PostgreSQL 17.4 Documentation
The PostgreSQL Global Development Group
PostgreSQL 17.4 Documentation
The PostgreSQL Global Development Group
Copyright © 1996–2025 The PostgreSQL Global Development Group
Legal Notice
PostgreSQL is Copyright © 1996–2025 by the PostgreSQL Global Development Group.
Postgres95 is Copyright © 1994–5 by the Regents of the University of California.
Permission to use, copy, modify, and distribute this software and its documentation for any purpose, without fee, and without a written
agreement is hereby granted, provided that the above copyright notice and this paragraph and the following two paragraphs appear in all copies.
IN NO EVENT SHALL THE UNIVERSITY OF CALIFORNIA BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL,
INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE
AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF CALIFORNIA HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGE.
THE UNIVERSITY OF CALIFORNIA SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED
HEREUNDER IS ON AN “AS-IS” BASIS, AND THE UNIVERSITY OF CALIFORNIA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
Table of Contents
Preface ................................................................................................................... xxxii
1. What Is PostgreSQL? .................................................................................... xxxii
2. A Brief History of PostgreSQL ....................................................................... xxxii
2.1. The Berkeley POSTGRES Project ........................................................ xxxiii
2.2. Postgres95 ....................................................................................... xxxiii
2.3. PostgreSQL ...................................................................................... xxxiv
3. Conventions ................................................................................................ xxxiv
4. Further Information ...................................................................................... xxxiv
5. Bug Reporting Guidelines .............................................................................. xxxv
5.1. Identifying Bugs ................................................................................ xxxv
5.2. What to Report ................................................................................. xxxvi
5.3. Where to Report Bugs ....................................................................... xxxvii
I. Tutorial .................................................................................................................... 1
1. Getting Started .................................................................................................. 3
1.1. Installation ............................................................................................. 3
1.2. Architectural Fundamentals ....................................................................... 3
1.3. Creating a Database ................................................................................. 3
1.4. Accessing a Database .............................................................................. 5
2. The SQL Language ............................................................................................ 7
2.1. Introduction ............................................................................................ 7
2.2. Concepts ................................................................................................ 7
2.3. Creating a New Table .............................................................................. 7
2.4. Populating a Table With Rows .................................................................. 8
2.5. Querying a Table .................................................................................... 9
2.6. Joins Between Tables ............................................................................. 11
2.7. Aggregate Functions .............................................................................. 13
2.8. Updates ............................................................................................... 15
2.9. Deletions .............................................................................................. 15
3. Advanced Features ........................................................................................... 17
3.1. Introduction .......................................................................................... 17
3.2. Views .................................................................................................. 17
3.3. Foreign Keys ........................................................................................ 17
3.4. Transactions ......................................................................................... 18
3.5. Window Functions ................................................................................. 20
3.6. Inheritance ........................................................................................... 23
3.7. Conclusion ........................................................................................... 24
II. The SQL Language ................................................................................................. 25
4. SQL Syntax .................................................................................................... 33
4.1. Lexical Structure ................................................................................... 33
4.2. Value Expressions ................................................................................. 42
4.3. Calling Functions .................................................................................. 56
5. Data Definition ................................................................................................ 59
5.1. Table Basics ......................................................................................... 59
5.2. Default Values ...................................................................................... 60
5.3. Identity Columns ................................................................................... 61
5.4. Generated Columns ................................................................................ 62
5.5. Constraints ........................................................................................... 64
5.6. System Columns ................................................................................... 73
5.7. Modifying Tables .................................................................................. 74
5.8. Privileges ............................................................................................. 76
5.9. Row Security Policies ............................................................................ 81
5.10. Schemas ............................................................................................. 87
5.11. Inheritance .......................................................................................... 92
5.12. Table Partitioning ................................................................................ 96
5.13. Foreign Data ..................................................................................... 110
iii
PostgreSQL 17.4 Documentation
5.14. Other Database Objects ....................................................................... 110
5.15. Dependency Tracking ......................................................................... 110
6. Data Manipulation .......................................................................................... 113
6.1. Inserting Data ..................................................................................... 113
6.2. Updating Data ..................................................................................... 114
6.3. Deleting Data ...................................................................................... 115
6.4. Returning Data from Modified Rows ....................................................... 115
7. Queries ......................................................................................................... 117
7.1. Overview ............................................................................................ 117
7.2. Table Expressions ................................................................................ 117
7.3. Select Lists ......................................................................................... 133
7.4. Combining Queries (UNION, INTERSECT, EXCEPT) ................................ 135
7.5. Sorting Rows (ORDER BY) .................................................................. 136
7.6. LIMIT and OFFSET ............................................................................ 137
7.7. VALUES Lists ..................................................................................... 137
7.8. WITH Queries (Common Table Expressions) ............................................ 138
8. Data Types .................................................................................................... 148
8.1. Numeric Types .................................................................................... 149
8.2. Monetary Types ................................................................................... 155
8.3. Character Types ................................................................................... 155
8.4. Binary Data Types ............................................................................... 158
8.5. Date/Time Types ................................................................................. 160
8.6. Boolean Type ...................................................................................... 169
8.7. Enumerated Types ............................................................................... 170
8.8. Geometric Types ................................................................................. 172
8.9. Network Address Types ........................................................................ 175
8.10. Bit String Types ................................................................................ 177
8.11. Text Search Types .............................................................................. 178
8.12. UUID Type ....................................................................................... 181
8.13. XML Type ........................................................................................ 181
8.14. JSON Types ...................................................................................... 183
8.15. Arrays .............................................................................................. 193
8.16. Composite Types ............................................................................... 203
8.17. Range Types ..................................................................................... 209
8.18. Domain Types ................................................................................... 215
8.19. Object Identifier Types ....................................................................... 216
8.20. pg_lsn Type ................................................................................... 218
8.21. Pseudo-Types .................................................................................... 219
9. Functions and Operators .................................................................................. 221
9.1. Logical Operators ................................................................................ 221
9.2. Comparison Functions and Operators ...................................................... 222
9.3. Mathematical Functions and Operators .................................................... 226
9.4. String Functions and Operators .............................................................. 234
9.5. Binary String Functions and Operators .................................................... 244
9.6. Bit String Functions and Operators ......................................................... 248
9.7. Pattern Matching ................................................................................. 250
9.8. Data Type Formatting Functions ............................................................. 269
9.9. Date/Time Functions and Operators ........................................................ 277
9.10. Enum Support Functions ..................................................................... 294
9.11. Geometric Functions and Operators ....................................................... 295
9.12. Network Address Functions and Operators .............................................. 302
9.13. Text Search Functions and Operators ..................................................... 305
9.14. UUID Functions ................................................................................ 311
9.15. XML Functions ................................................................................. 312
9.16. JSON Functions and Operators ............................................................. 326
9.17. Sequence Manipulation Functions ......................................................... 359
9.18. Conditional Expressions ...................................................................... 361
9.19. Array Functions and Operators ............................................................. 364
iv
PostgreSQL 17.4 Documentation
9.20. Range/Multirange Functions and Operators ............................................. 367
9.21. Aggregate Functions ........................................................................... 373
9.22. Window Functions ............................................................................. 381
9.23. Merge Support Functions ..................................................................... 382
9.24. Subquery Expressions ......................................................................... 383
9.25. Row and Array Comparisons ............................................................... 386
9.26. Set Returning Functions ...................................................................... 388
9.27. System Information Functions and Operators .......................................... 392
9.28. System Administration Functions .......................................................... 412
9.29. Trigger Functions ............................................................................... 430
9.30. Event Trigger Functions ...................................................................... 431
9.31. Statistics Information Functions ............................................................ 433
10. Type Conversion .......................................................................................... 435
10.1. Overview .......................................................................................... 435
10.2. Operators .......................................................................................... 436
10.3. Functions .......................................................................................... 440
10.4. Value Storage .................................................................................... 444
10.5. UNION, CASE, and Related Constructs .................................................. 445
10.6. SELECT Output Columns .................................................................... 446
11. Indexes ....................................................................................................... 448
11.1. Introduction ....................................................................................... 448
11.2. Index Types ...................................................................................... 449
11.3. Multicolumn Indexes .......................................................................... 451
11.4. Indexes and ORDER BY ..................................................................... 452
11.5. Combining Multiple Indexes ................................................................ 453
11.6. Unique Indexes .................................................................................. 454
11.7. Indexes on Expressions ....................................................................... 454
11.8. Partial Indexes ................................................................................... 455
11.9. Index-Only Scans and Covering Indexes ................................................ 458
11.10. Operator Classes and Operator Families ................................................ 460
11.11. Indexes and Collations ...................................................................... 462
11.12. Examining Index Usage ..................................................................... 462
12. Full Text Search ........................................................................................... 464
12.1. Introduction ....................................................................................... 464
12.2. Tables and Indexes ............................................................................. 468
12.3. Controlling Text Search ...................................................................... 470
12.4. Additional Features ............................................................................ 477
12.5. Parsers ............................................................................................. 483
12.6. Dictionaries ....................................................................................... 484
12.7. Configuration Example ....................................................................... 494
12.8. Testing and Debugging Text Search ...................................................... 495
12.9. Preferred Index Types for Text Search ................................................... 500
12.10. psql Support .................................................................................... 501
12.11. Limitations ...................................................................................... 504
13. Concurrency Control ..................................................................................... 505
13.1. Introduction ....................................................................................... 505
13.2. Transaction Isolation ........................................................................... 505
13.3. Explicit Locking ................................................................................ 511
13.4. Data Consistency Checks at the Application Level ................................... 517
13.5. Serialization Failure Handling .............................................................. 518
13.6. Caveats ............................................................................................. 519
13.7. Locking and Indexes ........................................................................... 519
14. Performance Tips ......................................................................................... 521
14.1. Using EXPLAIN ................................................................................ 521
14.2. Statistics Used by the Planner .............................................................. 534
14.3. Controlling the Planner with Explicit JOIN Clauses ................................. 540
14.4. Populating a Database ......................................................................... 542
14.5. Non-Durable Settings .......................................................................... 544
v
PostgreSQL 17.4 Documentation
15. Parallel Query .............................................................................................. 546
15.1. How Parallel Query Works .................................................................. 546
15.2. When Can Parallel Query Be Used? ...................................................... 547
15.3. Parallel Plans ..................................................................................... 548
15.4. Parallel Safety ................................................................................... 550
III. Server Administration ............................................................................................ 552
16. Installation from Binaries ............................................................................... 559
17. Installation from Source Code ......................................................................... 560
17.1. Requirements ..................................................................................... 560
17.2. Getting the Source .............................................................................. 562
17.3. Building and Installation with Autoconf and Make ................................... 562
17.4. Building and Installation with Meson ..................................................... 575
17.5. Post-Installation Setup ......................................................................... 586
17.6. Supported Platforms ........................................................................... 588
17.7. Platform-Specific Notes ....................................................................... 588
18. Server Setup and Operation ............................................................................ 595
18.1. The PostgreSQL User Account ............................................................. 595
18.2. Creating a Database Cluster ................................................................. 595
18.3. Starting the Database Server ................................................................ 597
18.4. Managing Kernel Resources ................................................................. 601
18.5. Shutting Down the Server .................................................................... 608
18.6. Upgrading a PostgreSQL Cluster .......................................................... 609
18.7. Preventing Server Spoofing .................................................................. 612
18.8. Encryption Options ............................................................................. 613
18.9. Secure TCP/IP Connections with SSL .................................................... 614
18.10. Secure TCP/IP Connections with GSSAPI Encryption ............................. 618
18.11. Secure TCP/IP Connections with SSH Tunnels ...................................... 618
18.12. Registering Event Log on Windows ..................................................... 619
19. Server Configuration ..................................................................................... 621
19.1. Setting Parameters .............................................................................. 621
19.2. File Locations .................................................................................... 625
19.3. Connections and Authentication ............................................................ 626
19.4. Resource Consumption ........................................................................ 632
19.5. Write Ahead Log ............................................................................... 641
19.6. Replication ........................................................................................ 652
19.7. Query Planning .................................................................................. 659
19.8. Error Reporting and Logging ............................................................... 666
19.9. Run-time Statistics ............................................................................. 680
19.10. Automatic Vacuuming ....................................................................... 682
19.11. Client Connection Defaults ................................................................. 683
19.12. Lock Management ............................................................................ 694
19.13. Version and Platform Compatibility ..................................................... 695
19.14. Error Handling ................................................................................. 697
19.15. Preset Options .................................................................................. 698
19.16. Customized Options .......................................................................... 700
19.17. Developer Options ............................................................................ 700
19.18. Short Options ................................................................................... 706
20. Client Authentication ..................................................................................... 707
20.1. The pg_hba.conf File ..................................................................... 707
20.2. User Name Maps ............................................................................... 716
20.3. Authentication Methods ....................................................................... 717
20.4. Trust Authentication ........................................................................... 718
20.5. Password Authentication ..................................................................... 719
20.6. GSSAPI Authentication ....................................................................... 720
20.7. SSPI Authentication ............................................................................ 721
20.8. Ident Authentication ........................................................................... 722
20.9. Peer Authentication ............................................................................ 723
20.10. LDAP Authentication ........................................................................ 723
vi
PostgreSQL 17.4 Documentation
20.11. RADIUS Authentication .................................................................... 726
20.12. Certificate Authentication ................................................................... 727
20.13. PAM Authentication ......................................................................... 727
20.14. BSD Authentication .......................................................................... 728
20.15. Authentication Problems .................................................................... 728
21. Database Roles ............................................................................................. 730
21.1. Database Roles .................................................................................. 730
21.2. Role Attributes .................................................................................. 731
21.3. Role Membership ............................................................................... 733
21.4. Dropping Roles .................................................................................. 734
21.5. Predefined Roles ................................................................................ 735
21.6. Function Security ............................................................................... 737
22. Managing Databases ..................................................................................... 738
22.1. Overview .......................................................................................... 738
22.2. Creating a Database ............................................................................ 738
22.3. Template Databases ............................................................................ 739
22.4. Database Configuration ....................................................................... 741
22.5. Destroying a Database ........................................................................ 741
22.6. Tablespaces ....................................................................................... 741
23. Localization ................................................................................................. 744
23.1. Locale Support .................................................................................. 744
23.2. Collation Support ............................................................................... 749
23.3. Character Set Support ......................................................................... 759
24. Routine Database Maintenance Tasks ............................................................... 769
24.1. Routine Vacuuming ............................................................................ 769
24.2. Routine Reindexing ............................................................................ 778
24.3. Log File Maintenance ......................................................................... 779
25. Backup and Restore ...................................................................................... 781
25.1. SQL Dump ....................................................................................... 781
25.2. File System Level Backup ................................................................... 784
25.3. Continuous Archiving and Point-in-Time Recovery (PITR) ........................ 785
26. High Availability, Load Balancing, and Replication ............................................ 797
26.1. Comparison of Different Solutions ........................................................ 797
26.2. Log-Shipping Standby Servers .............................................................. 800
26.3. Failover ............................................................................................ 809
26.4. Hot Standby ...................................................................................... 810
27. Monitoring Database Activity ......................................................................... 818
27.1. Standard Unix Tools ........................................................................... 818
27.2. The Cumulative Statistics System ......................................................... 819
27.3. Viewing Locks .................................................................................. 859
27.4. Progress Reporting ............................................................................. 860
27.5. Dynamic Tracing ............................................................................... 868
27.6. Monitoring Disk Usage ....................................................................... 877
28. Reliability and the Write-Ahead Log ................................................................ 879
28.1. Reliability ......................................................................................... 879
28.2. Data Checksums ................................................................................ 881
28.3. Write-Ahead Logging (WAL) ............................................................... 881
28.4. Asynchronous Commit ........................................................................ 882
28.5. WAL Configuration ............................................................................ 883
28.6. WAL Internals ................................................................................... 886
29. Logical Replication ....................................................................................... 888
29.1. Publication ........................................................................................ 888
29.2. Subscription ...................................................................................... 889
29.3. Logical Replication Failover ................................................................ 895
29.4. Row Filters ....................................................................................... 897
29.5. Column Lists ..................................................................................... 904
29.6. Conflicts ........................................................................................... 907
29.7. Restrictions ....................................................................................... 908
vii
PostgreSQL 17.4 Documentation
29.8. Architecture ...................................................................................... 908
29.9. Monitoring ........................................................................................ 909
29.10. Security .......................................................................................... 909
29.11. Configuration Settings ....................................................................... 910
29.12. Quick Setup ..................................................................................... 911
30. Just-in-Time Compilation (JIT) ....................................................................... 912
30.1. What Is JIT compilation? .................................................................... 912
30.2. When to JIT? .................................................................................... 912
30.3. Configuration .................................................................................... 914
30.4. Extensibility ...................................................................................... 914
31. Regression Tests ........................................................................................... 915
31.1. Running the Tests .............................................................................. 915
31.2. Test Evaluation .................................................................................. 919
31.3. Variant Comparison Files .................................................................... 921
31.4. TAP Tests ......................................................................................... 922
31.5. Test Coverage Examination ................................................................. 923
IV. Client Interfaces ................................................................................................... 925
32. libpq — C Library ........................................................................................ 930
32.1. Database Connection Control Functions ................................................. 930
32.2. Connection Status Functions ................................................................ 949
32.3. Command Execution Functions ............................................................. 956
32.4. Asynchronous Command Processing ...................................................... 972
32.5. Pipeline Mode ................................................................................... 976
32.6. Retrieving Query Results in Chunks ...................................................... 981
32.7. Canceling Queries in Progress .............................................................. 982
32.8. The Fast-Path Interface ....................................................................... 987
32.9. Asynchronous Notification ................................................................... 988
32.10. Functions Associated with the COPY Command ..................................... 989
32.11. Control Functions ............................................................................. 993
32.12. Miscellaneous Functions .................................................................... 995
32.13. Notice Processing ............................................................................. 999
32.14. Event System ................................................................................. 1000
32.15. Environment Variables ..................................................................... 1006
32.16. The Password File .......................................................................... 1008
32.17. The Connection Service File ............................................................. 1009
32.18. LDAP Lookup of Connection Parameters ............................................ 1009
32.19. SSL Support .................................................................................. 1010
32.20. Behavior in Threaded Programs ......................................................... 1014
32.21. Building libpq Programs .................................................................. 1015
32.22. Example Programs .......................................................................... 1016
33. Large Objects ............................................................................................. 1028
33.1. Introduction ..................................................................................... 1028
33.2. Implementation Features .................................................................... 1028
33.3. Client Interfaces ............................................................................... 1028
33.4. Server-Side Functions ....................................................................... 1033
33.5. Example Program ............................................................................. 1034
34. ECPG — Embedded SQL in C ..................................................................... 1040
34.1. The Concept .................................................................................... 1040
34.2. Managing Database Connections ......................................................... 1040
34.3. Running SQL Commands .................................................................. 1044
34.4. Using Host Variables ........................................................................ 1047
34.5. Dynamic SQL .................................................................................. 1061
34.6. pgtypes Library ................................................................................ 1063
34.7. Using Descriptor Areas ..................................................................... 1077
34.8. Error Handling ................................................................................. 1090
34.9. Preprocessor Directives ..................................................................... 1097
34.10. Processing Embedded SQL Programs ................................................. 1099
34.11. Library Functions ............................................................................ 1100
viii
PostgreSQL 17.4 Documentation
34.12. Large Objects ................................................................................. 1101
34.13. C++ Applications ............................................................................ 1102
34.14. Embedded SQL Commands .............................................................. 1106
34.15. Informix Compatibility Mode ............................................................ 1130
34.16. Oracle Compatibility Mode ............................................................... 1145
34.17. Internals ........................................................................................ 1145
35. The Information Schema .............................................................................. 1148
35.1. The Schema ..................................................................................... 1148
35.2. Data Types ...................................................................................... 1148
35.3. information_schema_catalog_name ........................................ 1149
35.4. administrable_role_authorizations .................................... 1149
35.5. applicable_roles ..................................................................... 1149
35.6. attributes ................................................................................. 1150
35.7. character_sets ......................................................................... 1152
35.8. check_constraint_routine_usage .......................................... 1153
35.9. check_constraints ................................................................... 1153
35.10. collations ................................................................................ 1154
35.11. collation_character_set_applicability .......................... 1154
35.12. column_column_usage .............................................................. 1155
35.13. column_domain_usage .............................................................. 1155
35.14. column_options ........................................................................ 1156
35.15. column_privileges .................................................................. 1156
35.16. column_udt_usage .................................................................... 1157
35.17. columns ...................................................................................... 1157
35.18. constraint_column_usage ...................................................... 1160
35.19. constraint_table_usage ........................................................ 1161
35.20. data_type_privileges ............................................................ 1161
35.21. domain_constraints ................................................................ 1162
35.22. domain_udt_usage .................................................................... 1163
35.23. domains ...................................................................................... 1163
35.24. element_types .......................................................................... 1165
35.25. enabled_roles .......................................................................... 1167
35.26. foreign_data_wrapper_options ............................................ 1167
35.27. foreign_data_wrappers .......................................................... 1168
35.28. foreign_server_options ........................................................ 1168
35.29. foreign_servers ...................................................................... 1168
35.30. foreign_table_options .......................................................... 1169
35.31. foreign_tables ........................................................................ 1169
35.32. key_column_usage .................................................................... 1170
35.33. parameters ................................................................................ 1170
35.34. referential_constraints ...................................................... 1172
35.35. role_column_grants ................................................................ 1173
35.36. role_routine_grants .............................................................. 1173
35.37. role_table_grants .................................................................. 1174
35.38. role_udt_grants ...................................................................... 1175
35.39. role_usage_grants .................................................................. 1175
35.40. routine_column_usage ............................................................ 1176
35.41. routine_privileges ................................................................ 1177
35.42. routine_routine_usage .......................................................... 1177
35.43. routine_sequence_usage ........................................................ 1178
35.44. routine_table_usage .............................................................. 1178
35.45. routines .................................................................................... 1179
35.46. schemata .................................................................................... 1183
35.47. sequences .................................................................................. 1184
35.48. sql_features ............................................................................ 1184
35.49. sql_implementation_info ...................................................... 1185
35.50. sql_parts .................................................................................. 1185
35.51. sql_sizing ................................................................................ 1186
ix
PostgreSQL 17.4 Documentation
35.52. table_constraints .................................................................. 1186
35.53. table_privileges .................................................................... 1187
35.54. tables ........................................................................................ 1188
35.55. transforms ................................................................................ 1188
35.56. triggered_update_columns .................................................... 1189
35.57. triggers .................................................................................... 1190
35.58. udt_privileges ........................................................................ 1191
35.59. usage_privileges .................................................................... 1192
35.60. user_defined_types ................................................................ 1192
35.61. user_mapping_options ............................................................ 1194
35.62. user_mappings .......................................................................... 1194
35.63. view_column_usage .................................................................. 1195
35.64. view_routine_usage ................................................................ 1195
35.65. view_table_usage .................................................................... 1196
35.66. views .......................................................................................... 1196
V. Server Programming ............................................................................................. 1198
36. Extending SQL ........................................................................................... 1204
36.1. How Extensibility Works ................................................................... 1204
36.2. The PostgreSQL Type System ............................................................ 1204
36.3. User-Defined Functions ..................................................................... 1207
36.4. User-Defined Procedures ................................................................... 1208
36.5. Query Language (SQL) Functions ....................................................... 1208
36.6. Function Overloading ........................................................................ 1225
36.7. Function Volatility Categories ............................................................. 1226
36.8. Procedural Language Functions ........................................................... 1227
36.9. Internal Functions ............................................................................. 1227
36.10. C-Language Functions ..................................................................... 1228
36.11. Function Optimization Information .................................................... 1251
36.12. User-Defined Aggregates ................................................................. 1252
36.13. User-Defined Types ........................................................................ 1259
36.14. User-Defined Operators ................................................................... 1263
36.15. Operator Optimization Information .................................................... 1264
36.16. Interfacing Extensions to Indexes ....................................................... 1268
36.17. Packaging Related Objects into an Extension ....................................... 1281
36.18. Extension Building Infrastructure ....................................................... 1289
37. Triggers ..................................................................................................... 1294
37.1. Overview of Trigger Behavior ............................................................ 1294
37.2. Visibility of Data Changes ................................................................. 1297
37.3. Writing Trigger Functions in C ........................................................... 1297
37.4. A Complete Trigger Example ............................................................. 1300
38. Event Triggers ............................................................................................ 1304
38.1. Overview of Event Trigger Behavior .................................................... 1304
38.2. Event Trigger Firing Matrix ............................................................... 1305
38.3. Writing Event Trigger Functions in C .................................................. 1308
38.4. A Complete Event Trigger Example .................................................... 1310
38.5. A Table Rewrite Event Trigger Example .............................................. 1311
38.6. A Database Login Event Trigger Example ............................................ 1312
39. The Rule System ........................................................................................ 1314
39.1. The Query Tree ................................................................................ 1314
39.2. Views and the Rule System ................................................................ 1316
39.3. Materialized Views ........................................................................... 1322
39.4. Rules on INSERT, UPDATE, and DELETE ........................................... 1325
39.5. Rules and Privileges .......................................................................... 1336
39.6. Rules and Command Status ................................................................ 1338
39.7. Rules Versus Triggers ....................................................................... 1338
40. Procedural Languages .................................................................................. 1342
40.1. Installing Procedural Languages .......................................................... 1342
41. PL/pgSQL — SQL Procedural Language ........................................................ 1345
x
PostgreSQL 17.4 Documentation
41.1. Overview ........................................................................................ 1345
41.2. Structure of PL/pgSQL ...................................................................... 1346
41.3. Declarations ..................................................................................... 1348
41.4. Expressions ..................................................................................... 1354
41.5. Basic Statements .............................................................................. 1355
41.6. Control Structures ............................................................................. 1363
41.7. Cursors ........................................................................................... 1378
41.8. Transaction Management ................................................................... 1384
41.9. Errors and Messages ......................................................................... 1385
41.10. Trigger Functions ............................................................................ 1387
41.11. PL/pgSQL under the Hood ............................................................... 1396
41.12. Tips for Developing in PL/pgSQL ..................................................... 1399
41.13. Porting from Oracle PL/SQL ............................................................ 1403
42. PL/Tcl — Tcl Procedural Language ............................................................... 1413
42.1. Overview ........................................................................................ 1413
42.2. PL/Tcl Functions and Arguments ........................................................ 1413
42.3. Data Values in PL/Tcl ....................................................................... 1415
42.4. Global Data in PL/Tcl ....................................................................... 1415
42.5. Database Access from PL/Tcl ............................................................. 1416
42.6. Trigger Functions in PL/Tcl ............................................................... 1418
42.7. Event Trigger Functions in PL/Tcl ....................................................... 1420
42.8. Error Handling in PL/Tcl ................................................................... 1420
42.9. Explicit Subtransactions in PL/Tcl ....................................................... 1421
42.10. Transaction Management .................................................................. 1422
42.11. PL/Tcl Configuration ....................................................................... 1423
42.12. Tcl Procedure Names ...................................................................... 1423
43. PL/Perl — Perl Procedural Language ............................................................. 1424
43.1. PL/Perl Functions and Arguments ....................................................... 1424
43.2. Data Values in PL/Perl ...................................................................... 1429
43.3. Built-in Functions ............................................................................. 1429
43.4. Global Values in PL/Perl ................................................................... 1434
43.5. Trusted and Untrusted PL/Perl ............................................................ 1435
43.6. PL/Perl Triggers ............................................................................... 1436
43.7. PL/Perl Event Triggers ...................................................................... 1438
43.8. PL/Perl Under the Hood .................................................................... 1438
44. PL/Python — Python Procedural Language ..................................................... 1440
44.1. PL/Python Functions ......................................................................... 1440
44.2. Data Values ..................................................................................... 1441
44.3. Sharing Data .................................................................................... 1447
44.4. Anonymous Code Blocks ................................................................... 1447
44.5. Trigger Functions ............................................................................. 1447
44.6. Database Access ............................................................................... 1448
44.7. Explicit Subtransactions ..................................................................... 1452
44.8. Transaction Management ................................................................... 1453
44.9. Utility Functions .............................................................................. 1453
44.10. Python 2 vs. Python 3 ..................................................................... 1454
44.11. Environment Variables ..................................................................... 1454
45. Server Programming Interface ....................................................................... 1456
45.1. Interface Functions ........................................................................... 1456
45.2. Interface Support Functions ................................................................ 1498
45.3. Memory Management ....................................................................... 1507
45.4. Transaction Management ................................................................... 1517
45.5. Visibility of Data Changes ................................................................. 1520
45.6. Examples ........................................................................................ 1520
46. Background Worker Processes ...................................................................... 1524
47. Logical Decoding ........................................................................................ 1527
47.1. Logical Decoding Examples ............................................................... 1527
47.2. Logical Decoding Concepts ................................................................ 1531
xi
PostgreSQL 17.4 Documentation
47.3. Streaming Replication Protocol Interface .............................................. 1533
47.4. Logical Decoding SQL Interface ......................................................... 1533
47.5. System Catalogs Related to Logical Decoding ....................................... 1533
47.6. Logical Decoding Output Plugins ........................................................ 1534
47.7. Logical Decoding Output Writers ........................................................ 1542
47.8. Synchronous Replication Support for Logical Decoding ........................... 1542
47.9. Streaming of Large Transactions for Logical Decoding ............................ 1542
47.10. Two-phase Commit Support for Logical Decoding ................................ 1544
48. Replication Progress Tracking ....................................................................... 1545
49. Archive Modules ........................................................................................ 1546
49.1. Initialization Functions ...................................................................... 1546
49.2. Archive Module Callbacks ................................................................. 1546
VI. Reference .......................................................................................................... 1549
I. SQL Commands ............................................................................................ 1554
ABORT .................................................................................................. 1558
ALTER AGGREGATE ............................................................................. 1559
ALTER COLLATION .............................................................................. 1561
ALTER CONVERSION ............................................................................ 1564
ALTER DATABASE ................................................................................ 1566
ALTER DEFAULT PRIVILEGES .............................................................. 1569
ALTER DOMAIN .................................................................................... 1573
ALTER EVENT TRIGGER ....................................................................... 1577
ALTER EXTENSION ............................................................................... 1578
ALTER FOREIGN DATA WRAPPER ........................................................ 1582
ALTER FOREIGN TABLE ....................................................................... 1584
ALTER FUNCTION ................................................................................. 1589
ALTER GROUP ...................................................................................... 1593
ALTER INDEX ....................................................................................... 1595
ALTER LANGUAGE ............................................................................... 1598
ALTER LARGE OBJECT ......................................................................... 1599
ALTER MATERIALIZED VIEW ............................................................... 1600
ALTER OPERATOR ................................................................................ 1602
ALTER OPERATOR CLASS .................................................................... 1604
ALTER OPERATOR FAMILY .................................................................. 1605
ALTER POLICY ..................................................................................... 1609
ALTER PROCEDURE .............................................................................. 1611
ALTER PUBLICATION ........................................................................... 1614
ALTER ROLE ......................................................................................... 1617
ALTER ROUTINE ................................................................................... 1621
ALTER RULE ......................................................................................... 1623
ALTER SCHEMA ................................................................................... 1624
ALTER SEQUENCE ................................................................................ 1625
ALTER SERVER ..................................................................................... 1628
ALTER STATISTICS ............................................................................... 1630
ALTER SUBSCRIPTION .......................................................................... 1631
ALTER SYSTEM .................................................................................... 1634
ALTER TABLE ....................................................................................... 1636
ALTER TABLESPACE ............................................................................ 1654
ALTER TEXT SEARCH CONFIGURATION .............................................. 1656
ALTER TEXT SEARCH DICTIONARY ..................................................... 1658
ALTER TEXT SEARCH PARSER ............................................................. 1660
ALTER TEXT SEARCH TEMPLATE ........................................................ 1661
ALTER TRIGGER ................................................................................... 1662
ALTER TYPE ......................................................................................... 1664
ALTER USER ......................................................................................... 1669
ALTER USER MAPPING ......................................................................... 1670
ALTER VIEW ......................................................................................... 1671
ANALYZE .............................................................................................. 1673
xii
PostgreSQL 17.4 Documentation
BEGIN ................................................................................................... 1677
CALL ..................................................................................................... 1679
CHECKPOINT ........................................................................................ 1681
CLOSE ................................................................................................... 1682
CLUSTER .............................................................................................. 1683
COMMENT ............................................................................................ 1686
COMMIT ................................................................................................ 1691
COMMIT PREPARED ............................................................................. 1692
COPY .................................................................................................... 1693
CREATE ACCESS METHOD ................................................................... 1704
CREATE AGGREGATE ........................................................................... 1705
CREATE CAST ....................................................................................... 1713
CREATE COLLATION ............................................................................ 1717
CREATE CONVERSION .......................................................................... 1720
CREATE DATABASE ............................................................................. 1722
CREATE DOMAIN ................................................................................. 1727
CREATE EVENT TRIGGER ..................................................................... 1730
CREATE EXTENSION ............................................................................ 1732
CREATE FOREIGN DATA WRAPPER ...................................................... 1735
CREATE FOREIGN TABLE ..................................................................... 1737
CREATE FUNCTION .............................................................................. 1742
CREATE GROUP .................................................................................... 1751
CREATE INDEX ..................................................................................... 1752
CREATE LANGUAGE ............................................................................. 1761
CREATE MATERIALIZED VIEW ............................................................. 1764
CREATE OPERATOR .............................................................................. 1766
CREATE OPERATOR CLASS .................................................................. 1769
CREATE OPERATOR FAMILY ................................................................ 1772
CREATE POLICY ................................................................................... 1773
CREATE PROCEDURE ........................................................................... 1779
CREATE PUBLICATION ......................................................................... 1783
CREATE ROLE ...................................................................................... 1787
CREATE RULE ...................................................................................... 1792
CREATE SCHEMA ................................................................................. 1795
CREATE SEQUENCE .............................................................................. 1798
CREATE SERVER .................................................................................. 1802
CREATE STATISTICS ............................................................................. 1804
CREATE SUBSCRIPTION ....................................................................... 1808
CREATE TABLE .................................................................................... 1813
CREATE TABLE AS ............................................................................... 1836
CREATE TABLESPACE .......................................................................... 1839
CREATE TEXT SEARCH CONFIGURATION ............................................ 1841
CREATE TEXT SEARCH DICTIONARY ................................................... 1842
CREATE TEXT SEARCH PARSER ........................................................... 1844
CREATE TEXT SEARCH TEMPLATE ...................................................... 1846
CREATE TRANSFORM ........................................................................... 1847
CREATE TRIGGER ................................................................................. 1849
CREATE TYPE ....................................................................................... 1856
CREATE USER ....................................................................................... 1865
CREATE USER MAPPING ....................................................................... 1866
CREATE VIEW ...................................................................................... 1868
DEALLOCATE ....................................................................................... 1874
DECLARE .............................................................................................. 1875
DELETE ................................................................................................. 1879
DISCARD ............................................................................................... 1882
DO ........................................................................................................ 1883
DROP ACCESS METHOD ....................................................................... 1885
DROP AGGREGATE ............................................................................... 1886
xiii
PostgreSQL 17.4 Documentation
DROP CAST ........................................................................................... 1888
DROP COLLATION ................................................................................ 1889
DROP CONVERSION .............................................................................. 1890
DROP DATABASE ................................................................................. 1891
DROP DOMAIN ...................................................................................... 1892
DROP EVENT TRIGGER ......................................................................... 1893
DROP EXTENSION ................................................................................. 1894
DROP FOREIGN DATA WRAPPER .......................................................... 1895
DROP FOREIGN TABLE ......................................................................... 1896
DROP FUNCTION .................................................................................. 1897
DROP GROUP ........................................................................................ 1899
DROP INDEX ......................................................................................... 1900
DROP LANGUAGE ................................................................................. 1902
DROP MATERIALIZED VIEW ................................................................. 1903
DROP OPERATOR .................................................................................. 1904
DROP OPERATOR CLASS ...................................................................... 1906
DROP OPERATOR FAMILY .................................................................... 1908
DROP OWNED ....................................................................................... 1910
DROP POLICY ....................................................................................... 1911
DROP PROCEDURE ............................................................................... 1912
DROP PUBLICATION ............................................................................. 1914
DROP ROLE .......................................................................................... 1915
DROP ROUTINE ..................................................................................... 1916
DROP RULE .......................................................................................... 1918
DROP SCHEMA ..................................................................................... 1919
DROP SEQUENCE .................................................................................. 1920
DROP SERVER ...................................................................................... 1921
DROP STATISTICS ................................................................................. 1922
DROP SUBSCRIPTION ............................................................................ 1923
DROP TABLE ........................................................................................ 1925
DROP TABLESPACE .............................................................................. 1926
DROP TEXT SEARCH CONFIGURATION ................................................ 1927
DROP TEXT SEARCH DICTIONARY ....................................................... 1928
DROP TEXT SEARCH PARSER ............................................................... 1929
DROP TEXT SEARCH TEMPLATE .......................................................... 1930
DROP TRANSFORM ............................................................................... 1931
DROP TRIGGER ..................................................................................... 1932
DROP TYPE ........................................................................................... 1933
DROP USER ........................................................................................... 1934
DROP USER MAPPING ........................................................................... 1935
DROP VIEW .......................................................................................... 1936
END ...................................................................................................... 1937
EXECUTE .............................................................................................. 1938
EXPLAIN ............................................................................................... 1939
FETCH ................................................................................................... 1945
GRANT .................................................................................................. 1949
IMPORT FOREIGN SCHEMA .................................................................. 1955
INSERT .................................................................................................. 1957
LISTEN .................................................................................................. 1965
LOAD .................................................................................................... 1967
LOCK .................................................................................................... 1968
MERGE .................................................................................................. 1971
MOVE ................................................................................................... 1978
NOTIFY ................................................................................................. 1980
PREPARE ............................................................................................... 1983
PREPARE TRANSACTION ...................................................................... 1986
REASSIGN OWNED ............................................................................... 1988
REFRESH MATERIALIZED VIEW ........................................................... 1989
xiv
PostgreSQL 17.4 Documentation
REINDEX ............................................................................................... 1991
RELEASE SAVEPOINT ........................................................................... 1996
RESET ................................................................................................... 1998
REVOKE ................................................................................................ 1999
ROLLBACK ........................................................................................... 2004
ROLLBACK PREPARED ......................................................................... 2005
ROLLBACK TO SAVEPOINT .................................................................. 2006
SAVEPOINT ........................................................................................... 2008
SECURITY LABEL ................................................................................. 2010
SELECT ................................................................................................. 2013
SELECT INTO ........................................................................................ 2035
SET ....................................................................................................... 2037
SET CONSTRAINTS ............................................................................... 2040
SET ROLE ............................................................................................. 2041
SET SESSION AUTHORIZATION ............................................................ 2043
SET TRANSACTION ............................................................................... 2045
SHOW ................................................................................................... 2048
START TRANSACTION .......................................................................... 2050
TRUNCATE ........................................................................................... 2051
UNLISTEN ............................................................................................. 2053
UPDATE ................................................................................................ 2055
VACUUM .............................................................................................. 2060
VALUES ................................................................................................ 2065
II. PostgreSQL Client Applications ..................................................................... 2068
clusterdb ................................................................................................. 2069
createdb .................................................................................................. 2072
createuser ................................................................................................ 2076
dropdb .................................................................................................... 2081
dropuser .................................................................................................. 2084
ecpg ....................................................................................................... 2087
pg_amcheck ............................................................................................ 2090
pg_basebackup ......................................................................................... 2096
pgbench .................................................................................................. 2105
pg_combinebackup ................................................................................... 2130
pg_config ................................................................................................ 2133
pg_dump ................................................................................................. 2136
pg_dumpall ............................................................................................. 2151
pg_isready ............................................................................................... 2158
pg_receivewal .......................................................................................... 2160
pg_recvlogical ......................................................................................... 2165
pg_restore ............................................................................................... 2169
pg_verifybackup ....................................................................................... 2179
psql ........................................................................................................ 2182
reindexdb ................................................................................................ 2226
vacuumdb ............................................................................................... 2230
III. PostgreSQL Server Applications .................................................................... 2235
initdb ..................................................................................................... 2236
pg_archivecleanup .................................................................................... 2241
pg_checksums .......................................................................................... 2243
pg_controldata ......................................................................................... 2245
pg_createsubscriber ................................................................................... 2246
pg_ctl ..................................................................................................... 2251
pg_resetwal ............................................................................................. 2257
pg_rewind ............................................................................................... 2261
pg_test_fsync ........................................................................................... 2265
pg_test_timing ......................................................................................... 2266
pg_upgrade .............................................................................................. 2270
pg_waldump ............................................................................................ 2280
xv
PostgreSQL 17.4 Documentation
pg_walsummary ....................................................................................... 2284
postgres .................................................................................................. 2285
VII. Internals ........................................................................................................... 2292
50. Overview of PostgreSQL Internals ................................................................. 2298
50.1. The Path of a Query ......................................................................... 2298
50.2. How Connections Are Established ....................................................... 2298
50.3. The Parser Stage .............................................................................. 2299
50.4. The PostgreSQL Rule System ............................................................. 2300
50.5. Planner/Optimizer ............................................................................. 2300
50.6. Executor ......................................................................................... 2301
51. System Catalogs ......................................................................................... 2303
51.1. Overview ........................................................................................ 2303
51.2. pg_aggregate ............................................................................. 2305
51.3. pg_am ........................................................................................... 2306
51.4. pg_amop ....................................................................................... 2307
51.5. pg_amproc ................................................................................... 2308
51.6. pg_attrdef ................................................................................. 2308
51.7. pg_attribute ............................................................................. 2309
51.8. pg_authid ................................................................................... 2311
51.9. pg_auth_members ....................................................................... 2312
51.10. pg_cast ...................................................................................... 2312
51.11. pg_class .................................................................................... 2313
51.12. pg_collation ............................................................................ 2316
51.13. pg_constraint .......................................................................... 2317
51.14. pg_conversion .......................................................................... 2318
51.15. pg_database .............................................................................. 2319
51.16. pg_db_role_setting ................................................................ 2320
51.17. pg_default_acl ........................................................................ 2321
51.18. pg_depend .................................................................................. 2321
51.19. pg_description ........................................................................ 2323
51.20. pg_enum ...................................................................................... 2324
51.21. pg_event_trigger .................................................................... 2324
51.22. pg_extension ............................................................................ 2325
51.23. pg_foreign_data_wrapper ...................................................... 2325
51.24. pg_foreign_server .................................................................. 2326
51.25. pg_foreign_table .................................................................... 2326
51.26. pg_index .................................................................................... 2327
51.27. pg_inherits .............................................................................. 2328
51.28. pg_init_privs .......................................................................... 2329
51.29. pg_language .............................................................................. 2329
51.30. pg_largeobject ........................................................................ 2330
51.31. pg_largeobject_metadata ...................................................... 2331
51.32. pg_namespace ............................................................................ 2331
51.33. pg_opclass ................................................................................ 2331
51.34. pg_operator .............................................................................. 2332
51.35. pg_opfamily .............................................................................. 2333
51.36. pg_parameter_acl .................................................................... 2333
51.37. pg_partitioned_table ............................................................ 2334
51.38. pg_policy .................................................................................. 2334
51.39. pg_proc ...................................................................................... 2335
51.40. pg_publication ........................................................................ 2337
51.41. pg_publication_namespace .................................................... 2338
51.42. pg_publication_rel ................................................................ 2338
51.43. pg_range .................................................................................... 2339
51.44. pg_replication_origin .......................................................... 2339
51.45. pg_rewrite ................................................................................ 2340
51.46. pg_seclabel .............................................................................. 2340
51.47. pg_sequence .............................................................................. 2341
xvi
PostgreSQL 17.4 Documentation
51.48. pg_shdepend .............................................................................. 2341
51.49. pg_shdescription .................................................................... 2342
51.50. pg_shseclabel .......................................................................... 2343
51.51. pg_statistic ............................................................................ 2343
51.52. pg_statistic_ext .................................................................... 2345
51.53. pg_statistic_ext_data .......................................................... 2345
51.54. pg_subscription ...................................................................... 2346
51.55. pg_subscription_rel .............................................................. 2347
51.56. pg_tablespace .......................................................................... 2348
51.57. pg_transform ............................................................................ 2348
51.58. pg_trigger ................................................................................ 2349
51.59. pg_ts_config ............................................................................ 2350
51.60. pg_ts_config_map .................................................................... 2351
51.61. pg_ts_dict ................................................................................ 2351
51.62. pg_ts_parser ............................................................................ 2352
51.63. pg_ts_template ........................................................................ 2352
51.64. pg_type ...................................................................................... 2353
51.65. pg_user_mapping ...................................................................... 2356
52. System Views ............................................................................................ 2357
52.1. Overview ........................................................................................ 2357
52.2. pg_available_extensions ........................................................ 2358
52.3. pg_available_extension_versions ........................................ 2358
52.4. pg_backend_memory_contexts .................................................. 2359
52.5. pg_config ................................................................................... 2360
52.6. pg_cursors ................................................................................. 2360
52.7. pg_file_settings ..................................................................... 2361
52.8. pg_group ..................................................................................... 2361
52.9. pg_hba_file_rules ................................................................... 2362
52.10. pg_ident_file_mappings ........................................................ 2363
52.11. pg_indexes ................................................................................ 2363
52.12. pg_locks .................................................................................... 2364
52.13. pg_matviews .............................................................................. 2366
52.14. pg_policies .............................................................................. 2367
52.15. pg_prepared_statements ........................................................ 2367
52.16. pg_prepared_xacts .................................................................. 2368
52.17. pg_publication_tables .......................................................... 2369
52.18. pg_replication_origin_status ............................................ 2369
52.19. pg_replication_slots ............................................................ 2370
52.20. pg_roles .................................................................................... 2371
52.21. pg_rules .................................................................................... 2372
52.22. pg_seclabels ............................................................................ 2373
52.23. pg_sequences ............................................................................ 2373
52.24. pg_settings .............................................................................. 2374
52.25. pg_shadow .................................................................................. 2376
52.26. pg_shmem_allocations ............................................................ 2377
52.27. pg_stats .................................................................................... 2377
52.28. pg_stats_ext ............................................................................ 2379
52.29. pg_stats_ext_exprs ................................................................ 2380
52.30. pg_tables .................................................................................. 2382
52.31. pg_timezone_abbrevs .............................................................. 2382
52.32. pg_timezone_names .................................................................. 2383
52.33. pg_user ...................................................................................... 2383
52.34. pg_user_mappings .................................................................... 2384
52.35. pg_views .................................................................................... 2384
52.36. pg_wait_events ........................................................................ 2385
53. Frontend/Backend Protocol ........................................................................... 2386
53.1. Overview ........................................................................................ 2386
53.2. Message Flow .................................................................................. 2387
xvii
PostgreSQL 17.4 Documentation
53.3. SASL Authentication ........................................................................ 2401
53.4. Streaming Replication Protocol ........................................................... 2403
53.5. Logical Streaming Replication Protocol ................................................ 2412
53.6. Message Data Types ......................................................................... 2414
53.7. Message Formats .............................................................................. 2414
53.8. Error and Notice Message Fields ......................................................... 2431
53.9. Logical Replication Message Formats .................................................. 2433
53.10. Summary of Changes since Protocol 2.0 ............................................. 2442
54. PostgreSQL Coding Conventions ................................................................... 2444
54.1. Formatting ....................................................................................... 2444
54.2. Reporting Errors Within the Server ...................................................... 2444
54.3. Error Message Style Guide ................................................................. 2448
54.4. Miscellaneous Coding Conventions ...................................................... 2452
55. Native Language Support ............................................................................. 2454
55.1. For the Translator ............................................................................. 2454
55.2. For the Programmer .......................................................................... 2456
56. Writing a Procedural Language Handler .......................................................... 2460
57. Writing a Foreign Data Wrapper .................................................................... 2462
57.1. Foreign Data Wrapper Functions ......................................................... 2462
57.2. Foreign Data Wrapper Callback Routines .............................................. 2462
57.3. Foreign Data Wrapper Helper Functions ............................................... 2478
57.4. Foreign Data Wrapper Query Planning ................................................. 2479
57.5. Row Locking in Foreign Data Wrappers ............................................... 2482
58. Writing a Table Sampling Method ................................................................. 2484
58.1. Sampling Method Support Functions .................................................... 2484
59. Writing a Custom Scan Provider .................................................................... 2487
59.1. Creating Custom Scan Paths ............................................................... 2487
59.2. Creating Custom Scan Plans ............................................................... 2488
59.3. Executing Custom Scans .................................................................... 2489
60. Genetic Query Optimizer .............................................................................. 2492
60.1. Query Handling as a Complex Optimization Problem .............................. 2492
60.2. Genetic Algorithms ........................................................................... 2492
60.3. Genetic Query Optimization (GEQO) in PostgreSQL .............................. 2493
60.4. Further Reading ............................................................................... 2495
61. Table Access Method Interface Definition ....................................................... 2496
62. Index Access Method Interface Definition ....................................................... 2497
62.1. Basic API Structure for Indexes .......................................................... 2497
62.2. Index Access Method Functions .......................................................... 2500
62.3. Index Scanning ................................................................................ 2506
62.4. Index Locking Considerations ............................................................. 2507
62.5. Index Uniqueness Checks .................................................................. 2508
62.6. Index Cost Estimation Functions ......................................................... 2510
63. Write Ahead Logging for Extensions .............................................................. 2513
63.1. Generic WAL Records ...................................................................... 2513
63.2. Custom WAL Resource Managers ....................................................... 2514
64. Built-in Index Access Methods ...................................................................... 2516
64.1. B-Tree Indexes ................................................................................ 2516
64.2. GiST Indexes ................................................................................... 2522
64.3. SP-GiST Indexes .............................................................................. 2539
64.4. GIN Indexes .................................................................................... 2552
64.5. BRIN Indexes .................................................................................. 2559
64.6. Hash Indexes ................................................................................... 2572
65. Database Physical Storage ............................................................................ 2574
65.1. Database File Layout ........................................................................ 2574
65.2. TOAST ........................................................................................... 2576
65.3. Free Space Map ............................................................................... 2579
65.4. Visibility Map .................................................................................. 2579
65.5. The Initialization Fork ....................................................................... 2580
xviii
PostgreSQL 17.4 Documentation
65.6. Database Page Layout ....................................................................... 2580
65.7. Heap-Only Tuples (HOT) .................................................................. 2583
66. Transaction Processing ................................................................................. 2584
66.1. Transactions and Identifiers ................................................................ 2584
66.2. Transactions and Locking .................................................................. 2584
66.3. Subtransactions ................................................................................ 2584
66.4. Two-Phase Transactions .................................................................... 2585
67. System Catalog Declarations and Initial Contents ............................................. 2586
67.1. System Catalog Declaration Rules ....................................................... 2586
67.2. System Catalog Initial Data ................................................................ 2587
67.3. BKI File Format ............................................................................... 2592
67.4. BKI Commands ............................................................................... 2592
67.5. Structure of the Bootstrap BKI File ..................................................... 2593
67.6. BKI Example ................................................................................... 2594
68. How the Planner Uses Statistics .................................................................... 2595
68.1. Row Estimation Examples ................................................................. 2595
68.2. Multivariate Statistics Examples .......................................................... 2600
68.3. Planner Statistics and Security ............................................................ 2604
69. Backup Manifest Format .............................................................................. 2605
69.1. Backup Manifest Top-level Object ....................................................... 2605
69.2. Backup Manifest File Object .............................................................. 2605
69.3. Backup Manifest WAL Range Object .................................................. 2606
VIII. Appendixes ...................................................................................................... 2607
A. PostgreSQL Error Codes ............................................................................... 2614
B. Date/Time Support ....................................................................................... 2623
B.1. Date/Time Input Interpretation ............................................................. 2623
B.2. Handling of Invalid or Ambiguous Timestamps ....................................... 2624
B.3. Date/Time Key Words ........................................................................ 2625
B.4. Date/Time Configuration Files ............................................................. 2626
B.5. POSIX Time Zone Specifications ......................................................... 2627
B.6. History of Units ................................................................................ 2629
B.7. Julian Dates ...................................................................................... 2630
C. SQL Key Words .......................................................................................... 2631
D. SQL Conformance ....................................................................................... 2656
D.1. Supported Features ............................................................................ 2657
D.2. Unsupported Features ......................................................................... 2669
D.3. XML Limits and Conformance to SQL/XML ......................................... 2677
E. Release Notes .............................................................................................. 2681
E.1. Release 17.4 ..................................................................................... 2681
E.2. Release 17.3 ..................................................................................... 2681
E.3. Release 17.2 ..................................................................................... 2688
E.4. Release 17.1 ..................................................................................... 2689
E.5. Release 17 ........................................................................................ 2694
E.6. Prior Releases ................................................................................... 2714
F. Additional Supplied Modules and Extensions .................................................... 2715
F.1. amcheck — tools to verify table and index consistency ............................. 2717
F.2. auth_delay — pause on authentication failure .......................................... 2723
F.3. auto_explain — log execution plans of slow queries ................................. 2724
F.4. basebackup_to_shell — example "shell" pg_basebackup module ................. 2727
F.5. basic_archive — an example WAL archive module .................................. 2728
F.6. bloom — bloom filter index access method ............................................ 2729
F.7. btree_gin — GIN operator classes with B-tree behavior ............................ 2733
F.8. btree_gist — GiST operator classes with B-tree behavior ........................... 2734
F.9. citext — a case-insensitive character string type ...................................... 2736
F.10. cube — a multi-dimensional cube data type .......................................... 2739
F.11. dblink — connect to other PostgreSQL databases ................................... 2744
F.12. dict_int — example full-text search dictionary for integers ....................... 2776
F.13. dict_xsyn — example synonym full-text search dictionary ....................... 2777
xix
PostgreSQL 17.4 Documentation
F.14. earthdistance — calculate great-circle distances ..................................... 2779
F.15. file_fdw — access data files in the server's file system ............................ 2781
F.16. fuzzystrmatch — determine string similarities and distance ...................... 2784
F.17. hstore — hstore key/value datatype ..................................................... 2789
F.18. intagg — integer aggregator and enumerator ......................................... 2797
F.19. intarray — manipulate arrays of integers .............................................. 2799
F.20. isn — data types for international standard numbers (ISBN, EAN, UPC,
etc.) ....................................................................................................... 2803
F.21. lo — manage large objects ................................................................. 2807
F.22. ltree — hierarchical tree-like data type ................................................. 2809
F.23. pageinspect — low-level inspection of database pages ............................. 2817
F.24. passwordcheck — verify password strength .......................................... 2828
F.25. pg_buffercache — inspect PostgreSQL buffer cache state ........................ 2829
F.26. pgcrypto — cryptographic functions .................................................... 2833
F.27. pg_freespacemap — examine the free space map ................................... 2843
F.28. pg_prewarm — preload relation data into buffer caches ........................... 2845
F.29. pgrowlocks — show a table's row locking information ............................ 2847
F.30. pg_stat_statements — track statistics of SQL planning and execution ......... 2849
F.31. pgstattuple — obtain tuple-level statistics ............................................. 2858
F.32. pg_surgery — perform low-level surgery on relation data ........................ 2863
F.33. pg_trgm — support for similarity of text using trigram matching ............... 2865
F.34. pg_visibility — visibility map information and utilities ............................ 2871
F.35. pg_walinspect — low-level WAL inspection ......................................... 2873
F.36. postgres_fdw — access data stored in external PostgreSQL servers ............ 2877
F.37. seg — a datatype for line segments or floating point intervals ................... 2888
F.38. sepgsql — SELinux-, label-based mandatory access control (MAC) security
module ................................................................................................... 2891
F.39. spi — Server Programming Interface features/examples ........................... 2899
F.40. sslinfo — obtain client SSL information ............................................... 2901
F.41. tablefunc — functions that return tables (crosstab and others) .............. 2903
F.42. tcn — a trigger function to notify listeners of changes to table content ........ 2913
F.43. test_decoding — SQL-based test/example module for WAL logical decoding ......................................................................................................... 2915
F.44. tsm_system_rows — the SYSTEM_ROWS sampling method for
TABLESAMPLE ....................................................................................... 2916
F.45. tsm_system_time — the SYSTEM_TIME sampling method for TABLESAMPLE ....................................................................................................... 2917
F.46. unaccent — a text search dictionary which removes diacritics ................... 2918
F.47. uuid-ossp — a UUID generator .......................................................... 2921
F.48. xml2 — XPath querying and XSLT functionality ................................... 2923
G. Additional Supplied Programs ........................................................................ 2928
G.1. Client Applications ............................................................................ 2928
G.2. Server Applications ............................................................................ 2935
H. External Projects .......................................................................................... 2936
H.1. Client Interfaces ................................................................................ 2936
H.2. Administration Tools .......................................................................... 2936
H.3. Procedural Languages ........................................................................ 2936
H.4. Extensions ........................................................................................ 2936
I. The Source Code Repository ........................................................................... 2937
I.1. Getting the Source via Git .................................................................... 2937
J. Documentation ............................................................................................. 2938
J.1. DocBook ........................................................................................... 2938
J.2. Tool Sets .......................................................................................... 2938
J.3. Building the Documentation with Make .................................................. 2940
J.4. Building the Documentation with Meson ................................................ 2942
J.5. Documentation Authoring .................................................................... 2942
J.6. Style Guide ....................................................................................... 2942
K. PostgreSQL Limits ....................................................................................... 2945
xx
PostgreSQL 17.4 Documentation
L. Acronyms ................................................................................................... 2946
M. Glossary .................................................................................................... 2953
N. Color Support .............................................................................................. 2967
N.1. When Color is Used .......................................................................... 2967
N.2. Configuring the Colors ....................................................................... 2967
O. Obsolete or Renamed Features ....................................................................... 2968
O.1. recovery.conf file merged into postgresql.conf ....................... 2968
O.2. Default Roles Renamed to Predefined Roles ........................................... 2968
O.3. pg_xlogdump renamed to pg_waldump ........................................... 2968
O.4. pg_resetxlog renamed to pg_resetwal ........................................ 2968
O.5. pg_receivexlog renamed to pg_receivewal ................................ 2968
Bibliography ............................................................................................................ 2970
Index ...................................................................................................................... 2972
xxi
List of Figures
60.1. Structure of a Genetic Algorithm ........................................................................ 2493
64.1. GIN Internals ................................................................................................... 2557
65.1. Page Layout .................................................................................................... 2582
xxii
List of Tables
4.1. Backslash Escape Sequences ................................................................................... 36
4.2. Operator Precedence (highest to lowest) .................................................................... 41
5.1. ACL Privilege Abbreviations ................................................................................... 79
5.2. Summary of Access Privileges ................................................................................. 80
8.1. Data Types ......................................................................................................... 148
8.2. Numeric Types .................................................................................................... 149
8.3. Monetary Types .................................................................................................. 155
8.4. Character Types .................................................................................................. 156
8.5. Special Character Types ........................................................................................ 157
8.6. Binary Data Types ............................................................................................... 158
8.7. bytea Literal Escaped Octets ............................................................................... 159
8.8. bytea Output Escaped Octets ............................................................................... 159
8.9. Date/Time Types ................................................................................................. 160
8.10. Date Input ......................................................................................................... 161
8.11. Time Input ........................................................................................................ 162
8.12. Time Zone Input ................................................................................................ 163
8.13. Special Date/Time Inputs ..................................................................................... 164
8.14. Date/Time Output Styles ..................................................................................... 165
8.15. Date Order Conventions ...................................................................................... 165
8.16. ISO 8601 Interval Unit Abbreviations .................................................................... 167
8.17. Interval Input ..................................................................................................... 168
8.18. Interval Output Style Examples ............................................................................ 169
8.19. Boolean Data Type ............................................................................................. 169
8.20. Geometric Types ................................................................................................ 172
8.21. Network Address Types ...................................................................................... 175
8.22. cidr Type Input Examples ................................................................................. 175
8.23. JSON Primitive Types and Corresponding PostgreSQL Types .................................... 184
8.24. jsonpath Variables ......................................................................................... 193
8.25. jsonpath Accessors ........................................................................................ 193
8.26. Object Identifier Types ....................................................................................... 216
8.27. Pseudo-Types .................................................................................................... 219
9.1. Comparison Operators .......................................................................................... 222
9.2. Comparison Predicates .......................................................................................... 222
9.3. Comparison Functions .......................................................................................... 226
9.4. Mathematical Operators ........................................................................................ 226
9.5. Mathematical Functions ........................................................................................ 228
9.6. Random Functions ............................................................................................... 231
9.7. Trigonometric Functions ....................................................................................... 232
9.8. Hyperbolic Functions ........................................................................................... 233
9.9. SQL String Functions and Operators ....................................................................... 234
9.10. Other String Functions and Operators .................................................................... 237
9.11. SQL Binary String Functions and Operators ........................................................... 244
9.12. Other Binary String Functions .............................................................................. 246
9.13. Text/Binary String Conversion Functions ............................................................... 247
9.14. Bit String Operators ........................................................................................... 248
9.15. Bit String Functions ........................................................................................... 249
9.16. Regular Expression Match Operators ..................................................................... 253
9.17. Regular Expression Atoms ................................................................................... 259
9.18. Regular Expression Quantifiers ............................................................................. 260
9.19. Regular Expression Constraints ............................................................................ 260
9.20. Regular Expression Character-Entry Escapes ........................................................... 262
9.21. Regular Expression Class-Shorthand Escapes .......................................................... 263
9.22. Regular Expression Constraint Escapes .................................................................. 263
9.23. Regular Expression Back References ..................................................................... 264
9.24. ARE Embedded-Option Letters ............................................................................ 264
xxiii
PostgreSQL 17.4 Documentation
9.25. Regular Expression Functions Equivalencies ........................................................... 268
9.26. Formatting Functions .......................................................................................... 269
9.27. Template Patterns for Date/Time Formatting ........................................................... 270
9.28. Template Pattern Modifiers for Date/Time Formatting .............................................. 272
9.29. Template Patterns for Numeric Formatting ............................................................. 274
9.30. Template Pattern Modifiers for Numeric Formatting ................................................. 276
9.31. to_char Examples ........................................................................................... 276
9.32. Date/Time Operators ........................................................................................... 277
9.33. Date/Time Functions ........................................................................................... 279
9.34. AT TIME ZONE and AT LOCAL Variants ........................................................... 290
9.35. Enum Support Functions ..................................................................................... 294
9.36. Geometric Operators ........................................................................................... 295
9.37. Geometric Functions ........................................................................................... 298
9.38. Geometric Type Conversion Functions ................................................................... 299
9.39. IP Address Operators .......................................................................................... 302
9.40. IP Address Functions .......................................................................................... 303
9.41. MAC Address Functions ..................................................................................... 304
9.42. Text Search Operators ......................................................................................... 305
9.43. Text Search Functions ......................................................................................... 306
9.44. Text Search Debugging Functions ......................................................................... 310
9.45. json and jsonb Operators ................................................................................ 327
9.46. Additional jsonb Operators ................................................................................ 328
9.47. JSON Creation Functions .................................................................................... 330
9.48. SQL/JSON Testing Functions ............................................................................... 332
9.49. JSON Processing Functions ................................................................................. 333
9.50. jsonpath Operators and Methods ...................................................................... 345
9.51. jsonpath Filter Expression Elements .................................................................. 349
9.52. SQL/JSON Query Functions ................................................................................ 351
9.53. Sequence Functions ............................................................................................ 359
9.54. Array Operators ................................................................................................. 364
9.55. Array Functions ................................................................................................. 365
9.56. Range Operators ................................................................................................ 367
9.57. Multirange Operators .......................................................................................... 369
9.58. Range Functions ................................................................................................ 371
9.59. Multirange Functions .......................................................................................... 372
9.60. General-Purpose Aggregate Functions .................................................................... 373
9.61. Aggregate Functions for Statistics ......................................................................... 377
9.62. Ordered-Set Aggregate Functions .......................................................................... 379
9.63. Hypothetical-Set Aggregate Functions ................................................................... 379
9.64. Grouping Operations ........................................................................................... 380
9.65. General-Purpose Window Functions ...................................................................... 381
9.66. Merge Support Functions ..................................................................................... 382
9.67. Series Generating Functions ................................................................................. 388
9.68. Subscript Generating Functions ............................................................................ 390
9.69. Session Information Functions .............................................................................. 392
9.70. Access Privilege Inquiry Functions ........................................................................ 395
9.71. aclitem Operators ........................................................................................... 397
9.72. aclitem Functions ........................................................................................... 397
9.73. Schema Visibility Inquiry Functions ...................................................................... 398
9.74. System Catalog Information Functions ................................................................... 399
9.75. Index Column Properties ..................................................................................... 404
9.76. Index Properties ................................................................................................. 404
9.77. Index Access Method Properties ........................................................................... 404
9.78. GUC Flags ........................................................................................................ 405
9.79. Object Information and Addressing Functions ......................................................... 405
9.80. Comment Information Functions ........................................................................... 406
9.81. Data Validity Checking Functions ......................................................................... 406
9.82. Transaction ID and Snapshot Information Functions ................................................. 407
xxiv
PostgreSQL 17.4 Documentation
9.83. Snapshot Components ......................................................................................... 408
9.84. Deprecated Transaction ID and Snapshot Information Functions ................................. 409
9.85. Committed Transaction Information Functions ........................................................ 409
9.86. Control Data Functions ....................................................................................... 410
9.87. pg_control_checkpoint Output Columns ...................................................... 410
9.88. pg_control_system Output Columns .............................................................. 410
9.89. pg_control_init Output Columns .................................................................. 411
9.90. pg_control_recovery Output Columns .......................................................... 411
9.91. Version Information Functions ............................................................................. 411
9.92. WAL Summarization Information Functions ........................................................... 412
9.93. Configuration Settings Functions .......................................................................... 413
9.94. Server Signaling Functions .................................................................................. 413
9.95. Backup Control Functions ................................................................................... 415
9.96. Recovery Information Functions ........................................................................... 417
9.97. Recovery Control Functions ................................................................................. 418
9.98. Snapshot Synchronization Functions ...................................................................... 419
9.99. Replication Management Functions ....................................................................... 420
9.100. Database Object Size Functions .......................................................................... 423
9.101. Database Object Location Functions .................................................................... 424
9.102. Collation Management Functions ........................................................................ 424
9.103. Partitioning Information Functions ....................................................................... 425
9.104. Index Maintenance Functions ............................................................................. 425
9.105. Generic File Access Functions ............................................................................ 426
9.106. Advisory Lock Functions ................................................................................... 429
9.107. Built-In Trigger Functions .................................................................................. 430
9.108. Table Rewrite Information Functions ................................................................... 433
12.1. Default Parser's Token Types ............................................................................... 483
13.1. Transaction Isolation Levels ................................................................................. 506
13.2. Conflicting Lock Modes ...................................................................................... 513
13.3. Conflicting Row-Level Locks ............................................................................... 515
18.1. System V IPC Parameters .................................................................................... 601
18.2. SSL Server File Usage ........................................................................................ 616
19.1. synchronous_commit Modes ................................................................................ 643
19.2. Message Severity Levels ..................................................................................... 671
19.3. Keys and Values of JSON Log Entries .................................................................. 678
19.4. Short Option Key ............................................................................................... 706
21.1. Predefined Roles ................................................................................................ 735
23.1. ICU Collation Levels .......................................................................................... 755
23.2. ICU Collation Settings ........................................................................................ 756
23.3. PostgreSQL Character Sets .................................................................................. 759
23.4. Built-in Client/Server Character Set Conversions ..................................................... 764
23.5. All Built-in Character Set Conversions .................................................................. 765
26.1. High Availability, Load Balancing, and Replication Feature Matrix ............................. 799
27.1. Dynamic Statistics Views .................................................................................... 820
27.2. Collected Statistics Views .................................................................................... 821
27.3. pg_stat_activity View ............................................................................... 824
27.4. Wait Event Types .............................................................................................. 825
27.5. Wait Events of Type Activity .......................................................................... 826
27.6. Wait Events of Type Bufferpin ........................................................................ 827
27.7. Wait Events of Type Client .............................................................................. 827
27.8. Wait Events of Type Extension ........................................................................ 827
27.9. Wait Events of Type Io ..................................................................................... 828
27.10. Wait Events of Type Ipc .................................................................................. 830
27.11. Wait Events of Type Lock ................................................................................ 833
27.12. Wait Events of Type Lwlock ............................................................................ 833
27.13. Wait Events of Type Timeout .......................................................................... 836
27.14. pg_stat_replication View ....................................................................... 838
27.15. pg_stat_replication_slots View ........................................................... 840
xxv
PostgreSQL 17.4 Documentation
27.16. pg_stat_wal_receiver View ..................................................................... 841
27.17. pg_stat_recovery_prefetch View ........................................................... 842
27.18. pg_stat_subscription View ..................................................................... 842
27.19. pg_stat_subscription_stats View ......................................................... 843
27.20. pg_stat_ssl View ....................................................................................... 843
27.21. pg_stat_gssapi View ................................................................................. 844
27.22. pg_stat_archiver View ............................................................................. 844
27.23. pg_stat_io View ......................................................................................... 845
27.24. pg_stat_bgwriter View ............................................................................. 847
27.25. pg_stat_checkpointer View ..................................................................... 848
27.26. pg_stat_wal View ....................................................................................... 848
27.27. pg_stat_database View ............................................................................. 849
27.28. pg_stat_database_conflicts View ......................................................... 851
27.29. pg_stat_all_tables View ......................................................................... 851
27.30. pg_stat_all_indexes View ....................................................................... 853
27.31. pg_statio_all_tables View ..................................................................... 854
27.32. pg_statio_all_indexes View ................................................................... 854
27.33. pg_statio_all_sequences View ............................................................... 855
27.34. pg_stat_user_functions View ................................................................. 855
27.35. pg_stat_slru View ..................................................................................... 856
27.36. Additional Statistics Functions ............................................................................ 857
27.37. Per-Backend Statistics Functions ......................................................................... 859
27.38. pg_stat_progress_analyze View ............................................................. 860
27.39. ANALYZE Phases ............................................................................................ 861
27.40. pg_stat_progress_cluster View ............................................................. 861
27.41. CLUSTER and VACUUM FULL Phases .............................................................. 862
27.42. pg_stat_progress_copy View ................................................................... 862
27.43. pg_stat_progress_create_index View ................................................... 863
27.44. CREATE INDEX Phases ................................................................................... 864
27.45. pg_stat_progress_vacuum View ............................................................... 865
27.46. VACUUM Phases ............................................................................................ 866
27.47. pg_stat_progress_basebackup View ....................................................... 867
27.48. Base Backup Phases ......................................................................................... 867
27.49. Built-in DTrace Probes ...................................................................................... 868
27.50. Defined Types Used in Probe Parameters ............................................................. 874
29.1. UPDATE Transformation Summary ....................................................................... 897
32.1. SSL Mode Descriptions ..................................................................................... 1013
32.2. Libpq/Client SSL File Usage .............................................................................. 1013
33.1. SQL-Oriented Large Object Functions .................................................................. 1033
34.1. Mapping Between PostgreSQL Data Types and C Variable Types ............................. 1049
34.2. Valid Input Formats for PGTYPESdate_from_asc ............................................ 1067
34.3. Valid Input Formats for PGTYPESdate_fmt_asc .............................................. 1069
34.4. Valid Input Formats for rdefmtdate ................................................................ 1070
34.5. Valid Input Formats for PGTYPEStimestamp_from_asc ................................... 1071
35.1. information_schema_catalog_name Columns ........................................... 1149
35.2. administrable_role_authorizations Columns ....................................... 1149
35.3. applicable_roles Columns ........................................................................ 1149
35.4. attributes Columns .................................................................................... 1150
35.5. character_sets Columns ............................................................................ 1152
35.6. check_constraint_routine_usage Columns ............................................. 1153
35.7. check_constraints Columns ...................................................................... 1154
35.8. collations Columns .................................................................................... 1154
35.9. collation_character_set_applicability Columns ............................... 1154
35.10. column_column_usage Columns ................................................................. 1155
35.11. column_domain_usage Columns ................................................................. 1155
35.12. column_options Columns ........................................................................... 1156
35.13. column_privileges Columns ..................................................................... 1156
35.14. column_udt_usage Columns ....................................................................... 1157
xxvi
PostgreSQL 17.4 Documentation
35.15. columns Columns ......................................................................................... 1157
35.16. constraint_column_usage Columns ......................................................... 1160
35.17. constraint_table_usage Columns ........................................................... 1161
35.18. data_type_privileges Columns ............................................................... 1162
35.19. domain_constraints Columns ................................................................... 1162
35.20. domain_udt_usage Columns ....................................................................... 1163
35.21. domains Columns ......................................................................................... 1163
35.22. element_types Columns ............................................................................. 1165
35.23. enabled_roles Columns ............................................................................. 1167
35.24. foreign_data_wrapper_options Columns ............................................... 1167
35.25. foreign_data_wrappers Columns ............................................................. 1168
35.26. foreign_server_options Columns ........................................................... 1168
35.27. foreign_servers Columns ......................................................................... 1169
35.28. foreign_table_options Columns ............................................................. 1169
35.29. foreign_tables Columns ........................................................................... 1169
35.30. key_column_usage Columns ....................................................................... 1170
35.31. parameters Columns ................................................................................... 1171
35.32. referential_constraints Columns ......................................................... 1172
35.33. role_column_grants Columns ................................................................... 1173
35.34. role_routine_grants Columns ................................................................. 1174
35.35. role_table_grants Columns ..................................................................... 1174
35.36. role_udt_grants Columns ......................................................................... 1175
35.37. role_usage_grants Columns ..................................................................... 1175
35.38. routine_column_usage Columns ............................................................... 1176
35.39. routine_privileges Columns ................................................................... 1177
35.40. routine_routine_usage Columns ............................................................. 1177
35.41. routine_sequence_usage Columns ........................................................... 1178
35.42. routine_table_usage Columns ................................................................. 1178
35.43. routines Columns ....................................................................................... 1179
35.44. schemata Columns ....................................................................................... 1183
35.45. sequences Columns ..................................................................................... 1184
35.46. sql_features Columns ............................................................................... 1185
35.47. sql_implementation_info Columns ......................................................... 1185
35.48. sql_parts Columns ..................................................................................... 1186
35.49. sql_sizing Columns ................................................................................... 1186
35.50. table_constraints Columns ..................................................................... 1186
35.51. table_privileges Columns ....................................................................... 1187
35.52. tables Columns ........................................................................................... 1188
35.53. transforms Columns ................................................................................... 1188
35.54. triggered_update_columns Columns ....................................................... 1189
35.55. triggers Columns ....................................................................................... 1190
35.56. udt_privileges Columns ........................................................................... 1191
35.57. usage_privileges Columns ....................................................................... 1192
35.58. user_defined_types Columns ................................................................... 1192
35.59. user_mapping_options Columns ............................................................... 1194
35.60. user_mappings Columns ............................................................................. 1194
35.61. view_column_usage Columns ..................................................................... 1195
35.62. view_routine_usage Columns ................................................................... 1195
35.63. view_table_usage Columns ....................................................................... 1196
35.64. views Columns ............................................................................................. 1196
36.1. Polymorphic Types ........................................................................................... 1205
36.2. Equivalent C Types for Built-in SQL Types .......................................................... 1231
36.3. B-Tree Strategies .............................................................................................. 1269
36.4. Hash Strategies ................................................................................................ 1269
36.5. GiST Two-Dimensional “R-tree” Strategies ........................................................... 1269
36.6. SP-GiST Point Strategies ................................................................................... 1269
36.7. GIN Array Strategies ........................................................................................ 1270
36.8. BRIN Minmax Strategies ................................................................................... 1270
xxvii
PostgreSQL 17.4 Documentation
36.9. B-Tree Support Functions .................................................................................. 1271
36.10. Hash Support Functions ................................................................................... 1271
36.11. GiST Support Functions ................................................................................... 1271
36.12. SP-GiST Support Functions .............................................................................. 1272
36.13. GIN Support Functions .................................................................................... 1272
36.14. BRIN Support Functions .................................................................................. 1273
38.1. Event Trigger Support by Command Tag .............................................................. 1305
41.1. Available Diagnostics Items ............................................................................... 1362
41.2. Error Diagnostics Items ..................................................................................... 1376
297. Policies Applied by Command Type ..................................................................... 1776
298. pgbench Automatic Variables .............................................................................. 2114
299. pgbench Operators ............................................................................................. 2117
300. pgbench Functions ............................................................................................. 2119
51.1. System Catalogs ............................................................................................... 2303
51.2. pg_aggregate Columns ................................................................................ 2305
51.3. pg_am Columns .............................................................................................. 2306
51.4. pg_amop Columns .......................................................................................... 2307
51.5. pg_amproc Columns ...................................................................................... 2308
51.6. pg_attrdef Columns .................................................................................... 2308
51.7. pg_attribute Columns ................................................................................ 2309
51.8. pg_authid Columns ...................................................................................... 2311
51.9. pg_auth_members Columns .......................................................................... 2312
51.10. pg_cast Columns ......................................................................................... 2313
51.11. pg_class Columns ....................................................................................... 2313
51.12. pg_collation Columns ............................................................................... 2316
51.13. pg_constraint Columns ............................................................................. 2317
51.14. pg_conversion Columns ............................................................................. 2318
51.15. pg_database Columns ................................................................................. 2319
51.16. pg_db_role_setting Columns ................................................................... 2320
51.17. pg_default_acl Columns ........................................................................... 2321
51.18. pg_depend Columns ..................................................................................... 2321
51.19. pg_description Columns ........................................................................... 2323
51.20. pg_enum Columns ......................................................................................... 2324
51.21. pg_event_trigger Columns ....................................................................... 2324
51.22. pg_extension Columns ............................................................................... 2325
51.23. pg_foreign_data_wrapper Columns ......................................................... 2325
51.24. pg_foreign_server Columns ..................................................................... 2326
51.25. pg_foreign_table Columns ....................................................................... 2327
51.26. pg_index Columns ....................................................................................... 2327
51.27. pg_inherits Columns ................................................................................. 2328
51.28. pg_init_privs Columns ............................................................................. 2329
51.29. pg_language Columns ................................................................................. 2329
51.30. pg_largeobject Columns ........................................................................... 2330
51.31. pg_largeobject_metadata Columns ......................................................... 2331
51.32. pg_namespace Columns ............................................................................... 2331
51.33. pg_opclass Columns ................................................................................... 2331
51.34. pg_operator Columns ................................................................................. 2332
51.35. pg_opfamily Columns ................................................................................. 2333
51.36. pg_parameter_acl Columns ....................................................................... 2333
51.37. pg_partitioned_table Columns ............................................................... 2334
51.38. pg_policy Columns ..................................................................................... 2334
51.39. pg_proc Columns ......................................................................................... 2335
51.40. pg_publication Columns ........................................................................... 2338
51.41. pg_publication_namespace Columns ....................................................... 2338
51.42. pg_publication_rel Columns ................................................................... 2338
51.43. pg_range Columns ....................................................................................... 2339
51.44. pg_replication_origin Columns ............................................................. 2340
51.45. pg_rewrite Columns ................................................................................... 2340
xxviii
PostgreSQL 17.4 Documentation
51.46. pg_seclabel Columns ................................................................................. 2341
51.47. pg_sequence Columns ................................................................................. 2341
51.48. pg_shdepend Columns ................................................................................. 2342
51.49. pg_shdescription Columns ....................................................................... 2343
51.50. pg_shseclabel Columns ............................................................................. 2343
51.51. pg_statistic Columns ............................................................................... 2344
51.52. pg_statistic_ext Columns ....................................................................... 2345
51.53. pg_statistic_ext_data Columns ............................................................. 2346
51.54. pg_subscription Columns ......................................................................... 2346
51.55. pg_subscription_rel Columns ................................................................. 2348
51.56. pg_tablespace Columns ............................................................................. 2348
51.57. pg_transform Columns ............................................................................... 2348
51.58. pg_trigger Columns ................................................................................... 2349
51.59. pg_ts_config Columns ............................................................................... 2350
51.60. pg_ts_config_map Columns ....................................................................... 2351
51.61. pg_ts_dict Columns ................................................................................... 2351
51.62. pg_ts_parser Columns ............................................................................... 2352
51.63. pg_ts_template Columns ........................................................................... 2352
51.64. pg_type Columns ......................................................................................... 2353
51.65. typcategory Codes .................................................................................... 2356
51.66. pg_user_mapping Columns ......................................................................... 2356
52.1. System Views .................................................................................................. 2357
52.2. pg_available_extensions Columns .......................................................... 2358
52.3. pg_available_extension_versions Columns ........................................... 2358
52.4. pg_backend_memory_contexts Columns ..................................................... 2359
52.5. pg_config Columns ...................................................................................... 2360
52.6. pg_cursors Columns .................................................................................... 2360
52.7. pg_file_settings Columns ........................................................................ 2361
52.8. pg_group Columns ........................................................................................ 2362
52.9. pg_hba_file_rules Columns ...................................................................... 2362
52.10. pg_ident_file_mappings Columns ........................................................... 2363
52.11. pg_indexes Columns ................................................................................... 2363
52.12. pg_locks Columns ....................................................................................... 2364
52.13. pg_matviews Columns ................................................................................. 2367
52.14. pg_policies Columns ................................................................................. 2367
52.15. pg_prepared_statements Columns ........................................................... 2368
52.16. pg_prepared_xacts Columns ..................................................................... 2368
52.17. pg_publication_tables Columns ............................................................. 2369
52.18. pg_replication_origin_status Columns ............................................... 2369
52.19. pg_replication_slots Columns ............................................................... 2370
52.20. pg_roles Columns ....................................................................................... 2372
52.21. pg_rules Columns ....................................................................................... 2372
52.22. pg_seclabels Columns ............................................................................... 2373
52.23. pg_sequences Columns ............................................................................... 2373
52.24. pg_settings Columns ................................................................................. 2374
52.25. pg_shadow Columns ..................................................................................... 2376
52.26. pg_shmem_allocations Columns ............................................................... 2377
52.27. pg_stats Columns ....................................................................................... 2378
52.28. pg_stats_ext Columns ............................................................................... 2379
52.29. pg_stats_ext_exprs Columns ................................................................... 2381
52.30. pg_tables Columns ..................................................................................... 2382
52.31. pg_timezone_abbrevs Columns ................................................................. 2382
52.32. pg_timezone_names Columns ..................................................................... 2383
52.33. pg_user Columns ......................................................................................... 2383
52.34. pg_user_mappings Columns ....................................................................... 2384
52.35. pg_views Columns ....................................................................................... 2384
52.36. pg_wait_events Columns ........................................................................... 2385
64.1. Built-in GiST Operator Classes ........................................................................... 2523
xxix
PostgreSQL 17.4 Documentation
64.2. Built-in SP-GiST Operator Classes ...................................................................... 2540
64.3. Built-in GIN Operator Classes ............................................................................ 2553
64.4. Built-in BRIN Operator Classes .......................................................................... 2560
64.5. Function and Support Numbers for Minmax Operator Classes ................................... 2569
64.6. Function and Support Numbers for Inclusion Operator Classes ................................. 2569
64.7. Procedure and Support Numbers for Bloom Operator Classes ................................... 2570
64.8. Procedure and Support Numbers for minmax-multi Operator Classes ......................... 2571
65.1. Contents of PGDATA ......................................................................................... 2574
65.2. Page Layout .................................................................................................... 2580
65.3. PageHeaderData Layout ..................................................................................... 2581
65.4. HeapTupleHeaderData Layout ............................................................................ 2582
A.1. PostgreSQL Error Codes ..................................................................................... 2614
B.1. Month Names ................................................................................................... 2625
B.2. Day of the Week Names ..................................................................................... 2625
B.3. Date/Time Field Modifiers .................................................................................. 2625
C.1. SQL Key Words ................................................................................................ 2631
F.1. Cube External Representations ............................................................................. 2739
F.2. Cube Operators .................................................................................................. 2739
F.3. Cube Functions .................................................................................................. 2740
F.4. Cube-Based Earthdistance Functions ..................................................................... 2779
F.5. Point-Based Earthdistance Operators ..................................................................... 2780
F.6. hstore Operators ............................................................................................. 2790
F.7. hstore Functions ............................................................................................. 2791
F.8. intarray Functions ......................................................................................... 2799
F.9. intarray Operators ......................................................................................... 2800
F.10. isn Data Types ............................................................................................... 2803
F.11. isn Functions ................................................................................................. 2804
F.12. ltree Operators ............................................................................................. 2810
F.13. ltree Functions ............................................................................................. 2812
F.14. pg_buffercache Columns ............................................................................ 2829
F.15. pg_buffercache_summary() Output Columns .............................................. 2830
F.16. pg_buffercache_usage_counts() Output Columns .................................... 2830
F.17. Supported Algorithms for crypt() ................................................................... 2834
F.18. Iteration Counts for crypt() ........................................................................... 2834
F.19. Hash Algorithm Speeds ..................................................................................... 2835
F.20. pgrowlocks Output Columns .......................................................................... 2847
F.21. pg_stat_statements Columns .................................................................... 2849
F.22. pg_stat_statements_info Columns .......................................................... 2853
F.23. pgstattuple Output Columns ........................................................................ 2858
F.24. pgstattuple_approx Output Columns .......................................................... 2861
F.25. pg_trgm Functions ......................................................................................... 2865
F.26. pg_trgm Operators ......................................................................................... 2866
F.27. seg External Representations ............................................................................. 2889
F.28. Examples of Valid seg Input ............................................................................. 2889
F.29. Seg GiST Operators .......................................................................................... 2889
F.30. Sepgsql Functions ............................................................................................ 2897
F.31. tablefunc Functions ..................................................................................... 2903
F.32. connectby Parameters ................................................................................... 2910
F.33. Functions for UUID Generation .......................................................................... 2921
F.34. Functions Returning UUID Constants .................................................................. 2922
F.35. xml2 Functions ............................................................................................... 2923
F.36. xpath_table Parameters ............................................................................... 2924
K.1. PostgreSQL Limitations ...................................................................................... 2945
xxx
List of Examples
8.1. Using the Character Types .................................................................................... 157
8.2. Using the boolean Type ..................................................................................... 170
8.3. Using the Bit String Types .................................................................................... 178
9.1. XSLT Stylesheet for Converting SQL/XML Output to HTML ..................................... 325
10.1. Square Root Operator Type Resolution .................................................................. 437
10.2. String Concatenation Operator Type Resolution ....................................................... 438
10.3. Absolute-Value and Negation Operator Type Resolution ........................................... 438
10.4. Array Inclusion Operator Type Resolution .............................................................. 439
10.5. Custom Operator on a Domain Type ..................................................................... 439
10.6. Rounding Function Argument Type Resolution ....................................................... 442
10.7. Variadic Function Resolution ............................................................................... 442
10.8. Substring Function Type Resolution ...................................................................... 443
10.9. character Storage Type Conversion .................................................................. 444
10.10. Type Resolution with Underspecified Types in a Union ........................................... 445
10.11. Type Resolution in a Simple Union ..................................................................... 445
10.12. Type Resolution in a Transposed Union ............................................................... 446
10.13. Type Resolution in a Nested Union ..................................................................... 446
11.1. Setting up a Partial Index to Exclude Common Values .............................................. 455
11.2. Setting up a Partial Index to Exclude Uninteresting Values ........................................ 456
11.3. Setting up a Partial Unique Index ......................................................................... 457
11.4. Do Not Use Partial Indexes as a Substitute for Partitioning ........................................ 457
20.1. Example pg_hba.conf Entries .......................................................................... 713
20.2. An Example pg_ident.conf File ..................................................................... 717
32.1. libpq Example Program 1 .................................................................................. 1017
32.2. libpq Example Program 2 .................................................................................. 1019
32.3. libpq Example Program 3 .................................................................................. 1022
33.1. Large Objects with libpq Example Program .......................................................... 1034
34.1. Example SQLDA Program ................................................................................. 1087
34.2. ECPG Program Accessing Large Objects .............................................................. 1101
40.1. Manual Installation of PL/Perl ............................................................................ 1343
41.1. Quoting Values in Dynamic Queries .................................................................... 1360
41.2. Exceptions with UPDATE/INSERT ...................................................................... 1375
41.3. A PL/pgSQL Trigger Function ............................................................................ 1389
41.4. A PL/pgSQL Trigger Function for Auditing .......................................................... 1390
41.5. A PL/pgSQL View Trigger Function for Auditing .................................................. 1391
41.6. A PL/pgSQL Trigger Function for Maintaining a Summary Table ............................. 1392
41.7. Auditing with Transition Tables .......................................................................... 1394
41.8. A PL/pgSQL Event Trigger Function ................................................................... 1396
41.9. Porting a Simple Function from PL/SQL to PL/pgSQL ............................................ 1404
41.10. Porting a Function that Creates Another Function from PL/SQL to PL/pgSQL ............ 1405
41.11. Porting a Procedure With String Manipulation and OUT Parameters from PL/SQL to
PL/pgSQL ............................................................................................................... 1406
41.12. Porting a Procedure from PL/SQL to PL/pgSQL .................................................. 1408
F.1. Create a Foreign Table for PostgreSQL CSV Logs ................................................... 2782
F.2. Create a Foreign Table with an Option on a Column ................................................ 2783
xxxi
Preface
This book is the official documentation of PostgreSQL. It has been written by the PostgreSQL developers and other volunteers in parallel to the development of the PostgreSQL software. It describes all
the functionality that the current version of PostgreSQL officially supports.
To make the large amount of information about PostgreSQL manageable, this book has been organized
in several parts. Each part is targeted at a different class of users, or at users in different stages of their
PostgreSQL experience:
• Part I is an informal introduction for new users.
• Part II documents the SQL query language environment, including data types and functions, as well
as user-level performance tuning. Every PostgreSQL user should read this.
• Part III describes the installation and administration of the server. Everyone who runs a PostgreSQL
server, be it for private use or for others, should read this part.
• Part IV describes the programming interfaces for PostgreSQL client programs.
• Part V contains information for advanced users about the extensibility capabilities of the server.
Topics include user-defined data types and functions.
• Part VI contains reference information about SQL commands, client and server programs. This part
supports the other parts with structured information sorted by command or program.
• Part VII contains assorted information that might be of use to PostgreSQL developers.
1. What Is PostgreSQL?
PostgreSQL is an object-relational database management system (ORDBMS) based on POSTGRES,
Version 4.21, developed at the University of California at Berkeley Computer Science Department.
POSTGRES pioneered many concepts that only became available in some commercial database systems much later.
PostgreSQL is an open-source descendant of this original Berkeley code. It supports a large part of
the SQL standard and offers many modern features:
• complex queries
• foreign keys
• triggers
• updatable views
• transactional integrity
• multiversion concurrency control
Also, PostgreSQL can be extended by the user in many ways, for example by adding new
• data types
• functions
• operators
• aggregate functions
• index methods
• procedural languages
And because of the liberal license, PostgreSQL can be used, modified, and distributed by anyone free
of charge for any purpose, be it private, commercial, or academic.
2. A Brief History of PostgreSQL
1
https://dsf.berkeley.edu/postgres.html
xxxii
Preface
The object-relational database management system now known as PostgreSQL is derived from the
POSTGRES package written at the University of California at Berkeley. With decades of development
behind it, PostgreSQL is now the most advanced open-source database available anywhere.
2.1. The Berkeley POSTGRES Project
The POSTGRES project, led by Professor Michael Stonebraker, was sponsored by the Defense Advanced Research Projects Agency (DARPA), the Army Research Office (ARO), the National Science
Foundation (NSF), and ESL, Inc. The implementation of POSTGRES began in 1986. The initial concepts for the system were presented in [ston86], and the definition of the initial data model appeared
in [rowe87]. The design of the rule system at that time was described in [ston87a]. The rationale and
architecture of the storage manager were detailed in [ston87b].
POSTGRES has undergone several major releases since then. The first “demoware” system became
operational in 1987 and was shown at the 1988 ACM-SIGMOD Conference. Version 1, described in
[ston90a], was released to a few external users in June 1989. In response to a critique of the first rule
system ([ston89]), the rule system was redesigned ([ston90b]), and Version 2 was released in June
1990 with the new rule system. Version 3 appeared in 1991 and added support for multiple storage
managers, an improved query executor, and a rewritten rule system. For the most part, subsequent
releases until Postgres95 (see below) focused on portability and reliability.
POSTGRES has been used to implement many different research and production applications. These
include: a financial data analysis system, a jet engine performance monitoring package, an asteroid tracking database, a medical information database, and several geographic information systems.
POSTGRES has also been used as an educational tool at several universities. Finally, Illustra Information Technologies (later merged into Informix2, which is now owned by IBM3) picked up the code
and commercialized it. In late 1992, POSTGRES became the primary data manager for the Sequoia
2000 scientific computing project described in [ston92].
The size of the external user community nearly doubled during 1993. It became increasingly obvious
that maintenance of the prototype code and support was taking up large amounts of time that should
have been devoted to database research. In an effort to reduce this support burden, the Berkeley POSTGRES project officially ended with Version 4.2.
2.2. Postgres95
In 1994, Andrew Yu and Jolly Chen added an SQL language interpreter to POSTGRES. Under a new
name, Postgres95 was subsequently released to the web to find its own way in the world as an opensource descendant of the original POSTGRES Berkeley code.
Postgres95 code was completely ANSI C and trimmed in size by 25%. Many internal changes improved performance and maintainability. Postgres95 release 1.0.x ran about 30–50% faster on the
Wisconsin Benchmark compared to POSTGRES, Version 4.2. Apart from bug fixes, the following
were the major enhancements:
• The query language PostQUEL was replaced with SQL (implemented in the server). (Interface library libpq was named after PostQUEL.) Subqueries were not supported until PostgreSQL (see below), but they could be imitated in Postgres95 with user-defined SQL functions. Aggregate functions were re-implemented. Support for the GROUP BY query clause was also added.
• A new program (psql) was provided for interactive SQL queries, which used GNU Readline. This
largely superseded the old monitor program.
• A new front-end library, libpgtcl, supported Tcl-based clients. A sample shell, pgtclsh, provided new Tcl commands to interface Tcl programs with the Postgres95 server.
2
3
https://www.ibm.com/analytics/informix
https://www.ibm.com/
xxxiii
Preface
• The large-object interface was overhauled. The inversion large objects were the only mechanism
for storing large objects. (The inversion file system was removed.)
• The instance-level rule system was removed. Rules were still available as rewrite rules.
• A short tutorial introducing regular SQL features as well as those of Postgres95 was distributed
with the source code
• GNU make (instead of BSD make) was used for the build. Also, Postgres95 could be compiled with
an unpatched GCC (data alignment of doubles was fixed).
2.3. PostgreSQL
By 1996, it became clear that the name “Postgres95” would not stand the test of time. We chose a new
name, PostgreSQL, to reflect the relationship between the original POSTGRES and the more recent
versions with SQL capability. At the same time, we set the version numbering to start at 6.0, putting
the numbers back into the sequence originally begun by the Berkeley POSTGRES project.
Many people continue to refer to PostgreSQL as “Postgres” (now rarely in all capital letters) because
of tradition or because it is easier to pronounce. This usage is widely accepted as a nickname or alias.
The emphasis during development of Postgres95 was on identifying and understanding existing problems in the server code. With PostgreSQL, the emphasis has shifted to augmenting features and capabilities, although work continues in all areas.
Details about what has happened in PostgreSQL since then can be found in Appendix E.
3. Conventions
The following conventions are used in the synopsis of a command: brackets ([ and ]) indicate optional
parts. Braces ({ and }) and vertical lines (|) indicate that you must choose one alternative. Dots (...)
mean that the preceding element can be repeated. All other symbols, including parentheses, should
be taken literally.
Where it enhances the clarity, SQL commands are preceded by the prompt =>, and shell commands
are preceded by the prompt $. Normally, prompts are not shown, though.
An administrator is generally a person who is in charge of installing and running the server. A user
could be anyone who is using, or wants to use, any part of the PostgreSQL system. These terms
should not be interpreted too narrowly; this book does not have fixed presumptions about system
administration procedures.
4. Further Information
Besides the documentation, that is, this book, there are other resources about PostgreSQL:
Wiki
The PostgreSQL wiki4 contains the project's FAQ5 (Frequently Asked Questions) list, TODO6
list, and detailed information about many more topics.
Web Site
The PostgreSQL web site7 carries details on the latest release and other information to make your
work or play with PostgreSQL more productive.
4
https://wiki.postgresql.org
https://wiki.postgresql.org/wiki/Frequently_Asked_Questions
6
https://wiki.postgresql.org/wiki/Todo
7
https://www.postgresql.org
5
xxxiv
Preface
Mailing Lists
The mailing lists are a good place to have your questions answered, to share experiences with
other users, and to contact the developers. Consult the PostgreSQL web site for details.
Yourself!
PostgreSQL is an open-source project. As such, it depends on the user community for ongoing
support. As you begin to use PostgreSQL, you will rely on others for help, either through the
documentation or through the mailing lists. Consider contributing your knowledge back. Read
the mailing lists and answer questions. If you learn something which is not in the documentation,
write it up and contribute it. If you add features to the code, contribute them.
5. Bug Reporting Guidelines
When you find a bug in PostgreSQL we want to hear about it. Your bug reports play an important part
in making PostgreSQL more reliable because even the utmost care cannot guarantee that every part
of PostgreSQL will work on every platform under every circumstance.
The following suggestions are intended to assist you in forming bug reports that can be handled in an
effective fashion. No one is required to follow them but doing so tends to be to everyone's advantage.
We cannot promise to fix every bug right away. If the bug is obvious, critical, or affects a lot of users,
chances are good that someone will look into it. It could also happen that we tell you to update to
a newer version to see if the bug happens there. Or we might decide that the bug cannot be fixed
before some major rewrite we might be planning is done. Or perhaps it is simply too hard and there are
more important things on the agenda. If you need help immediately, consider obtaining a commercial
support contract.
5.1. Identifying Bugs
Before you report a bug, please read and re-read the documentation to verify that you can really do
whatever it is you are trying. If it is not clear from the documentation whether you can do something
or not, please report that too; it is a bug in the documentation. If it turns out that a program does
something different from what the documentation says, that is a bug. That might include, but is not
limited to, the following circumstances:
• A program terminates with a fatal signal or an operating system error message that would point to
a problem in the program. (A counterexample might be a “disk full” message, since you have to
fix that yourself.)
• A program produces the wrong output for any given input.
• A program refuses to accept valid input (as defined in the documentation).
• A program accepts invalid input without a notice or error message. But keep in mind that your idea
of invalid input might be our idea of an extension or compatibility with traditional practice.
• PostgreSQL fails to compile, build, or install according to the instructions on supported platforms.
Here “program” refers to any executable, not only the backend process.
Being slow or resource-hogging is not necessarily a bug. Read the documentation or ask on one of
the mailing lists for help in tuning your applications. Failing to comply to the SQL standard is not
necessarily a bug either, unless compliance for the specific feature is explicitly claimed.
Before you continue, check on the TODO list and in the FAQ to see if your bug is already known.
If you cannot decode the information on the TODO list, report your problem. The least we can do is
make the TODO list clearer.
xxxv
Preface
5.2. What to Report
The most important thing to remember about bug reporting is to state all the facts and only facts. Do
not speculate what you think went wrong, what “it seemed to do”, or which part of the program has a
fault. If you are not familiar with the implementation you would probably guess wrong and not help
us a bit. And even if you are, educated explanations are a great supplement to but no substitute for
facts. If we are going to fix the bug we still have to see it happen for ourselves first. Reporting the
bare facts is relatively straightforward (you can probably copy and paste them from the screen) but
all too often important details are left out because someone thought it does not matter or the report
would be understood anyway.
The following items should be contained in every bug report:
• The exact sequence of steps from program start-up necessary to reproduce the problem. This should
be self-contained; it is not enough to send in a bare SELECT statement without the preceding CREATE TABLE and INSERT statements, if the output should depend on the data in the tables. We
do not have the time to reverse-engineer your database schema, and if we are supposed to make up
our own data we would probably miss the problem.
The best format for a test case for SQL-related problems is a file that can be run through the psql
frontend that shows the problem. (Be sure to not have anything in your ~/.psqlrc start-up file.)
An easy way to create this file is to use pg_dump to dump out the table declarations and data needed
to set the scene, then add the problem query. You are encouraged to minimize the size of your
example, but this is not absolutely necessary. If the bug is reproducible, we will find it either way.
If your application uses some other client interface, such as PHP, then please try to isolate the
offending queries. We will probably not set up a web server to reproduce your problem. In any case
remember to provide the exact input files; do not guess that the problem happens for “large files”
or “midsize databases”, etc. since this information is too inexact to be of use.
• The output you got. Please do not say that it “didn't work” or “crashed”. If there is an error message,
show it, even if you do not understand it. If the program terminates with an operating system error,
say which. If nothing at all happens, say so. Even if the result of your test case is a program crash
or otherwise obvious it might not happen on our platform. The easiest thing is to copy the output
from the terminal, if possible.
Note
If you are reporting an error message, please obtain the most verbose form of the message.
In psql, say \set VERBOSITY verbose beforehand. If you are extracting the message
from the server log, set the run-time parameter log_error_verbosity to verbose so that all
details are logged.
Note
In case of fatal errors, the error message reported by the client might not contain all the
information available. Please also look at the log output of the database server. If you do
not keep your server's log output, this would be a good time to start doing so.
• The output you expected is very important to state. If you just write “This command gives me that
output.” or “This is not what I expected.”, we might run it ourselves, scan the output, and think it
looks OK and is exactly what we expected. We should not have to spend the time to decode the
exact semantics behind your commands. Especially refrain from merely saying that “This is not
what SQL says/Oracle does.” Digging out the correct behavior from SQL is not a fun undertaking,
xxxvi
Preface
nor do we all know how all the other relational databases out there behave. (If your problem is a
program crash, you can obviously omit this item.)
• Any command line options and other start-up options, including any relevant environment variables
or configuration files that you changed from the default. Again, please provide exact information.
If you are using a prepackaged distribution that starts the database server at boot time, you should
try to find out how that is done.
• Anything you did at all differently from the installation instructions.
• The PostgreSQL version. You can run the command SELECT version(); to find out the version
of the server you are connected to. Most executable programs also support a --version option;
at least postgres --version and psql --version should work. If the function or the
options do not exist then your version is more than old enough to warrant an upgrade. If you run a
prepackaged version, such as RPMs, say so, including any subversion the package might have. If
you are talking about a Git snapshot, mention that, including the commit hash.
If your version is older than 17.4 we will almost certainly tell you to upgrade. There are many bug
fixes and improvements in each new release, so it is quite possible that a bug you have encountered
in an older release of PostgreSQL has already been fixed. We can only provide limited support
for sites using older releases of PostgreSQL; if you require more than we can provide, consider
acquiring a commercial support contract.
• Platform information. This includes the kernel name and version, C library, processor, memory
information, and so on. In most cases it is sufficient to report the vendor and version, but do not
assume everyone knows what exactly “Debian” contains or that everyone runs on x86_64. If you
have installation problems then information about the toolchain on your machine (compiler, make,
and so on) is also necessary.
Do not be afraid if your bug report becomes rather lengthy. That is a fact of life. It is better to report
everything the first time than us having to squeeze the facts out of you. On the other hand, if your
input files are huge, it is fair to ask first whether somebody is interested in looking into it. Here is an
article8 that outlines some more tips on reporting bugs.
Do not spend all your time to figure out which changes in the input make the problem go away. This
will probably not help solving it. If it turns out that the bug cannot be fixed right away, you will still
have time to find and share your work-around. Also, once again, do not waste your time guessing why
the bug exists. We will find that out soon enough.
When writing a bug report, please avoid confusing terminology. The software package in total is
called “PostgreSQL”, sometimes “Postgres” for short. If you are specifically talking about the backend
process, mention that, do not just say “PostgreSQL crashes”. A crash of a single backend process
is quite different from crash of the parent “postgres” process; please don't say “the server crashed”
when you mean a single backend process went down, nor vice versa. Also, client programs such as the
interactive frontend “psql” are completely separate from the backend. Please try to be specific about
whether the problem is on the client or server side.
5.3. Where to Report Bugs
In general, send bug reports to the bug report mailing list at <pgsql-bugs@lists.postgresql.org>. You are requested to use a descriptive subject for your email message, perhaps parts
of the error message.
Another method is to fill in the bug report web-form available at the project's web site9. Entering
a bug report this way causes it to be mailed to the <pgsql-bugs@lists.postgresql.org>
mailing list.
8
9
https://www.chiark.greenend.org.uk/~sgtatham/bugs.html
https://www.postgresql.org/
xxxvii
Preface
If your bug report has security implications and you'd prefer that it not become immediately visible
in public archives, don't send it to pgsql-bugs. Security issues can be reported privately to <security@postgresql.org>.
Do not send bug reports to any of the user mailing lists, such as <pgsql-sql@lists.postgresql.org> or <pgsql-general@lists.postgresql.org>. These mailing lists are for
answering user questions, and their subscribers normally do not wish to receive bug reports. More
importantly, they are unlikely to fix them.
Also, please do not send reports to the developers' mailing list <pgsql-hackers@lists.postgresql.org>. This list is for discussing the development of PostgreSQL, and it would be nice if we
could keep the bug reports separate. We might choose to take up a discussion about your bug report
on pgsql-hackers, if the problem needs more review.
If you have a problem with the documentation, the best place to report it is the documentation mailing
list <pgsql-docs@lists.postgresql.org>. Please be specific about what part of the documentation you are unhappy with.
If your bug is a portability problem on a non-supported platform, send mail to <pgsql-hackers@lists.postgresql.org>, so we (and you) can work on porting PostgreSQL to your platform.
Note
Due to the unfortunate amount of spam going around, all of the above lists will be moderated
unless you are subscribed. That means there will be some delay before the email is delivered.
If you wish to subscribe to the lists, please visit https://lists.postgresql.org/ for instructions.
xxxviii
Part I. Tutorial
Welcome to the PostgreSQL Tutorial. The tutorial is intended to give an introduction to PostgreSQL, relational
database concepts, and the SQL language. We assume some general knowledge about how to use computers and
no particular Unix or programming experience is required. This tutorial is intended to provide hands-on experience
with important aspects of the PostgreSQL system. It makes no attempt to be a comprehensive treatment of the
topics it covers.
After you have successfully completed this tutorial you will want to read the Part II section to gain a better understanding of the SQL language, or Part IV for information about developing applications with PostgreSQL. Those
who provision and manage their own PostgreSQL installation should also read Part III.
Table of Contents
1. Getting Started .......................................................................................................... 3
1.1. Installation ..................................................................................................... 3
1.2. Architectural Fundamentals ............................................................................... 3
1.3. Creating a Database ......................................................................................... 3
1.4. Accessing a Database ...................................................................................... 5
2. The SQL Language .................................................................................................... 7
2.1. Introduction .................................................................................................... 7
2.2. Concepts ........................................................................................................ 7
2.3. Creating a New Table ...................................................................................... 7
2.4. Populating a Table With Rows .......................................................................... 8
2.5. Querying a Table ............................................................................................ 9
2.6. Joins Between Tables ..................................................................................... 11
2.7. Aggregate Functions ...................................................................................... 13
2.8. Updates ....................................................................................................... 15
2.9. Deletions ...................................................................................................... 15
3. Advanced Features ................................................................................................... 17
3.1. Introduction .................................................................................................. 17
3.2. Views .......................................................................................................... 17
3.3. Foreign Keys ................................................................................................ 17
3.4. Transactions ................................................................................................. 18
3.5. Window Functions ......................................................................................... 20
3.6. Inheritance ................................................................................................... 23
3.7. Conclusion ................................................................................................... 24
2
Chapter 1. Getting Started
1.1. Installation
Before you can use PostgreSQL you need to install it, of course. It is possible that PostgreSQL is
already installed at your site, either because it was included in your operating system distribution
or because the system administrator already installed it. If that is the case, you should obtain information from the operating system documentation or your system administrator about how to access
PostgreSQL.
If you are not sure whether PostgreSQL is already available or whether you can use it for your experimentation then you can install it yourself. Doing so is not hard and it can be a good exercise. PostgreSQL can be installed by any unprivileged user; no superuser (root) access is required.
If you are installing PostgreSQL yourself, then refer to Chapter 17 for instructions on installation,
and return to this guide when the installation is complete. Be sure to follow closely the section about
setting up the appropriate environment variables.
If your site administrator has not set things up in the default way, you might have some more work to
do. For example, if the database server machine is a remote machine, you will need to set the PGHOST
environment variable to the name of the database server machine. The environment variable PGPORT
might also have to be set. The bottom line is this: if you try to start an application program and it
complains that it cannot connect to the database, you should consult your site administrator or, if
that is you, the documentation to make sure that your environment is properly set up. If you did not
understand the preceding paragraph then read the next section.
1.2. Architectural Fundamentals
Before we proceed, you should understand the basic PostgreSQL system architecture. Understanding
how the parts of PostgreSQL interact will make this chapter somewhat clearer.
In database jargon, PostgreSQL uses a client/server model. A PostgreSQL session consists of the
following cooperating processes (programs):
• A server process, which manages the database files, accepts connections to the database from client
applications, and performs database actions on behalf of the clients. The database server program
is called postgres.
• The user's client (frontend) application that wants to perform database operations. Client applications can be very diverse in nature: a client could be a text-oriented tool, a graphical application, a
web server that accesses the database to display web pages, or a specialized database maintenance
tool. Some client applications are supplied with the PostgreSQL distribution; most are developed
by users.
As is typical of client/server applications, the client and the server can be on different hosts. In that
case they communicate over a TCP/IP network connection. You should keep this in mind, because
the files that can be accessed on a client machine might not be accessible (or might only be accessible
using a different file name) on the database server machine.
The PostgreSQL server can handle multiple concurrent connections from clients. To achieve this it
starts (“forks”) a new process for each connection. From that point on, the client and the new server
process communicate without intervention by the original postgres process. Thus, the supervisor
server process is always running, waiting for client connections, whereas client and associated server
processes come and go. (All of this is of course invisible to the user. We only mention it here for
completeness.)
1.3. Creating a Database
3
Getting Started
The first test to see whether you can access the database server is to try to create a database. A running
PostgreSQL server can manage many databases. Typically, a separate database is used for each project
or for each user.
Possibly, your site administrator has already created a database for your use. In that case you can omit
this step and skip ahead to the next section.
To create a new database, in this example named mydb, you use the following command:
$ createdb mydb
If this produces no response then this step was successful and you can skip over the remainder of
this section.
If you see a message similar to:
createdb: command not found
then PostgreSQL was not installed properly. Either it was not installed at all or your shell's search path
was not set to include it. Try calling the command with an absolute path instead:
$ /usr/local/pgsql/bin/createdb mydb
The path at your site might be different. Contact your site administrator or check the installation instructions to correct the situation.
Another response could be this:
createdb: error: connection to server on socket "/
tmp/.s.PGSQL.5432" failed: No such file or directory
Is the server running locally and accepting connections on
that socket?
This means that the server was not started, or it is not listening where createdb expects to contact
it. Again, check the installation instructions or consult the administrator.
Another response could be this:
createdb: error: connection to server on socket "/
tmp/.s.PGSQL.5432" failed: FATAL: role "joe" does not exist
where your own login name is mentioned. This will happen if the administrator has not created a
PostgreSQL user account for you. (PostgreSQL user accounts are distinct from operating system user
accounts.) If you are the administrator, see Chapter 21 for help creating accounts. You will need to
become the operating system user under which PostgreSQL was installed (usually postgres) to
create the first user account. It could also be that you were assigned a PostgreSQL user name that is
different from your operating system user name; in that case you need to use the -U switch or set the
PGUSER environment variable to specify your PostgreSQL user name.
If you have a user account but it does not have the privileges required to create a database, you will
see the following:
createdb: error: database creation failed: ERROR:
denied to create database
4
permission
Getting Started
Not every user has authorization to create new databases. If PostgreSQL refuses to create databases
for you then the site administrator needs to grant you permission to create databases. Consult your
site administrator if this occurs. If you installed PostgreSQL yourself then you should log in for the
purposes of this tutorial under the user account that you started the server as. 1
You can also create databases with other names. PostgreSQL allows you to create any number of
databases at a given site. Database names must have an alphabetic first character and are limited to
63 bytes in length. A convenient choice is to create a database with the same name as your current
user name. Many tools assume that database name as the default, so it can save you some typing. To
create that database, simply type:
$ createdb
If you do not want to use your database anymore you can remove it. For example, if you are the owner
(creator) of the database mydb, you can destroy it using the following command:
$ dropdb mydb
(For this command, the database name does not default to the user account name. You always need to
specify it.) This action physically removes all files associated with the database and cannot be undone,
so this should only be done with a great deal of forethought.
More about createdb and dropdb can be found in createdb and dropdb respectively.
1.4. Accessing a Database
Once you have created a database, you can access it by:
• Running the PostgreSQL interactive terminal program, called psql, which allows you to interactively enter, edit, and execute SQL commands.
• Using an existing graphical frontend tool like pgAdmin or an office suite with ODBC or JDBC
support to create and manipulate a database. These possibilities are not covered in this tutorial.
• Writing a custom application, using one of the several available language bindings. These possibilities are discussed further in Part IV.
You probably want to start up psql to try the examples in this tutorial. It can be activated for the
mydb database by typing the command:
$ psql mydb
If you do not supply the database name then it will default to your user account name. You already
discovered this scheme in the previous section using createdb.
In psql, you will be greeted with the following message:
psql (17.4)
Type "help" for help.
mydb=>
The last line could also be:
1
As an explanation for why this works: PostgreSQL user names are separate from operating system user accounts. When you connect to a
database, you can choose what PostgreSQL user name to connect as; if you don't, it will default to the same name as your current operating
system account. As it happens, there will always be a PostgreSQL user account that has the same name as the operating system user that started
the server, and it also happens that that user always has permission to create databases. Instead of logging in as that user you can also specify
the -U option everywhere to select a PostgreSQL user name to connect as.
5
Getting Started
mydb=#
That would mean you are a database superuser, which is most likely the case if you installed the
PostgreSQL instance yourself. Being a superuser means that you are not subject to access controls.
For the purposes of this tutorial that is not important.
If you encounter problems starting psql then go back to the previous section. The diagnostics of
createdb and psql are similar, and if the former worked the latter should work as well.
The last line printed out by psql is the prompt, and it indicates that psql is listening to you and that
you can type SQL queries into a work space maintained by psql. Try out these commands:
mydb=> SELECT version();
version
----------------------------------------------------------------------------------------PostgreSQL 17.4 on x86_64-pc-linux-gnu, compiled by gcc (Debian
4.9.2-10) 4.9.2, 64-bit
(1 row)
mydb=> SELECT current_date;
date
-----------2016-01-07
(1 row)
mydb=> SELECT 2 + 2;
?column?
---------4
(1 row)
The psql program has a number of internal commands that are not SQL commands. They begin with
the backslash character, “\”. For example, you can get help on the syntax of various PostgreSQL SQL
commands by typing:
mydb=> \h
To get out of psql, type:
mydb=> \q
and psql will quit and return you to your command shell. (For more internal commands, type \? at
the psql prompt.) The full capabilities of psql are documented in psql. In this tutorial we will not
use these features explicitly, but you can use them yourself when it is helpful.
6
Chapter 2. The SQL Language
2.1. Introduction
This chapter provides an overview of how to use SQL to perform simple operations. This tutorial is
only intended to give you an introduction and is in no way a complete tutorial on SQL. Numerous
books have been written on SQL, including [melt93] and [date97]. You should be aware that some
PostgreSQL language features are extensions to the standard.
In the examples that follow, we assume that you have created a database named mydb, as described
in the previous chapter, and have been able to start psql.
Examples in this manual can also be found in the PostgreSQL source distribution in the directory
src/tutorial/. (Binary distributions of PostgreSQL might not provide those files.) To use those
files, first change to that directory and run make:
$ cd .../src/tutorial
$ make
This creates the scripts and compiles the C files containing user-defined functions and types. Then,
to start the tutorial, do the following:
$ psql -s mydb
...
mydb=> \i basics.sql
The \i command reads in commands from the specified file. psql's -s option puts you in single step
mode which pauses before sending each statement to the server. The commands used in this section
are in the file basics.sql.
2.2. Concepts
PostgreSQL is a relational database management system (RDBMS). That means it is a system for
managing data stored in relations. Relation is essentially a mathematical term for table. The notion
of storing data in tables is so commonplace today that it might seem inherently obvious, but there
are a number of other ways of organizing databases. Files and directories on Unix-like operating systems form an example of a hierarchical database. A more modern development is the object-oriented
database.
Each table is a named collection of rows. Each row of a given table has the same set of named
columns, and each column is of a specific data type. Whereas columns have a fixed order in each row,
it is important to remember that SQL does not guarantee the order of the rows within the table in any
way (although they can be explicitly sorted for display).
Tables are grouped into databases, and a collection of databases managed by a single PostgreSQL
server instance constitutes a database cluster.
2.3. Creating a New Table
You can create a new table by specifying the table name, along with all column names and their types:
7
The SQL Language
CREATE TABLE weather (
city
varchar(80),
temp_lo
int,
temp_hi
int,
prcp
real,
date
date
);
-- low temperature
-- high temperature
-- precipitation
You can enter this into psql with the line breaks. psql will recognize that the command is not
terminated until the semicolon.
White space (i.e., spaces, tabs, and newlines) can be used freely in SQL commands. That means you
can type the command aligned differently than above, or even all on one line. Two dashes (“--”)
introduce comments. Whatever follows them is ignored up to the end of the line. SQL is case-insensitive about key words and identifiers, except when identifiers are double-quoted to preserve the case
(not done above).
varchar(80) specifies a data type that can store arbitrary character strings up to 80 characters
in length. int is the normal integer type. real is a type for storing single precision floating-point
numbers. date should be self-explanatory. (Yes, the column of type date is also named date. This
might be convenient or confusing — you choose.)
PostgreSQL supports the standard SQL types int, smallint, real, double precision,
char(N), varchar(N), date, time, timestamp, and interval, as well as other types of
general utility and a rich set of geometric types. PostgreSQL can be customized with an arbitrary
number of user-defined data types. Consequently, type names are not key words in the syntax, except
where required to support special cases in the SQL standard.
The second example will store cities and their associated geographical location:
CREATE TABLE cities (
name
varchar(80),
location
point
);
The point type is an example of a PostgreSQL-specific data type.
Finally, it should be mentioned that if you don't need a table any longer or want to recreate it differently
you can remove it using the following command:
DROP TABLE tablename;
2.4. Populating a Table With Rows
The INSERT statement is used to populate a table with rows:
INSERT INTO weather VALUES ('San Francisco', 46, 50, 0.25,
'1994-11-27');
Note that all data types use rather obvious input formats. Constants that are not simple numeric values
usually must be surrounded by single quotes ('), as in the example. The date type is actually quite
flexible in what it accepts, but for this tutorial we will stick to the unambiguous format shown here.
The point type requires a coordinate pair as input, as shown here:
INSERT INTO cities VALUES ('San Francisco', '(-194.0, 53.0)');
8
The SQL Language
The syntax used so far requires you to remember the order of the columns. An alternative syntax allows
you to list the columns explicitly:
INSERT INTO weather (city, temp_lo, temp_hi, prcp, date)
VALUES ('San Francisco', 43, 57, 0.0, '1994-11-29');
You can list the columns in a different order if you wish or even omit some columns, e.g., if the
precipitation is unknown:
INSERT INTO weather (date, city, temp_hi, temp_lo)
VALUES ('1994-11-29', 'Hayward', 54, 37);
Many developers consider explicitly listing the columns better style than relying on the order implicitly.
Please enter all the commands shown above so you have some data to work with in the following
sections.
You could also have used COPY to load large amounts of data from flat-text files. This is usually
faster because the COPY command is optimized for this application while allowing less flexibility than
INSERT. An example would be:
COPY weather FROM '/home/user/weather.txt';
where the file name for the source file must be available on the machine running the backend process,
not the client, since the backend process reads the file directly. You can read more about the COPY
command in COPY.
2.5. Querying a Table
To retrieve data from a table, the table is queried. An SQL SELECT statement is used to do this. The
statement is divided into a select list (the part that lists the columns to be returned), a table list (the
part that lists the tables from which to retrieve the data), and an optional qualification (the part that
specifies any restrictions). For example, to retrieve all the rows of table weather, type:
SELECT * FROM weather;
Here * is a shorthand for “all columns”. 1 So the same result would be had with:
SELECT city, temp_lo, temp_hi, prcp, date FROM weather;
The output should be:
city
| temp_lo | temp_hi | prcp |
date
---------------+---------+---------+------+-----------San Francisco |
46 |
50 | 0.25 | 1994-11-27
San Francisco |
43 |
57 |
0 | 1994-11-29
Hayward
|
37 |
54 |
| 1994-11-29
(3 rows)
You can write expressions, not just simple column references, in the select list. For example, you can
do:
1
While SELECT * is useful for off-the-cuff queries, it is widely considered bad style in production code, since adding a column to the table
would change the results.
9
The SQL Language
SELECT city, (temp_hi+temp_lo)/2 AS temp_avg, date FROM weather;
This should give:
city
| temp_avg |
date
---------------+----------+-----------San Francisco |
48 | 1994-11-27
San Francisco |
50 | 1994-11-29
Hayward
|
45 | 1994-11-29
(3 rows)
Notice how the AS clause is used to relabel the output column. (The AS clause is optional.)
A query can be “qualified” by adding a WHERE clause that specifies which rows are wanted. The
WHERE clause contains a Boolean (truth value) expression, and only rows for which the Boolean
expression is true are returned. The usual Boolean operators (AND, OR, and NOT) are allowed in the
qualification. For example, the following retrieves the weather of San Francisco on rainy days:
SELECT * FROM weather
WHERE city = 'San Francisco' AND prcp > 0.0;
Result:
city
| temp_lo | temp_hi | prcp |
date
---------------+---------+---------+------+-----------San Francisco |
46 |
50 | 0.25 | 1994-11-27
(1 row)
You can request that the results of a query be returned in sorted order:
SELECT * FROM weather
ORDER BY city;
city
| temp_lo | temp_hi | prcp |
date
---------------+---------+---------+------+-----------Hayward
|
37 |
54 |
| 1994-11-29
San Francisco |
43 |
57 |
0 | 1994-11-29
San Francisco |
46 |
50 | 0.25 | 1994-11-27
In this example, the sort order isn't fully specified, and so you might get the San Francisco rows in
either order. But you'd always get the results shown above if you do:
SELECT * FROM weather
ORDER BY city, temp_lo;
You can request that duplicate rows be removed from the result of a query:
SELECT DISTINCT city
FROM weather;
city
---------------
10
The SQL Language
Hayward
San Francisco
(2 rows)
Here again, the result row ordering might vary. You can ensure consistent results by using DISTINCT
and ORDER BY together: 2
SELECT DISTINCT city
FROM weather
ORDER BY city;
2.6. Joins Between Tables
Thus far, our queries have only accessed one table at a time. Queries can access multiple tables at once,
or access the same table in such a way that multiple rows of the table are being processed at the same
time. Queries that access multiple tables (or multiple instances of the same table) at one time are called
join queries. They combine rows from one table with rows from a second table, with an expression
specifying which rows are to be paired. For example, to return all the weather records together with
the location of the associated city, the database needs to compare the city column of each row of the
weather table with the name column of all rows in the cities table, and select the pairs of rows
where these values match.3 This would be accomplished by the following query:
SELECT * FROM weather JOIN cities ON city = name;
city
| temp_lo | temp_hi | prcp |
date
|
name
| location
---------------+---------+---------+------+-----------+---------------+----------San Francisco |
46 |
50 | 0.25 | 1994-11-27 | San
Francisco | (-194,53)
San Francisco |
43 |
57 |
0 | 1994-11-29 | San
Francisco | (-194,53)
(2 rows)
Observe two things about the result set:
• There is no result row for the city of Hayward. This is because there is no matching entry in the
cities table for Hayward, so the join ignores the unmatched rows in the weather table. We
will see shortly how this can be fixed.
• There are two columns containing the city name. This is correct because the lists of columns from
the weather and cities tables are concatenated. In practice this is undesirable, though, so you
will probably want to list the output columns explicitly rather than using *:
SELECT city, temp_lo, temp_hi, prcp, date, location
FROM weather JOIN cities ON city = name;
Since the columns all had different names, the parser automatically found which table they belong
to. If there were duplicate column names in the two tables you'd need to qualify the column names
to show which one you meant, as in:
2
In some database systems, including older versions of PostgreSQL, the implementation of DISTINCT automatically orders the rows and
so ORDER BY is unnecessary. But this is not required by the SQL standard, and current PostgreSQL does not guarantee that DISTINCT
causes the rows to be ordered.
3
This is only a conceptual model. The join is usually performed in a more efficient manner than actually comparing each possible pair of
rows, but this is invisible to the user.
11
The SQL Language
SELECT weather.city, weather.temp_lo, weather.temp_hi,
weather.prcp, weather.date, cities.location
FROM weather JOIN cities ON weather.city = cities.name;
It is widely considered good style to qualify all column names in a join query, so that the query won't
fail if a duplicate column name is later added to one of the tables.
Join queries of the kind seen thus far can also be written in this form:
SELECT *
FROM weather, cities
WHERE city = name;
This syntax pre-dates the JOIN/ON syntax, which was introduced in SQL-92. The tables are simply
listed in the FROM clause, and the comparison expression is added to the WHERE clause. The results
from this older implicit syntax and the newer explicit JOIN/ON syntax are identical. But for a reader of
the query, the explicit syntax makes its meaning easier to understand: The join condition is introduced
by its own key word whereas previously the condition was mixed into the WHERE clause together
with other conditions.
Now we will figure out how we can get the Hayward records back in. What we want the query to do
is to scan the weather table and for each row to find the matching cities row(s). If no matching
row is found we want some “empty values” to be substituted for the cities table's columns. This
kind of query is called an outer join. (The joins we have seen so far are inner joins.) The command
looks like this:
SELECT *
FROM weather LEFT OUTER JOIN cities ON weather.city =
cities.name;
city
| temp_lo | temp_hi | prcp |
date
|
name
| location
---------------+---------+---------+------+-----------+---------------+----------Hayward
|
37 |
54 |
| 1994-11-29 |
|
San Francisco |
46 |
50 | 0.25 | 1994-11-27 | San
Francisco | (-194,53)
San Francisco |
43 |
57 |
0 | 1994-11-29 | San
Francisco | (-194,53)
(3 rows)
This query is called a left outer join because the table mentioned on the left of the join operator will
have each of its rows in the output at least once, whereas the table on the right will only have those
rows output that match some row of the left table. When outputting a left-table row for which there is
no right-table match, empty (null) values are substituted for the right-table columns.
Exercise:
There are also right outer joins and full outer joins. Try to find out what those do.
We can also join a table against itself. This is called a self join. As an example, suppose we wish to
find all the weather records that are in the temperature range of other weather records. So we need to
compare the temp_lo and temp_hi columns of each weather row to the temp_lo and temp_hi columns of all other weather rows. We can do this with the following query:
12
The SQL Language
SELECT w1.city, w1.temp_lo AS low, w1.temp_hi AS high,
w2.city, w2.temp_lo AS low, w2.temp_hi AS high
FROM weather w1 JOIN weather w2
ON w1.temp_lo < w2.temp_lo AND w1.temp_hi > w2.temp_hi;
city
| low | high |
city
| low | high
---------------+-----+------+---------------+-----+-----San Francisco | 43 |
57 | San Francisco | 46 |
50
Hayward
| 37 |
54 | San Francisco | 46 |
50
(2 rows)
Here we have relabeled the weather table as w1 and w2 to be able to distinguish the left and right side
of the join. You can also use these kinds of aliases in other queries to save some typing, e.g.:
SELECT *
FROM weather w JOIN cities c ON w.city = c.name;
You will encounter this style of abbreviating quite frequently.
2.7. Aggregate Functions
Like most other relational database products, PostgreSQL supports aggregate functions. An aggregate
function computes a single result from multiple input rows. For example, there are aggregates to compute the count, sum, avg (average), max (maximum) and min (minimum) over a set of rows.
As an example, we can find the highest low-temperature reading anywhere with:
SELECT max(temp_lo) FROM weather;
max
----46
(1 row)
If we wanted to know what city (or cities) that reading occurred in, we might try:
SELECT city FROM weather WHERE temp_lo = max(temp_lo);
WRONG
but this will not work since the aggregate max cannot be used in the WHERE clause. (This restriction
exists because the WHERE clause determines which rows will be included in the aggregate calculation;
so obviously it has to be evaluated before aggregate functions are computed.) However, as is often the
case the query can be restated to accomplish the desired result, here by using a subquery:
SELECT city FROM weather
WHERE temp_lo = (SELECT max(temp_lo) FROM weather);
city
--------------San Francisco
(1 row)
This is OK because the subquery is an independent computation that computes its own aggregate
separately from what is happening in the outer query.
13
The SQL Language
Aggregates are also very useful in combination with GROUP BY clauses. For example, we can get
the number of readings and the maximum low temperature observed in each city with:
SELECT city, count(*), max(temp_lo)
FROM weather
GROUP BY city;
city
| count | max
---------------+-------+----Hayward
|
1 | 37
San Francisco |
2 | 46
(2 rows)
which gives us one output row per city. Each aggregate result is computed over the table rows matching
that city. We can filter these grouped rows using HAVING:
SELECT city, count(*), max(temp_lo)
FROM weather
GROUP BY city
HAVING max(temp_lo) < 40;
city
| count | max
---------+-------+----Hayward |
1 | 37
(1 row)
which gives us the same results for only the cities that have all temp_lo values below 40. Finally,
if we only care about cities whose names begin with “S”, we might do:
SELECT city, count(*), max(temp_lo)
FROM weather
WHERE city LIKE 'S%'
-GROUP BY city;
1
city
| count | max
---------------+-------+----San Francisco |
2 | 46
(1 row)
1
The LIKE operator does pattern matching and is explained in Section 9.7.
It is important to understand the interaction between aggregates and SQL's WHERE and HAVING clauses. The fundamental difference between WHERE and HAVING is this: WHERE selects input rows before
groups and aggregates are computed (thus, it controls which rows go into the aggregate computation),
whereas HAVING selects group rows after groups and aggregates are computed. Thus, the WHERE
clause must not contain aggregate functions; it makes no sense to try to use an aggregate to determine
which rows will be inputs to the aggregates. On the other hand, the HAVING clause always contains
aggregate functions. (Strictly speaking, you are allowed to write a HAVING clause that doesn't use
aggregates, but it's seldom useful. The same condition could be used more efficiently at the WHERE
stage.)
In the previous example, we can apply the city name restriction in WHERE, since it needs no aggregate.
This is more efficient than adding the restriction to HAVING, because we avoid doing the grouping
and aggregate calculations for all rows that fail the WHERE check.
14
The SQL Language
Another way to select the rows that go into an aggregate computation is to use FILTER, which is a
per-aggregate option:
SELECT city, count(*) FILTER (WHERE temp_lo < 45), max(temp_lo)
FROM weather
GROUP BY city;
city
| count | max
---------------+-------+----Hayward
|
1 | 37
San Francisco |
1 | 46
(2 rows)
FILTER is much like WHERE, except that it removes rows only from the input of the particular aggregate function that it is attached to. Here, the count aggregate counts only rows with temp_lo
below 45; but the max aggregate is still applied to all rows, so it still finds the reading of 46.
2.8. Updates
You can update existing rows using the UPDATE command. Suppose you discover the temperature
readings are all off by 2 degrees after November 28. You can correct the data as follows:
UPDATE weather
SET temp_hi = temp_hi - 2,
WHERE date > '1994-11-28';
temp_lo = temp_lo - 2
Look at the new state of the data:
SELECT * FROM weather;
city
| temp_lo | temp_hi | prcp |
date
---------------+---------+---------+------+-----------San Francisco |
46 |
50 | 0.25 | 1994-11-27
San Francisco |
41 |
55 |
0 | 1994-11-29
Hayward
|
35 |
52 |
| 1994-11-29
(3 rows)
2.9. Deletions
Rows can be removed from a table using the DELETE command. Suppose you are no longer interested
in the weather of Hayward. Then you can do the following to delete those rows from the table:
DELETE FROM weather WHERE city = 'Hayward';
All weather records belonging to Hayward are removed.
SELECT * FROM weather;
city
| temp_lo | temp_hi | prcp |
date
---------------+---------+---------+------+-----------San Francisco |
46 |
50 | 0.25 | 1994-11-27
15
The SQL Language
San Francisco |
(2 rows)
41 |
55 |
0 | 1994-11-29
One should be wary of statements of the form
DELETE FROM tablename;
Without a qualification, DELETE will remove all rows from the given table, leaving it empty. The
system will not request confirmation before doing this!
16
Chapter 3. Advanced Features
3.1. Introduction
In the previous chapter we have covered the basics of using SQL to store and access your data in
PostgreSQL. We will now discuss some more advanced features of SQL that simplify management
and prevent loss or corruption of your data. Finally, we will look at some PostgreSQL extensions.
This chapter will on occasion refer to examples found in Chapter 2 to change or improve them, so
it will be useful to have read that chapter. Some examples from this chapter can also be found in
advanced.sql in the tutorial directory. This file also contains some sample data to load, which is
not repeated here. (Refer to Section 2.1 for how to use the file.)
3.2. Views
Refer back to the queries in Section 2.6. Suppose the combined listing of weather records and city
location is of particular interest to your application, but you do not want to type the query each time
you need it. You can create a view over the query, which gives a name to the query that you can refer
to like an ordinary table:
CREATE VIEW myview AS
SELECT name, temp_lo, temp_hi, prcp, date, location
FROM weather, cities
WHERE city = name;
SELECT * FROM myview;
Making liberal use of views is a key aspect of good SQL database design. Views allow you to encapsulate the details of the structure of your tables, which might change as your application evolves,
behind consistent interfaces.
Views can be used in almost any place a real table can be used. Building views upon other views is
not uncommon.
3.3. Foreign Keys
Recall the weather and cities tables from Chapter 2. Consider the following problem: You want
to make sure that no one can insert rows in the weather table that do not have a matching entry
in the cities table. This is called maintaining the referential integrity of your data. In simplistic
database systems this would be implemented (if at all) by first looking at the cities table to check
if a matching record exists, and then inserting or rejecting the new weather records. This approach
has a number of problems and is very inconvenient, so PostgreSQL can do this for you.
The new declaration of the tables would look like this:
CREATE TABLE cities (
name
varchar(80) primary key,
location point
);
CREATE TABLE weather (
city
varchar(80) references cities(name),
temp_lo
int,
17
Advanced Features
temp_hi
prcp
date
int,
real,
date
);
Now try inserting an invalid record:
INSERT INTO weather VALUES ('Berkeley', 45, 53, 0.0, '1994-11-28');
ERROR: insert or update on table "weather" violates foreign key
constraint "weather_city_fkey"
DETAIL: Key (city)=(Berkeley) is not present in table "cities".
The behavior of foreign keys can be finely tuned to your application. We will not go beyond this simple
example in this tutorial, but just refer you to Chapter 5 for more information. Making correct use of
foreign keys will definitely improve the quality of your database applications, so you are strongly
encouraged to learn about them.
3.4. Transactions
Transactions are a fundamental concept of all database systems. The essential point of a transaction is
that it bundles multiple steps into a single, all-or-nothing operation. The intermediate states between
the steps are not visible to other concurrent transactions, and if some failure occurs that prevents the
transaction from completing, then none of the steps affect the database at all.
For example, consider a bank database that contains balances for various customer accounts, as well as
total deposit balances for branches. Suppose that we want to record a payment of $100.00 from Alice's
account to Bob's account. Simplifying outrageously, the SQL commands for this might look like:
UPDATE accounts SET balance = balance - 100.00
WHERE name = 'Alice';
UPDATE branches SET balance = balance - 100.00
WHERE name = (SELECT branch_name FROM accounts WHERE name =
'Alice');
UPDATE accounts SET balance = balance + 100.00
WHERE name = 'Bob';
UPDATE branches SET balance = balance + 100.00
WHERE name = (SELECT branch_name FROM accounts WHERE name =
'Bob');
The details of these commands are not important here; the important point is that there are several
separate updates involved to accomplish this rather simple operation. Our bank's officers will want to
be assured that either all these updates happen, or none of them happen. It would certainly not do for
a system failure to result in Bob receiving $100.00 that was not debited from Alice. Nor would Alice
long remain a happy customer if she was debited without Bob being credited. We need a guarantee
that if something goes wrong partway through the operation, none of the steps executed so far will
take effect. Grouping the updates into a transaction gives us this guarantee. A transaction is said to be
atomic: from the point of view of other transactions, it either happens completely or not at all.
We also want a guarantee that once a transaction is completed and acknowledged by the database
system, it has indeed been permanently recorded and won't be lost even if a crash ensues shortly
thereafter. For example, if we are recording a cash withdrawal by Bob, we do not want any chance that
the debit to his account will disappear in a crash just after he walks out the bank door. A transactional
database guarantees that all the updates made by a transaction are logged in permanent storage (i.e.,
on disk) before the transaction is reported complete.
18
Advanced Features
Another important property of transactional databases is closely related to the notion of atomic updates: when multiple transactions are running concurrently, each one should not be able to see the
incomplete changes made by others. For example, if one transaction is busy totalling all the branch
balances, it would not do for it to include the debit from Alice's branch but not the credit to Bob's
branch, nor vice versa. So transactions must be all-or-nothing not only in terms of their permanent
effect on the database, but also in terms of their visibility as they happen. The updates made so far by
an open transaction are invisible to other transactions until the transaction completes, whereupon all
the updates become visible simultaneously.
In PostgreSQL, a transaction is set up by surrounding the SQL commands of the transaction with
BEGIN and COMMIT commands. So our banking transaction would actually look like:
BEGIN;
UPDATE accounts SET balance = balance - 100.00
WHERE name = 'Alice';
-- etc etc
COMMIT;
If, partway through the transaction, we decide we do not want to commit (perhaps we just noticed that
Alice's balance went negative), we can issue the command ROLLBACK instead of COMMIT, and all
our updates so far will be canceled.
PostgreSQL actually treats every SQL statement as being executed within a transaction. If you do not
issue a BEGIN command, then each individual statement has an implicit BEGIN and (if successful)
COMMIT wrapped around it. A group of statements surrounded by BEGIN and COMMIT is sometimes
called a transaction block.
Note
Some client libraries issue BEGIN and COMMIT commands automatically, so that you might
get the effect of transaction blocks without asking. Check the documentation for the interface
you are using.
It's possible to control the statements in a transaction in a more granular fashion through the use of
savepoints. Savepoints allow you to selectively discard parts of the transaction, while committing the
rest. After defining a savepoint with SAVEPOINT, you can if needed roll back to the savepoint with
ROLLBACK TO. All the transaction's database changes between defining the savepoint and rolling
back to it are discarded, but changes earlier than the savepoint are kept.
After rolling back to a savepoint, it continues to be defined, so you can roll back to it several times.
Conversely, if you are sure you won't need to roll back to a particular savepoint again, it can be
released, so the system can free some resources. Keep in mind that either releasing or rolling back to
a savepoint will automatically release all savepoints that were defined after it.
All this is happening within the transaction block, so none of it is visible to other database sessions.
When and if you commit the transaction block, the committed actions become visible as a unit to other
sessions, while the rolled-back actions never become visible at all.
Remembering the bank database, suppose we debit $100.00 from Alice's account, and credit Bob's
account, only to find later that we should have credited Wally's account. We could do it using savepoints like this:
BEGIN;
UPDATE accounts SET balance = balance - 100.00
WHERE name = 'Alice';
SAVEPOINT my_savepoint;
19
Advanced Features
UPDATE accounts SET balance = balance + 100.00
WHERE name = 'Bob';
-- oops ... forget that and use Wally's account
ROLLBACK TO my_savepoint;
UPDATE accounts SET balance = balance + 100.00
WHERE name = 'Wally';
COMMIT;
This example is, of course, oversimplified, but there's a lot of control possible in a transaction block
through the use of savepoints. Moreover, ROLLBACK TO is the only way to regain control of a
transaction block that was put in aborted state by the system due to an error, short of rolling it back
completely and starting again.
3.5. Window Functions
A window function performs a calculation across a set of table rows that are somehow related to the
current row. This is comparable to the type of calculation that can be done with an aggregate function.
However, window functions do not cause rows to become grouped into a single output row like nonwindow aggregate calls would. Instead, the rows retain their separate identities. Behind the scenes,
the window function is able to access more than just the current row of the query result.
Here is an example that shows how to compare each employee's salary with the average salary in his
or her department:
SELECT depname, empno, salary, avg(salary) OVER (PARTITION BY
depname) FROM empsalary;
depname | empno | salary |
avg
-----------+-------+--------+----------------------develop
|
11 |
5200 | 5020.0000000000000000
develop
|
7 |
4200 | 5020.0000000000000000
develop
|
9 |
4500 | 5020.0000000000000000
develop
|
8 |
6000 | 5020.0000000000000000
develop
|
10 |
5200 | 5020.0000000000000000
personnel |
5 |
3500 | 3700.0000000000000000
personnel |
2 |
3900 | 3700.0000000000000000
sales
|
3 |
4800 | 4866.6666666666666667
sales
|
1 |
5000 | 4866.6666666666666667
sales
|
4 |
4800 | 4866.6666666666666667
(10 rows)
The first three output columns come directly from the table empsalary, and there is one output row
for each row in the table. The fourth column represents an average taken across all the table rows
that have the same depname value as the current row. (This actually is the same function as the
non-window avg aggregate, but the OVER clause causes it to be treated as a window function and
computed across the window frame.)
A window function call always contains an OVER clause directly following the window function's
name and argument(s). This is what syntactically distinguishes it from a normal function or nonwindow aggregate. The OVER clause determines exactly how the rows of the query are split up for
processing by the window function. The PARTITION BY clause within OVER divides the rows into
groups, or partitions, that share the same values of the PARTITION BY expression(s). For each row,
the window function is computed across the rows that fall into the same partition as the current row.
You can also control the order in which rows are processed by window functions using ORDER BY
within OVER. (The window ORDER BY does not even have to match the order in which the rows are
output.) Here is an example:
20
Advanced Features
SELECT depname, empno, salary,
rank() OVER (PARTITION BY depname ORDER BY salary DESC)
FROM empsalary;
depname | empno | salary | rank
-----------+-------+--------+-----develop
|
8 |
6000 |
1
develop
|
10 |
5200 |
2
develop
|
11 |
5200 |
2
develop
|
9 |
4500 |
4
develop
|
7 |
4200 |
5
personnel |
2 |
3900 |
1
personnel |
5 |
3500 |
2
sales
|
1 |
5000 |
1
sales
|
4 |
4800 |
2
sales
|
3 |
4800 |
2
(10 rows)
As shown here, the rank function produces a numerical rank for each distinct ORDER BY value in
the current row's partition, using the order defined by the ORDER BY clause. rank needs no explicit
parameter, because its behavior is entirely determined by the OVER clause.
The rows considered by a window function are those of the “virtual table” produced by the query's
FROM clause as filtered by its WHERE, GROUP BY, and HAVING clauses if any. For example, a row
removed because it does not meet the WHERE condition is not seen by any window function. A query
can contain multiple window functions that slice up the data in different ways using different OVER
clauses, but they all act on the same collection of rows defined by this virtual table.
We already saw that ORDER BY can be omitted if the ordering of rows is not important. It is also
possible to omit PARTITION BY, in which case there is a single partition containing all rows.
There is another important concept associated with window functions: for each row, there is a set of
rows within its partition called its window frame. Some window functions act only on the rows of the
window frame, rather than of the whole partition. By default, if ORDER BY is supplied then the frame
consists of all rows from the start of the partition up through the current row, plus any following rows
that are equal to the current row according to the ORDER BY clause. When ORDER BY is omitted the
default frame consists of all rows in the partition. 1 Here is an example using sum:
SELECT salary, sum(salary) OVER () FROM empsalary;
salary | sum
--------+------5200 | 47100
5000 | 47100
3500 | 47100
4800 | 47100
3900 | 47100
4200 | 47100
4500 | 47100
4800 | 47100
6000 | 47100
5200 | 47100
(10 rows)
1
There are options to define the window frame in other ways, but this tutorial does not cover them. See Section 4.2.8 for details.
21
Advanced Features
Above, since there is no ORDER BY in the OVER clause, the window frame is the same as the partition,
which for lack of PARTITION BY is the whole table; in other words each sum is taken over the
whole table and so we get the same result for each output row. But if we add an ORDER BY clause,
we get very different results:
SELECT salary, sum(salary) OVER (ORDER BY salary) FROM empsalary;
salary | sum
--------+------3500 | 3500
3900 | 7400
4200 | 11600
4500 | 16100
4800 | 25700
4800 | 25700
5000 | 30700
5200 | 41100
5200 | 41100
6000 | 47100
(10 rows)
Here the sum is taken from the first (lowest) salary up through the current one, including any duplicates
of the current one (notice the results for the duplicated salaries).
Window functions are permitted only in the SELECT list and the ORDER BY clause of the query.
They are forbidden elsewhere, such as in GROUP BY, HAVING and WHERE clauses. This is because
they logically execute after the processing of those clauses. Also, window functions execute after
non-window aggregate functions. This means it is valid to include an aggregate function call in the
arguments of a window function, but not vice versa.
If there is a need to filter or group rows after the window calculations are performed, you can use a
sub-select. For example:
SELECT depname, empno, salary, enroll_date
FROM
(SELECT depname, empno, salary, enroll_date,
rank() OVER (PARTITION BY depname ORDER BY salary DESC,
empno) AS pos
FROM empsalary
) AS ss
WHERE pos < 3;
The above query only shows the rows from the inner query having rank less than 3.
When a query involves multiple window functions, it is possible to write out each one with a separate
OVER clause, but this is duplicative and error-prone if the same windowing behavior is wanted for
several functions. Instead, each windowing behavior can be named in a WINDOW clause and then
referenced in OVER. For example:
SELECT sum(salary) OVER w, avg(salary) OVER w
FROM empsalary
WINDOW w AS (PARTITION BY depname ORDER BY salary DESC);
More details about window functions can be found in Section 4.2.8, Section 9.22, Section 7.2.5, and
the SELECT reference page.
22
Advanced Features
3.6. Inheritance
Inheritance is a concept from object-oriented databases. It opens up interesting new possibilities of
database design.
Let's create two tables: A table cities and a table capitals. Naturally, capitals are also cities,
so you want some way to show the capitals implicitly when you list all cities. If you're really clever
you might invent some scheme like this:
CREATE TABLE capitals (
name
text,
population real,
elevation int,
-- (in ft)
state
char(2)
);
CREATE TABLE non_capitals (
name
text,
population real,
elevation int
-- (in ft)
);
CREATE VIEW cities AS
SELECT name, population, elevation FROM capitals
UNION
SELECT name, population, elevation FROM non_capitals;
This works OK as far as querying goes, but it gets ugly when you need to update several rows, for
one thing.
A better solution is this:
CREATE TABLE cities (
name
text,
population real,
elevation int
-- (in ft)
);
CREATE TABLE capitals (
state
char(2) UNIQUE NOT NULL
) INHERITS (cities);
In this case, a row of capitals inherits all columns (name, population, and elevation) from
its parent, cities. The type of the column name is text, a native PostgreSQL type for variable
length character strings. The capitals table has an additional column, state, which shows its
state abbreviation. In PostgreSQL, a table can inherit from zero or more other tables.
For example, the following query finds the names of all cities, including state capitals, that are located
at an elevation over 500 feet:
SELECT name, elevation
FROM cities
WHERE elevation > 500;
which returns:
23
Advanced Features
name
| elevation
-----------+----------Las Vegas |
2174
Mariposa |
1953
Madison
|
845
(3 rows)
On the other hand, the following query finds all the cities that are not state capitals and are situated
at an elevation over 500 feet:
SELECT name, elevation
FROM ONLY cities
WHERE elevation > 500;
name
| elevation
-----------+----------Las Vegas |
2174
Mariposa |
1953
(2 rows)
Here the ONLY before cities indicates that the query should be run over only the cities table, and
not tables below cities in the inheritance hierarchy. Many of the commands that we have already
discussed — SELECT, UPDATE, and DELETE — support this ONLY notation.
Note
Although inheritance is frequently useful, it has not been integrated with unique constraints or
foreign keys, which limits its usefulness. See Section 5.11 for more detail.
3.7. Conclusion
PostgreSQL has many features not touched upon in this tutorial introduction, which has been oriented
toward newer users of SQL. These features are discussed in more detail in the remainder of this book.
If you feel you need more introductory material, please visit the PostgreSQL web site2 for links to
more resources.
2
https://www.postgresql.org
24
Part II. The SQL Language
This part describes the use of the SQL language in PostgreSQL. We start with describing the general syntax of
SQL, then how to create tables, how to populate the database, and how to query it. The middle part lists the
available data types and functions for use in SQL commands. Lastly, we address several aspects of importance
for tuning a database.
The information is arranged so that a novice user can follow it from start to end and gain a full understanding
of the topics without having to refer forward too many times. The chapters are intended to be self-contained, so
that advanced users can read the chapters individually as they choose. The information is presented in narrative
form with topical units. Readers looking for a complete description of a particular command are encouraged to
review the Part VI.
Readers should know how to connect to a PostgreSQL database and issue SQL commands. Readers that are
unfamiliar with these issues are encouraged to read Part I first. SQL commands are typically entered using the
PostgreSQL interactive terminal psql, but other programs that have similar functionality can be used as well.
Table of Contents
4. SQL Syntax ............................................................................................................ 33
4.1. Lexical Structure ........................................................................................... 33
4.1.1. Identifiers and Key Words .................................................................... 33
4.1.2. Constants ........................................................................................... 35
4.1.3. Operators ........................................................................................... 40
4.1.4. Special Characters ............................................................................... 40
4.1.5. Comments ......................................................................................... 41
4.1.6. Operator Precedence ............................................................................ 41
4.2. Value Expressions ......................................................................................... 42
4.2.1. Column References ............................................................................. 43
4.2.2. Positional Parameters ........................................................................... 43
4.2.3. Subscripts .......................................................................................... 43
4.2.4. Field Selection .................................................................................... 44
4.2.5. Operator Invocations ........................................................................... 44
4.2.6. Function Calls .................................................................................... 45
4.2.7. Aggregate Expressions ......................................................................... 45
4.2.8. Window Function Calls ........................................................................ 48
4.2.9. Type Casts ......................................................................................... 50
4.2.10. Collation Expressions ......................................................................... 51
4.2.11. Scalar Subqueries .............................................................................. 52
4.2.12. Array Constructors ............................................................................ 52
4.2.13. Row Constructors .............................................................................. 54
4.2.14. Expression Evaluation Rules ............................................................... 55
4.3. Calling Functions .......................................................................................... 56
4.3.1. Using Positional Notation ..................................................................... 57
4.3.2. Using Named Notation ......................................................................... 57
4.3.3. Using Mixed Notation ......................................................................... 58
5. Data Definition ........................................................................................................ 59
5.1. Table Basics ................................................................................................. 59
5.2. Default Values .............................................................................................. 60
5.3. Identity Columns ........................................................................................... 61
5.4. Generated Columns ........................................................................................ 62
5.5. Constraints ................................................................................................... 64
5.5.1. Check Constraints ............................................................................... 64
5.5.2. Not-Null Constraints ............................................................................ 66
5.5.3. Unique Constraints .............................................................................. 67
5.5.4. Primary Keys ..................................................................................... 68
5.5.5. Foreign Keys ...................................................................................... 69
5.5.6. Exclusion Constraints .......................................................................... 72
5.6. System Columns ........................................................................................... 73
5.7. Modifying Tables .......................................................................................... 74
5.7.1. Adding a Column ............................................................................... 74
5.7.2. Removing a Column ............................................................................ 75
5.7.3. Adding a Constraint ............................................................................ 75
5.7.4. Removing a Constraint ........................................................................ 75
5.7.5. Changing a Column's Default Value ....................................................... 76
5.7.6. Changing a Column's Data Type ............................................................ 76
5.7.7. Renaming a Column ............................................................................ 76
5.7.8. Renaming a Table ............................................................................... 76
5.8. Privileges ..................................................................................................... 76
5.9. Row Security Policies .................................................................................... 81
5.10. Schemas ..................................................................................................... 87
5.10.1. Creating a Schema ............................................................................. 88
5.10.2. The Public Schema ............................................................................ 89
5.10.3. The Schema Search Path .................................................................... 89
26
The SQL Language
5.10.4. Schemas and Privileges ...................................................................... 91
5.10.5. The System Catalog Schema ............................................................... 91
5.10.6. Usage Patterns .................................................................................. 91
5.10.7. Portability ........................................................................................ 92
5.11. Inheritance .................................................................................................. 92
5.11.1. Caveats ............................................................................................ 95
5.12. Table Partitioning ........................................................................................ 96
5.12.1. Overview ......................................................................................... 96
5.12.2. Declarative Partitioning ...................................................................... 97
5.12.3. Partitioning Using Inheritance ............................................................ 102
5.12.4. Partition Pruning ............................................................................. 106
5.12.5. Partitioning and Constraint Exclusion .................................................. 108
5.12.6. Best Practices for Declarative Partitioning ............................................ 109
5.13. Foreign Data ............................................................................................. 110
5.14. Other Database Objects ............................................................................... 110
5.15. Dependency Tracking ................................................................................. 110
6. Data Manipulation .................................................................................................. 113
6.1. Inserting Data ............................................................................................. 113
6.2. Updating Data ............................................................................................. 114
6.3. Deleting Data .............................................................................................. 115
6.4. Returning Data from Modified Rows ............................................................... 115
7. Queries ................................................................................................................. 117
7.1. Overview .................................................................................................... 117
7.2. Table Expressions ........................................................................................ 117
7.2.1. The FROM Clause .............................................................................. 118
7.2.2. The WHERE Clause ............................................................................ 126
7.2.3. The GROUP BY and HAVING Clauses .................................................. 127
7.2.4. GROUPING SETS, CUBE, and ROLLUP .............................................. 130
7.2.5. Window Function Processing .............................................................. 133
7.3. Select Lists ................................................................................................. 133
7.3.1. Select-List Items ............................................................................... 133
7.3.2. Column Labels .................................................................................. 134
7.3.3. DISTINCT ...................................................................................... 134
7.4. Combining Queries (UNION, INTERSECT, EXCEPT) ........................................ 135
7.5. Sorting Rows (ORDER BY) .......................................................................... 136
7.6. LIMIT and OFFSET .................................................................................... 137
7.7. VALUES Lists ............................................................................................. 137
7.8. WITH Queries (Common Table Expressions) .................................................... 138
7.8.1. SELECT in WITH ............................................................................. 139
7.8.2. Recursive Queries ............................................................................. 139
7.8.3. Common Table Expression Materialization ............................................ 144
7.8.4. Data-Modifying Statements in WITH .................................................... 145
8. Data Types ............................................................................................................ 148
8.1. Numeric Types ............................................................................................ 149
8.1.1. Integer Types .................................................................................... 150
8.1.2. Arbitrary Precision Numbers ............................................................... 150
8.1.3. Floating-Point Types .......................................................................... 152
8.1.4. Serial Types ..................................................................................... 154
8.2. Monetary Types ........................................................................................... 155
8.3. Character Types ........................................................................................... 155
8.4. Binary Data Types ....................................................................................... 158
8.4.1. bytea Hex Format ........................................................................... 158
8.4.2. bytea Escape Format ....................................................................... 158
8.5. Date/Time Types ......................................................................................... 160
8.5.1. Date/Time Input ................................................................................ 161
8.5.2. Date/Time Output .............................................................................. 165
8.5.3. Time Zones ...................................................................................... 166
8.5.4. Interval Input .................................................................................... 167
27
The SQL Language
8.5.5. Interval Output .................................................................................. 169
8.6. Boolean Type .............................................................................................. 169
8.7. Enumerated Types ....................................................................................... 170
8.7.1. Declaration of Enumerated Types ......................................................... 170
8.7.2. Ordering .......................................................................................... 171
8.7.3. Type Safety ...................................................................................... 171
8.7.4. Implementation Details ....................................................................... 172
8.8. Geometric Types ......................................................................................... 172
8.8.1. Points .............................................................................................. 173
8.8.2. Lines ............................................................................................... 173
8.8.3. Line Segments .................................................................................. 173
8.8.4. Boxes .............................................................................................. 173
8.8.5. Paths ............................................................................................... 174
8.8.6. Polygons .......................................................................................... 174
8.8.7. Circles ............................................................................................. 174
8.9. Network Address Types ................................................................................ 175
8.9.1. inet .............................................................................................. 175
8.9.2. cidr .............................................................................................. 175
8.9.3. inet vs. cidr ................................................................................ 176
8.9.4. macaddr ........................................................................................ 176
8.9.5. macaddr8 ...................................................................................... 176
8.10. Bit String Types ........................................................................................ 177
8.11. Text Search Types ...................................................................................... 178
8.11.1. tsvector ..................................................................................... 178
8.11.2. tsquery ....................................................................................... 179
8.12. UUID Type ............................................................................................... 181
8.13. XML Type ................................................................................................ 181
8.13.1. Creating XML Values ...................................................................... 181
8.13.2. Encoding Handling .......................................................................... 182
8.13.3. Accessing XML Values .................................................................... 183
8.14. JSON Types .............................................................................................. 183
8.14.1. JSON Input and Output Syntax .......................................................... 185
8.14.2. Designing JSON Documents .............................................................. 186
8.14.3. jsonb Containment and Existence ..................................................... 186
8.14.4. jsonb Indexing .............................................................................. 188
8.14.5. jsonb Subscripting ......................................................................... 190
8.14.6. Transforms ..................................................................................... 192
8.14.7. jsonpath Type ................................................................................. 192
8.15. Arrays ...................................................................................................... 193
8.15.1. Declaration of Array Types ............................................................... 194
8.15.2. Array Value Input ............................................................................ 194
8.15.3. Accessing Arrays ............................................................................. 196
8.15.4. Modifying Arrays ............................................................................ 198
8.15.5. Searching in Arrays ......................................................................... 201
8.15.6. Array Input and Output Syntax .......................................................... 202
8.16. Composite Types ....................................................................................... 203
8.16.1. Declaration of Composite Types ......................................................... 203
8.16.2. Constructing Composite Values .......................................................... 204
8.16.3. Accessing Composite Types .............................................................. 205
8.16.4. Modifying Composite Types .............................................................. 205
8.16.5. Using Composite Types in Queries ..................................................... 206
8.16.6. Composite Type Input and Output Syntax ............................................ 208
8.17. Range Types ............................................................................................. 209
8.17.1. Built-in Range and Multirange Types .................................................. 210
8.17.2. Examples ....................................................................................... 210
8.17.3. Inclusive and Exclusive Bounds ......................................................... 210
8.17.4. Infinite (Unbounded) Ranges ............................................................. 211
8.17.5. Range Input/Output .......................................................................... 211
28
The SQL Language
8.17.6. Constructing Ranges and Multiranges .................................................. 212
8.17.7. Discrete Range Types ....................................................................... 213
8.17.8. Defining New Range Types ............................................................... 213
8.17.9. Indexing ......................................................................................... 214
8.17.10. Constraints on Ranges .................................................................... 214
8.18. Domain Types ........................................................................................... 215
8.19. Object Identifier Types ............................................................................... 216
8.20. pg_lsn Type ........................................................................................... 218
8.21. Pseudo-Types ............................................................................................ 219
9. Functions and Operators .......................................................................................... 221
9.1. Logical Operators ........................................................................................ 221
9.2. Comparison Functions and Operators .............................................................. 222
9.3. Mathematical Functions and Operators ............................................................ 226
9.4. String Functions and Operators ...................................................................... 234
9.4.1. format .......................................................................................... 242
9.5. Binary String Functions and Operators ............................................................ 244
9.6. Bit String Functions and Operators ................................................................. 248
9.7. Pattern Matching ......................................................................................... 250
9.7.1. LIKE .............................................................................................. 251
9.7.2. SIMILAR TO Regular Expressions ..................................................... 252
9.7.3. POSIX Regular Expressions ................................................................ 253
9.8. Data Type Formatting Functions ..................................................................... 269
9.9. Date/Time Functions and Operators ................................................................ 277
9.9.1. EXTRACT, date_part .................................................................... 284
9.9.2. date_trunc .................................................................................. 288
9.9.3. date_bin ...................................................................................... 289
9.9.4. AT TIME ZONE and AT LOCAL .................................................. 290
9.9.5. Current Date/Time ............................................................................. 292
9.9.6. Delaying Execution ........................................................................... 293
9.10. Enum Support Functions ............................................................................. 294
9.11. Geometric Functions and Operators ............................................................... 295
9.12. Network Address Functions and Operators ..................................................... 302
9.13. Text Search Functions and Operators ............................................................. 305
9.14. UUID Functions ........................................................................................ 311
9.15. XML Functions ......................................................................................... 312
9.15.1. Producing XML Content ................................................................... 312
9.15.2. XML Predicates .............................................................................. 316
9.15.3. Processing XML .............................................................................. 318
9.15.4. Mapping Tables to XML .................................................................. 323
9.16. JSON Functions and Operators ..................................................................... 326
9.16.1. Processing and Creating JSON Data .................................................... 327
9.16.2. The SQL/JSON Path Language .......................................................... 340
9.16.3. SQL/JSON Query Functions .............................................................. 351
9.16.4. JSON_TABLE ................................................................................ 354
9.17. Sequence Manipulation Functions ................................................................. 359
9.18. Conditional Expressions .............................................................................. 361
9.18.1. CASE ............................................................................................. 361
9.18.2. COALESCE ..................................................................................... 363
9.18.3. NULLIF ......................................................................................... 363
9.18.4. GREATEST and LEAST .................................................................... 363
9.19. Array Functions and Operators ..................................................................... 364
9.20. Range/Multirange Functions and Operators ..................................................... 367
9.21. Aggregate Functions ................................................................................... 373
9.22. Window Functions ..................................................................................... 381
9.23. Merge Support Functions ............................................................................. 382
9.24. Subquery Expressions ................................................................................. 383
9.24.1. EXISTS ......................................................................................... 383
9.24.2. IN ................................................................................................. 383
29
The SQL Language
9.24.3. NOT IN ........................................................................................ 384
9.24.4. ANY/SOME ...................................................................................... 384
9.24.5. ALL ............................................................................................... 385
9.24.6. Single-Row Comparison ................................................................... 385
9.25. Row and Array Comparisons ....................................................................... 386
9.25.1. IN ................................................................................................. 386
9.25.2. NOT IN ........................................................................................ 386
9.25.3. ANY/SOME (array) ............................................................................ 387
9.25.4. ALL (array) .................................................................................... 387
9.25.5. Row Constructor Comparison ............................................................ 387
9.25.6. Composite Type Comparison ............................................................. 388
9.26. Set Returning Functions .............................................................................. 388
9.27. System Information Functions and Operators .................................................. 392
9.27.1. Session Information Functions ........................................................... 392
9.27.2. Access Privilege Inquiry Functions ..................................................... 395
9.27.3. Schema Visibility Inquiry Functions .................................................... 398
9.27.4. System Catalog Information Functions ................................................ 399
9.27.5. Object Information and Addressing Functions ....................................... 405
9.27.6. Comment Information Functions ........................................................ 406
9.27.7. Data Validity Checking Functions ...................................................... 406
9.27.8. Transaction ID and Snapshot Information Functions ............................... 407
9.27.9. Committed Transaction Information Functions ...................................... 409
9.27.10. Control Data Functions ................................................................... 410
9.27.11. Version Information Functions ......................................................... 411
9.27.12. WAL Summarization Information Functions ....................................... 412
9.28. System Administration Functions .................................................................. 412
9.28.1. Configuration Settings Functions ........................................................ 412
9.28.2. Server Signaling Functions ................................................................ 413
9.28.3. Backup Control Functions ................................................................. 415
9.28.4. Recovery Control Functions .............................................................. 417
9.28.5. Snapshot Synchronization Functions ................................................... 419
9.28.6. Replication Management Functions ..................................................... 419
9.28.7. Database Object Management Functions .............................................. 423
9.28.8. Index Maintenance Functions ............................................................. 425
9.28.9. Generic File Access Functions ........................................................... 426
9.28.10. Advisory Lock Functions ................................................................ 428
9.29. Trigger Functions ....................................................................................... 430
9.30. Event Trigger Functions .............................................................................. 431
9.30.1. Capturing Changes at Command End .................................................. 431
9.30.2. Processing Objects Dropped by a DDL Command ................................. 431
9.30.3. Handling a Table Rewrite Event ......................................................... 433
9.31. Statistics Information Functions .................................................................... 433
9.31.1. Inspecting MCV Lists ...................................................................... 434
10. Type Conversion .................................................................................................. 435
10.1. Overview .................................................................................................. 435
10.2. Operators .................................................................................................. 436
10.3. Functions .................................................................................................. 440
10.4. Value Storage ............................................................................................ 444
10.5. UNION, CASE, and Related Constructs .......................................................... 445
10.6. SELECT Output Columns ............................................................................ 446
11. Indexes ............................................................................................................... 448
11.1. Introduction ............................................................................................... 448
11.2. Index Types .............................................................................................. 449
11.2.1. B-Tree ........................................................................................... 449
11.2.2. Hash .............................................................................................. 450
11.2.3. GiST ............................................................................................. 450
11.2.4. SP-GiST ......................................................................................... 450
11.2.5. GIN ............................................................................................... 450
30
The SQL Language
11.2.6. BRIN ............................................................................................. 451
11.3. Multicolumn Indexes .................................................................................. 451
11.4. Indexes and ORDER BY ............................................................................. 452
11.5. Combining Multiple Indexes ........................................................................ 453
11.6. Unique Indexes .......................................................................................... 454
11.7. Indexes on Expressions ............................................................................... 454
11.8. Partial Indexes ........................................................................................... 455
11.9. Index-Only Scans and Covering Indexes ........................................................ 458
11.10. Operator Classes and Operator Families ....................................................... 460
11.11. Indexes and Collations .............................................................................. 462
11.12. Examining Index Usage ............................................................................. 462
12. Full Text Search ................................................................................................... 464
12.1. Introduction ............................................................................................... 464
12.1.1. What Is a Document? ....................................................................... 465
12.1.2. Basic Text Matching ........................................................................ 465
12.1.3. Configurations ................................................................................. 467
12.2. Tables and Indexes ..................................................................................... 468
12.2.1. Searching a Table ............................................................................ 468
12.2.2. Creating Indexes .............................................................................. 469
12.3. Controlling Text Search .............................................................................. 470
12.3.1. Parsing Documents .......................................................................... 470
12.3.2. Parsing Queries ............................................................................... 471
12.3.3. Ranking Search Results .................................................................... 474
12.3.4. Highlighting Results ......................................................................... 476
12.4. Additional Features .................................................................................... 477
12.4.1. Manipulating Documents .................................................................. 477
12.4.2. Manipulating Queries ....................................................................... 478
12.4.3. Triggers for Automatic Updates ......................................................... 481
12.4.4. Gathering Document Statistics ........................................................... 482
12.5. Parsers ..................................................................................................... 483
12.6. Dictionaries ............................................................................................... 484
12.6.1. Stop Words .................................................................................... 485
12.6.2. Simple Dictionary ............................................................................ 486
12.6.3. Synonym Dictionary ........................................................................ 487
12.6.4. Thesaurus Dictionary ........................................................................ 489
12.6.5. Ispell Dictionary .............................................................................. 491
12.6.6. Snowball Dictionary ......................................................................... 493
12.7. Configuration Example ............................................................................... 494
12.8. Testing and Debugging Text Search .............................................................. 495
12.8.1. Configuration Testing ....................................................................... 495
12.8.2. Parser Testing ................................................................................. 498
12.8.3. Dictionary Testing ........................................................................... 499
12.9. Preferred Index Types for Text Search ........................................................... 500
12.10. psql Support ............................................................................................ 501
12.11. Limitations .............................................................................................. 504
13. Concurrency Control ............................................................................................. 505
13.1. Introduction ............................................................................................... 505
13.2. Transaction Isolation ................................................................................... 505
13.2.1. Read Committed Isolation Level ........................................................ 506
13.2.2. Repeatable Read Isolation Level ......................................................... 508
13.2.3. Serializable Isolation Level ................................................................ 509
13.3. Explicit Locking ........................................................................................ 511
13.3.1. Table-Level Locks ........................................................................... 512
13.3.2. Row-Level Locks ............................................................................ 514
13.3.3. Page-Level Locks ............................................................................ 515
13.3.4. Deadlocks ....................................................................................... 515
13.3.5. Advisory Locks ............................................................................... 516
13.4. Data Consistency Checks at the Application Level ........................................... 517
31
The SQL Language
13.4.1. Enforcing Consistency with Serializable Transactions ............................. 517
13.4.2. Enforcing Consistency with Explicit Blocking Locks .............................. 518
13.5. Serialization Failure Handling ...................................................................... 518
13.6. Caveats ..................................................................................................... 519
13.7. Locking and Indexes ................................................................................... 519
14. Performance Tips ................................................................................................. 521
14.1. Using EXPLAIN ........................................................................................ 521
14.1.1. EXPLAIN Basics ............................................................................. 521
14.1.2. EXPLAIN ANALYZE ...................................................................... 529
14.1.3. Caveats .......................................................................................... 533
14.2. Statistics Used by the Planner ...................................................................... 534
14.2.1. Single-Column Statistics ................................................................... 534
14.2.2. Extended Statistics ........................................................................... 536
14.3. Controlling the Planner with Explicit JOIN Clauses ......................................... 540
14.4. Populating a Database ................................................................................. 542
14.4.1. Disable Autocommit ........................................................................ 542
14.4.2. Use COPY ...................................................................................... 542
14.4.3. Remove Indexes .............................................................................. 542
14.4.4. Remove Foreign Key Constraints ....................................................... 543
14.4.5. Increase maintenance_work_mem ................................................. 543
14.4.6. Increase max_wal_size ................................................................ 543
14.4.7. Disable WAL Archival and Streaming Replication ................................. 543
14.4.8. Run ANALYZE Afterwards ................................................................ 543
14.4.9. Some Notes about pg_dump .............................................................. 544
14.5. Non-Durable Settings .................................................................................. 544
15. Parallel Query ...................................................................................................... 546
15.1. How Parallel Query Works .......................................................................... 546
15.2. When Can Parallel Query Be Used? .............................................................. 547
15.3. Parallel Plans ............................................................................................. 548
15.3.1. Parallel Scans .................................................................................. 548
15.3.2. Parallel Joins .................................................................................. 548
15.3.3. Parallel Aggregation ......................................................................... 549
15.3.4. Parallel Append ............................................................................... 549
15.3.5. Parallel Plan Tips ............................................................................ 549
15.4. Parallel Safety ........................................................................................... 550
15.4.1. Parallel Labeling for Functions and Aggregates ..................................... 550
32
Chapter 4. SQL Syntax
This chapter describes the syntax of SQL. It forms the foundation for understanding the following
chapters which will go into detail about how SQL commands are applied to define and modify data.
We also advise users who are already familiar with SQL to read this chapter carefully because it
contains several rules and concepts that are implemented inconsistently among SQL databases or that
are specific to PostgreSQL.
4.1. Lexical Structure
SQL input consists of a sequence of commands. A command is composed of a sequence of tokens,
terminated by a semicolon (“;”). The end of the input stream also terminates a command. Which tokens
are valid depends on the syntax of the particular command.
A token can be a key word, an identifier, a quoted identifier, a literal (or constant), or a special character
symbol. Tokens are normally separated by whitespace (space, tab, newline), but need not be if there
is no ambiguity (which is generally only the case if a special character is adjacent to some other token
type).
For example, the following is (syntactically) valid SQL input:
SELECT * FROM MY_TABLE;
UPDATE MY_TABLE SET A = 5;
INSERT INTO MY_TABLE VALUES (3, 'hi there');
This is a sequence of three commands, one per line (although this is not required; more than one
command can be on a line, and commands can usefully be split across lines).
Additionally, comments can occur in SQL input. They are not tokens, they are effectively equivalent
to whitespace.
The SQL syntax is not very consistent regarding what tokens identify commands and which are
operands or parameters. The first few tokens are generally the command name, so in the above example we would usually speak of a “SELECT”, an “UPDATE”, and an “INSERT” command. But for
instance the UPDATE command always requires a SET token to appear in a certain position, and this
particular variation of INSERT also requires a VALUES in order to be complete. The precise syntax
rules for each command are described in Part VI.
4.1.1. Identifiers and Key Words
Tokens such as SELECT, UPDATE, or VALUES in the example above are examples of key words,
that is, words that have a fixed meaning in the SQL language. The tokens MY_TABLE and A are
examples of identifiers. They identify names of tables, columns, or other database objects, depending
on the command they are used in. Therefore they are sometimes simply called “names”. Key words
and identifiers have the same lexical structure, meaning that one cannot know whether a token is an
identifier or a key word without knowing the language. A complete list of key words can be found
in Appendix C.
SQL identifiers and key words must begin with a letter (a-z, but also letters with diacritical marks
and non-Latin letters) or an underscore (_). Subsequent characters in an identifier or key word can be
letters, underscores, digits (0-9), or dollar signs ($). Note that dollar signs are not allowed in identifiers
according to the letter of the SQL standard, so their use might render applications less portable. The
SQL standard will not define a key word that contains digits or starts or ends with an underscore, so
identifiers of this form are safe against possible conflict with future extensions of the standard.
33
SQL Syntax
The system uses no more than NAMEDATALEN-1 bytes of an identifier; longer names can be written
in commands, but they will be truncated. By default, NAMEDATALEN is 64 so the maximum identifier
length is 63 bytes. If this limit is problematic, it can be raised by changing the NAMEDATALEN constant
in src/include/pg_config_manual.h.
Key words and unquoted identifiers are case-insensitive. Therefore:
UPDATE MY_TABLE SET A = 5;
can equivalently be written as:
uPDaTE my_TabLE SeT a = 5;
A convention often used is to write key words in upper case and names in lower case, e.g.:
UPDATE my_table SET a = 5;
There is a second kind of identifier: the delimited identifier or quoted identifier. It is formed by
enclosing an arbitrary sequence of characters in double-quotes ("). A delimited identifier is always
an identifier, never a key word. So "select" could be used to refer to a column or table named
“select”, whereas an unquoted select would be taken as a key word and would therefore provoke
a parse error when used where a table or column name is expected. The example can be written with
quoted identifiers like this:
UPDATE "my_table" SET "a" = 5;
Quoted identifiers can contain any character, except the character with code zero. (To include a double
quote, write two double quotes.) This allows constructing table or column names that would otherwise
not be possible, such as ones containing spaces or ampersands. The length limitation still applies.
Quoting an identifier also makes it case-sensitive, whereas unquoted names are always folded to lower
case. For example, the identifiers FOO, foo, and "foo" are considered the same by PostgreSQL, but
"Foo" and "FOO" are different from these three and each other. (The folding of unquoted names to
lower case in PostgreSQL is incompatible with the SQL standard, which says that unquoted names
should be folded to upper case. Thus, foo should be equivalent to "FOO" not "foo" according to
the standard. If you want to write portable applications you are advised to always quote a particular
name or never quote it.)
A variant of quoted identifiers allows including escaped Unicode characters identified by their code
points. This variant starts with U& (upper or lower case U followed by ampersand) immediately before
the opening double quote, without any spaces in between, for example U&"foo". (Note that this
creates an ambiguity with the operator &. Use spaces around the operator to avoid this problem.) Inside
the quotes, Unicode characters can be specified in escaped form by writing a backslash followed by
the four-digit hexadecimal code point number or alternatively a backslash followed by a plus sign
followed by a six-digit hexadecimal code point number. For example, the identifier "data" could
be written as
U&"d\0061t\+000061"
The following less trivial example writes the Russian word “slon” (elephant) in Cyrillic letters:
U&"\0441\043B\043E\043D"
If a different escape character than backslash is desired, it can be specified using the UESCAPE clause
after the string, for example:
34
SQL Syntax
U&"d!0061t!+000061" UESCAPE '!'
The escape character can be any single character other than a hexadecimal digit, the plus sign, a single
quote, a double quote, or a whitespace character. Note that the escape character is written in single
quotes, not double quotes, after UESCAPE.
To include the escape character in the identifier literally, write it twice.
Either the 4-digit or the 6-digit escape form can be used to specify UTF-16 surrogate pairs to compose characters with code points larger than U+FFFF, although the availability of the 6-digit form
technically makes this unnecessary. (Surrogate pairs are not stored directly, but are combined into a
single code point.)
If the server encoding is not UTF-8, the Unicode code point identified by one of these escape sequences
is converted to the actual server encoding; an error is reported if that's not possible.
4.1.2. Constants
There are three kinds of implicitly-typed constants in PostgreSQL: strings, bit strings, and numbers.
Constants can also be specified with explicit types, which can enable more accurate representation and
more efficient handling by the system. These alternatives are discussed in the following subsections.
4.1.2.1. String Constants
A string constant in SQL is an arbitrary sequence of characters bounded by single quotes ('), for
example 'This is a string'. To include a single-quote character within a string constant,
write two adjacent single quotes, e.g., 'Dianne''s horse'. Note that this is not the same as a
double-quote character (").
Two string constants that are only separated by whitespace with at least one newline are concatenated
and effectively treated as if the string had been written as one constant. For example:
SELECT 'foo'
'bar';
is equivalent to:
SELECT 'foobar';
but:
SELECT 'foo'
'bar';
is not valid syntax. (This slightly bizarre behavior is specified by SQL; PostgreSQL is following the
standard.)
4.1.2.2. String Constants with C-Style Escapes
PostgreSQL also accepts “escape” string constants, which are an extension to the SQL standard. An
escape string constant is specified by writing the letter E (upper or lower case) just before the opening
single quote, e.g., E'foo'. (When continuing an escape string constant across lines, write E only
before the first opening quote.) Within an escape string, a backslash character (\) begins a C-like
backslash escape sequence, in which the combination of backslash and following character(s) represent a special byte value, as shown in Table 4.1.
35
SQL Syntax
Table 4.1. Backslash Escape Sequences
Backslash Escape Sequence
Interpretation
\b
backspace
\f
form feed
\n
newline
\r
carriage return
\t
tab
\o, \oo, \ooo (o = 0–7)
octal byte value
\xh, \xhh (h = 0–9, A–F)
hexadecimal byte value
\uxxxx, \Uxxxxxxxx (x = 0–9, A–F)
16 or 32-bit hexadecimal Unicode character value
Any other character following a backslash is taken literally. Thus, to include a backslash character,
write two backslashes (\\). Also, a single quote can be included in an escape string by writing \',
in addition to the normal way of ''.
It is your responsibility that the byte sequences you create, especially when using the octal or hexadecimal escapes, compose valid characters in the server character set encoding. A useful alternative
is to use Unicode escapes or the alternative Unicode escape syntax, explained in Section 4.1.2.3; then
the server will check that the character conversion is possible.
Caution
If the configuration parameter standard_conforming_strings is off, then PostgreSQL recognizes backslash escapes in both regular and escape string constants. However, as of PostgreSQL 9.1, the default is on, meaning that backslash escapes are recognized only in escape string constants. This behavior is more standards-compliant, but might break applications
which rely on the historical behavior, where backslash escapes were always recognized. As
a workaround, you can set this parameter to off, but it is better to migrate away from using
backslash escapes. If you need to use a backslash escape to represent a special character, write
the string constant with an E.
In addition to standard_conforming_strings, the configuration parameters escape_string_warning and backslash_quote govern treatment of backslashes in string constants.
The character with the code zero cannot be in a string constant.
4.1.2.3. String Constants with Unicode Escapes
PostgreSQL also supports another type of escape syntax for strings that allows specifying arbitrary
Unicode characters by code point. A Unicode escape string constant starts with U& (upper or lower
case letter U followed by ampersand) immediately before the opening quote, without any spaces in
between, for example U&'foo'. (Note that this creates an ambiguity with the operator &. Use spaces
around the operator to avoid this problem.) Inside the quotes, Unicode characters can be specified
in escaped form by writing a backslash followed by the four-digit hexadecimal code point number
or alternatively a backslash followed by a plus sign followed by a six-digit hexadecimal code point
number. For example, the string 'data' could be written as
U&'d\0061t\+000061'
The following less trivial example writes the Russian word “slon” (elephant) in Cyrillic letters:
36
SQL Syntax
U&'\0441\043B\043E\043D'
If a different escape character than backslash is desired, it can be specified using the UESCAPE clause
after the string, for example:
U&'d!0061t!+000061' UESCAPE '!'
The escape character can be any single character other than a hexadecimal digit, the plus sign, a single
quote, a double quote, or a whitespace character.
To include the escape character in the string literally, write it twice.
Either the 4-digit or the 6-digit escape form can be used to specify UTF-16 surrogate pairs to compose characters with code points larger than U+FFFF, although the availability of the 6-digit form
technically makes this unnecessary. (Surrogate pairs are not stored directly, but are combined into a
single code point.)
If the server encoding is not UTF-8, the Unicode code point identified by one of these escape sequences
is converted to the actual server encoding; an error is reported if that's not possible.
Also, the Unicode escape syntax for string constants only works when the configuration parameter
standard_conforming_strings is turned on. This is because otherwise this syntax could confuse clients
that parse the SQL statements to the point that it could lead to SQL injections and similar security
issues. If the parameter is set to off, this syntax will be rejected with an error message.
4.1.2.4. Dollar-Quoted String Constants
While the standard syntax for specifying string constants is usually convenient, it can be difficult to
understand when the desired string contains many single quotes, since each of those must be doubled.
To allow more readable queries in such situations, PostgreSQL provides another way, called “dollar
quoting”, to write string constants. A dollar-quoted string constant consists of a dollar sign ($), an
optional “tag” of zero or more characters, another dollar sign, an arbitrary sequence of characters that
makes up the string content, a dollar sign, the same tag that began this dollar quote, and a dollar sign.
For example, here are two different ways to specify the string “Dianne's horse” using dollar quoting:
$$Dianne's horse$$
$SomeTag$Dianne's horse$SomeTag$
Notice that inside the dollar-quoted string, single quotes can be used without needing to be escaped.
Indeed, no characters inside a dollar-quoted string are ever escaped: the string content is always written
literally. Backslashes are not special, and neither are dollar signs, unless they are part of a sequence
matching the opening tag.
It is possible to nest dollar-quoted string constants by choosing different tags at each nesting level.
This is most commonly used in writing function definitions. For example:
$function$
BEGIN
RETURN ($1 ~ $q$[\t\r\n\v\\]$q$);
END;
$function$
Here, the sequence $q$[\t\r\n\v\\]$q$ represents a dollar-quoted literal string [\t\r\n\v
\\], which will be recognized when the function body is executed by PostgreSQL. But since the
sequence does not match the outer dollar quoting delimiter $function$, it is just some more characters within the constant so far as the outer string is concerned.
37
SQL Syntax
The tag, if any, of a dollar-quoted string follows the same rules as an unquoted identifier, except that it
cannot contain a dollar sign. Tags are case sensitive, so $tag$String content$tag$ is correct,
but $TAG$String content$tag$ is not.
A dollar-quoted string that follows a keyword or identifier must be separated from it by whitespace;
otherwise the dollar quoting delimiter would be taken as part of the preceding identifier.
Dollar quoting is not part of the SQL standard, but it is often a more convenient way to write complicated string literals than the standard-compliant single quote syntax. It is particularly useful when
representing string constants inside other constants, as is often needed in procedural function definitions. With single-quote syntax, each backslash in the above example would have to be written as four
backslashes, which would be reduced to two backslashes in parsing the original string constant, and
then to one when the inner string constant is re-parsed during function execution.
4.1.2.5. Bit-String Constants
Bit-string constants look like regular string constants with a B (upper or lower case) immediately
before the opening quote (no intervening whitespace), e.g., B'1001'. The only characters allowed
within bit-string constants are 0 and 1.
Alternatively, bit-string constants can be specified in hexadecimal notation, using a leading X (upper
or lower case), e.g., X'1FF'. This notation is equivalent to a bit-string constant with four binary digits
for each hexadecimal digit.
Both forms of bit-string constant can be continued across lines in the same way as regular string
constants. Dollar quoting cannot be used in a bit-string constant.
4.1.2.6. Numeric Constants
Numeric constants are accepted in these general forms:
digits
digits.[digits][e[+-]digits]
[digits].digits[e[+-]digits]
digitse[+-]digits
where digits is one or more decimal digits (0 through 9). At least one digit must be before or
after the decimal point, if one is used. At least one digit must follow the exponent marker (e), if
one is present. There cannot be any spaces or other characters embedded in the constant, except for
underscores, which can be used for visual grouping as described below. Note that any leading plus or
minus sign is not actually considered part of the constant; it is an operator applied to the constant.
These are some examples of valid numeric constants:
42
3.5
4.
.001
5e2
1.925e-3
Additionally, non-decimal integer constants are accepted in these forms:
0xhexdigits
0ooctdigits
0bbindigits
38
SQL Syntax
where hexdigits is one or more hexadecimal digits (0-9, A-F), octdigits is one or more octal
digits (0-7), and bindigits is one or more binary digits (0 or 1). Hexadecimal digits and the radix
prefixes can be in upper or lower case. Note that only integers can have non-decimal forms, not numbers with fractional parts.
These are some examples of valid non-decimal integer constants:
0b100101
0B10011001
0o273
0O755
0x42f
0XFFFF
For visual grouping, underscores can be inserted between digits. These have no further effect on the
value of the constant. For example:
1_500_000_000
0b10001000_00000000
0o_1_755
0xFFFF_FFFF
1.618_034
Underscores are not allowed at the start or end of a numeric constant or a group of digits (that is,
immediately before or after the decimal point or the exponent marker), and more than one underscore
in a row is not allowed.
A numeric constant that contains neither a decimal point nor an exponent is initially presumed to be
type integer if its value fits in type integer (32 bits); otherwise it is presumed to be type bigint
if its value fits in type bigint (64 bits); otherwise it is taken to be type numeric. Constants that
contain decimal points and/or exponents are always initially presumed to be type numeric.
The initially assigned data type of a numeric constant is just a starting point for the type resolution
algorithms. In most cases the constant will be automatically coerced to the most appropriate type depending on context. When necessary, you can force a numeric value to be interpreted as a specific data
type by casting it. For example, you can force a numeric value to be treated as type real (float4)
by writing:
REAL '1.23'
1.23::REAL
-- string style
-- PostgreSQL (historical) style
These are actually just special cases of the general casting notations discussed next.
4.1.2.7. Constants of Other Types
A constant of an arbitrary type can be entered using any one of the following notations:
type 'string'
'string'::type
CAST ( 'string' AS type )
The string constant's text is passed to the input conversion routine for the type called type. The result
is a constant of the indicated type. The explicit type cast can be omitted if there is no ambiguity as to
the type the constant must be (for example, when it is assigned directly to a table column), in which
case it is automatically coerced.
The string constant can be written using either regular SQL notation or dollar-quoting.
39
SQL Syntax
It is also possible to specify a type coercion using a function-like syntax:
typename ( 'string' )
but not all type names can be used in this way; see Section 4.2.9 for details.
The ::, CAST(), and function-call syntaxes can also be used to specify run-time type conversions of arbitrary expressions, as discussed in Section 4.2.9. To avoid syntactic ambiguity, the type
'string' syntax can only be used to specify the type of a simple literal constant. Another restriction
on the type 'string' syntax is that it does not work for array types; use :: or CAST() to specify
the type of an array constant.
The CAST() syntax conforms to SQL. The type 'string' syntax is a generalization of the
standard: SQL specifies this syntax only for a few data types, but PostgreSQL allows it for all types.
The syntax with :: is historical PostgreSQL usage, as is the function-call syntax.
4.1.3. Operators
An operator name is a sequence of up to NAMEDATALEN-1 (63 by default) characters from the following list:
+-*/<>=~!@#%^&|`?
There are a few restrictions on operator names, however:
• -- and /* cannot appear anywhere in an operator name, since they will be taken as the start of
a comment.
• A multiple-character operator name cannot end in + or -, unless the name also contains at least
one of these characters:
~!@#%^&|`?
For example, @- is an allowed operator name, but *- is not. This restriction allows PostgreSQL to
parse SQL-compliant queries without requiring spaces between tokens.
When working with non-SQL-standard operator names, you will usually need to separate adjacent
operators with spaces to avoid ambiguity. For example, if you have defined a prefix operator named
@, you cannot write X*@Y; you must write X* @Y to ensure that PostgreSQL reads it as two operator
names not one.
4.1.4. Special Characters
Some characters that are not alphanumeric have a special meaning that is different from being an operator. Details on the usage can be found at the location where the respective syntax element is described.
This section only exists to advise the existence and summarize the purposes of these characters.
• A dollar sign ($) followed by digits is used to represent a positional parameter in the body of a
function definition or a prepared statement. In other contexts the dollar sign can be part of an identifier or a dollar-quoted string constant.
• Parentheses (()) have their usual meaning to group expressions and enforce precedence. In some
cases parentheses are required as part of the fixed syntax of a particular SQL command.
• Brackets ([]) are used to select the elements of an array. See Section 8.15 for more information
on arrays.
40
SQL Syntax
• Commas (,) are used in some syntactical constructs to separate the elements of a list.
• The semicolon (;) terminates an SQL command. It cannot appear anywhere within a command,
except within a string constant or quoted identifier.
• The colon (:) is used to select “slices” from arrays. (See Section 8.15.) In certain SQL dialects
(such as Embedded SQL), the colon is used to prefix variable names.
• The asterisk (*) is used in some contexts to denote all the fields of a table row or composite value.
It also has a special meaning when used as the argument of an aggregate function, namely that the
aggregate does not require any explicit parameter.
• The period (.) is used in numeric constants, and to separate schema, table, and column names.
4.1.5. Comments
A comment is a sequence of characters beginning with double dashes and extending to the end of
the line, e.g.:
-- This is a standard SQL comment
Alternatively, C-style block comments can be used:
/* multiline comment
* with nesting: /* nested block comment */
*/
where the comment begins with /* and extends to the matching occurrence of */. These block comments nest, as specified in the SQL standard but unlike C, so that one can comment out larger blocks
of code that might contain existing block comments.
A comment is removed from the input stream before further syntax analysis and is effectively replaced
by whitespace.
4.1.6. Operator Precedence
Table 4.2 shows the precedence and associativity of the operators in PostgreSQL. Most operators have
the same precedence and are left-associative. The precedence and associativity of the operators is hardwired into the parser. Add parentheses if you want an expression with multiple operators to be parsed
in some other way than what the precedence rules imply.
Table 4.2. Operator Precedence (highest to lowest)
Operator/Element
Associativity
Description
.
left
table/column name separator
::
left
PostgreSQL-style typecast
[]
left
array element selection
+-
right
unary plus, unary minus
COLLATE
left
collation selection
AT
left
AT TIME ZONE, AT LOCAL
^
left
exponentiation
*/%
left
multiplication, division, modulo
+-
left
addition, subtraction
41
SQL Syntax
Operator/Element
Associativity
Description
(any other operator)
left
all other native and user-defined operators
BETWEEN IN LIKE ILIKE
SIMILAR
range containment, set membership,
string matching
< > = <= >= <>
comparison operators
IS ISNULL NOTNULL
IS TRUE, IS FALSE, IS NULL,
IS DISTINCT FROM, etc.
NOT
right
logical negation
AND
left
logical conjunction
OR
left
logical disjunction
Note that the operator precedence rules also apply to user-defined operators that have the same names
as the built-in operators mentioned above. For example, if you define a “+” operator for some custom
data type it will have the same precedence as the built-in “+” operator, no matter what yours does.
When a schema-qualified operator name is used in the OPERATOR syntax, as for example in:
SELECT 3 OPERATOR(pg_catalog.+) 4;
the OPERATOR construct is taken to have the default precedence shown in Table 4.2 for “any other
operator”. This is true no matter which specific operator appears inside OPERATOR().
Note
PostgreSQL versions before 9.5 used slightly different operator precedence rules. In particular, <= >= and <> used to be treated as generic operators; IS tests used to have higher priority; and NOT BETWEEN and related constructs acted inconsistently, being taken in some
cases as having the precedence of NOT rather than BETWEEN. These rules were changed for
better compliance with the SQL standard and to reduce confusion from inconsistent treatment
of logically equivalent constructs. In most cases, these changes will result in no behavioral
change, or perhaps in “no such operator” failures which can be resolved by adding parentheses.
However there are corner cases in which a query might change behavior without any parsing
error being reported.
4.2. Value Expressions
Value expressions are used in a variety of contexts, such as in the target list of the SELECT command,
as new column values in INSERT or UPDATE, or in search conditions in a number of commands. The
result of a value expression is sometimes called a scalar, to distinguish it from the result of a table
expression (which is a table). Value expressions are therefore also called scalar expressions (or even
simply expressions). The expression syntax allows the calculation of values from primitive parts using
arithmetic, logical, set, and other operations.
A value expression is one of the following:
• A constant or literal value
• A column reference
• A positional parameter reference, in the body of a function definition or prepared statement
• A subscripted expression
42
SQL Syntax
• A field selection expression
• An operator invocation
• A function call
• An aggregate expression
• A window function call
• A type cast
• A collation expression
• A scalar subquery
• An array constructor
• A row constructor
• Another value expression in parentheses (used to group subexpressions and override precedence)
In addition to this list, there are a number of constructs that can be classified as an expression but do
not follow any general syntax rules. These generally have the semantics of a function or operator and
are explained in the appropriate location in Chapter 9. An example is the IS NULL clause.
We have already discussed constants in Section 4.1.2. The following sections discuss the remaining
options.
4.2.1. Column References
A column can be referenced in the form:
correlation.columnname
correlation is the name of a table (possibly qualified with a schema name), or an alias for a table
defined by means of a FROM clause. The correlation name and separating dot can be omitted if the
column name is unique across all the tables being used in the current query. (See also Chapter 7.)
4.2.2. Positional Parameters
A positional parameter reference is used to indicate a value that is supplied externally to an SQL statement. Parameters are used in SQL function definitions and in prepared queries. Some client libraries
also support specifying data values separately from the SQL command string, in which case parameters are used to refer to the out-of-line data values. The form of a parameter reference is:
$number
For example, consider the definition of a function, dept, as:
CREATE FUNCTION dept(text) RETURNS dept
AS $$ SELECT * FROM dept WHERE name = $1 $$
LANGUAGE SQL;
Here the $1 references the value of the first function argument whenever the function is invoked.
4.2.3. Subscripts
43
SQL Syntax
If an expression yields a value of an array type, then a specific element of the array value can be
extracted by writing
expression[subscript]
or multiple adjacent elements (an “array slice”) can be extracted by writing
expression[lower_subscript:upper_subscript]
(Here, the brackets [ ] are meant to appear literally.) Each subscript is itself an expression, which
will be rounded to the nearest integer value.
In general the array expression must be parenthesized, but the parentheses can be omitted when
the expression to be subscripted is just a column reference or positional parameter. Also, multiple
subscripts can be concatenated when the original array is multidimensional. For example:
mytable.arraycolumn[4]
mytable.two_d_column[17][34]
$1[10:42]
(arrayfunction(a,b))[42]
The parentheses in the last example are required. See Section 8.15 for more about arrays.
4.2.4. Field Selection
If an expression yields a value of a composite type (row type), then a specific field of the row can
be extracted by writing
expression.fieldname
In general the row expression must be parenthesized, but the parentheses can be omitted when the
expression to be selected from is just a table reference or positional parameter. For example:
mytable.mycolumn
$1.somecolumn
(rowfunction(a,b)).col3
(Thus, a qualified column reference is actually just a special case of the field selection syntax.) An
important special case is extracting a field from a table column that is of a composite type:
(compositecol).somefield
(mytable.compositecol).somefield
The parentheses are required here to show that compositecol is a column name not a table name,
or that mytable is a table name not a schema name in the second case.
You can ask for all fields of a composite value by writing .*:
(compositecol).*
This notation behaves differently depending on context; see Section 8.16.5 for details.
4.2.5. Operator Invocations
44
SQL Syntax
There are two possible syntaxes for an operator invocation:
expression operator expression (binary infix operator)
operator expression (unary prefix operator)
where the operator token follows the syntax rules of Section 4.1.3, or is one of the key words AND,
OR, and NOT, or is a qualified operator name in the form:
OPERATOR(schema.operatorname)
Which particular operators exist and whether they are unary or binary depends on what operators have
been defined by the system or the user. Chapter 9 describes the built-in operators.
4.2.6. Function Calls
The syntax for a function call is the name of a function (possibly qualified with a schema name),
followed by its argument list enclosed in parentheses:
function_name ([expression [, expression ... ]] )
For example, the following computes the square root of 2:
sqrt(2)
The list of built-in functions is in Chapter 9. Other functions can be added by the user.
When issuing queries in a database where some users mistrust other users, observe security precautions
from Section 10.3 when writing function calls.
The arguments can optionally have names attached. See Section 4.3 for details.
Note
A function that takes a single argument of composite type can optionally be called using fieldselection syntax, and conversely field selection can be written in functional style. That is, the
notations col(table) and table.col are interchangeable. This behavior is not SQLstandard but is provided in PostgreSQL because it allows use of functions to emulate “computed fields”. For more information see Section 8.16.5.
4.2.7. Aggregate Expressions
An aggregate expression represents the application of an aggregate function across the rows selected
by a query. An aggregate function reduces multiple inputs to a single output value, such as the sum or
average of the inputs. The syntax of an aggregate expression is one of the following:
aggregate_name (expression [ , ... ] [ order_by_clause ] ) [ FILTER
( WHERE filter_clause ) ]
aggregate_name (ALL expression [ , ... ] [ order_by_clause ] )
[ FILTER ( WHERE filter_clause ) ]
aggregate_name (DISTINCT expression [ , ... ] [ order_by_clause ] )
[ FILTER ( WHERE filter_clause ) ]
aggregate_name ( * ) [ FILTER ( WHERE filter_clause ) ]
aggregate_name ( [ expression [ , ... ] ] ) WITHIN GROUP
( order_by_clause ) [ FILTER ( WHERE filter_clause ) ]
45
SQL Syntax
where aggregate_name is a previously defined aggregate (possibly qualified with a schema name)
and expression is any value expression that does not itself contain an aggregate expression or
a window function call. The optional order_by_clause and filter_clause are described
below.
The first form of aggregate expression invokes the aggregate once for each input row. The second
form is the same as the first, since ALL is the default. The third form invokes the aggregate once for
each distinct value of the expression (or distinct set of values, for multiple expressions) found in the
input rows. The fourth form invokes the aggregate once for each input row; since no particular input
value is specified, it is generally only useful for the count(*) aggregate function. The last form is
used with ordered-set aggregate functions, which are described below.
Most aggregate functions ignore null inputs, so that rows in which one or more of the expression(s)
yield null are discarded. This can be assumed to be true, unless otherwise specified, for all built-in
aggregates.
For example, count(*) yields the total number of input rows; count(f1) yields the number of
input rows in which f1 is non-null, since count ignores nulls; and count(distinct f1) yields
the number of distinct non-null values of f1.
Ordinarily, the input rows are fed to the aggregate function in an unspecified order. In many cases
this does not matter; for example, min produces the same result no matter what order it receives the
inputs in. However, some aggregate functions (such as array_agg and string_agg) produce
results that depend on the ordering of the input rows. When using such an aggregate, the optional
order_by_clause can be used to specify the desired ordering. The order_by_clause has
the same syntax as for a query-level ORDER BY clause, as described in Section 7.5, except that its
expressions are always just expressions and cannot be output-column names or numbers. For example:
WITH vals (v) AS ( VALUES (1),(3),(4),(3),(2) )
SELECT array_agg(v ORDER BY v DESC) FROM vals;
array_agg
------------{4,3,3,2,1}
Since jsonb only keeps the last matching key, ordering of its keys can be significant:
WITH vals (k, v) AS ( VALUES ('key0','1'), ('key1','3'),
('key1','2') )
SELECT jsonb_object_agg(k, v ORDER BY v) FROM vals;
jsonb_object_agg
---------------------------{"key0": "1", "key1": "3"}
When dealing with multiple-argument aggregate functions, note that the ORDER BY clause goes after
all the aggregate arguments. For example, write this:
SELECT string_agg(a, ',' ORDER BY a) FROM table;
not this:
SELECT string_agg(a ORDER BY a, ',') FROM table;
-- incorrect
The latter is syntactically valid, but it represents a call of a single-argument aggregate function with
two ORDER BY keys (the second one being rather useless since it's a constant).
If DISTINCT is specified with an order_by_clause, ORDER BY expressions can only reference
columns in the DISTINCT list. For example:
46
SQL Syntax
WITH vals (v) AS ( VALUES (1),(3),(4),(3),(2) )
SELECT array_agg(DISTINCT v ORDER BY v DESC) FROM vals;
array_agg
----------{4,3,2,1}
Placing ORDER BY within the aggregate's regular argument list, as described so far, is used when
ordering the input rows for general-purpose and statistical aggregates, for which ordering is optional. There is a subclass of aggregate functions called ordered-set aggregates for which an order_by_clause is required, usually because the aggregate's computation is only sensible in terms
of a specific ordering of its input rows. Typical examples of ordered-set aggregates include rank
and percentile calculations. For an ordered-set aggregate, the order_by_clause is written inside
WITHIN GROUP (...), as shown in the final syntax alternative above. The expressions in the
order_by_clause are evaluated once per input row just like regular aggregate arguments, sorted
as per the order_by_clause's requirements, and fed to the aggregate function as input arguments.
(This is unlike the case for a non-WITHIN GROUP order_by_clause, which is not treated as
argument(s) to the aggregate function.) The argument expressions preceding WITHIN GROUP, if
any, are called direct arguments to distinguish them from the aggregated arguments listed in the order_by_clause. Unlike regular aggregate arguments, direct arguments are evaluated only once
per aggregate call, not once per input row. This means that they can contain variables only if those
variables are grouped by GROUP BY; this restriction is the same as if the direct arguments were not
inside an aggregate expression at all. Direct arguments are typically used for things like percentile
fractions, which only make sense as a single value per aggregation calculation. The direct argument
list can be empty; in this case, write just () not (*). (PostgreSQL will actually accept either spelling,
but only the first way conforms to the SQL standard.)
An example of an ordered-set aggregate call is:
SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY income) FROM
households;
percentile_cont
----------------50489
which obtains the 50th percentile, or median, value of the income column from table households.
Here, 0.5 is a direct argument; it would make no sense for the percentile fraction to be a value varying
across rows.
If FILTER is specified, then only the input rows for which the filter_clause evaluates to true
are fed to the aggregate function; other rows are discarded. For example:
SELECT
count(*) AS unfiltered,
count(*) FILTER (WHERE i < 5) AS filtered
FROM generate_series(1,10) AS s(i);
unfiltered | filtered
------------+---------10 |
4
(1 row)
The predefined aggregate functions are described in Section 9.21. Other aggregate functions can be
added by the user.
An aggregate expression can only appear in the result list or HAVING clause of a SELECT command.
It is forbidden in other clauses, such as WHERE, because those clauses are logically evaluated before
the results of aggregates are formed.
47
SQL Syntax
When an aggregate expression appears in a subquery (see Section 4.2.11 and Section 9.24), the aggregate is normally evaluated over the rows of the subquery. But an exception occurs if the aggregate's
arguments (and filter_clause if any) contain only outer-level variables: the aggregate then belongs to the nearest such outer level, and is evaluated over the rows of that query. The aggregate expression as a whole is then an outer reference for the subquery it appears in, and acts as a constant over
any one evaluation of that subquery. The restriction about appearing only in the result list or HAVING
clause applies with respect to the query level that the aggregate belongs to.
4.2.8. Window Function Calls
A window function call represents the application of an aggregate-like function over some portion of
the rows selected by a query. Unlike non-window aggregate calls, this is not tied to grouping of the
selected rows into a single output row — each row remains separate in the query output. However the
window function has access to all the rows that would be part of the current row's group according
to the grouping specification (PARTITION BY list) of the window function call. The syntax of a
window function call is one of the following:
function_name ([expression [, expression ... ]]) [ FILTER
( WHERE filter_clause ) ] OVER window_name
function_name ([expression [, expression ... ]]) [ FILTER
( WHERE filter_clause ) ] OVER ( window_definition )
function_name ( * ) [ FILTER ( WHERE filter_clause ) ]
OVER window_name
function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER
( window_definition )
where window_definition has the syntax
[ existing_window_name ]
[ PARTITION BY expression [, ...] ]
[ ORDER BY expression [ ASC | DESC | USING operator ] [ NULLS
{ FIRST | LAST } ] [, ...] ]
[ frame_clause ]
The optional frame_clause can be one of
{ RANGE | ROWS | GROUPS } frame_start [ frame_exclusion ]
{ RANGE | ROWS | GROUPS } BETWEEN frame_start AND frame_end
[ frame_exclusion ]
where frame_start and frame_end can be one of
UNBOUNDED PRECEDING
offset PRECEDING
CURRENT ROW
offset FOLLOWING
UNBOUNDED FOLLOWING
and frame_exclusion can be one of
EXCLUDE CURRENT ROW
EXCLUDE GROUP
EXCLUDE TIES
EXCLUDE NO OTHERS
48
SQL Syntax
Here, expression represents any value expression that does not itself contain window function
calls.
window_name is a reference to a named window specification defined in the query's WINDOW clause.
Alternatively, a full window_definition can be given within parentheses, using the same syntax
as for defining a named window in the WINDOW clause; see the SELECT reference page for details. It's
worth pointing out that OVER wname is not exactly equivalent to OVER (wname ...); the latter
implies copying and modifying the window definition, and will be rejected if the referenced window
specification includes a frame clause.
The PARTITION BY clause groups the rows of the query into partitions, which are processed separately by the window function. PARTITION BY works similarly to a query-level GROUP BY clause,
except that its expressions are always just expressions and cannot be output-column names or numbers. Without PARTITION BY, all rows produced by the query are treated as a single partition. The
ORDER BY clause determines the order in which the rows of a partition are processed by the window
function. It works similarly to a query-level ORDER BY clause, but likewise cannot use output-column
names or numbers. Without ORDER BY, rows are processed in an unspecified order.
The frame_clause specifies the set of rows constituting the window frame, which is a subset of
the current partition, for those window functions that act on the frame instead of the whole partition.
The set of rows in the frame can vary depending on which row is the current row. The frame can be
specified in RANGE, ROWS or GROUPS mode; in each case, it runs from the frame_start to the
frame_end. If frame_end is omitted, the end defaults to CURRENT ROW.
A frame_start of UNBOUNDED PRECEDING means that the frame starts with the first row of
the partition, and similarly a frame_end of UNBOUNDED FOLLOWING means that the frame ends
with the last row of the partition.
In RANGE or GROUPS mode, a frame_start of CURRENT ROW means the frame starts with the
current row's first peer row (a row that the window's ORDER BY clause sorts as equivalent to the
current row), while a frame_end of CURRENT ROW means the frame ends with the current row's
last peer row. In ROWS mode, CURRENT ROW simply means the current row.
In the offset PRECEDING and offset FOLLOWING frame options, the offset must be an
expression not containing any variables, aggregate functions, or window functions. The meaning of
the offset depends on the frame mode:
• In ROWS mode, the offset must yield a non-null, non-negative integer, and the option means that
the frame starts or ends the specified number of rows before or after the current row.
• In GROUPS mode, the offset again must yield a non-null, non-negative integer, and the option
means that the frame starts or ends the specified number of peer groups before or after the current
row's peer group, where a peer group is a set of rows that are equivalent in the ORDER BY ordering.
(There must be an ORDER BY clause in the window definition to use GROUPS mode.)
• In RANGE mode, these options require that the ORDER BY clause specify exactly one column. The
offset specifies the maximum difference between the value of that column in the current row and
its value in preceding or following rows of the frame. The data type of the offset expression varies
depending on the data type of the ordering column. For numeric ordering columns it is typically
of the same type as the ordering column, but for datetime ordering columns it is an interval.
For example, if the ordering column is of type date or timestamp, one could write RANGE
BETWEEN '1 day' PRECEDING AND '10 days' FOLLOWING. The offset is still
required to be non-null and non-negative, though the meaning of “non-negative” depends on its
data type.
In any case, the distance to the end of the frame is limited by the distance to the end of the partition,
so that for rows near the partition ends the frame might contain fewer rows than elsewhere.
Notice that in both ROWS and GROUPS mode, 0 PRECEDING and 0 FOLLOWING are equivalent to
CURRENT ROW. This normally holds in RANGE mode as well, for an appropriate data-type-specific
meaning of “zero”.
49
SQL Syntax
The frame_exclusion option allows rows around the current row to be excluded from the frame,
even if they would be included according to the frame start and frame end options. EXCLUDE CURRENT ROW excludes the current row from the frame. EXCLUDE GROUP excludes the current row and
its ordering peers from the frame. EXCLUDE TIES excludes any peers of the current row from the
frame, but not the current row itself. EXCLUDE NO OTHERS simply specifies explicitly the default
behavior of not excluding the current row or its peers.
The default framing option is RANGE UNBOUNDED PRECEDING, which is the same as RANGE
BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW. With ORDER BY, this sets the frame
to be all rows from the partition start up through the current row's last ORDER BY peer. Without
ORDER BY, this means all rows of the partition are included in the window frame, since all rows
become peers of the current row.
Restrictions are that frame_start cannot be UNBOUNDED FOLLOWING, frame_end cannot
be UNBOUNDED PRECEDING, and the frame_end choice cannot appear earlier in the above list
of frame_start and frame_end options than the frame_start choice does — for example
RANGE BETWEEN CURRENT ROW AND offset PRECEDING is not allowed. But, for example,
ROWS BETWEEN 7 PRECEDING AND 8 PRECEDING is allowed, even though it would never
select any rows.
If FILTER is specified, then only the input rows for which the filter_clause evaluates to true
are fed to the window function; other rows are discarded. Only window functions that are aggregates
accept a FILTER clause.
The built-in window functions are described in Table 9.65. Other window functions can be added by
the user. Also, any built-in or user-defined general-purpose or statistical aggregate can be used as a
window function. (Ordered-set and hypothetical-set aggregates cannot presently be used as window
functions.)
The syntaxes using * are used for calling parameter-less aggregate functions as window functions, for
example count(*) OVER (PARTITION BY x ORDER BY y). The asterisk (*) is customarily not used for window-specific functions. Window-specific functions do not allow DISTINCT or
ORDER BY to be used within the function argument list.
Window function calls are permitted only in the SELECT list and the ORDER BY clause of the query.
More information about window functions can be found in Section 3.5, Section 9.22, and Section 7.2.5.
4.2.9. Type Casts
A type cast specifies a conversion from one data type to another. PostgreSQL accepts two equivalent
syntaxes for type casts:
CAST ( expression AS type )
expression::type
The CAST syntax conforms to SQL; the syntax with :: is historical PostgreSQL usage.
When a cast is applied to a value expression of a known type, it represents a run-time type conversion.
The cast will succeed only if a suitable type conversion operation has been defined. Notice that this
is subtly different from the use of casts with constants, as shown in Section 4.1.2.7. A cast applied
to an unadorned string literal represents the initial assignment of a type to a literal constant value,
and so it will succeed for any type (if the contents of the string literal are acceptable input syntax for
the data type).
An explicit type cast can usually be omitted if there is no ambiguity as to the type that a value expression must produce (for example, when it is assigned to a table column); the system will automatically
apply a type cast in such cases. However, automatic casting is only done for casts that are marked “OK
50
SQL Syntax
to apply implicitly” in the system catalogs. Other casts must be invoked with explicit casting syntax.
This restriction is intended to prevent surprising conversions from being applied silently.
It is also possible to specify a type cast using a function-like syntax:
typename ( expression )
However, this only works for types whose names are also valid as function names. For example, double precision cannot be used this way, but the equivalent float8 can. Also, the names interval, time, and timestamp can only be used in this fashion if they are double-quoted, because
of syntactic conflicts. Therefore, the use of the function-like cast syntax leads to inconsistencies and
should probably be avoided.
Note
The function-like syntax is in fact just a function call. When one of the two standard cast
syntaxes is used to do a run-time conversion, it will internally invoke a registered function
to perform the conversion. By convention, these conversion functions have the same name as
their output type, and thus the “function-like syntax” is nothing more than a direct invocation of
the underlying conversion function. Obviously, this is not something that a portable application
should rely on. For further details see CREATE CAST.
4.2.10. Collation Expressions
The COLLATE clause overrides the collation of an expression. It is appended to the expression it
applies to:
expr COLLATE collation
where collation is a possibly schema-qualified identifier. The COLLATE clause binds tighter than
operators; parentheses can be used when necessary.
If no collation is explicitly specified, the database system either derives a collation from the columns
involved in the expression, or it defaults to the default collation of the database if no column is involved
in the expression.
The two common uses of the COLLATE clause are overriding the sort order in an ORDER BY clause,
for example:
SELECT a, b, c FROM tbl WHERE ... ORDER BY a COLLATE "C";
and overriding the collation of a function or operator call that has locale-sensitive results, for example:
SELECT * FROM tbl WHERE a > 'foo' COLLATE "C";
Note that in the latter case the COLLATE clause is attached to an input argument of the operator we
wish to affect. It doesn't matter which argument of the operator or function call the COLLATE clause is
attached to, because the collation that is applied by the operator or function is derived by considering
all arguments, and an explicit COLLATE clause will override the collations of all other arguments.
(Attaching non-matching COLLATE clauses to more than one argument, however, is an error. For
more details see Section 23.2.) Thus, this gives the same result as the previous example:
51
SQL Syntax
SELECT * FROM tbl WHERE a COLLATE "C" > 'foo';
But this is an error:
SELECT * FROM tbl WHERE (a > 'foo') COLLATE "C";
because it attempts to apply a collation to the result of the > operator, which is of the non-collatable
data type boolean.
4.2.11. Scalar Subqueries
A scalar subquery is an ordinary SELECT query in parentheses that returns exactly one row with one
column. (See Chapter 7 for information about writing queries.) The SELECT query is executed and
the single returned value is used in the surrounding value expression. It is an error to use a query that
returns more than one row or more than one column as a scalar subquery. (But if, during a particular
execution, the subquery returns no rows, there is no error; the scalar result is taken to be null.) The
subquery can refer to variables from the surrounding query, which will act as constants during any
one evaluation of the subquery. See also Section 9.24 for other expressions involving subqueries.
For example, the following finds the largest city population in each state:
SELECT name, (SELECT max(pop) FROM cities WHERE cities.state =
states.name)
FROM states;
4.2.12. Array Constructors
An array constructor is an expression that builds an array value using values for its member elements. A
simple array constructor consists of the key word ARRAY, a left square bracket [, a list of expressions
(separated by commas) for the array element values, and finally a right square bracket ]. For example:
SELECT ARRAY[1,2,3+4];
array
--------{1,2,7}
(1 row)
By default, the array element type is the common type of the member expressions, determined using
the same rules as for UNION or CASE constructs (see Section 10.5). You can override this by explicitly
casting the array constructor to the desired type, for example:
SELECT ARRAY[1,2,22.7]::integer[];
array
---------{1,2,23}
(1 row)
This has the same effect as casting each expression to the array element type individually. For more
on casting, see Section 4.2.9.
Multidimensional array values can be built by nesting array constructors. In the inner constructors, the
key word ARRAY can be omitted. For example, these produce the same result:
SELECT ARRAY[ARRAY[1,2], ARRAY[3,4]];
52
SQL Syntax
array
--------------{{1,2},{3,4}}
(1 row)
SELECT ARRAY[[1,2],[3,4]];
array
--------------{{1,2},{3,4}}
(1 row)
Since multidimensional arrays must be rectangular, inner constructors at the same level must produce
sub-arrays of identical dimensions. Any cast applied to the outer ARRAY constructor propagates automatically to all the inner constructors.
Multidimensional array constructor elements can be anything yielding an array of the proper kind, not
only a sub-ARRAY construct. For example:
CREATE TABLE arr(f1 int[], f2 int[]);
INSERT INTO arr VALUES (ARRAY[[1,2],[3,4]], ARRAY[[5,6],[7,8]]);
SELECT ARRAY[f1, f2, '{{9,10},{11,12}}'::int[]] FROM arr;
array
-----------------------------------------------{{{1,2},{3,4}},{{5,6},{7,8}},{{9,10},{11,12}}}
(1 row)
You can construct an empty array, but since it's impossible to have an array with no type, you must
explicitly cast your empty array to the desired type. For example:
SELECT ARRAY[]::integer[];
array
------{}
(1 row)
It is also possible to construct an array from the results of a subquery. In this form, the array constructor is written with the key word ARRAY followed by a parenthesized (not bracketed) subquery. For
example:
SELECT ARRAY(SELECT oid FROM pg_proc WHERE proname LIKE 'bytea%');
array
-----------------------------------------------------------------{2011,1954,1948,1952,1951,1244,1950,2005,1949,1953,2006,31,2412}
(1 row)
SELECT ARRAY(SELECT ARRAY[i, i*2] FROM generate_series(1,5) AS
a(i));
array
---------------------------------{{1,2},{2,4},{3,6},{4,8},{5,10}}
(1 row)
The subquery must return a single column. If the subquery's output column is of a non-array type,
the resulting one-dimensional array will have an element for each row in the subquery result, with an
53
SQL Syntax
element type matching that of the subquery's output column. If the subquery's output column is of an
array type, the result will be an array of the same type but one higher dimension; in this case all the
subquery rows must yield arrays of identical dimensionality, else the result would not be rectangular.
The subscripts of an array value built with ARRAY always begin with one. For more information about
arrays, see Section 8.15.
4.2.13. Row Constructors
A row constructor is an expression that builds a row value (also called a composite value) using values
for its member fields. A row constructor consists of the key word ROW, a left parenthesis, zero or
more expressions (separated by commas) for the row field values, and finally a right parenthesis. For
example:
SELECT ROW(1,2.5,'this is a test');
The key word ROW is optional when there is more than one expression in the list.
A row constructor can include the syntax rowvalue.*, which will be expanded to a list of the
elements of the row value, just as occurs when the .* syntax is used at the top level of a SELECT list
(see Section 8.16.5). For example, if table t has columns f1 and f2, these are the same:
SELECT ROW(t.*, 42) FROM t;
SELECT ROW(t.f1, t.f2, 42) FROM t;
Note
Before PostgreSQL 8.2, the .* syntax was not expanded in row constructors, so that writing
ROW(t.*, 42) created a two-field row whose first field was another row value. The new
behavior is usually more useful. If you need the old behavior of nested row values, write the
inner row value without .*, for instance ROW(t, 42).
By default, the value created by a ROW expression is of an anonymous record type. If necessary, it can
be cast to a named composite type — either the row type of a table, or a composite type created with
CREATE TYPE AS. An explicit cast might be needed to avoid ambiguity. For example:
CREATE TABLE mytable(f1 int, f2 float, f3 text);
CREATE FUNCTION getf1(mytable) RETURNS int AS 'SELECT $1.f1'
LANGUAGE SQL;
-- No cast needed since only one getf1() exists
SELECT getf1(ROW(1,2.5,'this is a test'));
getf1
------1
(1 row)
CREATE TYPE myrowtype AS (f1 int, f2 text, f3 numeric);
CREATE FUNCTION getf1(myrowtype) RETURNS int AS 'SELECT $1.f1'
LANGUAGE SQL;
-- Now we need a cast to indicate which function to call:
54
SQL Syntax
SELECT getf1(ROW(1,2.5,'this is a test'));
ERROR: function getf1(record) is not unique
SELECT getf1(ROW(1,2.5,'this is a test')::mytable);
getf1
------1
(1 row)
SELECT getf1(CAST(ROW(11,'this is a test',2.5) AS myrowtype));
getf1
------11
(1 row)
Row constructors can be used to build composite values to be stored in a composite-type table column,
or to be passed to a function that accepts a composite parameter. Also, it is possible to test rows using
the standard comparison operators as described in Section 9.2, to compare one row against another as
described in Section 9.25, and to use them in connection with subqueries, as discussed in Section 9.24,
4.2.14. Expression Evaluation Rules
The order of evaluation of subexpressions is not defined. In particular, the inputs of an operator or
function are not necessarily evaluated left-to-right or in any other fixed order.
Furthermore, if the result of an expression can be determined by evaluating only some parts of it, then
other subexpressions might not be evaluated at all. For instance, if one wrote:
SELECT true OR somefunc();
then somefunc() would (probably) not be called at all. The same would be the case if one wrote:
SELECT somefunc() OR true;
Note that this is not the same as the left-to-right “short-circuiting” of Boolean operators that is found
in some programming languages.
As a consequence, it is unwise to use functions with side effects as part of complex expressions. It is
particularly dangerous to rely on side effects or evaluation order in WHERE and HAVING clauses, since
those clauses are extensively reprocessed as part of developing an execution plan. Boolean expressions
(AND/OR/NOT combinations) in those clauses can be reorganized in any manner allowed by the laws
of Boolean algebra.
When it is essential to force evaluation order, a CASE construct (see Section 9.18) can be used. For
example, this is an untrustworthy way of trying to avoid division by zero in a WHERE clause:
SELECT ... WHERE x > 0 AND y/x > 1.5;
But this is safe:
SELECT ... WHERE CASE WHEN x > 0 THEN y/x > 1.5 ELSE false END;
A CASE construct used in this fashion will defeat optimization attempts, so it should only be done
when necessary. (In this particular example, it would be better to sidestep the problem by writing y
> 1.5*x instead.)
55
SQL Syntax
CASE is not a cure-all for such issues, however. One limitation of the technique illustrated above is
that it does not prevent early evaluation of constant subexpressions. As described in Section 36.7,
functions and operators marked IMMUTABLE can be evaluated when the query is planned rather than
when it is executed. Thus for example
SELECT CASE WHEN x > 0 THEN x ELSE 1/0 END FROM tab;
is likely to result in a division-by-zero failure due to the planner trying to simplify the constant subexpression, even if every row in the table has x > 0 so that the ELSE arm would never be entered
at run time.
While that particular example might seem silly, related cases that don't obviously involve constants
can occur in queries executed within functions, since the values of function arguments and local variables can be inserted into queries as constants for planning purposes. Within PL/pgSQL functions, for
example, using an IF-THEN-ELSE statement to protect a risky computation is much safer than just
nesting it in a CASE expression.
Another limitation of the same kind is that a CASE cannot prevent evaluation of an aggregate expression contained within it, because aggregate expressions are computed before other expressions in a
SELECT list or HAVING clause are considered. For example, the following query can cause a division-by-zero error despite seemingly having protected against it:
SELECT CASE WHEN min(employees) > 0
THEN avg(expenses / employees)
END
FROM departments;
The min() and avg() aggregates are computed concurrently over all the input rows, so if any row
has employees equal to zero, the division-by-zero error will occur before there is any opportunity
to test the result of min(). Instead, use a WHERE or FILTER clause to prevent problematic input
rows from reaching an aggregate function in the first place.
4.3. Calling Functions
PostgreSQL allows functions that have named parameters to be called using either positional or named
notation. Named notation is especially useful for functions that have a large number of parameters,
since it makes the associations between parameters and actual arguments more explicit and reliable.
In positional notation, a function call is written with its argument values in the same order as they
are defined in the function declaration. In named notation, the arguments are matched to the function
parameters by name and can be written in any order. For each notation, also consider the effect of
function argument types, documented in Section 10.3.
In either notation, parameters that have default values given in the function declaration need not be
written in the call at all. But this is particularly useful in named notation, since any combination of
parameters can be omitted; while in positional notation parameters can only be omitted from right
to left.
PostgreSQL also supports mixed notation, which combines positional and named notation. In this case,
positional parameters are written first and named parameters appear after them.
The following examples will illustrate the usage of all three notations, using the following function
definition:
CREATE FUNCTION concat_lower_or_upper(a text, b text, uppercase
boolean DEFAULT false)
RETURNS text
56
SQL Syntax
AS
$$
SELECT CASE
WHEN $3 THEN UPPER($1 || ' ' || $2)
ELSE LOWER($1 || ' ' || $2)
END;
$$
LANGUAGE SQL IMMUTABLE STRICT;
Function concat_lower_or_upper has two mandatory parameters, a and b. Additionally there
is one optional parameter uppercase which defaults to false. The a and b inputs will be concatenated, and forced to either upper or lower case depending on the uppercase parameter. The remaining details of this function definition are not important here (see Chapter 36 for more information).
4.3.1. Using Positional Notation
Positional notation is the traditional mechanism for passing arguments to functions in PostgreSQL.
An example is:
SELECT concat_lower_or_upper('Hello', 'World', true);
concat_lower_or_upper
----------------------HELLO WORLD
(1 row)
All arguments are specified in order. The result is upper case since uppercase is specified as true.
Another example is:
SELECT concat_lower_or_upper('Hello', 'World');
concat_lower_or_upper
----------------------hello world
(1 row)
Here, the uppercase parameter is omitted, so it receives its default value of false, resulting in
lower case output. In positional notation, arguments can be omitted from right to left so long as they
have defaults.
4.3.2. Using Named Notation
In named notation, each argument's name is specified using => to separate it from the argument expression. For example:
SELECT concat_lower_or_upper(a => 'Hello', b => 'World');
concat_lower_or_upper
----------------------hello world
(1 row)
Again, the argument uppercase was omitted so it is set to false implicitly. One advantage of
using named notation is that the arguments may be specified in any order, for example:
SELECT concat_lower_or_upper(a => 'Hello', b => 'World', uppercase
=> true);
concat_lower_or_upper
57
SQL Syntax
----------------------HELLO WORLD
(1 row)
SELECT concat_lower_or_upper(a => 'Hello', uppercase => true, b =>
'World');
concat_lower_or_upper
----------------------HELLO WORLD
(1 row)
An older syntax based on ":=" is supported for backward compatibility:
SELECT concat_lower_or_upper(a := 'Hello', uppercase := true, b :=
'World');
concat_lower_or_upper
----------------------HELLO WORLD
(1 row)
4.3.3. Using Mixed Notation
The mixed notation combines positional and named notation. However, as already mentioned, named
arguments cannot precede positional arguments. For example:
SELECT concat_lower_or_upper('Hello', 'World', uppercase => true);
concat_lower_or_upper
----------------------HELLO WORLD
(1 row)
In the above query, the arguments a and b are specified positionally, while uppercase is specified
by name. In this example, that adds little except documentation. With a more complex function having
numerous parameters that have default values, named or mixed notation can save a great deal of writing
and reduce chances for error.
Note
Named and mixed call notations currently cannot be used when calling an aggregate function
(but they do work when an aggregate function is used as a window function).
58
Chapter 5. Data Definition
This chapter covers how one creates the database structures that will hold one's data. In a relational
database, the raw data is stored in tables, so the majority of this chapter is devoted to explaining how
tables are created and modified and what features are available to control what data is stored in the
tables. Subsequently, we discuss how tables can be organized into schemas, and how privileges can
be assigned to tables. Finally, we will briefly look at other features that affect the data storage, such
as inheritance, table partitioning, views, functions, and triggers.
5.1. Table Basics
A table in a relational database is much like a table on paper: It consists of rows and columns. The
number and order of the columns is fixed, and each column has a name. The number of rows is variable
— it reflects how much data is stored at a given moment. SQL does not make any guarantees about
the order of the rows in a table. When a table is read, the rows will appear in an unspecified order,
unless sorting is explicitly requested. This is covered in Chapter 7. Furthermore, SQL does not assign
unique identifiers to rows, so it is possible to have several completely identical rows in a table. This
is a consequence of the mathematical model that underlies SQL but is usually not desirable. Later in
this chapter we will see how to deal with this issue.
Each column has a data type. The data type constrains the set of possible values that can be assigned to
a column and assigns semantics to the data stored in the column so that it can be used for computations.
For instance, a column declared to be of a numerical type will not accept arbitrary text strings, and
the data stored in such a column can be used for mathematical computations. By contrast, a column
declared to be of a character string type will accept almost any kind of data but it does not lend itself
to mathematical calculations, although other operations such as string concatenation are available.
PostgreSQL includes a sizable set of built-in data types that fit many applications. Users can also
define their own data types. Most built-in data types have obvious names and semantics, so we defer
a detailed explanation to Chapter 8. Some of the frequently used data types are integer for whole
numbers, numeric for possibly fractional numbers, text for character strings, date for dates,
time for time-of-day values, and timestamp for values containing both date and time.
To create a table, you use the aptly named CREATE TABLE command. In this command you specify
at least a name for the new table, the names of the columns and the data type of each column. For
example:
CREATE TABLE my_first_table (
first_column text,
second_column integer
);
This creates a table named my_first_table with two columns. The first column is named
first_column and has a data type of text; the second column has the name second_column
and the type integer. The table and column names follow the identifier syntax explained in Section 4.1.1. The type names are usually also identifiers, but there are some exceptions. Note that the
column list is comma-separated and surrounded by parentheses.
Of course, the previous example was heavily contrived. Normally, you would give names to your
tables and columns that convey what kind of data they store. So let's look at a more realistic example:
CREATE TABLE products (
product_no integer,
name text,
price numeric
59
Data Definition
);
(The numeric type can store fractional components, as would be typical of monetary amounts.)
Tip
When you create many interrelated tables it is wise to choose a consistent naming pattern for
the tables and columns. For instance, there is a choice of using singular or plural nouns for
table names, both of which are favored by some theorist or other.
There is a limit on how many columns a table can contain. Depending on the column types, it is
between 250 and 1600. However, defining a table with anywhere near this many columns is highly
unusual and often a questionable design.
If you no longer need a table, you can remove it using the DROP TABLE command. For example:
DROP TABLE my_first_table;
DROP TABLE products;
Attempting to drop a table that does not exist is an error. Nevertheless, it is common in SQL script files
to unconditionally try to drop each table before creating it, ignoring any error messages, so that the
script works whether or not the table exists. (If you like, you can use the DROP TABLE IF EXISTS
variant to avoid the error messages, but this is not standard SQL.)
If you need to modify a table that already exists, see Section 5.7 later in this chapter.
With the tools discussed so far you can create fully functional tables. The remainder of this chapter is
concerned with adding features to the table definition to ensure data integrity, security, or convenience.
If you are eager to fill your tables with data now you can skip ahead to Chapter 6 and read the rest
of this chapter later.
5.2. Default Values
A column can be assigned a default value. When a new row is created and no values are specified
for some of the columns, those columns will be filled with their respective default values. A data
manipulation command can also request explicitly that a column be set to its default value, without
having to know what that value is. (Details about data manipulation commands are in Chapter 6.)
If no default value is declared explicitly, the default value is the null value. This usually makes sense
because a null value can be considered to represent unknown data.
In a table definition, default values are listed after the column data type. For example:
CREATE TABLE products (
product_no integer,
name text,
price numeric DEFAULT 9.99
);
The default value can be an expression, which will be evaluated whenever the default value is inserted
(not when the table is created). A common example is for a timestamp column to have a default of
CURRENT_TIMESTAMP, so that it gets set to the time of row insertion. Another common example is
generating a “serial number” for each row. In PostgreSQL this is typically done by something like:
60
Data Definition
CREATE TABLE products (
product_no integer DEFAULT nextval('products_product_no_seq'),
...
);
where the nextval() function supplies successive values from a sequence object (see Section 9.17).
This arrangement is sufficiently common that there's a special shorthand for it:
CREATE TABLE products (
product_no SERIAL,
...
);
The SERIAL shorthand is discussed further in Section 8.1.4.
5.3. Identity Columns
An identity column is a special column that is generated automatically from an implicit sequence. It
can be used to generate key values.
To create an identity column, use the GENERATED ... AS IDENTITY clause in CREATE TABLE,
for example:
CREATE TABLE people (
id bigint GENERATED ALWAYS AS IDENTITY,
...,
);
or alternatively
CREATE TABLE people (
id bigint GENERATED BY DEFAULT AS IDENTITY,
...,
);
See CREATE TABLE for more details.
If an INSERT command is executed on the table with the identity column and no value is explicitly
specified for the identity column, then a value generated by the implicit sequence is inserted. For
example, with the above definitions and assuming additional appropriate columns, writing
INSERT INTO people (name, address) VALUES ('A', 'foo');
INSERT INTO people (name, address) VALUES ('B', 'bar');
would generate values for the id column starting at 1 and result in the following table data:
id | name | address
----+------+--------1 | A
| foo
2 | B
| bar
Alternatively, the keyword DEFAULT can be specified in place of a value to explicitly request the
sequence-generated value, like
61
Data Definition
INSERT INTO people (id, name, address) VALUES (DEFAULT, 'C',
'baz');
Similarly, the keyword DEFAULT can be used in UPDATE commands.
Thus, in many ways, an identity column behaves like a column with a default value.
The clauses ALWAYS and BY DEFAULT in the column definition determine how explicitly user-specified values are handled in INSERT and UPDATE commands. In an INSERT command, if ALWAYS
is selected, a user-specified value is only accepted if the INSERT statement specifies OVERRIDING
SYSTEM VALUE. If BY DEFAULT is selected, then the user-specified value takes precedence. Thus,
using BY DEFAULT results in a behavior more similar to default values, where the default value can
be overridden by an explicit value, whereas ALWAYS provides some more protection against accidentally inserting an explicit value.
The data type of an identity column must be one of the data types supported by sequences. (See
CREATE SEQUENCE.) The properties of the associated sequence may be specified when creating
an identity column (see CREATE TABLE) or changed afterwards (see ALTER TABLE).
An identity column is automatically marked as NOT NULL. An identity column, however, does not
guarantee uniqueness. (A sequence normally returns unique values, but a sequence could be reset, or
values could be inserted manually into the identity column, as discussed above.) Uniqueness would
need to be enforced using a PRIMARY KEY or UNIQUE constraint.
In table inheritance hierarchies, identity columns and their properties in a child table are independent
of those in its parent tables. A child table does not inherit identity columns or their properties automatically from the parent. During INSERT or UPDATE, a column is treated as an identity column if
that column is an identity column in the table named in the statement, and the corresponding identity
properties are applied.
Partitions inherit identity columns from the partitioned table. They cannot have their own identity
columns. The properties of a given identity column are consistent across all the partitions in the partition hierarchy.
5.4. Generated Columns
A generated column is a special column that is always computed from other columns. Thus, it is for
columns what a view is for tables. There are two kinds of generated columns: stored and virtual. A
stored generated column is computed when it is written (inserted or updated) and occupies storage as
if it were a normal column. A virtual generated column occupies no storage and is computed when it is
read. Thus, a virtual generated column is similar to a view and a stored generated column is similar to a
materialized view (except that it is always updated automatically). PostgreSQL currently implements
only stored generated columns.
To create a generated column, use the GENERATED ALWAYS AS clause in CREATE TABLE, for
example:
CREATE TABLE people (
...,
height_cm numeric,
height_in numeric GENERATED ALWAYS AS (height_cm / 2.54) STORED
);
The keyword STORED must be specified to choose the stored kind of generated column. See CREATE
TABLE for more details.
A generated column cannot be written to directly. In INSERT or UPDATE commands, a value cannot
be specified for a generated column, but the keyword DEFAULT may be specified.
62
Data Definition
Consider the differences between a column with a default and a generated column. The column default
is evaluated once when the row is first inserted if no other value was provided; a generated column is
updated whenever the row changes and cannot be overridden. A column default may not refer to other
columns of the table; a generation expression would normally do so. A column default can use volatile
functions, for example random() or functions referring to the current time; this is not allowed for
generated columns.
Several restrictions apply to the definition of generated columns and tables involving generated
columns:
• The generation expression can only use immutable functions and cannot use subqueries or reference
anything other than the current row in any way.
• A generation expression cannot reference another generated column.
• A generation expression cannot reference a system column, except tableoid.
• A generated column cannot have a column default or an identity definition.
• A generated column cannot be part of a partition key.
• Foreign tables can have generated columns. See CREATE FOREIGN TABLE for details.
• For inheritance and partitioning:
• If a parent column is a generated column, its child column must also be a generated column;
however, the child column can have a different generation expression. The generation expression
that is actually applied during insert or update of a row is the one associated with the table that
the row is physically in. (This is unlike the behavior for column defaults: for those, the default
value associated with the table named in the query applies.)
• If a parent column is not a generated column, its child column must not be generated either.
• For inherited tables, if you write a child column definition without any GENERATED clause in
CREATE TABLE ... INHERITS, then its GENERATED clause will automatically be copied
from the parent. ALTER TABLE ... INHERIT will insist that parent and child columns
already match as to generation status, but it will not require their generation expressions to match.
• Similarly for partitioned tables, if you write a child column definition without any GENERATED
clause in CREATE TABLE ... PARTITION OF, then its GENERATED clause will automatically be copied from the parent. ALTER TABLE ... ATTACH PARTITION will insist
that parent and child columns already match as to generation status, but it will not require their
generation expressions to match.
• In case of multiple inheritance, if one parent column is a generated column, then all parent
columns must be generated columns. If they do not all have the same generation expression, then
the desired expression for the child must be specified explicitly.
Additional considerations apply to the use of generated columns.
• Generated columns maintain access privileges separately from their underlying base columns. So,
it is possible to arrange it so that a particular role can read from a generated column but not from
the underlying base columns.
• Generated columns are, conceptually, updated after BEFORE triggers have run. Therefore, changes
made to base columns in a BEFORE trigger will be reflected in generated columns. But conversely,
it is not allowed to access generated columns in BEFORE triggers.
• Generated columns are skipped for logical replication and cannot be specified in a CREATE PUBLICATION column list.
63
Data Definition
5.5. Constraints
Data types are a way to limit the kind of data that can be stored in a table. For many applications,
however, the constraint they provide is too coarse. For example, a column containing a product price
should probably only accept positive values. But there is no standard data type that accepts only positive numbers. Another issue is that you might want to constrain column data with respect to other
columns or rows. For example, in a table containing product information, there should be only one
row for each product number.
To that end, SQL allows you to define constraints on columns and tables. Constraints give you as
much control over the data in your tables as you wish. If a user attempts to store data in a column
that would violate a constraint, an error is raised. This applies even if the value came from the default
value definition.
5.5.1. Check Constraints
A check constraint is the most generic constraint type. It allows you to specify that the value in a certain column must satisfy a Boolean (truth-value) expression. For instance, to require positive product
prices, you could use:
CREATE TABLE products (
product_no integer,
name text,
price numeric CHECK (price > 0)
);
As you see, the constraint definition comes after the data type, just like default value definitions.
Default values and constraints can be listed in any order. A check constraint consists of the key word
CHECK followed by an expression in parentheses. The check constraint expression should involve the
column thus constrained, otherwise the constraint would not make too much sense.
You can also give the constraint a separate name. This clarifies error messages and allows you to refer
to the constraint when you need to change it. The syntax is:
CREATE TABLE products (
product_no integer,
name text,
price numeric CONSTRAINT positive_price CHECK (price > 0)
);
So, to specify a named constraint, use the key word CONSTRAINT followed by an identifier followed
by the constraint definition. (If you don't specify a constraint name in this way, the system chooses
a name for you.)
A check constraint can also refer to several columns. Say you store a regular price and a discounted
price, and you want to ensure that the discounted price is lower than the regular price:
CREATE TABLE products (
product_no integer,
name text,
price numeric CHECK (price > 0),
discounted_price numeric CHECK (discounted_price > 0),
CHECK (price > discounted_price)
);
64
Data Definition
The first two constraints should look familiar. The third one uses a new syntax. It is not attached to a
particular column, instead it appears as a separate item in the comma-separated column list. Column
definitions and these constraint definitions can be listed in mixed order.
We say that the first two constraints are column constraints, whereas the third one is a table constraint
because it is written separately from any one column definition. Column constraints can also be written
as table constraints, while the reverse is not necessarily possible, since a column constraint is supposed
to refer to only the column it is attached to. (PostgreSQL doesn't enforce that rule, but you should
follow it if you want your table definitions to work with other database systems.) The above example
could also be written as:
CREATE TABLE products (
product_no integer,
name text,
price numeric,
CHECK (price > 0),
discounted_price numeric,
CHECK (discounted_price > 0),
CHECK (price > discounted_price)
);
or even:
CREATE TABLE products (
product_no integer,
name text,
price numeric CHECK (price > 0),
discounted_price numeric,
CHECK (discounted_price > 0 AND price > discounted_price)
);
It's a matter of taste.
Names can be assigned to table constraints in the same way as column constraints:
CREATE TABLE products (
product_no integer,
name text,
price numeric,
CHECK (price > 0),
discounted_price numeric,
CHECK (discounted_price > 0),
CONSTRAINT valid_discount CHECK (price > discounted_price)
);
It should be noted that a check constraint is satisfied if the check expression evaluates to true or the
null value. Since most expressions will evaluate to the null value if any operand is null, they will not
prevent null values in the constrained columns. To ensure that a column does not contain null values,
the not-null constraint described in the next section can be used.
Note
PostgreSQL does not support CHECK constraints that reference table data other than the new
or updated row being checked. While a CHECK constraint that violates this rule may appear
to work in simple tests, it cannot guarantee that the database will not reach a state in which
65
Data Definition
the constraint condition is false (due to subsequent changes of the other row(s) involved). This
would cause a database dump and restore to fail. The restore could fail even when the complete
database state is consistent with the constraint, due to rows not being loaded in an order that
will satisfy the constraint. If possible, use UNIQUE, EXCLUDE, or FOREIGN KEY constraints
to express cross-row and cross-table restrictions.
If what you desire is a one-time check against other rows at row insertion, rather than a continuously-maintained consistency guarantee, a custom trigger can be used to implement that.
(This approach avoids the dump/restore problem because pg_dump does not reinstall triggers
until after restoring data, so that the check will not be enforced during a dump/restore.)
Note
PostgreSQL assumes that CHECK constraints' conditions are immutable, that is, they will always give the same result for the same input row. This assumption is what justifies examining CHECK constraints only when rows are inserted or updated, and not at other times. (The
warning above about not referencing other table data is really a special case of this restriction.)
An example of a common way to break this assumption is to reference a user-defined function
in a CHECK expression, and then change the behavior of that function. PostgreSQL does not
disallow that, but it will not notice if there are rows in the table that now violate the CHECK
constraint. That would cause a subsequent database dump and restore to fail. The recommended way to handle such a change is to drop the constraint (using ALTER TABLE), adjust the
function definition, and re-add the constraint, thereby rechecking it against all table rows.
5.5.2. Not-Null Constraints
A not-null constraint simply specifies that a column must not assume the null value. A syntax example:
CREATE TABLE products (
product_no integer NOT NULL,
name text NOT NULL,
price numeric
);
A not-null constraint is always written as a column constraint. A not-null constraint is functionally
equivalent to creating a check constraint CHECK (column_name IS NOT NULL), but in PostgreSQL creating an explicit not-null constraint is more efficient. The drawback is that you cannot give
explicit names to not-null constraints created this way.
Of course, a column can have more than one constraint. Just write the constraints one after another:
CREATE TABLE products (
product_no integer NOT NULL,
name text NOT NULL,
price numeric NOT NULL CHECK (price > 0)
);
The order doesn't matter. It does not necessarily determine in which order the constraints are checked.
The NOT NULL constraint has an inverse: the NULL constraint. This does not mean that the column
must be null, which would surely be useless. Instead, this simply selects the default behavior that the
column might be null. The NULL constraint is not present in the SQL standard and should not be used
in portable applications. (It was only added to PostgreSQL to be compatible with some other database
66
Data Definition
systems.) Some users, however, like it because it makes it easy to toggle the constraint in a script file.
For example, you could start with:
CREATE TABLE products (
product_no integer NULL,
name text NULL,
price numeric NULL
);
and then insert the NOT key word where desired.
Tip
In most database designs the majority of columns should be marked not null.
5.5.3. Unique Constraints
Unique constraints ensure that the data contained in a column, or a group of columns, is unique among
all the rows in the table. The syntax is:
CREATE TABLE products (
product_no integer UNIQUE,
name text,
price numeric
);
when written as a column constraint, and:
CREATE TABLE products (
product_no integer,
name text,
price numeric,
UNIQUE (product_no)
);
when written as a table constraint.
To define a unique constraint for a group of columns, write it as a table constraint with the column
names separated by commas:
CREATE TABLE example (
a integer,
b integer,
c integer,
UNIQUE (a, c)
);
This specifies that the combination of values in the indicated columns is unique across the whole table,
though any one of the columns need not be (and ordinarily isn't) unique.
You can assign your own name for a unique constraint, in the usual way:
CREATE TABLE products (
67
Data Definition
product_no integer CONSTRAINT must_be_different UNIQUE,
name text,
price numeric
);
Adding a unique constraint will automatically create a unique B-tree index on the column or group of
columns listed in the constraint. A uniqueness restriction covering only some rows cannot be written
as a unique constraint, but it is possible to enforce such a restriction by creating a unique partial index.
In general, a unique constraint is violated if there is more than one row in the table where the values of
all of the columns included in the constraint are equal. By default, two null values are not considered
equal in this comparison. That means even in the presence of a unique constraint it is possible to store
duplicate rows that contain a null value in at least one of the constrained columns. This behavior can
be changed by adding the clause NULLS NOT DISTINCT, like
CREATE TABLE products (
product_no integer UNIQUE NULLS NOT DISTINCT,
name text,
price numeric
);
or
CREATE TABLE products (
product_no integer,
name text,
price numeric,
UNIQUE NULLS NOT DISTINCT (product_no)
);
The default behavior can be specified explicitly using NULLS DISTINCT. The default null treatment
in unique constraints is implementation-defined according to the SQL standard, and other implementations have a different behavior. So be careful when developing applications that are intended to be
portable.
5.5.4. Primary Keys
A primary key constraint indicates that a column, or group of columns, can be used as a unique identifier for rows in the table. This requires that the values be both unique and not null. So, the following
two table definitions accept the same data:
CREATE TABLE products (
product_no integer UNIQUE NOT NULL,
name text,
price numeric
);
CREATE TABLE products (
product_no integer PRIMARY KEY,
name text,
price numeric
);
Primary keys can span more than one column; the syntax is similar to unique constraints:
68
Data Definition
CREATE TABLE example (
a integer,
b integer,
c integer,
PRIMARY KEY (a, c)
);
Adding a primary key will automatically create a unique B-tree index on the column or group of
columns listed in the primary key, and will force the column(s) to be marked NOT NULL.
A table can have at most one primary key. (There can be any number of unique and not-null constraints,
which are functionally almost the same thing, but only one can be identified as the primary key.)
Relational database theory dictates that every table must have a primary key. This rule is not enforced
by PostgreSQL, but it is usually best to follow it.
Primary keys are useful both for documentation purposes and for client applications. For example, a
GUI application that allows modifying row values probably needs to know the primary key of a table
to be able to identify rows uniquely. There are also various ways in which the database system makes
use of a primary key if one has been declared; for example, the primary key defines the default target
column(s) for foreign keys referencing its table.
5.5.5. Foreign Keys
A foreign key constraint specifies that the values in a column (or a group of columns) must match the
values appearing in some row of another table. We say this maintains the referential integrity between
two related tables.
Say you have the product table that we have used several times already:
CREATE TABLE products (
product_no integer PRIMARY KEY,
name text,
price numeric
);
Let's also assume you have a table storing orders of those products. We want to ensure that the orders
table only contains orders of products that actually exist. So we define a foreign key constraint in the
orders table that references the products table:
CREATE TABLE orders (
order_id integer PRIMARY KEY,
product_no integer REFERENCES products (product_no),
quantity integer
);
Now it is impossible to create orders with non-NULL product_no entries that do not appear in
the products table.
We say that in this situation the orders table is the referencing table and the products table is the
referenced table. Similarly, there are referencing and referenced columns.
You can also shorten the above command to:
CREATE TABLE orders (
order_id integer PRIMARY KEY,
69
Data Definition
product_no integer REFERENCES products,
quantity integer
);
because in absence of a column list the primary key of the referenced table is used as the referenced
column(s).
You can assign your own name for a foreign key constraint, in the usual way.
A foreign key can also constrain and reference a group of columns. As usual, it then needs to be written
in table constraint form. Here is a contrived syntax example:
CREATE TABLE t1 (
a integer PRIMARY KEY,
b integer,
c integer,
FOREIGN KEY (b, c) REFERENCES other_table (c1, c2)
);
Of course, the number and type of the constrained columns need to match the number and type of
the referenced columns.
Sometimes it is useful for the “other table” of a foreign key constraint to be the same table; this is
called a self-referential foreign key. For example, if you want rows of a table to represent nodes of
a tree structure, you could write
CREATE TABLE tree (
node_id integer PRIMARY KEY,
parent_id integer REFERENCES tree,
name text,
...
);
A top-level node would have NULL parent_id, while non-NULL parent_id entries would be
constrained to reference valid rows of the table.
A table can have more than one foreign key constraint. This is used to implement many-to-many
relationships between tables. Say you have tables about products and orders, but now you want to
allow one order to contain possibly many products (which the structure above did not allow). You
could use this table structure:
CREATE TABLE products (
product_no integer PRIMARY KEY,
name text,
price numeric
);
CREATE TABLE orders (
order_id integer PRIMARY KEY,
shipping_address text,
...
);
CREATE TABLE order_items (
product_no integer REFERENCES products,
order_id integer REFERENCES orders,
70
Data Definition
quantity integer,
PRIMARY KEY (product_no, order_id)
);
Notice that the primary key overlaps with the foreign keys in the last table.
We know that the foreign keys disallow creation of orders that do not relate to any products. But what
if a product is removed after an order is created that references it? SQL allows you to handle that as
well. Intuitively, we have a few options:
• Disallow deleting a referenced product
• Delete the orders as well
• Something else?
To illustrate this, let's implement the following policy on the many-to-many relationship example above: when someone wants to remove a product that is still referenced by an order (via order_items), we disallow it. If someone removes an order, the order items are removed as well:
CREATE TABLE products (
product_no integer PRIMARY KEY,
name text,
price numeric
);
CREATE TABLE orders (
order_id integer PRIMARY KEY,
shipping_address text,
...
);
CREATE TABLE order_items (
product_no integer REFERENCES products ON DELETE RESTRICT,
order_id integer REFERENCES orders ON DELETE CASCADE,
quantity integer,
PRIMARY KEY (product_no, order_id)
);
Restricting and cascading deletes are the two most common options. RESTRICT prevents deletion of
a referenced row. NO ACTION means that if any referencing rows still exist when the constraint is
checked, an error is raised; this is the default behavior if you do not specify anything. (The essential
difference between these two choices is that NO ACTION allows the check to be deferred until later
in the transaction, whereas RESTRICT does not.) CASCADE specifies that when a referenced row is
deleted, row(s) referencing it should be automatically deleted as well. There are two other options:
SET NULL and SET DEFAULT. These cause the referencing column(s) in the referencing row(s) to
be set to nulls or their default values, respectively, when the referenced row is deleted. Note that these
do not excuse you from observing any constraints. For example, if an action specifies SET DEFAULT
but the default value would not satisfy the foreign key constraint, the operation will fail.
The appropriate choice of ON DELETE action depends on what kinds of objects the related tables
represent. When the referencing table represents something that is a component of what is represented
by the referenced table and cannot exist independently, then CASCADE could be appropriate. If the
two tables represent independent objects, then RESTRICT or NO ACTION is more appropriate; an
application that actually wants to delete both objects would then have to be explicit about this and
run two delete commands. In the above example, order items are part of an order, and it is convenient
if they are deleted automatically if an order is deleted. But products and orders are different things,
and so making a deletion of a product automatically cause the deletion of some order items could be
considered problematic. The actions SET NULL or SET DEFAULT can be appropriate if a foreign-key
71
Data Definition
relationship represents optional information. For example, if the products table contained a reference
to a product manager, and the product manager entry gets deleted, then setting the product's product
manager to null or a default might be useful.
The actions SET NULL and SET DEFAULT can take a column list to specify which columns to set.
Normally, all columns of the foreign-key constraint are set; setting only a subset is useful in some
special cases. Consider the following example:
CREATE TABLE tenants (
tenant_id integer PRIMARY KEY
);
CREATE TABLE users (
tenant_id integer REFERENCES tenants ON DELETE CASCADE,
user_id integer NOT NULL,
PRIMARY KEY (tenant_id, user_id)
);
CREATE TABLE posts (
tenant_id integer REFERENCES tenants ON DELETE CASCADE,
post_id integer NOT NULL,
author_id integer,
PRIMARY KEY (tenant_id, post_id),
FOREIGN KEY (tenant_id, author_id) REFERENCES users ON DELETE
SET NULL (author_id)
);
Without the specification of the column, the foreign key would also set the column tenant_id to
null, but that column is still required as part of the primary key.
Analogous to ON DELETE there is also ON UPDATE which is invoked when a referenced column
is changed (updated). The possible actions are the same, except that column lists cannot be specified
for SET NULL and SET DEFAULT. In this case, CASCADE means that the updated values of the
referenced column(s) should be copied into the referencing row(s).
Normally, a referencing row need not satisfy the foreign key constraint if any of its referencing
columns are null. If MATCH FULL is added to the foreign key declaration, a referencing row escapes
satisfying the constraint only if all its referencing columns are null (so a mix of null and non-null
values is guaranteed to fail a MATCH FULL constraint). If you don't want referencing rows to be able
to avoid satisfying the foreign key constraint, declare the referencing column(s) as NOT NULL.
A foreign key must reference columns that either are a primary key or form a unique constraint, or
are columns from a non-partial unique index. This means that the referenced columns always have
an index to allow efficient lookups on whether a referencing row has a match. Since a DELETE of
a row from the referenced table or an UPDATE of a referenced column will require a scan of the
referencing table for rows matching the old value, it is often a good idea to index the referencing
columns too. Because this is not always needed, and there are many choices available on how to index,
the declaration of a foreign key constraint does not automatically create an index on the referencing
columns.
More information about updating and deleting data is in Chapter 6. Also see the description of foreign
key constraint syntax in the reference documentation for CREATE TABLE.
5.5.6. Exclusion Constraints
Exclusion constraints ensure that if any two rows are compared on the specified columns or expressions
using the specified operators, at least one of these operator comparisons will return false or null. The
syntax is:
72
Data Definition
CREATE TABLE circles (
c circle,
EXCLUDE USING gist (c WITH &&)
);
See also CREATE TABLE ... CONSTRAINT ... EXCLUDE for details.
Adding an exclusion constraint will automatically create an index of the type specified in the constraint
declaration.
5.6. System Columns
Every table has several system columns that are implicitly defined by the system. Therefore, these
names cannot be used as names of user-defined columns. (Note that these restrictions are separate from
whether the name is a key word or not; quoting a name will not allow you to escape these restrictions.)
You do not really need to be concerned about these columns; just know they exist.
tableoid
The OID of the table containing this row. This column is particularly handy for queries that select from partitioned tables (see Section 5.12) or inheritance hierarchies (see Section 5.11), since
without it, it's difficult to tell which individual table a row came from. The tableoid can be
joined against the oid column of pg_class to obtain the table name.
xmin
The identity (transaction ID) of the inserting transaction for this row version. (A row version is an
individual state of a row; each update of a row creates a new row version for the same logical row.)
cmin
The command identifier (starting at zero) within the inserting transaction.
xmax
The identity (transaction ID) of the deleting transaction, or zero for an undeleted row version. It
is possible for this column to be nonzero in a visible row version. That usually indicates that the
deleting transaction hasn't committed yet, or that an attempted deletion was rolled back.
cmax
The command identifier within the deleting transaction, or zero.
ctid
The physical location of the row version within its table. Note that although the ctid can be used
to locate the row version very quickly, a row's ctid will change if it is updated or moved by
VACUUM FULL. Therefore ctid is useless as a long-term row identifier. A primary key should
be used to identify logical rows.
Transaction identifiers are also 32-bit quantities. In a long-lived database it is possible for transaction
IDs to wrap around. This is not a fatal problem given appropriate maintenance procedures; see Chapter 24 for details. It is unwise, however, to depend on the uniqueness of transaction IDs over the long
term (more than one billion transactions).
Command identifiers are also 32-bit quantities. This creates a hard limit of 232 (4 billion) SQL commands within a single transaction. In practice this limit is not a problem — note that the limit is on
the number of SQL commands, not the number of rows processed. Also, only commands that actually
modify the database contents will consume a command identifier.
73
Data Definition
5.7. Modifying Tables
When you create a table and you realize that you made a mistake, or the requirements of the application
change, you can drop the table and create it again. But this is not a convenient option if the table is
already filled with data, or if the table is referenced by other database objects (for instance a foreign key
constraint). Therefore PostgreSQL provides a family of commands to make modifications to existing
tables. Note that this is conceptually distinct from altering the data contained in the table: here we are
interested in altering the definition, or structure, of the table.
You can:
• Add columns
• Remove columns
• Add constraints
• Remove constraints
• Change default values
• Change column data types
• Rename columns
• Rename tables
All these actions are performed using the ALTER TABLE command, whose reference page contains
details beyond those given here.
5.7.1. Adding a Column
To add a column, use a command like:
ALTER TABLE products ADD COLUMN description text;
The new column is initially filled with whatever default value is given (null if you don't specify a
DEFAULT clause).
Tip
From PostgreSQL 11, adding a column with a constant default value no longer means that
each row of the table needs to be updated when the ALTER TABLE statement is executed.
Instead, the default value will be returned the next time the row is accessed, and applied when
the table is rewritten, making the ALTER TABLE very fast even on large tables.
However, if the default value is volatile (e.g., clock_timestamp()) each row will need
to be updated with the value calculated at the time ALTER TABLE is executed. To avoid a
potentially lengthy update operation, particularly if you intend to fill the column with mostly
nondefault values anyway, it may be preferable to add the column with no default, insert the
correct values using UPDATE, and then add any desired default as described below.
You can also define constraints on the column at the same time, using the usual syntax:
ALTER TABLE products ADD COLUMN description text CHECK (description
<> '');
In fact all the options that can be applied to a column description in CREATE TABLE can be used here.
Keep in mind however that the default value must satisfy the given constraints, or the ADD will fail.
Alternatively, you can add constraints later (see below) after you've filled in the new column correctly.
74
Data Definition
5.7.2. Removing a Column
To remove a column, use a command like:
ALTER TABLE products DROP COLUMN description;
Whatever data was in the column disappears. Table constraints involving the column are dropped, too.
However, if the column is referenced by a foreign key constraint of another table, PostgreSQL will
not silently drop that constraint. You can authorize dropping everything that depends on the column
by adding CASCADE:
ALTER TABLE products DROP COLUMN description CASCADE;
See Section 5.15 for a description of the general mechanism behind this.
5.7.3. Adding a Constraint
To add a constraint, the table constraint syntax is used. For example:
ALTER TABLE products ADD CHECK (name <> '');
ALTER TABLE products ADD CONSTRAINT some_name UNIQUE (product_no);
ALTER TABLE products ADD FOREIGN KEY (product_group_id) REFERENCES
product_groups;
To add a not-null constraint, which cannot be written as a table constraint, use this syntax:
ALTER TABLE products ALTER COLUMN product_no SET NOT NULL;
The constraint will be checked immediately, so the table data must satisfy the constraint before it can
be added.
5.7.4. Removing a Constraint
To remove a constraint you need to know its name. If you gave it a name then that's easy. Otherwise
the system assigned a generated name, which you need to find out. The psql command \d tablename can be helpful here; other interfaces might also provide a way to inspect table details. Then
the command is:
ALTER TABLE products DROP CONSTRAINT some_name;
(If you are dealing with a generated constraint name like $2, don't forget that you'll need to double-quote it to make it a valid identifier.)
As with dropping a column, you need to add CASCADE if you want to drop a constraint that something
else depends on. An example is that a foreign key constraint depends on a unique or primary key
constraint on the referenced column(s).
This works the same for all constraint types except not-null constraints. To drop a not-null constraint
use:
ALTER TABLE products ALTER COLUMN product_no DROP NOT NULL;
(Recall that not-null constraints do not have names.)
75
Data Definition
5.7.5. Changing a Column's Default Value
To set a new default for a column, use a command like:
ALTER TABLE products ALTER COLUMN price SET DEFAULT 7.77;
Note that this doesn't affect any existing rows in the table, it just changes the default for future INSERT
commands.
To remove any default value, use:
ALTER TABLE products ALTER COLUMN price DROP DEFAULT;
This is effectively the same as setting the default to null. As a consequence, it is not an error to drop
a default where one hadn't been defined, because the default is implicitly the null value.
5.7.6. Changing a Column's Data Type
To convert a column to a different data type, use a command like:
ALTER TABLE products ALTER COLUMN price TYPE numeric(10,2);
This will succeed only if each existing entry in the column can be converted to the new type by an
implicit cast. If a more complex conversion is needed, you can add a USING clause that specifies how
to compute the new values from the old.
PostgreSQL will attempt to convert the column's default value (if any) to the new type, as well as
any constraints that involve the column. But these conversions might fail, or might produce surprising
results. It's often best to drop any constraints on the column before altering its type, and then add back
suitably modified constraints afterwards.
5.7.7. Renaming a Column
To rename a column:
ALTER TABLE products RENAME COLUMN product_no TO product_number;
5.7.8. Renaming a Table
To rename a table:
ALTER TABLE products RENAME TO items;
5.8. Privileges
When an object is created, it is assigned an owner. The owner is normally the role that executed the
creation statement. For most kinds of objects, the initial state is that only the owner (or a superuser)
can do anything with the object. To allow other roles to use it, privileges must be granted.
There are different kinds of privileges: SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, TRIGGER, CREATE, CONNECT, TEMPORARY, EXECUTE, USAGE, SET, ALTER SYSTEM,
and MAINTAIN. The privileges applicable to a particular object vary depending on the object's type
76
Data Definition
(table, function, etc.). More detail about the meanings of these privileges appears below. The following sections and chapters will also show you how these privileges are used.
The right to modify or destroy an object is inherent in being the object's owner, and cannot be granted
or revoked in itself. (However, like all privileges, that right can be inherited by members of the owning
role; see Section 21.3.)
An object can be assigned to a new owner with an ALTER command of the appropriate kind for the
object, for example
ALTER TABLE table_name OWNER TO new_owner;
Superusers can always do this; ordinary roles can only do it if they are both the current owner of the
object (or inherit the privileges of the owning role) and able to SET ROLE to the new owning role.
To assign privileges, the GRANT command is used. For example, if joe is an existing role, and
accounts is an existing table, the privilege to update the table can be granted with:
GRANT UPDATE ON accounts TO joe;
Writing ALL in place of a specific privilege grants all privileges that are relevant for the object type.
The special “role” name PUBLIC can be used to grant a privilege to every role on the system. Also,
“group” roles can be set up to help manage privileges when there are many users of a database —
for details see Chapter 21.
To revoke a previously-granted privilege, use the fittingly named REVOKE command:
REVOKE ALL ON accounts FROM PUBLIC;
Ordinarily, only the object's owner (or a superuser) can grant or revoke privileges on an object. However, it is possible to grant a privilege “with grant option”, which gives the recipient the right to grant
it in turn to others. If the grant option is subsequently revoked then all who received the privilege from
that recipient (directly or through a chain of grants) will lose the privilege. For details see the GRANT
and REVOKE reference pages.
An object's owner can choose to revoke their own ordinary privileges, for example to make a table
read-only for themselves as well as others. But owners are always treated as holding all grant options,
so they can always re-grant their own privileges.
The available privileges are:
SELECT
Allows SELECT from any column, or specific column(s), of a table, view, materialized view, or
other table-like object. Also allows use of COPY TO. This privilege is also needed to reference
existing column values in UPDATE, DELETE, or MERGE. For sequences, this privilege also allows
use of the currval function. For large objects, this privilege allows the object to be read.
INSERT
Allows INSERT of a new row into a table, view, etc. Can be granted on specific column(s), in
which case only those columns may be assigned to in the INSERT command (other columns will
therefore receive default values). Also allows use of COPY FROM.
UPDATE
Allows UPDATE of any column, or specific column(s), of a table, view, etc. (In practice, any
nontrivial UPDATE command will require SELECT privilege as well, since it must reference
77
Data Definition
table columns to determine which rows to update, and/or to compute new values for columns.)
SELECT ... FOR UPDATE and SELECT ... FOR SHARE also require this privilege on
at least one column, in addition to the SELECT privilege. For sequences, this privilege allows
use of the nextval and setval functions. For large objects, this privilege allows writing or
truncating the object.
DELETE
Allows DELETE of a row from a table, view, etc. (In practice, any nontrivial DELETE command
will require SELECT privilege as well, since it must reference table columns to determine which
rows to delete.)
TRUNCATE
Allows TRUNCATE on a table.
REFERENCES
Allows creation of a foreign key constraint referencing a table, or specific column(s) of a table.
TRIGGER
Allows creation of a trigger on a table, view, etc.
CREATE
For databases, allows new schemas and publications to be created within the database, and allows
trusted extensions to be installed within the database.
For schemas, allows new objects to be created within the schema. To rename an existing object,
you must own the object and have this privilege for the containing schema.
For tablespaces, allows tables, indexes, and temporary files to be created within the tablespace,
and allows databases to be created that have the tablespace as their default tablespace.
Note that revoking this privilege will not alter the existence or location of existing objects.
CONNECT
Allows the grantee to connect to the database. This privilege is checked at connection startup (in
addition to checking any restrictions imposed by pg_hba.conf).
TEMPORARY
Allows temporary tables to be created while using the database.
EXECUTE
Allows calling a function or procedure, including use of any operators that are implemented on
top of the function. This is the only type of privilege that is applicable to functions and procedures.
USAGE
For procedural languages, allows use of the language for the creation of functions in that language.
This is the only type of privilege that is applicable to procedural languages.
For schemas, allows access to objects contained in the schema (assuming that the objects' own
privilege requirements are also met). Essentially this allows the grantee to “look up” objects within
the schema. Without this permission, it is still possible to see the object names, e.g., by querying
system catalogs. Also, after revoking this permission, existing sessions might have statements
that have previously performed this lookup, so this is not a completely secure way to prevent
object access.
78
Data Definition
For sequences, allows use of the currval and nextval functions.
For types and domains, allows use of the type or domain in the creation of tables, functions, and
other schema objects. (Note that this privilege does not control all “usage” of the type, such as
values of the type appearing in queries. It only prevents objects from being created that depend on
the type. The main purpose of this privilege is controlling which users can create dependencies
on a type, which could prevent the owner from changing the type later.)
For foreign-data wrappers, allows creation of new servers using the foreign-data wrapper.
For foreign servers, allows creation of foreign tables using the server. Grantees may also create,
alter, or drop their own user mappings associated with that server.
SET
Allows a server configuration parameter to be set to a new value within the current session. (While
this privilege can be granted on any parameter, it is meaningless except for parameters that would
normally require superuser privilege to set.)
ALTER SYSTEM
Allows a server configuration parameter to be configured to a new value using the ALTER
SYSTEM command.
MAINTAIN
Allows VACUUM, ANALYZE, CLUSTER, REFRESH MATERIALIZED VIEW, REINDEX, and
LOCK TABLE on a relation.
The privileges required by other commands are listed on the reference page of the respective command.
PostgreSQL grants privileges on some types of objects to PUBLIC by default when the objects are
created. No privileges are granted to PUBLIC by default on tables, table columns, sequences, foreign
data wrappers, foreign servers, large objects, schemas, tablespaces, or configuration parameters. For
other types of objects, the default privileges granted to PUBLIC are as follows: CONNECT and TEMPORARY (create temporary tables) privileges for databases; EXECUTE privilege for functions and
procedures; and USAGE privilege for languages and data types (including domains). The object owner
can, of course, REVOKE both default and expressly granted privileges. (For maximum security, issue
the REVOKE in the same transaction that creates the object; then there is no window in which another
user can use the object.) Also, these default privilege settings can be overridden using the ALTER
DEFAULT PRIVILEGES command.
Table 5.1 shows the one-letter abbreviations that are used for these privilege types in ACL (Access
Control List) values. You will see these letters in the output of the psql commands listed below, or
when looking at ACL columns of system catalogs.
Table 5.1. ACL Privilege Abbreviations
Privilege
Abbreviation
Applicable Object Types
SELECT
r (“read”)
LARGE OBJECT, SEQUENCE, TABLE (and table-like objects), table column
INSERT
a (“append”)
TABLE, table column
UPDATE
w (“write”)
LARGE OBJECT, SEQUENCE, TABLE, table
column
DELETE
d
TABLE
TRUNCATE
D
TABLE
REFERENCES
x
TABLE, table column
TRIGGER
t
TABLE
79
Data Definition
Privilege
Abbreviation
Applicable Object Types
CREATE
C
DATABASE, SCHEMA, TABLESPACE
CONNECT
c
DATABASE
TEMPORARY
T
DATABASE
EXECUTE
X
FUNCTION, PROCEDURE
USAGE
U
DOMAIN, FOREIGN DATA WRAPPER,
FOREIGN SERVER, LANGUAGE, SCHEMA,
SEQUENCE, TYPE
SET
s
PARAMETER
ALTER SYSTEM
A
PARAMETER
MAINTAIN
m
TABLE
Table 5.2 summarizes the privileges available for each type of SQL object, using the abbreviations
shown above. It also shows the psql command that can be used to examine privilege settings for each
object type.
Table 5.2. Summary of Access Privileges
Object Type
All Privileges
Default PUBLIC
Privileges
psql Command
DATABASE
CTc
Tc
\l
DOMAIN
U
U
\dD+
FUNCTION or PROCEDURE
X
X
\df+
FOREIGN DATA WRAPPER
U
none
\dew+
FOREIGN SERVER
U
none
\des+
LANGUAGE
U
U
\dL+
LARGE OBJECT
rw
none
\dl+
PARAMETER
sA
none
\dconfig+
SCHEMA
UC
none
\dn+
SEQUENCE
rwU
none
\dp
TABLE (and table-like objects)
arwdDxtm
none
\dp
Table column
arwx
none
\dp
TABLESPACE
C
none
\db+
TYPE
U
U
\dT+
The privileges that have been granted for a particular object are displayed as a list of aclitem
entries, each having the format:
grantee=privilege-abbreviation[*].../grantor
Each aclitem lists all the permissions of one grantee that have been granted by a particular grantor.
Specific privileges are represented by one-letter abbreviations from Table 5.1, with * appended if the
privilege was granted with grant option. For example, calvin=r*w/hobbes specifies that the role
calvin has the privilege SELECT (r) with grant option (*) as well as the non-grantable privilege
UPDATE (w), both granted by the role hobbes. If calvin also has some privileges on the same
object granted by a different grantor, those would appear as a separate aclitem entry. An empty
grantee field in an aclitem stands for PUBLIC.
As an example, suppose that user miriam creates table mytable and does:
80
Data Definition
GRANT SELECT ON mytable TO PUBLIC;
GRANT SELECT, UPDATE, INSERT ON mytable TO admin;
GRANT SELECT (col1), UPDATE (col1) ON mytable TO miriam_rw;
Then psql's \dp command would show:
=> \dp mytable
Access privileges
Schema | Name
| Type |
Access privileges
|
Column
privileges
| Policies
--------+---------+-------+-----------------------+-----------------------+---------public | mytable | table | miriam=arwdDxtm/miriam+| col1:
+|
|
|
| =r/miriam
+|
miriam_rw=rw/miriam |
|
|
| admin=arw/miriam
|
|
(1 row)
If the “Access privileges” column is empty for a given object, it means the object has default privileges
(that is, its privileges entry in the relevant system catalog is null). Default privileges always include all
privileges for the owner, and can include some privileges for PUBLIC depending on the object type,
as explained above. The first GRANT or REVOKE on an object will instantiate the default privileges
(producing, for example, miriam=arwdDxt/miriam) and then modify them per the specified request. Similarly, entries are shown in “Column privileges” only for columns with nondefault privileges. (Note: for this purpose, “default privileges” always means the built-in default privileges for the
object's type. An object whose privileges have been affected by an ALTER DEFAULT PRIVILEGES
command will always be shown with an explicit privilege entry that includes the effects of the ALTER.)
Notice that the owner's implicit grant options are not marked in the access privileges display. A * will
appear only when grant options have been explicitly granted to someone.
The “Access privileges” column shows (none) when the object's privileges entry is non-null but
empty. This means that no privileges are granted at all, even to the object's owner — a rare situation.
(The owner still has implicit grant options in this case, and so could re-grant her own privileges; but
she has none at the moment.)
5.9. Row Security Policies
In addition to the SQL-standard privilege system available through GRANT, tables can have row
security policies that restrict, on a per-user basis, which rows can be returned by normal queries or
inserted, updated, or deleted by data modification commands. This feature is also known as Row-Level
Security. By default, tables do not have any policies, so that if a user has access privileges to a table
according to the SQL privilege system, all rows within it are equally available for querying or updating.
When row security is enabled on a table (with ALTER TABLE ... ENABLE ROW LEVEL SECURITY), all normal access to the table for selecting rows or modifying rows must be allowed by a row
security policy. (However, the table's owner is typically not subject to row security policies.) If no
policy exists for the table, a default-deny policy is used, meaning that no rows are visible or can be
modified. Operations that apply to the whole table, such as TRUNCATE and REFERENCES, are not
subject to row security.
Row security policies can be specific to commands, or to roles, or to both. A policy can be specified
to apply to ALL commands, or to SELECT, INSERT, UPDATE, or DELETE. Multiple roles can be
assigned to a given policy, and normal role membership and inheritance rules apply.
81
Data Definition
To specify which rows are visible or modifiable according to a policy, an expression is required that
returns a Boolean result. This expression will be evaluated for each row prior to any conditions or
functions coming from the user's query. (The only exceptions to this rule are leakproof functions,
which are guaranteed to not leak information; the optimizer may choose to apply such functions ahead
of the row-security check.) Rows for which the expression does not return true will not be processed.
Separate expressions may be specified to provide independent control over the rows which are visible
and the rows which are allowed to be modified. Policy expressions are run as part of the query and
with the privileges of the user running the query, although security-definer functions can be used to
access data not available to the calling user.
Superusers and roles with the BYPASSRLS attribute always bypass the row security system when
accessing a table. Table owners normally bypass row security as well, though a table owner can choose
to be subject to row security with ALTER TABLE ... FORCE ROW LEVEL SECURITY.
Enabling and disabling row security, as well as adding policies to a table, is always the privilege of
the table owner only.
Policies are created using the CREATE POLICY command, altered using the ALTER POLICY command, and dropped using the DROP POLICY command. To enable and disable row security for a
given table, use the ALTER TABLE command.
Each policy has a name and multiple policies can be defined for a table. As policies are table-specific,
each policy for a table must have a unique name. Different tables may have policies with the same
name.
When multiple policies apply to a given query, they are combined using either OR (for permissive
policies, which are the default) or using AND (for restrictive policies). This is similar to the rule that a
given role has the privileges of all roles that they are a member of. Permissive vs. restrictive policies
are discussed further below.
As a simple example, here is how to create a policy on the account relation to allow only members
of the managers role to access rows, and only rows of their accounts:
CREATE TABLE accounts (manager text, company text, contact_email
text);
ALTER TABLE accounts ENABLE ROW LEVEL SECURITY;
CREATE POLICY account_managers ON accounts TO managers
USING (manager = current_user);
The policy above implicitly provides a WITH CHECK clause identical to its USING clause, so that
the constraint applies both to rows selected by a command (so a manager cannot SELECT, UPDATE,
or DELETE existing rows belonging to a different manager) and to rows modified by a command (so
rows belonging to a different manager cannot be created via INSERT or UPDATE).
If no role is specified, or the special user name PUBLIC is used, then the policy applies to all users
on the system. To allow all users to access only their own row in a users table, a simple policy
can be used:
CREATE POLICY user_policy ON users
USING (user_name = current_user);
This works similarly to the previous example.
To use a different policy for rows that are being added to the table compared to those rows that are
visible, multiple policies can be combined. This pair of policies would allow all users to view all rows
in the users table, but only modify their own:
82
Data Definition
CREATE POLICY user_sel_policy ON users
FOR SELECT
USING (true);
CREATE POLICY user_mod_policy ON users
USING (user_name = current_user);
In a SELECT command, these two policies are combined using OR, with the net effect being that all
rows can be selected. In other command types, only the second policy applies, so that the effects are
the same as before.
Row security can also be disabled with the ALTER TABLE command. Disabling row security does
not remove any policies that are defined on the table; they are simply ignored. Then all rows in the
table are visible and modifiable, subject to the standard SQL privileges system.
Below is a larger example of how this feature can be used in production environments. The table
passwd emulates a Unix password file:
-- Simple passwd-file based example
CREATE TABLE passwd (
user_name
text UNIQUE NOT NULL,
pwhash
text,
uid
int PRIMARY KEY,
gid
int NOT NULL,
real_name
text NOT NULL,
home_phone
text,
extra_info
text,
home_dir
text NOT NULL,
shell
text NOT NULL
);
CREATE ROLE admin;
CREATE ROLE bob;
CREATE ROLE alice;
-- Administrator
-- Normal user
-- Normal user
-- Populate the table
INSERT INTO passwd VALUES
('admin','xxx',0,0,'Admin','111-222-3333',null,'/root','/bin/
dash');
INSERT INTO passwd VALUES
('bob','xxx',1,1,'Bob','123-456-7890',null,'/home/bob','/bin/
zsh');
INSERT INTO passwd VALUES
('alice','xxx',2,1,'Alice','098-765-4321',null,'/home/alice','/
bin/zsh');
-- Be sure to enable row-level security on the table
ALTER TABLE passwd ENABLE ROW LEVEL SECURITY;
-- Create policies
-- Administrator can see all rows and add any rows
CREATE POLICY admin_all ON passwd TO admin USING (true) WITH CHECK
(true);
-- Normal users can view all rows
CREATE POLICY all_view ON passwd FOR SELECT USING (true);
-- Normal users can update their own records, but
-- limit which shells a normal user is allowed to set
CREATE POLICY user_mod ON passwd FOR UPDATE
83
Data Definition
USING (current_user = user_name)
WITH CHECK (
current_user = user_name AND
shell IN ('/bin/bash','/bin/sh','/bin/dash','/bin/zsh','/bin/
tcsh')
);
-- Allow admin all normal rights
GRANT SELECT, INSERT, UPDATE, DELETE ON passwd TO admin;
-- Users only get select access on public columns
GRANT SELECT
(user_name, uid, gid, real_name, home_phone, extra_info,
home_dir, shell)
ON passwd TO public;
-- Allow users to update certain columns
GRANT UPDATE
(pwhash, real_name, home_phone, extra_info, shell)
ON passwd TO public;
As with any security settings, it's important to test and ensure that the system is behaving as expected.
Using the example above, this demonstrates that the permission system is working properly.
-- admin can view all rows and fields
postgres=> set role admin;
SET
postgres=> table passwd;
user_name | pwhash | uid | gid | real_name | home_phone |
extra_info | home_dir
|
shell
-----------+--------+-----+-----+-----------+-------------+------------+-------------+----------admin
| xxx
|
0 |
0 | Admin
| 111-222-3333 |
| /root
| /bin/dash
bob
| xxx
|
1 |
1 | Bob
| 123-456-7890 |
| /home/bob
| /bin/zsh
alice
| xxx
|
2 |
1 | Alice
| 098-765-4321 |
| /home/alice | /bin/zsh
(3 rows)
-- Test what Alice is able to do
postgres=> set role alice;
SET
postgres=> table passwd;
ERROR: permission denied for table passwd
postgres=> select
user_name,real_name,home_phone,extra_info,home_dir,shell from
passwd;
user_name | real_name | home_phone | extra_info | home_dir
|
shell
-----------+-----------+--------------+------------+------------+----------admin
| Admin
| 111-222-3333 |
| /root
| /bin/dash
bob
| Bob
| 123-456-7890 |
| /home/bob
| /bin/zsh
alice
| Alice
| 098-765-4321 |
| /home/alice
| /bin/zsh
(3 rows)
84
Data Definition
postgres=> update passwd set user_name = 'joe';
ERROR: permission denied for table passwd
-- Alice is allowed to change her own real_name, but no others
postgres=> update passwd set real_name = 'Alice Doe';
UPDATE 1
postgres=> update passwd set real_name = 'John Doe' where user_name
= 'admin';
UPDATE 0
postgres=> update passwd set shell = '/bin/xx';
ERROR: new row violates WITH CHECK OPTION for "passwd"
postgres=> delete from passwd;
ERROR: permission denied for table passwd
postgres=> insert into passwd (user_name) values ('xxx');
ERROR: permission denied for table passwd
-- Alice can change her own password; RLS silently prevents
updating other rows
postgres=> update passwd set pwhash = 'abc';
UPDATE 1
All of the policies constructed thus far have been permissive policies, meaning that when multiple
policies are applied they are combined using the “OR” Boolean operator. While permissive policies
can be constructed to only allow access to rows in the intended cases, it can be simpler to combine
permissive policies with restrictive policies (which the records must pass and which are combined
using the “AND” Boolean operator). Building on the example above, we add a restrictive policy to
require the administrator to be connected over a local Unix socket to access the records of the passwd
table:
CREATE POLICY admin_local_only ON passwd AS RESTRICTIVE TO admin
USING (pg_catalog.inet_client_addr() IS NULL);
We can then see that an administrator connecting over a network will not see any records, due to the
restrictive policy:
=> SELECT current_user;
current_user
-------------admin
(1 row)
=> select inet_client_addr();
inet_client_addr
-----------------127.0.0.1
(1 row)
=> TABLE passwd;
user_name | pwhash | uid | gid | real_name | home_phone |
extra_info | home_dir | shell
-----------+--------+-----+-----+-----------+-----------+------------+----------+------(0 rows)
=> UPDATE passwd set pwhash = NULL;
UPDATE 0
85
Data Definition
Referential integrity checks, such as unique or primary key constraints and foreign key references,
always bypass row security to ensure that data integrity is maintained. Care must be taken when developing schemas and row level policies to avoid “covert channel” leaks of information through such
referential integrity checks.
In some contexts it is important to be sure that row security is not being applied. For example, when
taking a backup, it could be disastrous if row security silently caused some rows to be omitted from
the backup. In such a situation, you can set the row_security configuration parameter to off. This
does not in itself bypass row security; what it does is throw an error if any query's results would get
filtered by a policy. The reason for the error can then be investigated and fixed.
In the examples above, the policy expressions consider only the current values in the row to be accessed or updated. This is the simplest and best-performing case; when possible, it's best to design row
security applications to work this way. If it is necessary to consult other rows or other tables to make
a policy decision, that can be accomplished using sub-SELECTs, or functions that contain SELECTs,
in the policy expressions. Be aware however that such accesses can create race conditions that could
allow information leakage if care is not taken. As an example, consider the following table design:
-- definition of privilege groups
CREATE TABLE groups (group_id int PRIMARY KEY,
group_name text NOT NULL);
INSERT INTO groups VALUES
(1, 'low'),
(2, 'medium'),
(5, 'high');
GRANT ALL ON groups TO alice; -- alice is the administrator
GRANT SELECT ON groups TO public;
-- definition of users' privilege levels
CREATE TABLE users (user_name text PRIMARY KEY,
group_id int NOT NULL REFERENCES groups);
INSERT INTO users VALUES
('alice', 5),
('bob', 2),
('mallory', 2);
GRANT ALL ON users TO alice;
GRANT SELECT ON users TO public;
-- table holding the information to be protected
CREATE TABLE information (info text,
group_id int NOT NULL REFERENCES groups);
INSERT INTO information VALUES
('barely secret', 1),
('slightly secret', 2),
('very secret', 5);
ALTER TABLE information ENABLE ROW LEVEL SECURITY;
-- a row should be visible to/updatable by users whose security
group_id is
-- greater than or equal to the row's group_id
CREATE POLICY fp_s ON information FOR SELECT
86
Data Definition
USING (group_id <= (SELECT group_id FROM users WHERE user_name =
current_user));
CREATE POLICY fp_u ON information FOR UPDATE
USING (group_id <= (SELECT group_id FROM users WHERE user_name =
current_user));
-- we rely only on RLS to protect the information table
GRANT ALL ON information TO public;
Now suppose that alice wishes to change the “slightly secret” information, but decides that mallory should not be trusted with the new content of that row, so she does:
BEGIN;
UPDATE users SET group_id = 1 WHERE user_name = 'mallory';
UPDATE information SET info = 'secret from mallory' WHERE group_id
= 2;
COMMIT;
That looks safe; there is no window wherein mallory should be able to see the “secret from mallory”
string. However, there is a race condition here. If mallory is concurrently doing, say,
SELECT * FROM information WHERE group_id = 2 FOR UPDATE;
and her transaction is in READ COMMITTED mode, it is possible for her to see “secret from mallory”.
That happens if her transaction reaches the information row just after alice's does. It blocks
waiting for alice's transaction to commit, then fetches the updated row contents thanks to the FOR
UPDATE clause. However, it does not fetch an updated row for the implicit SELECT from users,
because that sub-SELECT did not have FOR UPDATE; instead the users row is read with the snapshot taken at the start of the query. Therefore, the policy expression tests the old value of mallory's
privilege level and allows her to see the updated row.
There are several ways around this problem. One simple answer is to use SELECT ... FOR
SHARE in sub-SELECTs in row security policies. However, that requires granting UPDATE privilege
on the referenced table (here users) to the affected users, which might be undesirable. (But another
row security policy could be applied to prevent them from actually exercising that privilege; or the
sub-SELECT could be embedded into a security definer function.) Also, heavy concurrent use of row
share locks on the referenced table could pose a performance problem, especially if updates of it are
frequent. Another solution, practical if updates of the referenced table are infrequent, is to take an
ACCESS EXCLUSIVE lock on the referenced table when updating it, so that no concurrent transactions could be examining old row values. Or one could just wait for all concurrent transactions to end
after committing an update of the referenced table and before making changes that rely on the new
security situation.
For additional details see CREATE POLICY and ALTER TABLE.
5.10. Schemas
A PostgreSQL database cluster contains one or more named databases. Roles and a few other object
types are shared across the entire cluster. A client connection to the server can only access data in a
single database, the one specified in the connection request.
Note
Users of a cluster do not necessarily have the privilege to access every database in the cluster.
Sharing of role names means that there cannot be different roles named, say, joe in two
87
Data Definition
databases in the same cluster; but the system can be configured to allow joe access to only
some of the databases.
A database contains one or more named schemas, which in turn contain tables. Schemas also contain
other kinds of named objects, including data types, functions, and operators. Within one schema, two
objects of the same type cannot have the same name. Furthermore, tables, sequences, indexes, views,
materialized views, and foreign tables share the same namespace, so that, for example, an index and a
table must have different names if they are in the same schema. The same object name can be used in
different schemas without conflict; for example, both schema1 and myschema can contain tables
named mytable. Unlike databases, schemas are not rigidly separated: a user can access objects in
any of the schemas in the database they are connected to, if they have privileges to do so.
There are several reasons why one might want to use schemas:
• To allow many users to use one database without interfering with each other.
• To organize database objects into logical groups to make them more manageable.
• Third-party applications can be put into separate schemas so they do not collide with the names
of other objects.
Schemas are analogous to directories at the operating system level, except that schemas cannot be
nested.
5.10.1. Creating a Schema
To create a schema, use the CREATE SCHEMA command. Give the schema a name of your choice.
For example:
CREATE SCHEMA myschema;
To create or access objects in a schema, write a qualified name consisting of the schema name and
table name separated by a dot:
schema.table
This works anywhere a table name is expected, including the table modification commands and the
data access commands discussed in the following chapters. (For brevity we will speak of tables only,
but the same ideas apply to other kinds of named objects, such as types and functions.)
Actually, the even more general syntax
database.schema.table
can be used too, but at present this is just for pro forma compliance with the SQL standard. If you
write a database name, it must be the same as the database you are connected to.
So to create a table in the new schema, use:
CREATE TABLE myschema.mytable (
...
88
Data Definition
);
To drop a schema if it's empty (all objects in it have been dropped), use:
DROP SCHEMA myschema;
To drop a schema including all contained objects, use:
DROP SCHEMA myschema CASCADE;
See Section 5.15 for a description of the general mechanism behind this.
Often you will want to create a schema owned by someone else (since this is one of the ways to restrict
the activities of your users to well-defined namespaces). The syntax for that is:
CREATE SCHEMA schema_name AUTHORIZATION user_name;
You can even omit the schema name, in which case the schema name will be the same as the user
name. See Section 5.10.6 for how this can be useful.
Schema names beginning with pg_ are reserved for system purposes and cannot be created by users.
5.10.2. The Public Schema
In the previous sections we created tables without specifying any schema names. By default such
tables (and other objects) are automatically put into a schema named “public”. Every new database
contains such a schema. Thus, the following are equivalent:
CREATE TABLE products ( ... );
and:
CREATE TABLE public.products ( ... );
5.10.3. The Schema Search Path
Qualified names are tedious to write, and it's often best not to wire a particular schema name into
applications anyway. Therefore tables are often referred to by unqualified names, which consist of
just the table name. The system determines which table is meant by following a search path, which is
a list of schemas to look in. The first matching table in the search path is taken to be the one wanted.
If there is no match in the search path, an error is reported, even if matching table names exist in other
schemas in the database.
The ability to create like-named objects in different schemas complicates writing a query that references precisely the same objects every time. It also opens up the potential for users to change the behavior of other users' queries, maliciously or accidentally. Due to the prevalence of unqualified names
in queries and their use in PostgreSQL internals, adding a schema to search_path effectively trusts
all users having CREATE privilege on that schema. When you run an ordinary query, a malicious user
able to create objects in a schema of your search path can take control and execute arbitrary SQL
functions as though you executed them.
89
Data Definition
The first schema named in the search path is called the current schema. Aside from being the first
schema searched, it is also the schema in which new tables will be created if the CREATE TABLE
command does not specify a schema name.
To show the current search path, use the following command:
SHOW search_path;
In the default setup this returns:
search_path
-------------"$user", public
The first element specifies that a schema with the same name as the current user is to be searched.
If no such schema exists, the entry is ignored. The second element refers to the public schema that
we have seen already.
The first schema in the search path that exists is the default location for creating new objects. That
is the reason that by default objects are created in the public schema. When objects are referenced
in any other context without schema qualification (table modification, data modification, or query
commands) the search path is traversed until a matching object is found. Therefore, in the default
configuration, any unqualified access again can only refer to the public schema.
To put our new schema in the path, we use:
SET search_path TO myschema,public;
(We omit the $user here because we have no immediate need for it.) And then we can access the
table without schema qualification:
DROP TABLE mytable;
Also, since myschema is the first element in the path, new objects would by default be created in it.
We could also have written:
SET search_path TO myschema;
Then we no longer have access to the public schema without explicit qualification. There is nothing
special about the public schema except that it exists by default. It can be dropped, too.
See also Section 9.27 for other ways to manipulate the schema search path.
The search path works in the same way for data type names, function names, and operator names as it
does for table names. Data type and function names can be qualified in exactly the same way as table
names. If you need to write a qualified operator name in an expression, there is a special provision:
you must write
OPERATOR(schema.operator)
This is needed to avoid syntactic ambiguity. An example is:
90
Data Definition
SELECT 3 OPERATOR(pg_catalog.+) 4;
In practice one usually relies on the search path for operators, so as not to have to write anything so
ugly as that.
5.10.4. Schemas and Privileges
By default, users cannot access any objects in schemas they do not own. To allow that, the owner of
the schema must grant the USAGE privilege on the schema. By default, everyone has that privilege
on the schema public. To allow users to make use of the objects in a schema, additional privileges
might need to be granted, as appropriate for the object.
A user can also be allowed to create objects in someone else's schema. To allow that, the CREATE
privilege on the schema needs to be granted. In databases upgraded from PostgreSQL 14 or earlier,
everyone has that privilege on the schema public. Some usage patterns call for revoking that privilege:
REVOKE CREATE ON SCHEMA public FROM PUBLIC;
(The first “public” is the schema, the second “public” means “every user”. In the first sense it is an
identifier, in the second sense it is a key word, hence the different capitalization; recall the guidelines
from Section 4.1.1.)
5.10.5. The System Catalog Schema
In addition to public and user-created schemas, each database contains a pg_catalog schema,
which contains the system tables and all the built-in data types, functions, and operators. pg_catalog is always effectively part of the search path. If it is not named explicitly in the path then it is
implicitly searched before searching the path's schemas. This ensures that built-in names will always
be findable. However, you can explicitly place pg_catalog at the end of your search path if you
prefer to have user-defined names override built-in names.
Since system table names begin with pg_, it is best to avoid such names to ensure that you won't suffer
a conflict if some future version defines a system table named the same as your table. (With the default
search path, an unqualified reference to your table name would then be resolved as the system table
instead.) System tables will continue to follow the convention of having names beginning with pg_,
so that they will not conflict with unqualified user-table names so long as users avoid the pg_ prefix.
5.10.6. Usage Patterns
Schemas can be used to organize your data in many ways. A secure schema usage pattern prevents
untrusted users from changing the behavior of other users' queries. When a database does not use
a secure schema usage pattern, users wishing to securely query that database would take protective action at the beginning of each session. Specifically, they would begin each session by setting
search_path to the empty string or otherwise removing schemas that are writable by non-superusers from search_path. There are a few usage patterns easily supported by the default configuration:
• Constrain ordinary users to user-private schemas. To implement this pattern, first ensure that no
schemas have public CREATE privileges. Then, for every user needing to create non-temporary
objects, create a schema with the same name as that user, for example CREATE SCHEMA alice
AUTHORIZATION alice. (Recall that the default search path starts with $user, which resolves
to the user name. Therefore, if each user has a separate schema, they access their own schemas
by default.) This pattern is a secure schema usage pattern unless an untrusted user is the database
owner or has been granted ADMIN OPTION on a relevant role, in which case no secure schema
usage pattern exists.
91
Data Definition
In PostgreSQL 15 and later, the default configuration supports this usage pattern. In prior versions,
or when using a database that has been upgraded from a prior version, you will need to remove
the public CREATE privilege from the public schema (issue REVOKE CREATE ON SCHEMA
public FROM PUBLIC). Then consider auditing the public schema for objects named like
objects in schema pg_catalog.
• Remove the public schema from the default search path, by modifying postgresql.conf or by
issuing ALTER ROLE ALL SET search_path = "$user". Then, grant privileges to create
in the public schema. Only qualified names will choose public schema objects. While qualified table
references are fine, calls to functions in the public schema will be unsafe or unreliable. If you create
functions or extensions in the public schema, use the first pattern instead. Otherwise, like the first
pattern, this is secure unless an untrusted user is the database owner or has been granted ADMIN
OPTION on a relevant role.
• Keep the default search path, and grant privileges to create in the public schema. All users access the
public schema implicitly. This simulates the situation where schemas are not available at all, giving
a smooth transition from the non-schema-aware world. However, this is never a secure pattern. It is
acceptable only when the database has a single user or a few mutually-trusting users. In databases
upgraded from PostgreSQL 14 or earlier, this is the default.
For any pattern, to install shared applications (tables to be used by everyone, additional functions provided by third parties, etc.), put them into separate schemas. Remember to grant appropriate privileges
to allow the other users to access them. Users can then refer to these additional objects by qualifying
the names with a schema name, or they can put the additional schemas into their search path, as they
choose.
5.10.7. Portability
In the SQL standard, the notion of objects in the same schema being owned by different users does
not exist. Moreover, some implementations do not allow you to create schemas that have a different
name than their owner. In fact, the concepts of schema and user are nearly equivalent in a database
system that implements only the basic schema support specified in the standard. Therefore, many users
consider qualified names to really consist of user_name.table_name. This is how PostgreSQL
will effectively behave if you create a per-user schema for every user.
Also, there is no concept of a public schema in the SQL standard. For maximum conformance to
the standard, you should not use the public schema.
Of course, some SQL database systems might not implement schemas at all, or provide namespace
support by allowing (possibly limited) cross-database access. If you need to work with those systems,
then maximum portability would be achieved by not using schemas at all.
5.11. Inheritance
PostgreSQL implements table inheritance, which can be a useful tool for database designers.
(SQL:1999 and later define a type inheritance feature, which differs in many respects from the features
described here.)
Let's start with an example: suppose we are trying to build a data model for cities. Each state has many
cities, but only one capital. We want to be able to quickly retrieve the capital city for any particular
state. This can be done by creating two tables, one for state capitals and one for cities that are not
capitals. However, what happens when we want to ask for data about a city, regardless of whether it is
a capital or not? The inheritance feature can help to resolve this problem. We define the capitals
table so that it inherits from cities:
92
Data Definition
CREATE TABLE cities (
name
text,
population
float,
elevation
int
);
-- in feet
CREATE TABLE capitals (
state
char(2)
) INHERITS (cities);
In this case, the capitals table inherits all the columns of its parent table, cities. State capitals
also have an extra column, state, that shows their state.
In PostgreSQL, a table can inherit from zero or more other tables, and a query can reference either
all rows of a table or all rows of a table plus all of its descendant tables. The latter behavior is the
default. For example, the following query finds the names of all cities, including state capitals, that
are located at an elevation over 500 feet:
SELECT name, elevation
FROM cities
WHERE elevation > 500;
Given the sample data from the PostgreSQL tutorial (see Section 2.1), this returns:
name
| elevation
-----------+----------Las Vegas |
2174
Mariposa |
1953
Madison
|
845
On the other hand, the following query finds all the cities that are not state capitals and are situated
at an elevation over 500 feet:
SELECT name, elevation
FROM ONLY cities
WHERE elevation > 500;
name
| elevation
-----------+----------Las Vegas |
2174
Mariposa |
1953
Here the ONLY keyword indicates that the query should apply only to cities, and not any tables
below cities in the inheritance hierarchy. Many of the commands that we have already discussed
— SELECT, UPDATE and DELETE — support the ONLY keyword.
You can also write the table name with a trailing * to explicitly specify that descendant tables are
included:
SELECT name, elevation
FROM cities*
WHERE elevation > 500;
Writing * is not necessary, since this behavior is always the default. However, this syntax is still
supported for compatibility with older releases where the default could be changed.
93
Data Definition
In some cases you might wish to know which table a particular row originated from. There is a system
column called tableoid in each table which can tell you the originating table:
SELECT c.tableoid, c.name, c.elevation
FROM cities c
WHERE c.elevation > 500;
which returns:
tableoid |
name
| elevation
----------+-----------+----------139793 | Las Vegas |
2174
139793 | Mariposa |
1953
139798 | Madison
|
845
(If you try to reproduce this example, you will probably get different numeric OIDs.) By doing a join
with pg_class you can see the actual table names:
SELECT p.relname, c.name, c.elevation
FROM cities c, pg_class p
WHERE c.elevation > 500 AND c.tableoid = p.oid;
which returns:
relname |
name
| elevation
----------+-----------+----------cities
| Las Vegas |
2174
cities
| Mariposa |
1953
capitals | Madison
|
845
Another way to get the same effect is to use the regclass alias type, which will print the table OID
symbolically:
SELECT c.tableoid::regclass, c.name, c.elevation
FROM cities c
WHERE c.elevation > 500;
Inheritance does not automatically propagate data from INSERT or COPY commands to other tables
in the inheritance hierarchy. In our example, the following INSERT statement will fail:
INSERT INTO cities (name, population, elevation, state)
VALUES ('Albany', NULL, NULL, 'NY');
We might hope that the data would somehow be routed to the capitals table, but this does not
happen: INSERT always inserts into exactly the table specified. In some cases it is possible to redirect
the insertion using a rule (see Chapter 39). However that does not help for the above case because
the cities table does not contain the column state, and so the command will be rejected before
the rule can be applied.
All check constraints and not-null constraints on a parent table are automatically inherited by its children, unless explicitly specified otherwise with NO INHERIT clauses. Other types of constraints
(unique, primary key, and foreign key constraints) are not inherited.
A table can inherit from more than one parent table, in which case it has the union of the columns
defined by the parent tables. Any columns declared in the child table's definition are added to these.
94
Data Definition
If the same column name appears in multiple parent tables, or in both a parent table and the child's
definition, then these columns are “merged” so that there is only one such column in the child table. To
be merged, columns must have the same data types, else an error is raised. Inheritable check constraints
and not-null constraints are merged in a similar fashion. Thus, for example, a merged column will be
marked not-null if any one of the column definitions it came from is marked not-null. Check constraints
are merged if they have the same name, and the merge will fail if their conditions are different.
Table inheritance is typically established when the child table is created, using the INHERITS clause
of the CREATE TABLE statement. Alternatively, a table which is already defined in a compatible way
can have a new parent relationship added, using the INHERIT variant of ALTER TABLE. To do this
the new child table must already include columns with the same names and types as the columns of the
parent. It must also include check constraints with the same names and check expressions as those of
the parent. Similarly an inheritance link can be removed from a child using the NO INHERIT variant
of ALTER TABLE. Dynamically adding and removing inheritance links like this can be useful when
the inheritance relationship is being used for table partitioning (see Section 5.12).
One convenient way to create a compatible table that will later be made a new child is to use the
LIKE clause in CREATE TABLE. This creates a new table with the same columns as the source table.
If there are any CHECK constraints defined on the source table, the INCLUDING CONSTRAINTS
option to LIKE should be specified, as the new child must have constraints matching the parent to
be considered compatible.
A parent table cannot be dropped while any of its children remain. Neither can columns or check
constraints of child tables be dropped or altered if they are inherited from any parent tables. If you
wish to remove a table and all of its descendants, one easy way is to drop the parent table with the
CASCADE option (see Section 5.15).
ALTER TABLE will propagate any changes in column data definitions and check constraints down
the inheritance hierarchy. Again, dropping columns that are depended on by other tables is only possible when using the CASCADE option. ALTER TABLE follows the same rules for duplicate column
merging and rejection that apply during CREATE TABLE.
Inherited queries perform access permission checks on the parent table only. Thus, for example, granting UPDATE permission on the cities table implies permission to update rows in the capitals
table as well, when they are accessed through cities. This preserves the appearance that the data
is (also) in the parent table. But the capitals table could not be updated directly without an additional grant. In a similar way, the parent table's row security policies (see Section 5.9) are applied to
rows coming from child tables during an inherited query. A child table's policies, if any, are applied
only when it is the table explicitly named in the query; and in that case, any policies attached to its
parent(s) are ignored.
Foreign tables (see Section 5.13) can also be part of inheritance hierarchies, either as parent or child
tables, just as regular tables can be. If a foreign table is part of an inheritance hierarchy then any
operations not supported by the foreign table are not supported on the whole hierarchy either.
5.11.1. Caveats
Note that not all SQL commands are able to work on inheritance hierarchies. Commands that are used
for data querying, data modification, or schema modification (e.g., SELECT, UPDATE, DELETE, most
variants of ALTER TABLE, but not INSERT or ALTER TABLE ... RENAME) typically default
to including child tables and support the ONLY notation to exclude them. Commands that do database
maintenance and tuning (e.g., REINDEX, VACUUM) typically only work on individual, physical tables
and do not support recursing over inheritance hierarchies. The respective behavior of each individual
command is documented in its reference page (SQL Commands).
A serious limitation of the inheritance feature is that indexes (including unique constraints) and foreign
key constraints only apply to single tables, not to their inheritance children. This is true on both the
referencing and referenced sides of a foreign key constraint. Thus, in the terms of the above example:
95
Data Definition
• If we declared cities.name to be UNIQUE or a PRIMARY KEY, this would not stop the capitals table from having rows with names duplicating rows in cities. And those duplicate rows
would by default show up in queries from cities. In fact, by default capitals would have no
unique constraint at all, and so could contain multiple rows with the same name. You could add a
unique constraint to capitals, but this would not prevent duplication compared to cities.
• Similarly, if we were to specify that cities.name REFERENCES some other table, this constraint
would not automatically propagate to capitals. In this case you could work around it by manually
adding the same REFERENCES constraint to capitals.
• Specifying that another table's column REFERENCES cities(name) would allow the other
table to contain city names, but not capital names. There is no good workaround for this case.
Some functionality not implemented for inheritance hierarchies is implemented for declarative partitioning. Considerable care is needed in deciding whether partitioning with legacy inheritance is useful
for your application.
5.12. Table Partitioning
PostgreSQL supports basic table partitioning. This section describes why and how to implement partitioning as part of your database design.
5.12.1. Overview
Partitioning refers to splitting what is logically one large table into smaller physical pieces. Partitioning
can provide several benefits:
• Query performance can be improved dramatically in certain situations, particularly when most of
the heavily accessed rows of the table are in a single partition or a small number of partitions.
Partitioning effectively substitutes for the upper tree levels of indexes, making it more likely that
the heavily-used parts of the indexes fit in memory.
• When queries or updates access a large percentage of a single partition, performance can be improved by using a sequential scan of that partition instead of using an index, which would require
random-access reads scattered across the whole table.
• Bulk loads and deletes can be accomplished by adding or removing partitions, if the usage pattern is
accounted for in the partitioning design. Dropping an individual partition using DROP TABLE, or
doing ALTER TABLE DETACH PARTITION, is far faster than a bulk operation. These commands
also entirely avoid the VACUUM overhead caused by a bulk DELETE.
• Seldom-used data can be migrated to cheaper and slower storage media.
These benefits will normally be worthwhile only when a table would otherwise be very large. The
exact point at which a table will benefit from partitioning depends on the application, although a rule
of thumb is that the size of the table should exceed the physical memory of the database server.
PostgreSQL offers built-in support for the following forms of partitioning:
Range Partitioning
The table is partitioned into “ranges” defined by a key column or set of columns, with no overlap
between the ranges of values assigned to different partitions. For example, one might partition by
date ranges, or by ranges of identifiers for particular business objects. Each range's bounds are
understood as being inclusive at the lower end and exclusive at the upper end. For example, if
one partition's range is from 1 to 10, and the next one's range is from 10 to 20, then value 10
belongs to the second partition not the first.
96
Data Definition
List Partitioning
The table is partitioned by explicitly listing which key value(s) appear in each partition.
Hash Partitioning
The table is partitioned by specifying a modulus and a remainder for each partition. Each partition
will hold the rows for which the hash value of the partition key divided by the specified modulus
will produce the specified remainder.
If your application needs to use other forms of partitioning not listed above, alternative methods such
as inheritance and UNION ALL views can be used instead. Such methods offer flexibility but do not
have some of the performance benefits of built-in declarative partitioning.
5.12.2. Declarative Partitioning
PostgreSQL allows you to declare that a table is divided into partitions. The table that is divided is
referred to as a partitioned table. The declaration includes the partitioning method as described above,
plus a list of columns or expressions to be used as the partition key.
The partitioned table itself is a “virtual” table having no storage of its own. Instead, the storage belongs
to partitions, which are otherwise-ordinary tables associated with the partitioned table. Each partition
stores a subset of the data as defined by its partition bounds. All rows inserted into a partitioned table
will be routed to the appropriate one of the partitions based on the values of the partition key column(s).
Updating the partition key of a row will cause it to be moved into a different partition if it no longer
satisfies the partition bounds of its original partition.
Partitions may themselves be defined as partitioned tables, resulting in sub-partitioning. Although
all partitions must have the same columns as their partitioned parent, partitions may have their own
indexes, constraints and default values, distinct from those of other partitions. See CREATE TABLE
for more details on creating partitioned tables and partitions.
It is not possible to turn a regular table into a partitioned table or vice versa. However, it is possible to
add an existing regular or partitioned table as a partition of a partitioned table, or remove a partition
from a partitioned table turning it into a standalone table; this can simplify and speed up many maintenance processes. See ALTER TABLE to learn more about the ATTACH PARTITION and DETACH
PARTITION sub-commands.
Partitions can also be foreign tables, although considerable care is needed because it is then the user's
responsibility that the contents of the foreign table satisfy the partitioning rule. There are some other
restrictions as well. See CREATE FOREIGN TABLE for more information.
5.12.2.1. Example
Suppose we are constructing a database for a large ice cream company. The company measures peak
temperatures every day as well as ice cream sales in each region. Conceptually, we want a table like:
CREATE TABLE measurement (
city_id
int not null,
logdate
date not null,
peaktemp
int,
unitsales
int
);
We know that most queries will access just the last week's, month's or quarter's data, since the main
use of this table will be to prepare online reports for management. To reduce the amount of old data
that needs to be stored, we decide to keep only the most recent 3 years worth of data. At the beginning
97
Data Definition
of each month we will remove the oldest month's data. In this situation we can use partitioning to help
us meet all of our different requirements for the measurements table.
To use declarative partitioning in this case, use the following steps:
1. Create the measurement table as a partitioned table by specifying the PARTITION BY clause,
which includes the partitioning method (RANGE in this case) and the list of column(s) to use as
the partition key.
CREATE TABLE measurement (
city_id
int not null,
logdate
date not null,
peaktemp
int,
unitsales
int
) PARTITION BY RANGE (logdate);
2. Create partitions. Each partition's definition must specify bounds that correspond to the partitioning
method and partition key of the parent. Note that specifying bounds such that the new partition's
values would overlap with those in one or more existing partitions will cause an error.
Partitions thus created are in every way normal PostgreSQL tables (or, possibly, foreign tables).
It is possible to specify a tablespace and storage parameters for each partition separately.
For our example, each partition should hold one month's worth of data, to match the requirement
of deleting one month's data at a time. So the commands might look like:
CREATE TABLE measurement_y2006m02 PARTITION OF measurement
FOR VALUES FROM ('2006-02-01') TO ('2006-03-01');
CREATE TABLE measurement_y2006m03 PARTITION OF measurement
FOR VALUES FROM ('2006-03-01') TO ('2006-04-01');
...
CREATE TABLE measurement_y2007m11 PARTITION OF measurement
FOR VALUES FROM ('2007-11-01') TO ('2007-12-01');
CREATE TABLE measurement_y2007m12 PARTITION OF measurement
FOR VALUES FROM ('2007-12-01') TO ('2008-01-01')
TABLESPACE fasttablespace;
CREATE TABLE measurement_y2008m01 PARTITION OF measurement
FOR VALUES FROM ('2008-01-01') TO ('2008-02-01')
WITH (parallel_workers = 4)
TABLESPACE fasttablespace;
(Recall that adjacent partitions can share a bound value, since range upper bounds are treated as
exclusive bounds.)
If you wish to implement sub-partitioning, again specify the PARTITION BY clause in the commands used to create individual partitions, for example:
CREATE TABLE measurement_y2006m02 PARTITION OF measurement
FOR VALUES FROM ('2006-02-01') TO ('2006-03-01')
PARTITION BY RANGE (peaktemp);
After creating partitions of measurement_y2006m02, any data inserted into measurement
that is mapped to measurement_y2006m02 (or data that is directly inserted into measurement_y2006m02, which is allowed provided its partition constraint is satisfied) will be further
98
Data Definition
redirected to one of its partitions based on the peaktemp column. The partition key specified may
overlap with the parent's partition key, although care should be taken when specifying the bounds
of a sub-partition such that the set of data it accepts constitutes a subset of what the partition's own
bounds allow; the system does not try to check whether that's really the case.
Inserting data into the parent table that does not map to one of the existing partitions will cause an
error; an appropriate partition must be added manually.
It is not necessary to manually create table constraints describing the partition boundary conditions
for partitions. Such constraints will be created automatically.
3. Create an index on the key column(s), as well as any other indexes you might want, on the partitioned table. (The key index is not strictly necessary, but in most scenarios it is helpful.) This
automatically creates a matching index on each partition, and any partitions you create or attach
later will also have such an index. An index or unique constraint declared on a partitioned table
is “virtual” in the same way that the partitioned table is: the actual data is in child indexes on the
individual partition tables.
CREATE INDEX ON measurement (logdate);
4. Ensure that the enable_partition_pruning configuration parameter is not disabled in postgresql.conf. If it is, queries will not be optimized as desired.
In the above example we would be creating a new partition each month, so it might be wise to write
a script that generates the required DDL automatically.
5.12.2.2. Partition Maintenance
Normally the set of partitions established when initially defining the table is not intended to remain
static. It is common to want to remove partitions holding old data and periodically add new partitions
for new data. One of the most important advantages of partitioning is precisely that it allows this
otherwise painful task to be executed nearly instantaneously by manipulating the partition structure,
rather than physically moving large amounts of data around.
The simplest option for removing old data is to drop the partition that is no longer necessary:
DROP TABLE measurement_y2006m02;
This can very quickly delete millions of records because it doesn't have to individually delete every
record. Note however that the above command requires taking an ACCESS EXCLUSIVE lock on
the parent table.
Another option that is often preferable is to remove the partition from the partitioned table but retain
access to it as a table in its own right. This has two forms:
ALTER TABLE measurement DETACH PARTITION measurement_y2006m02;
ALTER TABLE measurement DETACH PARTITION measurement_y2006m02
CONCURRENTLY;
These allow further operations to be performed on the data before it is dropped. For example, this is
often a useful time to back up the data using COPY, pg_dump, or similar tools. It might also be a useful
time to aggregate data into smaller formats, perform other data manipulations, or run reports. The
first form of the command requires an ACCESS EXCLUSIVE lock on the parent table. Adding the
CONCURRENTLY qualifier as in the second form allows the detach operation to require only SHARE
UPDATE EXCLUSIVE lock on the parent table, but see ALTER TABLE ... DETACH PARTITION
for details on the restrictions.
Similarly we can add a new partition to handle new data. We can create an empty partition in the
partitioned table just as the original partitions were created above:
99
Data Definition
CREATE TABLE measurement_y2008m02 PARTITION OF measurement
FOR VALUES FROM ('2008-02-01') TO ('2008-03-01')
TABLESPACE fasttablespace;
As an alternative to creating a new partition, it is sometimes more convenient to create a new table separate from the partition structure and attach it as a partition later. This allows new data to be
loaded, checked, and transformed prior to it appearing in the partitioned table. Moreover, the ATTACH
PARTITION operation requires only a SHARE UPDATE EXCLUSIVE lock on the partitioned table
rather than the ACCESS EXCLUSIVE lock required by CREATE TABLE ... PARTITION OF,
so it is more friendly to concurrent operations on the partitioned table; see ALTER TABLE ...
ATTACH PARTITION for additional details. The CREATE TABLE ... LIKE option can be
helpful to avoid tediously repeating the parent table's definition; for example:
CREATE TABLE measurement_y2008m02
(LIKE measurement INCLUDING DEFAULTS INCLUDING CONSTRAINTS)
TABLESPACE fasttablespace;
ALTER TABLE measurement_y2008m02 ADD CONSTRAINT y2008m02
CHECK ( logdate >= DATE '2008-02-01' AND logdate < DATE
'2008-03-01' );
\copy measurement_y2008m02 from 'measurement_y2008m02'
-- possibly some other data preparation work
ALTER TABLE measurement ATTACH PARTITION measurement_y2008m02
FOR VALUES FROM ('2008-02-01') TO ('2008-03-01' );
Note that when running the ATTACH PARTITION command, the table will be scanned to validate
the partition constraint while holding an ACCESS EXCLUSIVE lock on that partition. As shown
above, it is recommended to avoid this scan by creating a CHECK constraint matching the expected
partition constraint on the table prior to attaching it. Once the ATTACH PARTITION is complete, it
is recommended to drop the now-redundant CHECK constraint. If the table being attached is itself a
partitioned table, then each of its sub-partitions will be recursively locked and scanned until either a
suitable CHECK constraint is encountered or the leaf partitions are reached.
Similarly, if the partitioned table has a DEFAULT partition, it is recommended to create a CHECK
constraint which excludes the to-be-attached partition's constraint. If this is not done, the DEFAULT
partition will be scanned to verify that it contains no records which should be located in the partition
being attached. This operation will be performed whilst holding an ACCESS EXCLUSIVE lock on the
DEFAULT partition. If the DEFAULT partition is itself a partitioned table, then each of its partitions
will be recursively checked in the same way as the table being attached, as mentioned above.
As mentioned earlier, it is possible to create indexes on partitioned tables so that they are applied automatically to the entire hierarchy. This can be very convenient as not only will all existing partitions be
indexed, but any future partitions will be as well. However, one limitation when creating new indexes
on partitioned tables is that it is not possible to use the CONCURRENTLY qualifier, which could lead to
long lock times. To avoid this, you can use CREATE INDEX ON ONLY the partitioned table, which
creates the new index marked as invalid, preventing automatic application to existing partitions. Instead, indexes can then be created individually on each partition using CONCURRENTLY and attached
to the partitioned index on the parent using ALTER INDEX ... ATTACH PARTITION. Once
indexes for all the partitions are attached to the parent index, the parent index will be marked valid
automatically. Example:
CREATE INDEX measurement_usls_idx ON ONLY measurement (unitsales);
CREATE INDEX CONCURRENTLY measurement_usls_200602_idx
100
Data Definition
ON measurement_y2006m02 (unitsales);
ALTER INDEX measurement_usls_idx
ATTACH PARTITION measurement_usls_200602_idx;
...
This technique can be used with UNIQUE and PRIMARY KEY constraints too; the indexes are created
implicitly when the constraint is created. Example:
ALTER TABLE ONLY measurement ADD UNIQUE (city_id, logdate);
ALTER TABLE measurement_y2006m02 ADD UNIQUE (city_id, logdate);
ALTER INDEX measurement_city_id_logdate_key
ATTACH PARTITION measurement_y2006m02_city_id_logdate_key;
...
5.12.2.3. Limitations
The following limitations apply to partitioned tables:
• To create a unique or primary key constraint on a partitioned table, the partition keys must not include any expressions or function calls and the constraint's columns must include all of the partition
key columns. This limitation exists because the individual indexes making up the constraint can
only directly enforce uniqueness within their own partitions; therefore, the partition structure itself
must guarantee that there are not duplicates in different partitions.
• Similarly an exclusion constraint must include all the partition key columns. Furthermore the constraint must compare those columns for equality (not e.g. &&). Again, this limitation stems from not
being able to enforce cross-partition restrictions. The constraint may include additional columns
that aren't part of the partition key, and it may compare those with any operators you like.
• BEFORE ROW triggers on INSERT cannot change which partition is the final destination for a
new row.
• Mixing temporary and permanent relations in the same partition tree is not allowed. Hence, if the
partitioned table is permanent, so must be its partitions and likewise if the partitioned table is temporary. When using temporary relations, all members of the partition tree have to be from the same
session.
Individual partitions are linked to their partitioned table using inheritance behind-the-scenes. However,
it is not possible to use all of the generic features of inheritance with declaratively partitioned tables
or their partitions, as discussed below. Notably, a partition cannot have any parents other than the
partitioned table it is a partition of, nor can a table inherit from both a partitioned table and a regular
table. That means partitioned tables and their partitions never share an inheritance hierarchy with
regular tables.
Since a partition hierarchy consisting of the partitioned table and its partitions is still an inheritance
hierarchy, tableoid and all the normal rules of inheritance apply as described in Section 5.11, with
a few exceptions:
• Partitions cannot have columns that are not present in the parent. It is not possible to specify columns
when creating partitions with CREATE TABLE, nor is it possible to add columns to partitions
after-the-fact using ALTER TABLE. Tables may be added as a partition with ALTER TABLE ...
ATTACH PARTITION only if their columns exactly match the parent.
• Both CHECK and NOT NULL constraints of a partitioned table are always inherited by all its partitions. CHECK constraints that are marked NO INHERIT are not allowed to be created on partitioned
tables. You cannot drop a NOT NULL constraint on a partition's column if the same constraint is
present in the parent table.
101
Data Definition
• Using ONLY to add or drop a constraint on only the partitioned table is supported as long as there
are no partitions. Once partitions exist, using ONLY will result in an error for any constraints other
than UNIQUE and PRIMARY KEY. Instead, constraints on the partitions themselves can be added
and (if they are not present in the parent table) dropped.
• As a partitioned table does not have any data itself, attempts to use TRUNCATE ONLY on a partitioned table will always return an error.
5.12.3. Partitioning Using Inheritance
While the built-in declarative partitioning is suitable for most common use cases, there are some
circumstances where a more flexible approach may be useful. Partitioning can be implemented using
table inheritance, which allows for several features not supported by declarative partitioning, such as:
• For declarative partitioning, partitions must have exactly the same set of columns as the partitioned
table, whereas with table inheritance, child tables may have extra columns not present in the parent.
• Table inheritance allows for multiple inheritance.
• Declarative partitioning only supports range, list and hash partitioning, whereas table inheritance
allows data to be divided in a manner of the user's choosing. (Note, however, that if constraint
exclusion is unable to prune child tables effectively, query performance might be poor.)
5.12.3.1. Example
This example builds a partitioning structure equivalent to the declarative partitioning example above.
Use the following steps:
1. Create the “root” table, from which all of the “child” tables will inherit. This table will contain
no data. Do not define any check constraints on this table, unless you intend them to be applied
equally to all child tables. There is no point in defining any indexes or unique constraints on it,
either. For our example, the root table is the measurement table as originally defined:
CREATE TABLE measurement (
city_id
int not null,
logdate
date not null,
peaktemp
int,
unitsales
int
);
2. Create several “child” tables that each inherit from the root table. Normally, these tables will not
add any columns to the set inherited from the root. Just as with declarative partitioning, these tables
are in every way normal PostgreSQL tables (or foreign tables).
CREATE TABLE measurement_y2006m02 () INHERITS (measurement);
CREATE TABLE measurement_y2006m03 () INHERITS (measurement);
...
CREATE TABLE measurement_y2007m11 () INHERITS (measurement);
CREATE TABLE measurement_y2007m12 () INHERITS (measurement);
CREATE TABLE measurement_y2008m01 () INHERITS (measurement);
3. Add non-overlapping table constraints to the child tables to define the allowed key values in each.
Typical examples would be:
CHECK ( x = 1 )
CHECK ( county IN ( 'Oxfordshire', 'Buckinghamshire',
'Warwickshire' ))
CHECK ( outletID >= 100 AND outletID < 200 )
102
Data Definition
Ensure that the constraints guarantee that there is no overlap between the key values permitted in
different child tables. A common mistake is to set up range constraints like:
CHECK ( outletID BETWEEN 100 AND 200 )
CHECK ( outletID BETWEEN 200 AND 300 )
This is wrong since it is not clear which child table the key value 200 belongs in. Instead, ranges
should be defined in this style:
CREATE TABLE measurement_y2006m02 (
CHECK ( logdate >= DATE '2006-02-01' AND logdate < DATE
'2006-03-01' )
) INHERITS (measurement);
CREATE TABLE measurement_y2006m03 (
CHECK ( logdate >= DATE '2006-03-01' AND logdate < DATE
'2006-04-01' )
) INHERITS (measurement);
...
CREATE TABLE measurement_y2007m11 (
CHECK ( logdate >= DATE '2007-11-01' AND logdate < DATE
'2007-12-01' )
) INHERITS (measurement);
CREATE TABLE measurement_y2007m12 (
CHECK ( logdate >= DATE '2007-12-01' AND logdate < DATE
'2008-01-01' )
) INHERITS (measurement);
CREATE TABLE measurement_y2008m01 (
CHECK ( logdate >= DATE '2008-01-01' AND logdate < DATE
'2008-02-01' )
) INHERITS (measurement);
4. For each child table, create an index on the key column(s), as well as any other indexes you might
want.
CREATE INDEX measurement_y2006m02_logdate ON
measurement_y2006m02 (logdate);
CREATE INDEX measurement_y2006m03_logdate ON
measurement_y2006m03 (logdate);
CREATE INDEX measurement_y2007m11_logdate ON
measurement_y2007m11 (logdate);
CREATE INDEX measurement_y2007m12_logdate ON
measurement_y2007m12 (logdate);
CREATE INDEX measurement_y2008m01_logdate ON
measurement_y2008m01 (logdate);
5. We want our application to be able to say INSERT INTO measurement ... and have the
data be redirected into the appropriate child table. We can arrange that by attaching a suitable
trigger function to the root table. If data will be added only to the latest child, we can use a very
simple trigger function:
CREATE OR REPLACE FUNCTION measurement_insert_trigger()
RETURNS TRIGGER AS $$
103
Data Definition
BEGIN
INSERT INTO measurement_y2008m01 VALUES (NEW.*);
RETURN NULL;
END;
$$
LANGUAGE plpgsql;
After creating the function, we create a trigger which calls the trigger function:
CREATE TRIGGER insert_measurement_trigger
BEFORE INSERT ON measurement
FOR EACH ROW EXECUTE FUNCTION measurement_insert_trigger();
We must redefine the trigger function each month so that it always inserts into the current child
table. The trigger definition does not need to be updated, however.
We might want to insert data and have the server automatically locate the child table into which
the row should be added. We could do this with a more complex trigger function, for example:
CREATE OR REPLACE FUNCTION measurement_insert_trigger()
RETURNS TRIGGER AS $$
BEGIN
IF ( NEW.logdate >= DATE '2006-02-01' AND
NEW.logdate < DATE '2006-03-01' ) THEN
INSERT INTO measurement_y2006m02 VALUES (NEW.*);
ELSIF ( NEW.logdate >= DATE '2006-03-01' AND
NEW.logdate < DATE '2006-04-01' ) THEN
INSERT INTO measurement_y2006m03 VALUES (NEW.*);
...
ELSIF ( NEW.logdate >= DATE '2008-01-01' AND
NEW.logdate < DATE '2008-02-01' ) THEN
INSERT INTO measurement_y2008m01 VALUES (NEW.*);
ELSE
RAISE EXCEPTION 'Date out of range. Fix the
measurement_insert_trigger() function!';
END IF;
RETURN NULL;
END;
$$
104
Data Definition
LANGUAGE plpgsql;
The trigger definition is the same as before. Note that each IF test must exactly match the CHECK
constraint for its child table.
While this function is more complex than the single-month case, it doesn't need to be updated as
often, since branches can be added in advance of being needed.
Note
In practice, it might be best to check the newest child first, if most inserts go into that
child. For simplicity, we have shown the trigger's tests in the same order as in other parts
of this example.
A different approach to redirecting inserts into the appropriate child table is to set up rules, instead
of a trigger, on the root table. For example:
CREATE RULE measurement_insert_y2006m02 AS
ON INSERT TO measurement WHERE
( logdate >= DATE '2006-02-01' AND logdate < DATE
'2006-03-01' )
DO INSTEAD
INSERT INTO measurement_y2006m02 VALUES (NEW.*);
...
CREATE RULE measurement_insert_y2008m01 AS
ON INSERT TO measurement WHERE
( logdate >= DATE '2008-01-01' AND logdate < DATE
'2008-02-01' )
DO INSTEAD
INSERT INTO measurement_y2008m01 VALUES (NEW.*);
A rule has significantly more overhead than a trigger, but the overhead is paid once per query
rather than once per row, so this method might be advantageous for bulk-insert situations. In most
cases, however, the trigger method will offer better performance.
Be aware that COPY ignores rules. If you want to use COPY to insert data, you'll need to copy into
the correct child table rather than directly into the root. COPY does fire triggers, so you can use
it normally if you use the trigger approach.
Another disadvantage of the rule approach is that there is no simple way to force an error if the set
of rules doesn't cover the insertion date; the data will silently go into the root table instead.
6. Ensure that the constraint_exclusion configuration parameter is not disabled in postgresql.conf; otherwise child tables may be accessed unnecessarily.
As we can see, a complex table hierarchy could require a substantial amount of DDL. In the above
example we would be creating a new child table each month, so it might be wise to write a script that
generates the required DDL automatically.
5.12.3.2. Maintenance for Inheritance Partitioning
To remove old data quickly, simply drop the child table that is no longer necessary:
DROP TABLE measurement_y2006m02;
To remove the child table from the inheritance hierarchy table but retain access to it as a table in its
own right:
105
Data Definition
ALTER TABLE measurement_y2006m02 NO INHERIT measurement;
To add a new child table to handle new data, create an empty child table just as the original children
were created above:
CREATE TABLE measurement_y2008m02 (
CHECK ( logdate >= DATE '2008-02-01' AND logdate < DATE
'2008-03-01' )
) INHERITS (measurement);
Alternatively, one may want to create and populate the new child table before adding it to the table
hierarchy. This could allow data to be loaded, checked, and transformed before being made visible
to queries on the parent table.
CREATE TABLE measurement_y2008m02
(LIKE measurement INCLUDING DEFAULTS INCLUDING CONSTRAINTS);
ALTER TABLE measurement_y2008m02 ADD CONSTRAINT y2008m02
CHECK ( logdate >= DATE '2008-02-01' AND logdate < DATE
'2008-03-01' );
\copy measurement_y2008m02 from 'measurement_y2008m02'
-- possibly some other data preparation work
ALTER TABLE measurement_y2008m02 INHERIT measurement;
5.12.3.3. Caveats
The following caveats apply to partitioning implemented using inheritance:
• There is no automatic way to verify that all of the CHECK constraints are mutually exclusive. It is
safer to create code that generates child tables and creates and/or modifies associated objects than
to write each by hand.
• Indexes and foreign key constraints apply to single tables and not to their inheritance children, hence
they have some caveats to be aware of.
• The schemes shown here assume that the values of a row's key column(s) never change, or at least do
not change enough to require it to move to another partition. An UPDATE that attempts to do that will
fail because of the CHECK constraints. If you need to handle such cases, you can put suitable update
triggers on the child tables, but it makes management of the structure much more complicated.
• If you are using manual VACUUM or ANALYZE commands, don't forget that you need to run them
on each child table individually. A command like:
ANALYZE measurement;
will only process the root table.
• INSERT statements with ON CONFLICT clauses are unlikely to work as expected, as the ON
CONFLICT action is only taken in case of unique violations on the specified target relation, not
its child relations.
• Triggers or rules will be needed to route rows to the desired child table, unless the application is
explicitly aware of the partitioning scheme. Triggers may be complicated to write, and will be much
slower than the tuple routing performed internally by declarative partitioning.
5.12.4. Partition Pruning
106
Data Definition
Partition pruning is a query optimization technique that improves performance for declaratively partitioned tables. As an example:
SET enable_partition_pruning = on;
-- the default
SELECT count(*) FROM measurement WHERE logdate >= DATE
'2008-01-01';
Without partition pruning, the above query would scan each of the partitions of the measurement
table. With partition pruning enabled, the planner will examine the definition of each partition and
prove that the partition need not be scanned because it could not contain any rows meeting the query's
WHERE clause. When the planner can prove this, it excludes (prunes) the partition from the query plan.
By using the EXPLAIN command and the enable_partition_pruning configuration parameter, it's possible to show the difference between a plan for which partitions have been pruned and one for which
they have not. A typical unoptimized plan for this type of table setup is:
SET enable_partition_pruning = off;
EXPLAIN SELECT count(*) FROM measurement WHERE logdate >= DATE
'2008-01-01';
QUERY PLAN
---------------------------------------------------------------------------------Aggregate (cost=188.76..188.77 rows=1 width=8)
-> Append (cost=0.00..181.05 rows=3085 width=0)
-> Seq Scan on measurement_y2006m02 (cost=0.00..33.12
rows=617 width=0)
Filter: (logdate >= '2008-01-01'::date)
-> Seq Scan on measurement_y2006m03 (cost=0.00..33.12
rows=617 width=0)
Filter: (logdate >= '2008-01-01'::date)
...
-> Seq Scan on measurement_y2007m11 (cost=0.00..33.12
rows=617 width=0)
Filter: (logdate >= '2008-01-01'::date)
-> Seq Scan on measurement_y2007m12 (cost=0.00..33.12
rows=617 width=0)
Filter: (logdate >= '2008-01-01'::date)
-> Seq Scan on measurement_y2008m01 (cost=0.00..33.12
rows=617 width=0)
Filter: (logdate >= '2008-01-01'::date)
Some or all of the partitions might use index scans instead of full-table sequential scans, but the point
here is that there is no need to scan the older partitions at all to answer this query. When we enable
partition pruning, we get a significantly cheaper plan that will deliver the same answer:
SET enable_partition_pruning = on;
EXPLAIN SELECT count(*) FROM measurement WHERE logdate >= DATE
'2008-01-01';
QUERY PLAN
---------------------------------------------------------------------------------Aggregate (cost=37.75..37.76 rows=1 width=8)
-> Seq Scan on measurement_y2008m01 (cost=0.00..33.12 rows=617
width=0)
Filter: (logdate >= '2008-01-01'::date)
107
Data Definition
Note that partition pruning is driven only by the constraints defined implicitly by the partition keys,
not by the presence of indexes. Therefore it isn't necessary to define indexes on the key columns.
Whether an index needs to be created for a given partition depends on whether you expect that queries
that scan the partition will generally scan a large part of the partition or just a small part. An index
will be helpful in the latter case but not the former.
Partition pruning can be performed not only during the planning of a given query, but also during its
execution. This is useful as it can allow more partitions to be pruned when clauses contain expressions
whose values are not known at query planning time, for example, parameters defined in a PREPARE
statement, using a value obtained from a subquery, or using a parameterized value on the inner side of
a nested loop join. Partition pruning during execution can be performed at any of the following times:
• During initialization of the query plan. Partition pruning can be performed here for parameter values
which are known during the initialization phase of execution. Partitions which are pruned during
this stage will not show up in the query's EXPLAIN or EXPLAIN ANALYZE. It is possible to determine the number of partitions which were removed during this phase by observing the “Subplans
Removed” property in the EXPLAIN output.
• During actual execution of the query plan. Partition pruning may also be performed here to remove
partitions using values which are only known during actual query execution. This includes values
from subqueries and values from execution-time parameters such as those from parameterized nested loop joins. Since the value of these parameters may change many times during the execution of
the query, partition pruning is performed whenever one of the execution parameters being used by
partition pruning changes. Determining if partitions were pruned during this phase requires careful
inspection of the loops property in the EXPLAIN ANALYZE output. Subplans corresponding to
different partitions may have different values for it depending on how many times each of them
was pruned during execution. Some may be shown as (never executed) if they were pruned
every time.
Partition pruning can be disabled using the enable_partition_pruning setting.
5.12.5. Partitioning and Constraint Exclusion
Constraint exclusion is a query optimization technique similar to partition pruning. While it is primarily used for partitioning implemented using the legacy inheritance method, it can be used for other
purposes, including with declarative partitioning.
Constraint exclusion works in a very similar way to partition pruning, except that it uses each table's
CHECK constraints — which gives it its name — whereas partition pruning uses the table's partition
bounds, which exist only in the case of declarative partitioning. Another difference is that constraint
exclusion is only applied at plan time; there is no attempt to remove partitions at execution time.
The fact that constraint exclusion uses CHECK constraints, which makes it slow compared to partition
pruning, can sometimes be used as an advantage: because constraints can be defined even on declaratively-partitioned tables, in addition to their internal partition bounds, constraint exclusion may be
able to elide additional partitions from the query plan.
The default (and recommended) setting of constraint_exclusion is neither on nor off, but an intermediate setting called partition, which causes the technique to be applied only to queries that are
likely to be working on inheritance partitioned tables. The on setting causes the planner to examine
CHECK constraints in all queries, even simple ones that are unlikely to benefit.
The following caveats apply to constraint exclusion:
• Constraint exclusion is only applied during query planning, unlike partition pruning, which can also
be applied during query execution.
• Constraint exclusion only works when the query's WHERE clause contains constants (or externally
supplied parameters). For example, a comparison against a non-immutable function such as CUR-
108
Data Definition
RENT_TIMESTAMP cannot be optimized, since the planner cannot know which child table the
function's value might fall into at run time.
• Keep the partitioning constraints simple, else the planner may not be able to prove that child tables
might not need to be visited. Use simple equality conditions for list partitioning, or simple range
tests for range partitioning, as illustrated in the preceding examples. A good rule of thumb is that
partitioning constraints should contain only comparisons of the partitioning column(s) to constants
using B-tree-indexable operators, because only B-tree-indexable column(s) are allowed in the partition key.
• All constraints on all children of the parent table are examined during constraint exclusion, so large
numbers of children are likely to increase query planning time considerably. So the legacy inheritance based partitioning will work well with up to perhaps a hundred child tables; don't try to use
many thousands of children.
5.12.6. Best Practices for Declarative Partitioning
The choice of how to partition a table should be made carefully, as the performance of query planning
and execution can be negatively affected by poor design.
One of the most critical design decisions will be the column or columns by which you partition your
data. Often the best choice will be to partition by the column or set of columns which most commonly
appear in WHERE clauses of queries being executed on the partitioned table. WHERE clauses that are
compatible with the partition bound constraints can be used to prune unneeded partitions. However,
you may be forced into making other decisions by requirements for the PRIMARY KEY or a UNIQUE
constraint. Removal of unwanted data is also a factor to consider when planning your partitioning
strategy. An entire partition can be detached fairly quickly, so it may be beneficial to design the partition strategy in such a way that all data to be removed at once is located in a single partition.
Choosing the target number of partitions that the table should be divided into is also a critical decision
to make. Not having enough partitions may mean that indexes remain too large and that data locality
remains poor which could result in low cache hit ratios. However, dividing the table into too many
partitions can also cause issues. Too many partitions can mean longer query planning times and higher
memory consumption during both query planning and execution, as further described below. When
choosing how to partition your table, it's also important to consider what changes may occur in the
future. For example, if you choose to have one partition per customer and you currently have a small
number of large customers, consider the implications if in several years you instead find yourself with
a large number of small customers. In this case, it may be better to choose to partition by HASH and
choose a reasonable number of partitions rather than trying to partition by LIST and hoping that the
number of customers does not increase beyond what it is practical to partition the data by.
Sub-partitioning can be useful to further divide partitions that are expected to become larger than other
partitions. Another option is to use range partitioning with multiple columns in the partition key. Either
of these can easily lead to excessive numbers of partitions, so restraint is advisable.
It is important to consider the overhead of partitioning during query planning and execution. The
query planner is generally able to handle partition hierarchies with up to a few thousand partitions
fairly well, provided that typical queries allow the query planner to prune all but a small number
of partitions. Planning times become longer and memory consumption becomes higher when more
partitions remain after the planner performs partition pruning. Another reason to be concerned about
having a large number of partitions is that the server's memory consumption may grow significantly
over time, especially if many sessions touch large numbers of partitions. That's because each partition
requires its metadata to be loaded into the local memory of each session that touches it.
With data warehouse type workloads, it can make sense to use a larger number of partitions than with
an OLTP type workload. Generally, in data warehouses, query planning time is less of a concern as
the majority of processing time is spent during query execution. With either of these two types of
workload, it is important to make the right decisions early, as re-partitioning large quantities of data
can be painfully slow. Simulations of the intended workload are often beneficial for optimizing the
109
Data Definition
partitioning strategy. Never just assume that more partitions are better than fewer partitions, nor viceversa.
5.13. Foreign Data
PostgreSQL implements portions of the SQL/MED specification, allowing you to access data that
resides outside PostgreSQL using regular SQL queries. Such data is referred to as foreign data. (Note
that this usage is not to be confused with foreign keys, which are a type of constraint within the
database.)
Foreign data is accessed with help from a foreign data wrapper. A foreign data wrapper is a library
that can communicate with an external data source, hiding the details of connecting to the data source
and obtaining data from it. There are some foreign data wrappers available as contrib modules; see
Appendix F. Other kinds of foreign data wrappers might be found as third party products. If none of
the existing foreign data wrappers suit your needs, you can write your own; see Chapter 57.
To access foreign data, you need to create a foreign server object, which defines how to connect to
a particular external data source according to the set of options used by its supporting foreign data
wrapper. Then you need to create one or more foreign tables, which define the structure of the remote
data. A foreign table can be used in queries just like a normal table, but a foreign table has no storage
in the PostgreSQL server. Whenever it is used, PostgreSQL asks the foreign data wrapper to fetch data
from the external source, or transmit data to the external source in the case of update commands.
Accessing remote data may require authenticating to the external data source. This information can
be provided by a user mapping, which can provide additional data such as user names and passwords
based on the current PostgreSQL role.
For additional information, see CREATE FOREIGN DATA WRAPPER, CREATE SERVER, CREATE USER MAPPING, CREATE FOREIGN TABLE, and IMPORT FOREIGN SCHEMA.
5.14. Other Database Objects
Tables are the central objects in a relational database structure, because they hold your data. But they
are not the only objects that exist in a database. Many other kinds of objects can be created to make the
use and management of the data more efficient or convenient. They are not discussed in this chapter,
but we give you a list here so that you are aware of what is possible:
• Views
• Functions, procedures, and operators
• Data types and domains
• Triggers and rewrite rules
Detailed information on these topics appears in Part V.
5.15. Dependency Tracking
When you create complex database structures involving many tables with foreign key constraints,
views, triggers, functions, etc. you implicitly create a net of dependencies between the objects. For
instance, a table with a foreign key constraint depends on the table it references.
To ensure the integrity of the entire database structure, PostgreSQL makes sure that you cannot drop
objects that other objects still depend on. For example, attempting to drop the products table we considered in Section 5.5.5, with the orders table depending on it, would result in an error message like
this:
110
Data Definition
DROP TABLE products;
ERROR: cannot drop table products because other objects depend on
it
DETAIL: constraint orders_product_no_fkey on table orders depends
on table products
HINT: Use DROP ... CASCADE to drop the dependent objects too.
The error message contains a useful hint: if you do not want to bother deleting all the dependent objects
individually, you can run:
DROP TABLE products CASCADE;
and all the dependent objects will be removed, as will any objects that depend on them, recursively.
In this case, it doesn't remove the orders table, it only removes the foreign key constraint. It stops
there because nothing depends on the foreign key constraint. (If you want to check what DROP ...
CASCADE will do, run DROP without CASCADE and read the DETAIL output.)
Almost all DROP commands in PostgreSQL support specifying CASCADE. Of course, the nature of
the possible dependencies varies with the type of the object. You can also write RESTRICT instead
of CASCADE to get the default behavior, which is to prevent dropping objects that any other objects
depend on.
Note
According to the SQL standard, specifying either RESTRICT or CASCADE is required in
a DROP command. No database system actually enforces that rule, but whether the default
behavior is RESTRICT or CASCADE varies across systems.
If a DROP command lists multiple objects, CASCADE is only required when there are dependencies
outside the specified group. For example, when saying DROP TABLE tab1, tab2 the existence
of a foreign key referencing tab1 from tab2 would not mean that CASCADE is needed to succeed.
For a user-defined function or procedure whose body is defined as a string literal, PostgreSQL tracks
dependencies associated with the function's externally-visible properties, such as its argument and
result types, but not dependencies that could only be known by examining the function body. As an
example, consider this situation:
CREATE TYPE rainbow AS ENUM ('red', 'orange', 'yellow',
'green', 'blue', 'purple');
CREATE TABLE my_colors (color rainbow, note text);
CREATE FUNCTION get_color_note (rainbow) RETURNS text AS
'SELECT note FROM my_colors WHERE color = $1'
LANGUAGE SQL;
(See Section 36.5 for an explanation of SQL-language functions.) PostgreSQL will be aware that the
get_color_note function depends on the rainbow type: dropping the type would force dropping
the function, because its argument type would no longer be defined. But PostgreSQL will not consider
get_color_note to depend on the my_colors table, and so will not drop the function if the table
is dropped. While there are disadvantages to this approach, there are also benefits. The function is still
valid in some sense if the table is missing, though executing it would cause an error; creating a new
table of the same name would allow the function to work again.
111
Data Definition
On the other hand, for an SQL-language function or procedure whose body is written in SQL-standard
style, the body is parsed at function definition time and all dependencies recognized by the parser are
stored. Thus, if we write the function above as
CREATE FUNCTION get_color_note (rainbow) RETURNS text
BEGIN ATOMIC
SELECT note FROM my_colors WHERE color = $1;
END;
then the function's dependency on the my_colors table will be known and enforced by DROP.
112
Chapter 6. Data Manipulation
The previous chapter discussed how to create tables and other structures to hold your data. Now it is
time to fill the tables with data. This chapter covers how to insert, update, and delete table data. The
chapter after this will finally explain how to extract your long-lost data from the database.
6.1. Inserting Data
When a table is created, it contains no data. The first thing to do before a database can be of much use
is to insert data. Data is inserted one row at a time. You can also insert more than one row in a single
command, but it is not possible to insert something that is not a complete row. Even if you know only
some column values, a complete row must be created.
To create a new row, use the INSERT command. The command requires the table name and column
values. For example, consider the products table from Chapter 5:
CREATE TABLE products (
product_no integer,
name text,
price numeric
);
An example command to insert a row would be:
INSERT INTO products VALUES (1, 'Cheese', 9.99);
The data values are listed in the order in which the columns appear in the table, separated by commas.
Usually, the data values will be literals (constants), but scalar expressions are also allowed.
The above syntax has the drawback that you need to know the order of the columns in the table. To
avoid this you can also list the columns explicitly. For example, both of the following commands have
the same effect as the one above:
INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese',
9.99);
INSERT INTO products (name, price, product_no) VALUES ('Cheese',
9.99, 1);
Many users consider it good practice to always list the column names.
If you don't have values for all the columns, you can omit some of them. In that case, the columns will
be filled with their default values. For example:
INSERT INTO products (product_no, name) VALUES (1, 'Cheese');
INSERT INTO products VALUES (1, 'Cheese');
The second form is a PostgreSQL extension. It fills the columns from the left with as many values as
are given, and the rest will be defaulted.
For clarity, you can also request default values explicitly, for individual columns or for the entire row:
INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese',
DEFAULT);
113
Data Manipulation
INSERT INTO products DEFAULT VALUES;
You can insert multiple rows in a single command:
INSERT INTO products (product_no, name, price) VALUES
(1, 'Cheese', 9.99),
(2, 'Bread', 1.99),
(3, 'Milk', 2.99);
It is also possible to insert the result of a query (which might be no rows, one row, or many rows):
INSERT INTO products (product_no, name, price)
SELECT product_no, name, price FROM new_products
WHERE release_date = 'today';
This provides the full power of the SQL query mechanism (Chapter 7) for computing the rows to be
inserted.
Tip
When inserting a lot of data at the same time, consider using the COPY command. It is not
as flexible as the INSERT command, but is more efficient. Refer to Section 14.4 for more
information on improving bulk loading performance.
6.2. Updating Data
The modification of data that is already in the database is referred to as updating. You can update
individual rows, all the rows in a table, or a subset of all rows. Each column can be updated separately;
the other columns are not affected.
To update existing rows, use the UPDATE command. This requires three pieces of information:
1. The name of the table and column to update
2. The new value of the column
3. Which row(s) to update
Recall from Chapter 5 that SQL does not, in general, provide a unique identifier for rows. Therefore it
is not always possible to directly specify which row to update. Instead, you specify which conditions
a row must meet in order to be updated. Only if you have a primary key in the table (independent
of whether you declared it or not) can you reliably address individual rows by choosing a condition
that matches the primary key. Graphical database access tools rely on this fact to allow you to update
rows individually.
For example, this command updates all products that have a price of 5 to have a price of 10:
UPDATE products SET price = 10 WHERE price = 5;
This might cause zero, one, or many rows to be updated. It is not an error to attempt an update that
does not match any rows.
Let's look at that command in detail. First is the key word UPDATE followed by the table name. As
usual, the table name can be schema-qualified, otherwise it is looked up in the path. Next is the key
word SET followed by the column name, an equal sign, and the new column value. The new column
value can be any scalar expression, not just a constant. For example, if you want to raise the price of
all products by 10% you could use:
114
Data Manipulation
UPDATE products SET price = price * 1.10;
As you see, the expression for the new value can refer to the existing value(s) in the row. We also left
out the WHERE clause. If it is omitted, it means that all rows in the table are updated. If it is present,
only those rows that match the WHERE condition are updated. Note that the equals sign in the SET
clause is an assignment while the one in the WHERE clause is a comparison, but this does not create any
ambiguity. Of course, the WHERE condition does not have to be an equality test. Many other operators
are available (see Chapter 9). But the expression needs to evaluate to a Boolean result.
You can update more than one column in an UPDATE command by listing more than one assignment
in the SET clause. For example:
UPDATE mytable SET a = 5, b = 3, c = 1 WHERE a > 0;
6.3. Deleting Data
So far we have explained how to add data to tables and how to change data. What remains is to discuss
how to remove data that is no longer needed. Just as adding data is only possible in whole rows, you
can only remove entire rows from a table. In the previous section we explained that SQL does not
provide a way to directly address individual rows. Therefore, removing rows can only be done by
specifying conditions that the rows to be removed have to match. If you have a primary key in the table
then you can specify the exact row. But you can also remove groups of rows matching a condition,
or you can remove all rows in the table at once.
You use the DELETE command to remove rows; the syntax is very similar to the UPDATE command.
For instance, to remove all rows from the products table that have a price of 10, use:
DELETE FROM products WHERE price = 10;
If you simply write:
DELETE FROM products;
then all rows in the table will be deleted! Caveat programmer.
6.4. Returning Data from Modified Rows
Sometimes it is useful to obtain data from modified rows while they are being manipulated. The
INSERT, UPDATE, DELETE, and MERGE commands all have an optional RETURNING clause that
supports this. Use of RETURNING avoids performing an extra database query to collect the data, and
is especially valuable when it would otherwise be difficult to identify the modified rows reliably.
The allowed contents of a RETURNING clause are the same as a SELECT command's output list (see
Section 7.3). It can contain column names of the command's target table, or value expressions using
those columns. A common shorthand is RETURNING *, which selects all columns of the target table
in order.
In an INSERT, the data available to RETURNING is the row as it was inserted. This is not so useful in
trivial inserts, since it would just repeat the data provided by the client. But it can be very handy when
relying on computed default values. For example, when using a serial column to provide unique
identifiers, RETURNING can return the ID assigned to a new row:
CREATE TABLE users (firstname text, lastname text, id serial
primary key);
115
Data Manipulation
INSERT INTO users (firstname, lastname) VALUES ('Joe', 'Cool')
RETURNING id;
The RETURNING clause is also very useful with INSERT ... SELECT.
In an UPDATE, the data available to RETURNING is the new content of the modified row. For example:
UPDATE products SET price = price * 1.10
WHERE price <= 99.99
RETURNING name, price AS new_price;
In a DELETE, the data available to RETURNING is the content of the deleted row. For example:
DELETE FROM products
WHERE obsoletion_date = 'today'
RETURNING *;
In a MERGE, the data available to RETURNING is the content of the source row plus the content of
the inserted, updated, or deleted target row. Since it is quite common for the source and target to have
many of the same columns, specifying RETURNING * can lead to a lot of duplicated columns, so it
is often more useful to qualify it so as to return just the source or target row. For example:
MERGE INTO products p USING new_products n ON p.product_no =
n.product_no
WHEN NOT MATCHED THEN INSERT VALUES (n.product_no, n.name,
n.price)
WHEN MATCHED THEN UPDATE SET name = n.name, price = n.price
RETURNING p.*;
If there are triggers (Chapter 37) on the target table, the data available to RETURNING is the row as
modified by the triggers. Thus, inspecting columns computed by triggers is another common use-case
for RETURNING.
116
Chapter 7. Queries
The previous chapters explained how to create tables, how to fill them with data, and how to manipulate
that data. Now we finally discuss how to retrieve the data from the database.
7.1. Overview
The process of retrieving or the command to retrieve data from a database is called a query. In SQL
the SELECT command is used to specify queries. The general syntax of the SELECT command is
[WITH with_queries] SELECT select_list FROM table_expression
[sort_specification]
The following sections describe the details of the select list, the table expression, and the sort specification. WITH queries are treated last since they are an advanced feature.
A simple kind of query has the form:
SELECT * FROM table1;
Assuming that there is a table called table1, this command would retrieve all rows and all userdefined columns from table1. (The method of retrieval depends on the client application. For example, the psql program will display an ASCII-art table on the screen, while client libraries will offer
functions to extract individual values from the query result.) The select list specification * means all
columns that the table expression happens to provide. A select list can also select a subset of the available columns or make calculations using the columns. For example, if table1 has columns named
a, b, and c (and perhaps others) you can make the following query:
SELECT a, b + c FROM table1;
(assuming that b and c are of a numerical data type). See Section 7.3 for more details.
FROM table1 is a simple kind of table expression: it reads just one table. In general, table expressions can be complex constructs of base tables, joins, and subqueries. But you can also omit the table
expression entirely and use the SELECT command as a calculator:
SELECT 3 * 4;
This is more useful if the expressions in the select list return varying results. For example, you could
call a function this way:
SELECT random();
7.2. Table Expressions
A table expression computes a table. The table expression contains a FROM clause that is optionally
followed by WHERE, GROUP BY, and HAVING clauses. Trivial table expressions simply refer to a
table on disk, a so-called base table, but more complex expressions can be used to modify or combine
base tables in various ways.
The optional WHERE, GROUP BY, and HAVING clauses in the table expression specify a pipeline of
successive transformations performed on the table derived in the FROM clause. All these transforma-
117
Queries
tions produce a virtual table that provides the rows that are passed to the select list to compute the
output rows of the query.
7.2.1. The FROM Clause
The FROM clause derives a table from one or more other tables given in a comma-separated table
reference list.
FROM table_reference [, table_reference [, ...]]
A table reference can be a table name (possibly schema-qualified), or a derived table such as a subquery, a JOIN construct, or complex combinations of these. If more than one table reference is listed
in the FROM clause, the tables are cross-joined (that is, the Cartesian product of their rows is formed;
see below). The result of the FROM list is an intermediate virtual table that can then be subject to transformations by the WHERE, GROUP BY, and HAVING clauses and is finally the result of the overall
table expression.
When a table reference names a table that is the parent of a table inheritance hierarchy, the table
reference produces rows of not only that table but all of its descendant tables, unless the key word
ONLY precedes the table name. However, the reference produces only the columns that appear in the
named table — any columns added in subtables are ignored.
Instead of writing ONLY before the table name, you can write * after the table name to explicitly
specify that descendant tables are included. There is no real reason to use this syntax any more, because searching descendant tables is now always the default behavior. However, it is supported for
compatibility with older releases.
7.2.1.1. Joined Tables
A joined table is a table derived from two other (real or derived) tables according to the rules of the
particular join type. Inner, outer, and cross-joins are available. The general syntax of a joined table is
T1 join_type T2 [ join_condition ]
Joins of all types can be chained together, or nested: either or both T1 and T2 can be joined tables.
Parentheses can be used around JOIN clauses to control the join order. In the absence of parentheses,
JOIN clauses nest left-to-right.
Join Types
Cross join
T1 CROSS JOIN T2
For every possible combination of rows from T1 and T2 (i.e., a Cartesian product), the joined
table will contain a row consisting of all columns in T1 followed by all columns in T2. If the
tables have N and M rows respectively, the joined table will have N * M rows.
FROM T1 CROSS JOIN T2 is equivalent to FROM T1 INNER JOIN T2 ON TRUE (see
below). It is also equivalent to FROM T1, T2.
Note
This latter equivalence does not hold exactly when more than two tables appear, because
JOIN binds more tightly than comma. For example FROM T1 CROSS JOIN T2
INNER JOIN T3 ON condition is not the same as FROM T1, T2 INNER JOIN
118
Queries
T3 ON condition because the condition can reference T1 in the first case but
not the second.
Qualified joins
T1 { [INNER] | { LEFT | RIGHT | FULL } [OUTER] } JOIN T2
ON boolean_expression
T1 { [INNER] | { LEFT | RIGHT | FULL } [OUTER] } JOIN T2 USING
( join column list )
T1 NATURAL { [INNER] | { LEFT | RIGHT | FULL } [OUTER] } JOIN T2
The words INNER and OUTER are optional in all forms. INNER is the default; LEFT, RIGHT,
and FULL imply an outer join.
The join condition is specified in the ON or USING clause, or implicitly by the word NATURAL.
The join condition determines which rows from the two source tables are considered to “match”,
as explained in detail below.
The possible types of qualified join are:
INNER JOIN
For each row R1 of T1, the joined table has a row for each row in T2 that satisfies the join
condition with R1.
LEFT OUTER JOIN
First, an inner join is performed. Then, for each row in T1 that does not satisfy the join
condition with any row in T2, a joined row is added with null values in columns of T2. Thus,
the joined table always has at least one row for each row in T1.
RIGHT OUTER JOIN
First, an inner join is performed. Then, for each row in T2 that does not satisfy the join
condition with any row in T1, a joined row is added with null values in columns of T1. This
is the converse of a left join: the result table will always have a row for each row in T2.
FULL OUTER JOIN
First, an inner join is performed. Then, for each row in T1 that does not satisfy the join
condition with any row in T2, a joined row is added with null values in columns of T2. Also,
for each row of T2 that does not satisfy the join condition with any row in T1, a joined row
with null values in the columns of T1 is added.
The ON clause is the most general kind of join condition: it takes a Boolean value expression of
the same kind as is used in a WHERE clause. A pair of rows from T1 and T2 match if the ON
expression evaluates to true.
The USING clause is a shorthand that allows you to take advantage of the specific situation where
both sides of the join use the same name for the joining column(s). It takes a comma-separated
list of the shared column names and forms a join condition that includes an equality comparison
for each one. For example, joining T1 and T2 with USING (a, b) produces the join condition
ON T1.a = T2.a AND T1.b = T2.b.
Furthermore, the output of JOIN USING suppresses redundant columns: there is no need to print
both of the matched columns, since they must have equal values. While JOIN ON produces all
columns from T1 followed by all columns from T2, JOIN USING produces one output column
for each of the listed column pairs (in the listed order), followed by any remaining columns from
T1, followed by any remaining columns from T2.
119
Queries
Finally, NATURAL is a shorthand form of USING: it forms a USING list consisting of all column
names that appear in both input tables. As with USING, these columns appear only once in the
output table. If there are no common column names, NATURAL JOIN behaves like CROSS
JOIN.
Note
USING is reasonably safe from column changes in the joined relations since only the listed
columns are combined. NATURAL is considerably more risky since any schema changes
to either relation that cause a new matching column name to be present will cause the join
to combine that new column as well.
To put this together, assume we have tables t1:
num | name
-----+-----1 | a
2 | b
3 | c
and t2:
num | value
-----+------1 | xxx
3 | yyy
5 | zzz
then we get the following results for the various joins:
=> SELECT * FROM t1 CROSS JOIN t2;
num | name | num | value
-----+------+-----+------1 | a
|
1 | xxx
1 | a
|
3 | yyy
1 | a
|
5 | zzz
2 | b
|
1 | xxx
2 | b
|
3 | yyy
2 | b
|
5 | zzz
3 | c
|
1 | xxx
3 | c
|
3 | yyy
3 | c
|
5 | zzz
(9 rows)
=> SELECT * FROM t1 INNER JOIN t2 ON t1.num = t2.num;
num | name | num | value
-----+------+-----+------1 | a
|
1 | xxx
3 | c
|
3 | yyy
(2 rows)
=> SELECT * FROM t1 INNER JOIN t2 USING (num);
num | name | value
-----+------+------1 | a
| xxx
120
Queries
3 | c
(2 rows)
| yyy
=> SELECT * FROM t1 NATURAL INNER JOIN t2;
num | name | value
-----+------+------1 | a
| xxx
3 | c
| yyy
(2 rows)
=> SELECT * FROM t1 LEFT JOIN t2 ON t1.num = t2.num;
num | name | num | value
-----+------+-----+------1 | a
|
1 | xxx
2 | b
|
|
3 | c
|
3 | yyy
(3 rows)
=> SELECT * FROM t1 LEFT JOIN t2 USING (num);
num | name | value
-----+------+------1 | a
| xxx
2 | b
|
3 | c
| yyy
(3 rows)
=> SELECT * FROM t1 RIGHT JOIN t2 ON t1.num = t2.num;
num | name | num | value
-----+------+-----+------1 | a
|
1 | xxx
3 | c
|
3 | yyy
|
|
5 | zzz
(3 rows)
=> SELECT * FROM t1 FULL JOIN t2 ON t1.num = t2.num;
num | name | num | value
-----+------+-----+------1 | a
|
1 | xxx
2 | b
|
|
3 | c
|
3 | yyy
|
|
5 | zzz
(4 rows)
The join condition specified with ON can also contain conditions that do not relate directly to the join.
This can prove useful for some queries but needs to be thought out carefully. For example:
=> SELECT * FROM t1 LEFT JOIN t2 ON t1.num = t2.num AND t2.value =
'xxx';
num | name | num | value
-----+------+-----+------1 | a
|
1 | xxx
2 | b
|
|
3 | c
|
|
(3 rows)
Notice that placing the restriction in the WHERE clause produces a different result:
121
Queries
=> SELECT * FROM t1 LEFT JOIN t2 ON t1.num = t2.num WHERE t2.value
= 'xxx';
num | name | num | value
-----+------+-----+------1 | a
|
1 | xxx
(1 row)
This is because a restriction placed in the ON clause is processed before the join, while a restriction
placed in the WHERE clause is processed after the join. That does not matter with inner joins, but it
matters a lot with outer joins.
7.2.1.2. Table and Column Aliases
A temporary name can be given to tables and complex table references to be used for references to
the derived table in the rest of the query. This is called a table alias.
To create a table alias, write
FROM table_reference AS alias
or
FROM table_reference alias
The AS key word is optional noise. alias can be any identifier.
A typical application of table aliases is to assign short identifiers to long table names to keep the join
clauses readable. For example:
SELECT * FROM some_very_long_table_name s JOIN
another_fairly_long_name a ON s.id = a.num;
The alias becomes the new name of the table reference so far as the current query is concerned — it
is not allowed to refer to the table by the original name elsewhere in the query. Thus, this is not valid:
SELECT * FROM my_table AS m WHERE my_table.a > 5;
-- wrong
Table aliases are mainly for notational convenience, but it is necessary to use them when joining a
table to itself, e.g.:
SELECT * FROM people AS mother JOIN people AS child ON mother.id =
child.mother_id;
Parentheses are used to resolve ambiguities. In the following example, the first statement assigns the
alias b to the second instance of my_table, but the second statement assigns the alias to the result
of the join:
SELECT * FROM my_table AS a CROSS JOIN my_table AS b ...
SELECT * FROM (my_table AS a CROSS JOIN my_table) AS b ...
Another form of table aliasing gives temporary names to the columns of the table, as well as the table
itself:
FROM table_reference [AS] alias ( column1 [, column2 [, ...]] )
122
Queries
If fewer column aliases are specified than the actual table has columns, the remaining columns are not
renamed. This syntax is especially useful for self-joins or subqueries.
When an alias is applied to the output of a JOIN clause, the alias hides the original name(s) within
the JOIN. For example:
SELECT a.* FROM my_table AS a JOIN your_table AS b ON ...
is valid SQL, but:
SELECT a.* FROM (my_table AS a JOIN your_table AS b ON ...) AS c
is not valid; the table alias a is not visible outside the alias c.
7.2.1.3. Subqueries
Subqueries specifying a derived table must be enclosed in parentheses. They may be assigned a table
alias name, and optionally column alias names (as in Section 7.2.1.2). For example:
FROM (SELECT * FROM table1) AS alias_name
This example is equivalent to FROM table1 AS alias_name. More interesting cases, which
cannot be reduced to a plain join, arise when the subquery involves grouping or aggregation.
A subquery can also be a VALUES list:
FROM (VALUES ('anne', 'smith'), ('bob', 'jones'), ('joe', 'blow'))
AS names(first, last)
Again, a table alias is optional. Assigning alias names to the columns of the VALUES list is optional,
but is good practice. For more information see Section 7.7.
According to the SQL standard, a table alias name must be supplied for a subquery. PostgreSQL allows
AS and the alias to be omitted, but writing one is good practice in SQL code that might be ported to
another system.
7.2.1.4. Table Functions
Table functions are functions that produce a set of rows, made up of either base data types (scalar
types) or composite data types (table rows). They are used like a table, view, or subquery in the FROM
clause of a query. Columns returned by table functions can be included in SELECT, JOIN, or WHERE
clauses in the same manner as columns of a table, view, or subquery.
Table functions may also be combined using the ROWS FROM syntax, with the results returned in
parallel columns; the number of result rows in this case is that of the largest function result, with
smaller results padded with null values to match.
function_call [WITH ORDINALITY] [[AS] table_alias [(column_alias
[, ... ])]]
ROWS FROM( function_call [, ... ] ) [WITH ORDINALITY]
[[AS] table_alias [(column_alias [, ... ])]]
If the WITH ORDINALITY clause is specified, an additional column of type bigint will be added
to the function result columns. This column numbers the rows of the function result set, starting from
1. (This is a generalization of the SQL-standard syntax for UNNEST ... WITH ORDINALITY.)
123
Queries
By default, the ordinal column is called ordinality, but a different column name can be assigned
to it using an AS clause.
The special table function UNNEST may be called with any number of array parameters, and it returns
a corresponding number of columns, as if UNNEST (Section 9.19) had been called on each parameter
separately and combined using the ROWS FROM construct.
UNNEST( array_expression [, ... ] ) [WITH ORDINALITY]
[[AS] table_alias [(column_alias [, ... ])]]
If no table_alias is specified, the function name is used as the table name; in the case of a ROWS
FROM() construct, the first function's name is used.
If column aliases are not supplied, then for a function returning a base data type, the column name is
also the same as the function name. For a function returning a composite type, the result columns get
the names of the individual attributes of the type.
Some examples:
CREATE TABLE foo (fooid int, foosubid int, fooname text);
CREATE FUNCTION getfoo(int) RETURNS SETOF foo AS $$
SELECT * FROM foo WHERE fooid = $1;
$$ LANGUAGE SQL;
SELECT * FROM getfoo(1) AS t1;
SELECT * FROM foo
WHERE foosubid IN (
SELECT foosubid
FROM getfoo(foo.fooid) z
WHERE z.fooid = foo.fooid
);
CREATE VIEW vw_getfoo AS SELECT * FROM getfoo(1);
SELECT * FROM vw_getfoo;
In some cases it is useful to define table functions that can return different column sets depending on
how they are invoked. To support this, the table function can be declared as returning the pseudo-type
record with no OUT parameters. When such a function is used in a query, the expected row structure
must be specified in the query itself, so that the system can know how to parse and plan the query.
This syntax looks like:
function_call [AS] alias (column_definition [, ... ])
function_call AS [alias] (column_definition [, ... ])
ROWS FROM( ... function_call AS (column_definition [, ... ])
[, ... ] )
When not using the ROWS FROM() syntax, the column_definition list replaces the column
alias list that could otherwise be attached to the FROM item; the names in the column definitions serve
as column aliases. When using the ROWS FROM() syntax, a column_definition list can be
attached to each member function separately; or if there is only one member function and no WITH
ORDINALITY clause, a column_definition list can be written in place of a column alias list
following ROWS FROM().
Consider this example:
124
Queries
SELECT *
FROM dblink('dbname=mydb', 'SELECT proname, prosrc FROM
pg_proc')
AS t1(proname name, prosrc text)
WHERE proname LIKE 'bytea%';
The dblink function (part of the dblink module) executes a remote query. It is declared to return
record since it might be used for any kind of query. The actual column set must be specified in the
calling query so that the parser knows, for example, what * should expand to.
This example uses ROWS FROM:
SELECT *
FROM ROWS FROM
(
json_to_recordset('[{"a":40,"b":"foo"},
{"a":"100","b":"bar"}]')
AS (a INTEGER, b TEXT),
generate_series(1, 3)
) AS x (p, q, s)
ORDER BY p;
p | q | s
-----+-----+--40 | foo | 1
100 | bar | 2
|
| 3
It joins two functions into a single FROM target. json_to_recordset() is instructed to return
two columns, the first integer and the second text. The result of generate_series() is used
directly. The ORDER BY clause sorts the column values as integers.
7.2.1.5. LATERAL Subqueries
Subqueries appearing in FROM can be preceded by the key word LATERAL. This allows them to reference columns provided by preceding FROM items. (Without LATERAL, each subquery is evaluated
independently and so cannot cross-reference any other FROM item.)
Table functions appearing in FROM can also be preceded by the key word LATERAL, but for functions
the key word is optional; the function's arguments can contain references to columns provided by
preceding FROM items in any case.
A LATERAL item can appear at the top level in the FROM list, or within a JOIN tree. In the latter case
it can also refer to any items that are on the left-hand side of a JOIN that it is on the right-hand side of.
When a FROM item contains LATERAL cross-references, evaluation proceeds as follows: for each
row of the FROM item providing the cross-referenced column(s), or set of rows of multiple FROM
items providing the columns, the LATERAL item is evaluated using that row or row set's values of
the columns. The resulting row(s) are joined as usual with the rows they were computed from. This is
repeated for each row or set of rows from the column source table(s).
A trivial example of LATERAL is
SELECT * FROM foo, LATERAL (SELECT * FROM bar WHERE bar.id =
foo.bar_id) ss;
This is not especially useful since it has exactly the same result as the more conventional
125
Queries
SELECT * FROM foo, bar WHERE bar.id = foo.bar_id;
LATERAL is primarily useful when the cross-referenced column is necessary for computing the row(s)
to be joined. A common application is providing an argument value for a set-returning function. For
example, supposing that vertices(polygon) returns the set of vertices of a polygon, we could
identify close-together vertices of polygons stored in a table with:
SELECT p1.id, p2.id, v1, v2
FROM polygons p1, polygons p2,
LATERAL vertices(p1.poly) v1,
LATERAL vertices(p2.poly) v2
WHERE (v1 <-> v2) < 10 AND p1.id != p2.id;
This query could also be written
SELECT p1.id, p2.id, v1, v2
FROM polygons p1 CROSS JOIN LATERAL vertices(p1.poly) v1,
polygons p2 CROSS JOIN LATERAL vertices(p2.poly) v2
WHERE (v1 <-> v2) < 10 AND p1.id != p2.id;
or in several other equivalent formulations. (As already mentioned, the LATERAL key word is unnecessary in this example, but we use it for clarity.)
It is often particularly handy to LEFT JOIN to a LATERAL subquery, so that source rows will appear
in the result even if the LATERAL subquery produces no rows for them. For example, if get_product_names() returns the names of products made by a manufacturer, but some manufacturers in
our table currently produce no products, we could find out which ones those are like this:
SELECT m.name
FROM manufacturers m LEFT JOIN LATERAL get_product_names(m.id)
pname ON true
WHERE pname IS NULL;
7.2.2. The WHERE Clause
The syntax of the WHERE clause is
WHERE search_condition
where search_condition is any value expression (see Section 4.2) that returns a value of type
boolean.
After the processing of the FROM clause is done, each row of the derived virtual table is checked
against the search condition. If the result of the condition is true, the row is kept in the output table,
otherwise (i.e., if the result is false or null) it is discarded. The search condition typically references
at least one column of the table generated in the FROM clause; this is not required, but otherwise the
WHERE clause will be fairly useless.
Note
The join condition of an inner join can be written either in the WHERE clause or in the JOIN
clause. For example, these table expressions are equivalent:
126
Queries
FROM a, b WHERE a.id = b.id AND b.val > 5
and:
FROM a INNER JOIN b ON (a.id = b.id) WHERE b.val > 5
or perhaps even:
FROM a NATURAL JOIN b WHERE b.val > 5
Which one of these you use is mainly a matter of style. The JOIN syntax in the FROM clause
is probably not as portable to other SQL database management systems, even though it is in
the SQL standard. For outer joins there is no choice: they must be done in the FROM clause.
The ON or USING clause of an outer join is not equivalent to a WHERE condition, because
it results in the addition of rows (for unmatched input rows) as well as the removal of rows
in the final result.
Here are some examples of WHERE clauses:
SELECT ... FROM fdt WHERE c1 > 5
SELECT ... FROM fdt WHERE c1 IN (1, 2, 3)
SELECT ... FROM fdt WHERE c1 IN (SELECT c1 FROM t2)
SELECT ... FROM fdt WHERE c1 IN (SELECT c3 FROM t2 WHERE c2 =
fdt.c1 + 10)
SELECT ... FROM fdt WHERE c1 BETWEEN (SELECT c3 FROM t2 WHERE c2 =
fdt.c1 + 10) AND 100
SELECT ... FROM fdt WHERE EXISTS (SELECT c1 FROM t2 WHERE c2 >
fdt.c1)
fdt is the table derived in the FROM clause. Rows that do not meet the search condition of the WHERE
clause are eliminated from fdt. Notice the use of scalar subqueries as value expressions. Just like any
other query, the subqueries can employ complex table expressions. Notice also how fdt is referenced
in the subqueries. Qualifying c1 as fdt.c1 is only necessary if c1 is also the name of a column
in the derived input table of the subquery. But qualifying the column name adds clarity even when
it is not needed. This example shows how the column naming scope of an outer query extends into
its inner queries.
7.2.3. The GROUP BY and HAVING Clauses
After passing the WHERE filter, the derived input table might be subject to grouping, using the GROUP
BY clause, and elimination of group rows using the HAVING clause.
SELECT select_list
FROM ...
[WHERE ...]
GROUP BY grouping_column_reference
[, grouping_column_reference]...
The GROUP BY clause is used to group together those rows in a table that have the same values in
all the columns listed. The order in which the columns are listed does not matter. The effect is to
127
Queries
combine each set of rows having common values into one group row that represents all rows in the
group. This is done to eliminate redundancy in the output and/or compute aggregates that apply to
these groups. For instance:
=> SELECT * FROM test1;
x | y
---+--a | 3
c | 2
b | 5
a | 1
(4 rows)
=> SELECT x FROM test1 GROUP BY x;
x
--a
b
c
(3 rows)
In the second query, we could not have written SELECT * FROM test1 GROUP BY x, because
there is no single value for the column y that could be associated with each group. The grouped-by
columns can be referenced in the select list since they have a single value in each group.
In general, if a table is grouped, columns that are not listed in GROUP BY cannot be referenced except
in aggregate expressions. An example with aggregate expressions is:
=> SELECT x, sum(y) FROM test1 GROUP BY x;
x | sum
---+----a |
4
b |
5
c |
2
(3 rows)
Here sum is an aggregate function that computes a single value over the entire group. More information
about the available aggregate functions can be found in Section 9.21.
Tip
Grouping without aggregate expressions effectively calculates the set of distinct values in a
column. This can also be achieved using the DISTINCT clause (see Section 7.3.3).
Here is another example: it calculates the total sales for each product (rather than the total sales of
all products):
SELECT product_id, p.name, (sum(s.units) * p.price) AS sales
FROM products p LEFT JOIN sales s USING (product_id)
GROUP BY product_id, p.name, p.price;
In this example, the columns product_id, p.name, and p.price must be in the GROUP BY
clause since they are referenced in the query select list (but see below). The column s.units does
not have to be in the GROUP BY list since it is only used in an aggregate expression (sum(...)),
128
Queries
which represents the sales of a product. For each product, the query returns a summary row about all
sales of the product.
If the products table is set up so that, say, product_id is the primary key, then it would be enough to
group by product_id in the above example, since name and price would be functionally dependent
on the product ID, and so there would be no ambiguity about which name and price value to return
for each product ID group.
In strict SQL, GROUP BY can only group by columns of the source table but PostgreSQL extends
this to also allow GROUP BY to group by columns in the select list. Grouping by value expressions
instead of simple column names is also allowed.
If a table has been grouped using GROUP BY, but only certain groups are of interest, the HAVING
clause can be used, much like a WHERE clause, to eliminate groups from the result. The syntax is:
SELECT select_list FROM ... [WHERE ...] GROUP BY ...
HAVING boolean_expression
Expressions in the HAVING clause can refer both to grouped expressions and to ungrouped expressions
(which necessarily involve an aggregate function).
Example:
=> SELECT x, sum(y) FROM test1 GROUP BY x HAVING sum(y) > 3;
x | sum
---+----a |
4
b |
5
(2 rows)
=> SELECT x, sum(y) FROM test1 GROUP BY x HAVING x < 'c';
x | sum
---+----a |
4
b |
5
(2 rows)
Again, a more realistic example:
SELECT product_id, p.name, (sum(s.units) * (p.price - p.cost)) AS
profit
FROM products p LEFT JOIN sales s USING (product_id)
WHERE s.date > CURRENT_DATE - INTERVAL '4 weeks'
GROUP BY product_id, p.name, p.price, p.cost
HAVING sum(p.price * s.units) > 5000;
In the example above, the WHERE clause is selecting rows by a column that is not grouped (the expression is only true for sales during the last four weeks), while the HAVING clause restricts the output
to groups with total gross sales over 5000. Note that the aggregate expressions do not necessarily need
to be the same in all parts of the query.
If a query contains aggregate function calls, but no GROUP BY clause, grouping still occurs: the result
is a single group row (or perhaps no rows at all, if the single row is then eliminated by HAVING).
The same is true if it contains a HAVING clause, even without any aggregate function calls or GROUP
BY clause.
129
Queries
7.2.4. GROUPING SETS, CUBE, and ROLLUP
More complex grouping operations than those described above are possible using the concept of grouping sets. The data selected by the FROM and WHERE clauses is grouped separately by each specified
grouping set, aggregates computed for each group just as for simple GROUP BY clauses, and then
the results returned. For example:
=> SELECT * FROM items_sold;
brand | size | sales
-------+------+------Foo
| L
| 10
Foo
| M
| 20
Bar
| M
| 15
Bar
| L
| 5
(4 rows)
=> SELECT brand, size, sum(sales) FROM items_sold GROUP BY GROUPING
SETS ((brand), (size), ());
brand | size | sum
-------+------+----Foo
|
| 30
Bar
|
| 20
| L
| 15
| M
| 35
|
| 50
(5 rows)
Each sublist of GROUPING SETS may specify zero or more columns or expressions and is interpreted
the same way as though it were directly in the GROUP BY clause. An empty grouping set means that
all rows are aggregated down to a single group (which is output even if no input rows were present),
as described above for the case of aggregate functions with no GROUP BY clause.
References to the grouping columns or expressions are replaced by null values in result rows for
grouping sets in which those columns do not appear. To distinguish which grouping a particular output
row resulted from, see Table 9.64.
A shorthand notation is provided for specifying two common types of grouping set. A clause of the
form
ROLLUP ( e1, e2, e3, ... )
represents the given list of expressions and all prefixes of the list including the empty list; thus it is
equivalent to
GROUPING SETS (
( e1, e2, e3, ... ),
...
( e1, e2 ),
( e1 ),
( )
)
This is commonly used for analysis over hierarchical data; e.g., total salary by department, division,
and company-wide total.
A clause of the form
130
Queries
CUBE ( e1, e2, ... )
represents the given list and all of its possible subsets (i.e., the power set). Thus
CUBE ( a, b, c )
is equivalent to
GROUPING SETS (
( a, b, c ),
( a, b
),
( a,
c ),
( a
),
(
b, c ),
(
b
),
(
c ),
(
)
)
The individual elements of a CUBE or ROLLUP clause may be either individual expressions, or sublists
of elements in parentheses. In the latter case, the sublists are treated as single units for the purposes
of generating the individual grouping sets. For example:
CUBE ( (a, b), (c, d) )
is equivalent to
GROUPING SETS (
( a, b, c, d ),
( a, b
),
(
c, d ),
(
)
)
and
ROLLUP ( a, (b, c), d )
is equivalent to
GROUPING SETS (
( a, b, c, d ),
( a, b, c
),
( a
),
(
)
)
The CUBE and ROLLUP constructs can be used either directly in the GROUP BY clause, or nested
inside a GROUPING SETS clause. If one GROUPING SETS clause is nested inside another, the
effect is the same as if all the elements of the inner clause had been written directly in the outer clause.
If multiple grouping items are specified in a single GROUP BY clause, then the final list of grouping
sets is the Cartesian product of the individual items. For example:
131
Queries
GROUP BY a, CUBE (b, c), GROUPING SETS ((d), (e))
is equivalent to
GROUP BY GROUPING SETS (
(a, b, c, d), (a, b, c, e),
(a, b, d),
(a, b, e),
(a, c, d),
(a, c, e),
(a, d),
(a, e)
)
When specifying multiple grouping items together, the final set of grouping sets might contain duplicates. For example:
GROUP BY ROLLUP (a, b), ROLLUP (a, c)
is equivalent to
GROUP BY GROUPING SETS (
(a, b, c),
(a, b),
(a, b),
(a, c),
(a),
(a),
(a, c),
(a),
()
)
If these duplicates are undesirable, they can be removed using the DISTINCT clause directly on the
GROUP BY. Therefore:
GROUP BY DISTINCT ROLLUP (a, b), ROLLUP (a, c)
is equivalent to
GROUP BY GROUPING SETS (
(a, b, c),
(a, b),
(a, c),
(a),
()
)
This is not the same as using SELECT DISTINCT because the output rows may still contain duplicates. If any of the ungrouped columns contains NULL, it will be indistinguishable from the NULL
used when that same column is grouped.
Note
The construct (a, b) is normally recognized in expressions as a row constructor. Within the
GROUP BY clause, this does not apply at the top levels of expressions, and (a, b) is parsed
132
Queries
as a list of expressions as described above. If for some reason you need a row constructor in
a grouping expression, use ROW(a, b).
7.2.5. Window Function Processing
If the query contains any window functions (see Section 3.5, Section 9.22 and Section 4.2.8), these
functions are evaluated after any grouping, aggregation, and HAVING filtering is performed. That is, if
the query uses any aggregates, GROUP BY, or HAVING, then the rows seen by the window functions
are the group rows instead of the original table rows from FROM/WHERE.
When multiple window functions are used, all the window functions having syntactically equivalent
PARTITION BY and ORDER BY clauses in their window definitions are guaranteed to be evaluated
in a single pass over the data. Therefore they will see the same sort ordering, even if the ORDER BY
does not uniquely determine an ordering. However, no guarantees are made about the evaluation of
functions having different PARTITION BY or ORDER BY specifications. (In such cases a sort step is
typically required between the passes of window function evaluations, and the sort is not guaranteed
to preserve ordering of rows that its ORDER BY sees as equivalent.)
Currently, window functions always require presorted data, and so the query output will be ordered
according to one or another of the window functions' PARTITION BY/ORDER BY clauses. It is not
recommended to rely on this, however. Use an explicit top-level ORDER BY clause if you want to be
sure the results are sorted in a particular way.
7.3. Select Lists
As shown in the previous section, the table expression in the SELECT command constructs an intermediate virtual table by possibly combining tables, views, eliminating rows, grouping, etc. This table
is finally passed on to processing by the select list. The select list determines which columns of the
intermediate table are actually output.
7.3.1. Select-List Items
The simplest kind of select list is * which emits all columns that the table expression produces. Otherwise, a select list is a comma-separated list of value expressions (as defined in Section 4.2). For
instance, it could be a list of column names:
SELECT a, b, c FROM ...
The columns names a, b, and c are either the actual names of the columns of tables referenced in the
FROM clause, or the aliases given to them as explained in Section 7.2.1.2. The name space available
in the select list is the same as in the WHERE clause, unless grouping is used, in which case it is the
same as in the HAVING clause.
If more than one table has a column of the same name, the table name must also be given, as in:
SELECT tbl1.a, tbl2.a, tbl1.b FROM ...
When working with multiple tables, it can also be useful to ask for all the columns of a particular table:
SELECT tbl1.*, tbl2.a FROM ...
See Section 8.16.5 for more about the table_name.* notation.
If an arbitrary value expression is used in the select list, it conceptually adds a new virtual column to
the returned table. The value expression is evaluated once for each result row, with the row's values
133
Queries
substituted for any column references. But the expressions in the select list do not have to reference
any columns in the table expression of the FROM clause; they can be constant arithmetic expressions,
for instance.
7.3.2. Column Labels
The entries in the select list can be assigned names for subsequent processing, such as for use in an
ORDER BY clause or for display by the client application. For example:
SELECT a AS value, b + c AS sum FROM ...
If no output column name is specified using AS, the system assigns a default column name. For simple
column references, this is the name of the referenced column. For function calls, this is the name of
the function. For complex expressions, the system will generate a generic name.
The AS key word is usually optional, but in some cases where the desired column name matches a
PostgreSQL key word, you must write AS or double-quote the column name in order to avoid ambiguity. (Appendix C shows which key words require AS to be used as a column label.) For example,
FROM is one such key word, so this does not work:
SELECT a from, b + c AS sum FROM ...
but either of these do:
SELECT a AS from, b + c AS sum FROM ...
SELECT a "from", b + c AS sum FROM ...
For greatest safety against possible future key word additions, it is recommended that you always
either write AS or double-quote the output column name.
Note
The naming of output columns here is different from that done in the FROM clause (see Section 7.2.1.2). It is possible to rename the same column twice, but the name assigned in the
select list is the one that will be passed on.
7.3.3. DISTINCT
After the select list has been processed, the result table can optionally be subject to the elimination of
duplicate rows. The DISTINCT key word is written directly after SELECT to specify this:
SELECT DISTINCT select_list ...
(Instead of DISTINCT the key word ALL can be used to specify the default behavior of retaining
all rows.)
Obviously, two rows are considered distinct if they differ in at least one column value. Null values
are considered equal in this comparison.
Alternatively, an arbitrary expression can determine what rows are to be considered distinct:
134
Queries
SELECT DISTINCT ON (expression [, expression ...]) select_list ...
Here expression is an arbitrary value expression that is evaluated for all rows. A set of rows for
which all the expressions are equal are considered duplicates, and only the first row of the set is kept
in the output. Note that the “first row” of a set is unpredictable unless the query is sorted on enough
columns to guarantee a unique ordering of the rows arriving at the DISTINCT filter. (DISTINCT
ON processing occurs after ORDER BY sorting.)
The DISTINCT ON clause is not part of the SQL standard and is sometimes considered bad style
because of the potentially indeterminate nature of its results. With judicious use of GROUP BY and
subqueries in FROM, this construct can be avoided, but it is often the most convenient alternative.
7.4. Combining Queries (UNION, INTERSECT,
EXCEPT)
The results of two queries can be combined using the set operations union, intersection, and difference.
The syntax is
query1 UNION [ALL] query2
query1 INTERSECT [ALL] query2
query1 EXCEPT [ALL] query2
where query1 and query2 are queries that can use any of the features discussed up to this point.
UNION effectively appends the result of query2 to the result of query1 (although there is no guarantee that this is the order in which the rows are actually returned). Furthermore, it eliminates duplicate
rows from its result, in the same way as DISTINCT, unless UNION ALL is used.
INTERSECT returns all rows that are both in the result of query1 and in the result of query2.
Duplicate rows are eliminated unless INTERSECT ALL is used.
EXCEPT returns all rows that are in the result of query1 but not in the result of query2. (This is
sometimes called the difference between two queries.) Again, duplicates are eliminated unless EXCEPT ALL is used.
In order to calculate the union, intersection, or difference of two queries, the two queries must be
“union compatible”, which means that they return the same number of columns and the corresponding
columns have compatible data types, as described in Section 10.5.
Set operations can be combined, for example
query1 UNION query2 EXCEPT query3
which is equivalent to
(query1 UNION query2) EXCEPT query3
As shown here, you can use parentheses to control the order of evaluation. Without parentheses,
UNION and EXCEPT associate left-to-right, but INTERSECT binds more tightly than those two operators. Thus
query1 UNION query2 INTERSECT query3
means
135
Queries
query1 UNION (query2 INTERSECT query3)
You can also surround an individual query with parentheses. This is important if the query needs
to use any of the clauses discussed in following sections, such as LIMIT. Without parentheses, you'll
get a syntax error, or else the clause will be understood as applying to the output of the set operation
rather than one of its inputs. For example,
SELECT a FROM b UNION SELECT x FROM y LIMIT 10
is accepted, but it means
(SELECT a FROM b UNION SELECT x FROM y) LIMIT 10
not
SELECT a FROM b UNION (SELECT x FROM y LIMIT 10)
7.5. Sorting Rows (ORDER BY)
After a query has produced an output table (after the select list has been processed) it can optionally
be sorted. If sorting is not chosen, the rows will be returned in an unspecified order. The actual order
in that case will depend on the scan and join plan types and the order on disk, but it must not be relied
on. A particular output ordering can only be guaranteed if the sort step is explicitly chosen.
The ORDER BY clause specifies the sort order:
SELECT select_list
FROM table_expression
ORDER BY sort_expression1 [ASC | DESC] [NULLS { FIRST | LAST }]
[, sort_expression2 [ASC | DESC] [NULLS { FIRST |
LAST }] ...]
The sort expression(s) can be any expression that would be valid in the query's select list. An example
is:
SELECT a, b FROM table1 ORDER BY a + b, c;
When more than one expression is specified, the later values are used to sort rows that are equal
according to the earlier values. Each expression can be followed by an optional ASC or DESC keyword
to set the sort direction to ascending or descending. ASC order is the default. Ascending order puts
smaller values first, where “smaller” is defined in terms of the < operator. Similarly, descending order
is determined with the > operator. 1
The NULLS FIRST and NULLS LAST options can be used to determine whether nulls appear before
or after non-null values in the sort ordering. By default, null values sort as if larger than any non-null
value; that is, NULLS FIRST is the default for DESC order, and NULLS LAST otherwise.
Note that the ordering options are considered independently for each sort column. For example ORDER
BY x, y DESC means ORDER BY x ASC, y DESC, which is not the same as ORDER BY
x DESC, y DESC.
1
Actually, PostgreSQL uses the default B-tree operator class for the expression's data type to determine the sort ordering for ASC and DESC.
Conventionally, data types will be set up so that the < and > operators correspond to this sort ordering, but a user-defined data type's designer
could choose to do something different.
136
Queries
A sort_expression can also be the column label or number of an output column, as in:
SELECT a + b AS sum, c FROM table1 ORDER BY sum;
SELECT a, max(b) FROM table1 GROUP BY a ORDER BY 1;
both of which sort by the first output column. Note that an output column name has to stand alone,
that is, it cannot be used in an expression — for example, this is not correct:
SELECT a + b AS sum, c FROM table1 ORDER BY sum + c;
wrong
--
This restriction is made to reduce ambiguity. There is still ambiguity if an ORDER BY item is a simple
name that could match either an output column name or a column from the table expression. The
output column is used in such cases. This would only cause confusion if you use AS to rename an
output column to match some other table column's name.
ORDER BY can be applied to the result of a UNION, INTERSECT, or EXCEPT combination, but in
this case it is only permitted to sort by output column names or numbers, not by expressions.
7.6. LIMIT and OFFSET
LIMIT and OFFSET allow you to retrieve just a portion of the rows that are generated by the rest
of the query:
SELECT select_list
FROM table_expression
[ ORDER BY ... ]
[ LIMIT { count | ALL } ]
[ OFFSET start ]
If a limit count is given, no more than that many rows will be returned (but possibly fewer, if the
query itself yields fewer rows). LIMIT ALL is the same as omitting the LIMIT clause, as is LIMIT
with a NULL argument.
OFFSET says to skip that many rows before beginning to return rows. OFFSET 0 is the same as
omitting the OFFSET clause, as is OFFSET with a NULL argument.
If both OFFSET and LIMIT appear, then OFFSET rows are skipped before starting to count the
LIMIT rows that are returned.
When using LIMIT, it is important to use an ORDER BY clause that constrains the result rows into a
unique order. Otherwise you will get an unpredictable subset of the query's rows. You might be asking
for the tenth through twentieth rows, but tenth through twentieth in what ordering? The ordering is
unknown, unless you specified ORDER BY.
The query optimizer takes LIMIT into account when generating query plans, so you are very likely
to get different plans (yielding different row orders) depending on what you give for LIMIT and
OFFSET. Thus, using different LIMIT/OFFSET values to select different subsets of a query result
will give inconsistent results unless you enforce a predictable result ordering with ORDER BY. This
is not a bug; it is an inherent consequence of the fact that SQL does not promise to deliver the results
of a query in any particular order unless ORDER BY is used to constrain the order.
The rows skipped by an OFFSET clause still have to be computed inside the server; therefore a large
OFFSET might be inefficient.
7.7. VALUES Lists
137
Queries
VALUES provides a way to generate a “constant table” that can be used in a query without having to
actually create and populate a table on-disk. The syntax is
VALUES ( expression [, ...] ) [, ...]
Each parenthesized list of expressions generates a row in the table. The lists must all have the same
number of elements (i.e., the number of columns in the table), and corresponding entries in each
list must have compatible data types. The actual data type assigned to each column of the result is
determined using the same rules as for UNION (see Section 10.5).
As an example:
VALUES (1, 'one'), (2, 'two'), (3, 'three');
will return a table of two columns and three rows. It's effectively equivalent to:
SELECT 1 AS column1, 'one' AS column2
UNION ALL
SELECT 2, 'two'
UNION ALL
SELECT 3, 'three';
By default, PostgreSQL assigns the names column1, column2, etc. to the columns of a VALUES
table. The column names are not specified by the SQL standard and different database systems do it
differently, so it's usually better to override the default names with a table alias list, like this:
=> SELECT * FROM (VALUES (1, 'one'), (2, 'two'), (3, 'three')) AS t
(num,letter);
num | letter
-----+-------1 | one
2 | two
3 | three
(3 rows)
Syntactically, VALUES followed by expression lists is treated as equivalent to:
SELECT select_list FROM table_expression
and can appear anywhere a SELECT can. For example, you can use it as part of a UNION, or attach a
sort_specification (ORDER BY, LIMIT, and/or OFFSET) to it. VALUES is most commonly
used as the data source in an INSERT command, and next most commonly as a subquery.
For more information see VALUES.
7.8. WITH Queries (Common Table Expressions)
WITH provides a way to write auxiliary statements for use in a larger query. These statements, which
are often referred to as Common Table Expressions or CTEs, can be thought of as defining temporary
tables that exist just for one query. Each auxiliary statement in a WITH clause can be a SELECT,
INSERT, UPDATE, DELETE, or MERGE; and the WITH clause itself is attached to a primary statement
that can also be a SELECT, INSERT, UPDATE, DELETE, or MERGE.
138
Queries
7.8.1. SELECT in WITH
The basic value of SELECT in WITH is to break down complicated queries into simpler parts. An
example is:
WITH regional_sales AS (
SELECT region, SUM(amount) AS total_sales
FROM orders
GROUP BY region
), top_regions AS (
SELECT region
FROM regional_sales
WHERE total_sales > (SELECT SUM(total_sales)/10 FROM
regional_sales)
)
SELECT region,
product,
SUM(quantity) AS product_units,
SUM(amount) AS product_sales
FROM orders
WHERE region IN (SELECT region FROM top_regions)
GROUP BY region, product;
which displays per-product sales totals in only the top sales regions. The WITH clause defines two
auxiliary statements named regional_sales and top_regions, where the output of regional_sales is used in top_regions and the output of top_regions is used in the primary
SELECT query. This example could have been written without WITH, but we'd have needed two levels
of nested sub-SELECTs. It's a bit easier to follow this way.
7.8.2. Recursive Queries
The optional RECURSIVE modifier changes WITH from a mere syntactic convenience into a feature
that accomplishes things not otherwise possible in standard SQL. Using RECURSIVE, a WITH query
can refer to its own output. A very simple example is this query to sum the integers from 1 through 100:
WITH RECURSIVE t(n) AS (
VALUES (1)
UNION ALL
SELECT n+1 FROM t WHERE n < 100
)
SELECT sum(n) FROM t;
The general form of a recursive WITH query is always a non-recursive term, then UNION (or UNION
ALL), then a recursive term, where only the recursive term can contain a reference to the query's own
output. Such a query is executed as follows:
Recursive Query Evaluation
1.
Evaluate the non-recursive term. For UNION (but not UNION ALL), discard duplicate rows.
Include all remaining rows in the result of the recursive query, and also place them in a temporary
working table.
2.
So long as the working table is not empty, repeat these steps:
a.
Evaluate the recursive term, substituting the current contents of the working table for the
recursive self-reference. For UNION (but not UNION ALL), discard duplicate rows and
139
Queries
rows that duplicate any previous result row. Include all remaining rows in the result of the
recursive query, and also place them in a temporary intermediate table.
b.
Replace the contents of the working table with the contents of the intermediate table, then
empty the intermediate table.
Note
While RECURSIVE allows queries to be specified recursively, internally such queries are
evaluated iteratively.
In the example above, the working table has just a single row in each step, and it takes on the values
from 1 through 100 in successive steps. In the 100th step, there is no output because of the WHERE
clause, and so the query terminates.
Recursive queries are typically used to deal with hierarchical or tree-structured data. A useful example
is this query to find all the direct and indirect sub-parts of a product, given only a table that shows
immediate inclusions:
WITH RECURSIVE included_parts(sub_part, part, quantity) AS (
SELECT sub_part, part, quantity FROM parts WHERE part =
'our_product'
UNION ALL
SELECT p.sub_part, p.part, p.quantity * pr.quantity
FROM included_parts pr, parts p
WHERE p.part = pr.sub_part
)
SELECT sub_part, SUM(quantity) as total_quantity
FROM included_parts
GROUP BY sub_part
7.8.2.1. Search Order
When computing a tree traversal using a recursive query, you might want to order the results in either
depth-first or breadth-first order. This can be done by computing an ordering column alongside the
other data columns and using that to sort the results at the end. Note that this does not actually control in
which order the query evaluation visits the rows; that is as always in SQL implementation-dependent.
This approach merely provides a convenient way to order the results afterwards.
To create a depth-first order, we compute for each result row an array of rows that we have visited so
far. For example, consider the following query that searches a table tree using a link field:
WITH RECURSIVE search_tree(id, link, data) AS (
SELECT t.id, t.link, t.data
FROM tree t
UNION ALL
SELECT t.id, t.link, t.data
FROM tree t, search_tree st
WHERE t.id = st.link
)
SELECT * FROM search_tree;
To add depth-first ordering information, you can write this:
140
Queries
WITH RECURSIVE search_tree(id, link, data, path) AS (
SELECT t.id, t.link, t.data, ARRAY[t.id]
FROM tree t
UNION ALL
SELECT t.id, t.link, t.data, path || t.id
FROM tree t, search_tree st
WHERE t.id = st.link
)
SELECT * FROM search_tree ORDER BY path;
In the general case where more than one field needs to be used to identify a row, use an array of rows.
For example, if we needed to track fields f1 and f2:
WITH RECURSIVE search_tree(id, link, data, path) AS (
SELECT t.id, t.link, t.data, ARRAY[ROW(t.f1, t.f2)]
FROM tree t
UNION ALL
SELECT t.id, t.link, t.data, path || ROW(t.f1, t.f2)
FROM tree t, search_tree st
WHERE t.id = st.link
)
SELECT * FROM search_tree ORDER BY path;
Tip
Omit the ROW() syntax in the common case where only one field needs to be tracked. This
allows a simple array rather than a composite-type array to be used, gaining efficiency.
To create a breadth-first order, you can add a column that tracks the depth of the search, for example:
WITH RECURSIVE search_tree(id, link, data, depth) AS (
SELECT t.id, t.link, t.data, 0
FROM tree t
UNION ALL
SELECT t.id, t.link, t.data, depth + 1
FROM tree t, search_tree st
WHERE t.id = st.link
)
SELECT * FROM search_tree ORDER BY depth;
To get a stable sort, add data columns as secondary sorting columns.
Tip
The recursive query evaluation algorithm produces its output in breadth-first search order.
However, this is an implementation detail and it is perhaps unsound to rely on it. The order of
the rows within each level is certainly undefined, so some explicit ordering might be desired
in any case.
There is built-in syntax to compute a depth- or breadth-first sort column. For example:
WITH RECURSIVE search_tree(id, link, data) AS (
141
Queries
SELECT t.id, t.link, t.data
FROM tree t
UNION ALL
SELECT t.id, t.link, t.data
FROM tree t, search_tree st
WHERE t.id = st.link
) SEARCH DEPTH FIRST BY id SET ordercol
SELECT * FROM search_tree ORDER BY ordercol;
WITH RECURSIVE search_tree(id, link, data) AS (
SELECT t.id, t.link, t.data
FROM tree t
UNION ALL
SELECT t.id, t.link, t.data
FROM tree t, search_tree st
WHERE t.id = st.link
) SEARCH BREADTH FIRST BY id SET ordercol
SELECT * FROM search_tree ORDER BY ordercol;
This syntax is internally expanded to something similar to the above hand-written forms. The SEARCH
clause specifies whether depth- or breadth first search is wanted, the list of columns to track for sorting,
and a column name that will contain the result data that can be used for sorting. That column will
implicitly be added to the output rows of the CTE.
7.8.2.2. Cycle Detection
When working with recursive queries it is important to be sure that the recursive part of the query will
eventually return no tuples, or else the query will loop indefinitely. Sometimes, using UNION instead
of UNION ALL can accomplish this by discarding rows that duplicate previous output rows. However,
often a cycle does not involve output rows that are completely duplicate: it may be necessary to check
just one or a few fields to see if the same point has been reached before. The standard method for
handling such situations is to compute an array of the already-visited values. For example, consider
again the following query that searches a table graph using a link field:
WITH RECURSIVE search_graph(id, link, data, depth) AS (
SELECT g.id, g.link, g.data, 0
FROM graph g
UNION ALL
SELECT g.id, g.link, g.data, sg.depth + 1
FROM graph g, search_graph sg
WHERE g.id = sg.link
)
SELECT * FROM search_graph;
This query will loop if the link relationships contain cycles. Because we require a “depth” output,
just changing UNION ALL to UNION would not eliminate the looping. Instead we need to recognize
whether we have reached the same row again while following a particular path of links. We add two
columns is_cycle and path to the loop-prone query:
WITH RECURSIVE search_graph(id, link, data, depth, is_cycle, path)
AS (
SELECT g.id, g.link, g.data, 0,
false,
ARRAY[g.id]
FROM graph g
UNION ALL
SELECT g.id, g.link, g.data, sg.depth + 1,
142
Queries
g.id = ANY(path),
path || g.id
FROM graph g, search_graph sg
WHERE g.id = sg.link AND NOT is_cycle
)
SELECT * FROM search_graph;
Aside from preventing cycles, the array value is often useful in its own right as representing the “path”
taken to reach any particular row.
In the general case where more than one field needs to be checked to recognize a cycle, use an array
of rows. For example, if we needed to compare fields f1 and f2:
WITH RECURSIVE search_graph(id, link, data, depth, is_cycle, path)
AS (
SELECT g.id, g.link, g.data, 0,
false,
ARRAY[ROW(g.f1, g.f2)]
FROM graph g
UNION ALL
SELECT g.id, g.link, g.data, sg.depth + 1,
ROW(g.f1, g.f2) = ANY(path),
path || ROW(g.f1, g.f2)
FROM graph g, search_graph sg
WHERE g.id = sg.link AND NOT is_cycle
)
SELECT * FROM search_graph;
Tip
Omit the ROW() syntax in the common case where only one field needs to be checked to
recognize a cycle. This allows a simple array rather than a composite-type array to be used,
gaining efficiency.
There is built-in syntax to simplify cycle detection. The above query can also be written like this:
WITH RECURSIVE search_graph(id, link, data, depth) AS (
SELECT g.id, g.link, g.data, 1
FROM graph g
UNION ALL
SELECT g.id, g.link, g.data, sg.depth + 1
FROM graph g, search_graph sg
WHERE g.id = sg.link
) CYCLE id SET is_cycle USING path
SELECT * FROM search_graph;
and it will be internally rewritten to the above form. The CYCLE clause specifies first the list of
columns to track for cycle detection, then a column name that will show whether a cycle has been
detected, and finally the name of another column that will track the path. The cycle and path columns
will implicitly be added to the output rows of the CTE.
Tip
The cycle path column is computed in the same way as the depth-first ordering column show in
the previous section. A query can have both a SEARCH and a CYCLE clause, but a depth-first
143
Queries
search specification and a cycle detection specification would create redundant computations,
so it's more efficient to just use the CYCLE clause and order by the path column. If breadthfirst ordering is wanted, then specifying both SEARCH and CYCLE can be useful.
A helpful trick for testing queries when you are not certain if they might loop is to place a LIMIT in
the parent query. For example, this query would loop forever without the LIMIT:
WITH RECURSIVE t(n) AS (
SELECT 1
UNION ALL
SELECT n+1 FROM t
)
SELECT n FROM t LIMIT 100;
This works because PostgreSQL's implementation evaluates only as many rows of a WITH query as
are actually fetched by the parent query. Using this trick in production is not recommended, because
other systems might work differently. Also, it usually won't work if you make the outer query sort the
recursive query's results or join them to some other table, because in such cases the outer query will
usually try to fetch all of the WITH query's output anyway.
7.8.3. Common Table Expression Materialization
A useful property of WITH queries is that they are normally evaluated only once per execution of the
parent query, even if they are referred to more than once by the parent query or sibling WITH queries.
Thus, expensive calculations that are needed in multiple places can be placed within a WITH query
to avoid redundant work. Another possible application is to prevent unwanted multiple evaluations
of functions with side-effects. However, the other side of this coin is that the optimizer is not able to
push restrictions from the parent query down into a multiply-referenced WITH query, since that might
affect all uses of the WITH query's output when it should affect only one. The multiply-referenced
WITH query will be evaluated as written, without suppression of rows that the parent query might
discard afterwards. (But, as mentioned above, evaluation might stop early if the reference(s) to the
query demand only a limited number of rows.)
However, if a WITH query is non-recursive and side-effect-free (that is, it is a SELECT containing no volatile functions) then it can be folded into the parent query, allowing joint optimization of
the two query levels. By default, this happens if the parent query references the WITH query just
once, but not if it references the WITH query more than once. You can override that decision by
specifying MATERIALIZED to force separate calculation of the WITH query, or by specifying NOT
MATERIALIZED to force it to be merged into the parent query. The latter choice risks duplicate computation of the WITH query, but it can still give a net savings if each usage of the WITH query needs
only a small part of the WITH query's full output.
A simple example of these rules is
WITH w AS (
SELECT * FROM big_table
)
SELECT * FROM w WHERE key = 123;
This WITH query will be folded, producing the same execution plan as
SELECT * FROM big_table WHERE key = 123;
In particular, if there's an index on key, it will probably be used to fetch just the rows having key
= 123. On the other hand, in
144
Queries
WITH w AS (
SELECT * FROM big_table
)
SELECT * FROM w AS w1 JOIN w AS w2 ON w1.key = w2.ref
WHERE w2.key = 123;
the WITH query will be materialized, producing a temporary copy of big_table that is then joined
with itself — without benefit of any index. This query will be executed much more efficiently if
written as
WITH w AS NOT MATERIALIZED (
SELECT * FROM big_table
)
SELECT * FROM w AS w1 JOIN w AS w2 ON w1.key = w2.ref
WHERE w2.key = 123;
so that the parent query's restrictions can be applied directly to scans of big_table.
An example where NOT MATERIALIZED could be undesirable is
WITH w AS (
SELECT key, very_expensive_function(val) as f FROM some_table
)
SELECT * FROM w AS w1 JOIN w AS w2 ON w1.f = w2.f;
Here, materialization of the WITH query ensures that very_expensive_function is evaluated
only once per table row, not twice.
The examples above only show WITH being used with SELECT, but it can be attached in the same way
to INSERT, UPDATE, DELETE, or MERGE. In each case it effectively provides temporary table(s)
that can be referred to in the main command.
7.8.4. Data-Modifying Statements in WITH
You can use data-modifying statements (INSERT, UPDATE, DELETE, or MERGE) in WITH. This
allows you to perform several different operations in the same query. An example is:
WITH moved_rows AS (
DELETE FROM products
WHERE
"date" >= '2010-10-01' AND
"date" < '2010-11-01'
RETURNING *
)
INSERT INTO products_log
SELECT * FROM moved_rows;
This query effectively moves rows from products to products_log. The DELETE in WITH
deletes the specified rows from products, returning their contents by means of its RETURNING
clause; and then the primary query reads that output and inserts it into products_log.
A fine point of the above example is that the WITH clause is attached to the INSERT, not the subSELECT within the INSERT. This is necessary because data-modifying statements are only allowed
in WITH clauses that are attached to the top-level statement. However, normal WITH visibility rules
apply, so it is possible to refer to the WITH statement's output from the sub-SELECT.
145
Queries
Data-modifying statements in WITH usually have RETURNING clauses (see Section 6.4), as shown
in the example above. It is the output of the RETURNING clause, not the target table of the data-modifying statement, that forms the temporary table that can be referred to by the rest of the query. If a
data-modifying statement in WITH lacks a RETURNING clause, then it forms no temporary table and
cannot be referred to in the rest of the query. Such a statement will be executed nonetheless. A notparticularly-useful example is:
WITH t AS (
DELETE FROM foo
)
DELETE FROM bar;
This example would remove all rows from tables foo and bar. The number of affected rows reported
to the client would only include rows removed from bar.
Recursive self-references in data-modifying statements are not allowed. In some cases it is possible
to work around this limitation by referring to the output of a recursive WITH, for example:
WITH RECURSIVE included_parts(sub_part, part) AS (
SELECT sub_part, part FROM parts WHERE part = 'our_product'
UNION ALL
SELECT p.sub_part, p.part
FROM included_parts pr, parts p
WHERE p.part = pr.sub_part
)
DELETE FROM parts
WHERE part IN (SELECT part FROM included_parts);
This query would remove all direct and indirect subparts of a product.
Data-modifying statements in WITH are executed exactly once, and always to completion, independently of whether the primary query reads all (or indeed any) of their output. Notice that this is different from the rule for SELECT in WITH: as stated in the previous section, execution of a SELECT is
carried only as far as the primary query demands its output.
The sub-statements in WITH are executed concurrently with each other and with the main query.
Therefore, when using data-modifying statements in WITH, the order in which the specified updates
actually happen is unpredictable. All the statements are executed with the same snapshot (see Chapter 13), so they cannot “see” one another's effects on the target tables. This alleviates the effects of the
unpredictability of the actual order of row updates, and means that RETURNING data is the only way
to communicate changes between different WITH sub-statements and the main query. An example of
this is that in
WITH t AS (
UPDATE products SET price = price * 1.05
RETURNING *
)
SELECT * FROM products;
the outer SELECT would return the original prices before the action of the UPDATE, while in
WITH t AS (
UPDATE products SET price = price * 1.05
RETURNING *
)
SELECT * FROM t;
146
Queries
the outer SELECT would return the updated data.
Trying to update the same row twice in a single statement is not supported. Only one of the modifications takes place, but it is not easy (and sometimes not possible) to reliably predict which one. This also
applies to deleting a row that was already updated in the same statement: only the update is performed.
Therefore you should generally avoid trying to modify a single row twice in a single statement. In
particular avoid writing WITH sub-statements that could affect the same rows changed by the main
statement or a sibling sub-statement. The effects of such a statement will not be predictable.
At present, any table used as the target of a data-modifying statement in WITH must not have a conditional rule, nor an ALSO rule, nor an INSTEAD rule that expands to multiple statements.
147
Chapter 8. Data Types
PostgreSQL has a rich set of native data types available to users. Users can add new types to PostgreSQL using the CREATE TYPE command.
Table 8.1 shows all the built-in general-purpose data types. Most of the alternative names listed in
the “Aliases” column are the names used internally by PostgreSQL for historical reasons. In addition,
some internally used or deprecated types are available, but are not listed here.
Table 8.1. Data Types
Name
Aliases
Description
bigint
int8
signed eight-byte integer
bigserial
serial8
autoincrementing eight-byte integer
fixed-length bit string
bit [ (n) ]
bit varying [ (n) ]
varbit
[ (n) ]
variable-length bit string
boolean
bool
logical Boolean (true/false)
box
rectangular box on a plane
bytea
binary data (“byte array”)
character [ (n) ]
char [ (n) ] fixed-length character string
character varying [ (n) ]
varchar
[ (n) ]
variable-length character string
cidr
IPv4 or IPv6 network address
circle
circle on a plane
date
calendar date (year, month, day)
double precision
float8
IPv4 or IPv6 host address
inet
integer
double precision floating-point number (8 bytes)
int, int4
signed four-byte integer
interval [ fields ]
[ (p) ]
time span
json
textual JSON data
jsonb
binary JSON data, decomposed
line
infinite line on a plane
lseg
line segment on a plane
macaddr
MAC (Media Access Control) address
macaddr8
MAC (Media Access Control) address
(EUI-64 format)
money
currency amount
numeric [ (p, s) ]
decimal
[ (p, s) ]
exact numeric of selectable precision
path
geometric path on a plane
pg_lsn
PostgreSQL Log Sequence Number
pg_snapshot
user-level transaction ID snapshot
point
geometric point on a plane
148
Data Types
Name
Aliases
Description
closed geometric path on a plane
polygon
real
float4
single precision floating-point number
(4 bytes)
smallint
int2
signed two-byte integer
smallserial
serial2
autoincrementing two-byte integer
serial
serial4
autoincrementing four-byte integer
text
variable-length character string
time [ (p) ] [ without
time zone ]
time of day (no time zone)
time [ (p) ] with time
zone
timetz
date and time (no time zone)
timestamp [ (p) ] [ without time zone ]
timestamp [ (p) ] with
time zone
time of day, including time zone
timestamptz
date and time, including time zone
tsquery
text search query
tsvector
text search document
txid_snapshot
user-level transaction ID snapshot
(deprecated; see pg_snapshot)
uuid
universally unique identifier
xml
XML data
Compatibility
The following types (or spellings thereof) are specified by SQL: bigint, bit, bit varying, boolean, char, character varying, character, varchar, date, double precision, integer, interval, numeric, decimal, real, smallint,
time (with or without time zone), timestamp (with or without time zone), xml.
Each data type has an external representation determined by its input and output functions. Many of the
built-in types have obvious external formats. However, several types are either unique to PostgreSQL,
such as geometric paths, or have several possible formats, such as the date and time types. Some of the
input and output functions are not invertible, i.e., the result of an output function might lose accuracy
when compared to the original input.
8.1. Numeric Types
Numeric types consist of two-, four-, and eight-byte integers, four- and eight-byte floating-point numbers, and selectable-precision decimals. Table 8.2 lists the available types.
Table 8.2. Numeric Types
Name
Storage Size Description
Range
smallint
2 bytes
small-range integer
-32768 to +32767
integer
4 bytes
typical choice for integer
-2147483648 to
+2147483647
bigint
8 bytes
large-range integer
-9223372036854775808 to
+9223372036854775807
149
Data Types
Name
Storage Size Description
Range
decimal
variable
user-specified precision,
exact
up to 131072 digits before
the decimal point; up to
16383 digits after the decimal point
numeric
variable
user-specified precision,
exact
up to 131072 digits before
the decimal point; up to
16383 digits after the decimal point
real
4 bytes
variable-precision, inexact 6 decimal digits precision
double precision
8 bytes
variable-precision, inexact 15 decimal digits precision
smallserial
2 bytes
small autoincrementing in- 1 to 32767
teger
serial
4 bytes
autoincrementing integer
bigserial
8 bytes
large autoincrementing in- 1 to
teger
9223372036854775807
1 to 2147483647
The syntax of constants for the numeric types is described in Section 4.1.2. The numeric types have a
full set of corresponding arithmetic operators and functions. Refer to Chapter 9 for more information.
The following sections describe the types in detail.
8.1.1. Integer Types
The types smallint, integer, and bigint store whole numbers, that is, numbers without fractional components, of various ranges. Attempts to store values outside of the allowed range will result
in an error.
The type integer is the common choice, as it offers the best balance between range, storage size, and
performance. The smallint type is generally only used if disk space is at a premium. The bigint
type is designed to be used when the range of the integer type is insufficient.
SQL only specifies the integer types integer (or int), smallint, and bigint. The type names
int2, int4, and int8 are extensions, which are also used by some other SQL database systems.
8.1.2. Arbitrary Precision Numbers
The type numeric can store numbers with a very large number of digits. It is especially recommended for storing monetary amounts and other quantities where exactness is required. Calculations with
numeric values yield exact results where possible, e.g., addition, subtraction, multiplication. However, calculations on numeric values are very slow compared to the integer types, or to the floating-point types described in the next section.
We use the following terms below: The precision of a numeric is the total count of significant digits
in the whole number, that is, the number of digits to both sides of the decimal point. The scale of a
numeric is the count of decimal digits in the fractional part, to the right of the decimal point. So the
number 23.5141 has a precision of 6 and a scale of 4. Integers can be considered to have a scale of zero.
Both the maximum precision and the maximum scale of a numeric column can be configured. To
declare a column of type numeric use the syntax:
NUMERIC(precision, scale)
The precision must be positive, while the scale may be positive or negative (see below). Alternatively:
150
Data Types
NUMERIC(precision)
selects a scale of 0. Specifying:
NUMERIC
without any precision or scale creates an “unconstrained numeric” column in which numeric values
of any length can be stored, up to the implementation limits. A column of this kind will not coerce
input values to any particular scale, whereas numeric columns with a declared scale will coerce
input values to that scale. (The SQL standard requires a default scale of 0, i.e., coercion to integer
precision. We find this a bit useless. If you're concerned about portability, always specify the precision
and scale explicitly.)
Note
The maximum precision that can be explicitly specified in a numeric type declaration is
1000. An unconstrained numeric column is subject to the limits described in Table 8.2.
If the scale of a value to be stored is greater than the declared scale of the column, the system will
round the value to the specified number of fractional digits. Then, if the number of digits to the left
of the decimal point exceeds the declared precision minus the declared scale, an error is raised. For
example, a column declared as
NUMERIC(3, 1)
will round values to 1 decimal place and can store values between -99.9 and 99.9, inclusive.
Beginning in PostgreSQL 15, it is allowed to declare a numeric column with a negative scale. Then
values will be rounded to the left of the decimal point. The precision still represents the maximum
number of non-rounded digits. Thus, a column declared as
NUMERIC(2, -3)
will round values to the nearest thousand and can store values between -99000 and 99000, inclusive.
It is also allowed to declare a scale larger than the declared precision. Such a column can only hold
fractional values, and it requires the number of zero digits just to the right of the decimal point to be
at least the declared scale minus the declared precision. For example, a column declared as
NUMERIC(3, 5)
will round values to 5 decimal places and can store values between -0.00999 and 0.00999, inclusive.
Note
PostgreSQL permits the scale in a numeric type declaration to be any value in the range
-1000 to 1000. However, the SQL standard requires the scale to be in the range 0 to precision. Using scales outside that range may not be portable to other database systems.
Numeric values are physically stored without any extra leading or trailing zeroes. Thus, the declared
precision and scale of a column are maximums, not fixed allocations. (In this sense the numeric
type is more akin to varchar(n) than to char(n).) The actual storage requirement is two bytes
for each group of four decimal digits, plus three to eight bytes overhead.
151
Data Types
In addition to ordinary numeric values, the numeric type has several special values:
Infinity
-Infinity
NaN
These are adapted from the IEEE 754 standard, and represent “infinity”, “negative infinity”, and “nota-number”, respectively. When writing these values as constants in an SQL command, you must put
quotes around them, for example UPDATE table SET x = '-Infinity'. On input, these
strings are recognized in a case-insensitive manner. The infinity values can alternatively be spelled
inf and -inf.
The infinity values behave as per mathematical expectations. For example, Infinity plus any finite
value equals Infinity, as does Infinity plus Infinity; but Infinity minus Infinity
yields NaN (not a number), because it has no well-defined interpretation. Note that an infinity can only
be stored in an unconstrained numeric column, because it notionally exceeds any finite precision
limit.
The NaN (not a number) value is used to represent undefined calculational results. In general, any
operation with a NaN input yields another NaN. The only exception is when the operation's other inputs
are such that the same output would be obtained if the NaN were to be replaced by any finite or infinite
numeric value; then, that output value is used for NaN too. (An example of this principle is that NaN
raised to the zero power yields one.)
Note
In most implementations of the “not-a-number” concept, NaN is not considered equal to any
other numeric value (including NaN). In order to allow numeric values to be sorted and used
in tree-based indexes, PostgreSQL treats NaN values as equal, and greater than all non-NaN
values.
The types decimal and numeric are equivalent. Both types are part of the SQL standard.
When rounding values, the numeric type rounds ties away from zero, while (on most machines) the
real and double precision types round ties to the nearest even number. For example:
SELECT x,
round(x::numeric) AS num_round,
round(x::double precision) AS dbl_round
FROM generate_series(-3.5, 3.5, 1) as x;
x
| num_round | dbl_round
------+-----------+-----------3.5 |
-4 |
-4
-2.5 |
-3 |
-2
-1.5 |
-2 |
-2
-0.5 |
-1 |
-0
0.5 |
1 |
0
1.5 |
2 |
2
2.5 |
3 |
2
3.5 |
4 |
4
(8 rows)
8.1.3. Floating-Point Types
The data types real and double precision are inexact, variable-precision numeric types. On
all currently supported platforms, these types are implementations of IEEE Standard 754 for Binary
152
Data Types
Floating-Point Arithmetic (single and double precision, respectively), to the extent that the underlying
processor, operating system, and compiler support it.
Inexact means that some values cannot be converted exactly to the internal format and are stored as
approximations, so that storing and retrieving a value might show slight discrepancies. Managing these
errors and how they propagate through calculations is the subject of an entire branch of mathematics
and computer science and will not be discussed here, except for the following points:
• If you require exact storage and calculations (such as for monetary amounts), use the numeric
type instead.
• If you want to do complicated calculations with these types for anything important, especially if
you rely on certain behavior in boundary cases (infinity, underflow), you should evaluate the implementation carefully.
• Comparing two floating-point values for equality might not always work as expected.
On all currently supported platforms, the real type has a range of around 1E-37 to 1E+37 with a
precision of at least 6 decimal digits. The double precision type has a range of around 1E-307
to 1E+308 with a precision of at least 15 digits. Values that are too large or too small will cause an
error. Rounding might take place if the precision of an input number is too high. Numbers too close
to zero that are not representable as distinct from zero will cause an underflow error.
By default, floating point values are output in text form in their shortest precise decimal representation; the decimal value produced is closer to the true stored binary value than to any other value representable in the same binary precision. (However, the output value is currently never exactly midway
between two representable values, in order to avoid a widespread bug where input routines do not
properly respect the round-to-nearest-even rule.) This value will use at most 17 significant decimal
digits for float8 values, and at most 9 digits for float4 values.
Note
This shortest-precise output format is much faster to generate than the historical rounded format.
For compatibility with output generated by older versions of PostgreSQL, and to allow the output
precision to be reduced, the extra_float_digits parameter can be used to select rounded decimal output
instead. Setting a value of 0 restores the previous default of rounding the value to 6 (for float4)
or 15 (for float8) significant decimal digits. Setting a negative value reduces the number of digits
further; for example -2 would round output to 4 or 13 digits respectively.
Any value of extra_float_digits greater than 0 selects the shortest-precise format.
Note
Applications that wanted precise values have historically had to set extra_float_digits to 3 to
obtain them. For maximum compatibility between versions, they should continue to do so.
In addition to ordinary numeric values, the floating-point types have several special values:
Infinity
-Infinity
NaN
These represent the IEEE 754 special values “infinity”, “negative infinity”, and “not-a-number”, respectively. When writing these values as constants in an SQL command, you must put quotes around
153
Data Types
them, for example UPDATE table SET x = '-Infinity'. On input, these strings are recognized in a case-insensitive manner. The infinity values can alternatively be spelled inf and -inf.
Note
IEEE 754 specifies that NaN should not compare equal to any other floating-point value (including NaN). In order to allow floating-point values to be sorted and used in tree-based indexes, PostgreSQL treats NaN values as equal, and greater than all non-NaN values.
PostgreSQL also supports the SQL-standard notations float and float(p) for specifying inexact
numeric types. Here, p specifies the minimum acceptable precision in binary digits. PostgreSQL accepts float(1) to float(24) as selecting the real type, while float(25) to float(53)
select double precision. Values of p outside the allowed range draw an error. float with no
precision specified is taken to mean double precision.
8.1.4. Serial Types
Note
This section describes a PostgreSQL-specific way to create an autoincrementing column. Another way is to use the SQL-standard identity column feature, described at Section 5.3.
The data types smallserial, serial and bigserial are not true types, but merely a notational convenience for creating unique identifier columns (similar to the AUTO_INCREMENT property
supported by some other databases). In the current implementation, specifying:
CREATE TABLE tablename (
colname SERIAL
);
is equivalent to specifying:
CREATE SEQUENCE tablename_colname_seq AS integer;
CREATE TABLE tablename (
colname integer NOT NULL DEFAULT
nextval('tablename_colname_seq')
);
ALTER SEQUENCE tablename_colname_seq OWNED BY tablename.colname;
Thus, we have created an integer column and arranged for its default values to be assigned from a
sequence generator. A NOT NULL constraint is applied to ensure that a null value cannot be inserted.
(In most cases you would also want to attach a UNIQUE or PRIMARY KEY constraint to prevent
duplicate values from being inserted by accident, but this is not automatic.) Lastly, the sequence is
marked as “owned by” the column, so that it will be dropped if the column or table is dropped.
Note
Because smallserial, serial and bigserial are implemented using sequences, there
may be "holes" or gaps in the sequence of values which appears in the column, even if no rows
are ever deleted. A value allocated from the sequence is still "used up" even if a row containing
that value is never successfully inserted into the table column. This may happen, for example,
if the inserting transaction rolls back. See nextval() in Section 9.17 for details.
154
Data Types
To insert the next value of the sequence into the serial column, specify that the serial column
should be assigned its default value. This can be done either by excluding the column from the list of
columns in the INSERT statement, or through the use of the DEFAULT key word.
The type names serial and serial4 are equivalent: both create integer columns. The type
names bigserial and serial8 work the same way, except that they create a bigint column.
bigserial should be used if you anticipate the use of more than 231 identifiers over the lifetime of
the table. The type names smallserial and serial2 also work the same way, except that they
create a smallint column.
The sequence created for a serial column is automatically dropped when the owning column is
dropped. You can drop the sequence without dropping the column, but this will force removal of the
column default expression.
8.2. Monetary Types
The money type stores a currency amount with a fixed fractional precision; see Table 8.3. The fractional precision is determined by the database's lc_monetary setting. The range shown in the table
assumes there are two fractional digits. Input is accepted in a variety of formats, including integer
and floating-point literals, as well as typical currency formatting, such as '$1,000.00'. Output is
generally in the latter form but depends on the locale.
Table 8.3. Monetary Types
Name
Storage Size Description
Range
money
8 bytes
-92233720368547758.08
to
+92233720368547758.07
currency amount
Since the output of this data type is locale-sensitive, it might not work to load money data into a
database that has a different setting of lc_monetary. To avoid problems, before restoring a dump
into a new database make sure lc_monetary has the same or equivalent value as in the database
that was dumped.
Values of the numeric, int, and bigint data types can be cast to money. Conversion from the
real and double precision data types can be done by casting to numeric first, for example:
SELECT '12.34'::float8::numeric::money;
However, this is not recommended. Floating point numbers should not be used to handle money due
to the potential for rounding errors.
A money value can be cast to numeric without loss of precision. Conversion to other types could
potentially lose precision, and must also be done in two stages:
SELECT '52093.89'::money::numeric::float8;
Division of a money value by an integer value is performed with truncation of the fractional part
towards zero. To get a rounded result, divide by a floating-point value, or cast the money value to
numeric before dividing and back to money afterwards. (The latter is preferable to avoid risking
precision loss.) When a money value is divided by another money value, the result is double precision (i.e., a pure number, not money); the currency units cancel each other out in the division.
8.3. Character Types
155
Data Types
Table 8.4. Character Types
Name
Description
character varying(n), varchar(n)
variable-length with limit
character(n), char(n), bpchar(n)
fixed-length, blank-padded
bpchar
variable unlimited length, blank-trimmed
text
variable unlimited length
Table 8.4 shows the general-purpose character types available in PostgreSQL.
SQL defines two primary character types: character varying(n) and character(n), where
n is a positive integer. Both of these types can store strings up to n characters (not bytes) in length. An
attempt to store a longer string into a column of these types will result in an error, unless the excess
characters are all spaces, in which case the string will be truncated to the maximum length. (This
somewhat bizarre exception is required by the SQL standard.) However, if one explicitly casts a value
to character varying(n) or character(n), then an over-length value will be truncated to
n characters without raising an error. (This too is required by the SQL standard.) If the string to be
stored is shorter than the declared length, values of type character will be space-padded; values
of type character varying will simply store the shorter string.
In addition, PostgreSQL provides the text type, which stores strings of any length. Although the
text type is not in the SQL standard, several other SQL database management systems have it as
well. text is PostgreSQL's native string data type, in that most built-in functions operating on strings
are declared to take or return text not character varying. For many purposes, character
varying acts as though it were a domain over text.
The type name varchar is an alias for character varying, while bpchar (with length specifier) and char are aliases for character. The varchar and char aliases are defined in the SQL
standard; bpchar is a PostgreSQL extension.
If specified, the length n must be greater than zero and cannot exceed 10,485,760. If character
varying (or varchar) is used without length specifier, the type accepts strings of any length. If
bpchar lacks a length specifier, it also accepts strings of any length, but trailing spaces are semantically insignificant. If character (or char) lacks a specifier, it is equivalent to character(1).
Values of type character are physically padded with spaces to the specified width n, and are stored
and displayed that way. However, trailing spaces are treated as semantically insignificant and disregarded when comparing two values of type character. In collations where whitespace is significant, this behavior can produce unexpected results; for example SELECT 'a '::CHAR(2) collate "C" < E'a\n'::CHAR(2) returns true, even though C locale would consider a space
to be greater than a newline. Trailing spaces are removed when converting a character value to
one of the other string types. Note that trailing spaces are semantically significant in character
varying and text values, and when using pattern matching, that is LIKE and regular expressions.
The characters that can be stored in any of these data types are determined by the database character set,
which is selected when the database is created. Regardless of the specific character set, the character
with code zero (sometimes called NUL) cannot be stored. For more information refer to Section 23.3.
The storage requirement for a short string (up to 126 bytes) is 1 byte plus the actual string, which
includes the space padding in the case of character. Longer strings have 4 bytes of overhead instead
of 1. Long strings are compressed by the system automatically, so the physical requirement on disk
might be less. Very long values are also stored in background tables so that they do not interfere with
rapid access to shorter column values. In any case, the longest possible character string that can be
stored is about 1 GB. (The maximum value that will be allowed for n in the data type declaration is less
than that. It wouldn't be useful to change this because with multibyte character encodings the number
of characters and bytes can be quite different. If you desire to store long strings with no specific upper
limit, use text or character varying without a length specifier, rather than making up an
arbitrary length limit.)
156
Data Types
Tip
There is no performance difference among these three types, apart from increased storage
space when using the blank-padded type, and a few extra CPU cycles to check the length
when storing into a length-constrained column. While character(n) has performance advantages in some other database systems, there is no such advantage in PostgreSQL; in fact
character(n) is usually the slowest of the three because of its additional storage costs. In
most situations text or character varying should be used instead.
Refer to Section 4.1.2.1 for information about the syntax of string literals, and to Chapter 9 for information about available operators and functions.
Example 8.1. Using the Character Types
CREATE TABLE test1 (a character(4));
INSERT INTO test1 VALUES ('ok');
SELECT a, char_length(a) FROM test1; --
1
a
| char_length
------+------------ok
|
2
CREATE TABLE test2 (b varchar(5));
INSERT INTO test2 VALUES ('ok');
INSERT INTO test2 VALUES ('good
');
INSERT INTO test2 VALUES ('too long');
ERROR: value too long for type character varying(5)
INSERT INTO test2 VALUES ('too long'::varchar(5)); -- explicit
truncation
SELECT b, char_length(b) FROM test2;
b
| char_length
-------+------------ok
|
2
good |
5
too l |
5
1
The char_length function is discussed in Section 9.4.
There are two other fixed-length character types in PostgreSQL, shown in Table 8.5. These are not
intended for general-purpose use, only for use in the internal system catalogs. The name type is used
to store identifiers. Its length is currently defined as 64 bytes (63 usable characters plus terminator)
but should be referenced using the constant NAMEDATALEN in C source code. The length is set at
compile time (and is therefore adjustable for special uses); the default maximum length might change
in a future release. The type "char" (note the quotes) is different from char(1) in that it only uses
one byte of storage, and therefore can store only a single ASCII character. It is used in the system
catalogs as a simplistic enumeration type.
Table 8.5. Special Character Types
Name
Storage Size
Description
"char"
1 byte
single-byte internal type
157
Data Types
Name
Storage Size
Description
name
64 bytes
internal type for object names
8.4. Binary Data Types
The bytea data type allows storage of binary strings; see Table 8.6.
Table 8.6. Binary Data Types
Name
Storage Size
Description
bytea
1 or 4 bytes plus the actual binary string
variable-length binary string
A binary string is a sequence of octets (or bytes). Binary strings are distinguished from character
strings in two ways. First, binary strings specifically allow storing octets of value zero and other “nonprintable” octets (usually, octets outside the decimal range 32 to 126). Character strings disallow zero
octets, and also disallow any other octet values and sequences of octet values that are invalid according
to the database's selected character set encoding. Second, operations on binary strings process the
actual bytes, whereas the processing of character strings depends on locale settings. In short, binary
strings are appropriate for storing data that the programmer thinks of as “raw bytes”, whereas character
strings are appropriate for storing text.
The bytea type supports two formats for input and output: “hex” format and PostgreSQL's historical “escape” format. Both of these are always accepted on input. The output format depends on the
configuration parameter bytea_output; the default is hex. (Note that the hex format was introduced in
PostgreSQL 9.0; earlier versions and some tools don't understand it.)
The SQL standard defines a different binary string type, called BLOB or BINARY LARGE OBJECT.
The input format is different from bytea, but the provided functions and operators are mostly the
same.
8.4.1. bytea Hex Format
The “hex” format encodes binary data as 2 hexadecimal digits per byte, most significant nibble first.
The entire string is preceded by the sequence \x (to distinguish it from the escape format). In some
contexts, the initial backslash may need to be escaped by doubling it (see Section 4.1.2.1). For input,
the hexadecimal digits can be either upper or lower case, and whitespace is permitted between digit
pairs (but not within a digit pair nor in the starting \x sequence). The hex format is compatible with a
wide range of external applications and protocols, and it tends to be faster to convert than the escape
format, so its use is preferred.
Example:
SET bytea_output = 'hex';
SELECT '\xDEADBEEF'::bytea;
bytea
-----------\xdeadbeef
8.4.2. bytea Escape Format
The “escape” format is the traditional PostgreSQL format for the bytea type. It takes the approach of
representing a binary string as a sequence of ASCII characters, while converting those bytes that cannot
be represented as an ASCII character into special escape sequences. If, from the point of view of the
application, representing bytes as characters makes sense, then this representation can be convenient.
158
Data Types
But in practice it is usually confusing because it fuzzes up the distinction between binary strings and
character strings, and also the particular escape mechanism that was chosen is somewhat unwieldy.
Therefore, this format should probably be avoided for most new applications.
When entering bytea values in escape format, octets of certain values must be escaped, while all
octet values can be escaped. In general, to escape an octet, convert it into its three-digit octal value and
precede it by a backslash. Backslash itself (octet decimal value 92) can alternatively be represented
by double backslashes. Table 8.7 shows the characters that must be escaped, and gives the alternative
escape sequences where applicable.
Table 8.7. bytea Literal Escaped Octets
Decimal Octet
Value
Description
Escaped Input
Representation
Example
Hex Representation
0
zero octet
'\000'
'\000'::bytea
\x00
39
single quote
'''' or
'\047'
''''::bytea
\x27
92
backslash
'\\' or
'\134'
'\\'::bytea
\x5c
0 to 31 and 127
to 255
“non-printable”
octets
'\xxx' (octal
value)
'\001'::bytea
\x01
The requirement to escape non-printable octets varies depending on locale settings. In some instances
you can get away with leaving them unescaped.
The reason that single quotes must be doubled, as shown in Table 8.7, is that this is true for any string
literal in an SQL command. The generic string-literal parser consumes the outermost single quotes
and reduces any pair of single quotes to one data character. What the bytea input function sees is just
one single quote, which it treats as a plain data character. However, the bytea input function treats
backslashes as special, and the other behaviors shown in Table 8.7 are implemented by that function.
In some contexts, backslashes must be doubled compared to what is shown above, because the generic
string-literal parser will also reduce pairs of backslashes to one data character; see Section 4.1.2.1.
Bytea octets are output in hex format by default. If you change bytea_output to escape, “nonprintable” octets are converted to their equivalent three-digit octal value and preceded by one backslash. Most “printable” octets are output by their standard representation in the client character set, e.g.:
SET bytea_output = 'escape';
SELECT 'abc \153\154\155 \052\251\124'::bytea;
bytea
---------------abc klm *\251T
The octet with decimal value 92 (backslash) is doubled in the output. Details are in Table 8.8.
Table 8.8. bytea Output Escaped Octets
Decimal Octet
Value
Description
Escaped Output Example
Representation
Output Result
92
backslash
\\
\\
0 to 31 and 127
to 255
“non-printable”
octets
\xxx (octal val- '\001'::bytea
ue)
32 to 126
“printable” octets client character
'\176'::bytea
set representation
'\134'::bytea
159
\001
~
Data Types
Depending on the front end to PostgreSQL you use, you might have additional work to do in terms of
escaping and unescaping bytea strings. For example, you might also have to escape line feeds and
carriage returns if your interface automatically translates these.
8.5. Date/Time Types
PostgreSQL supports the full set of SQL date and time types, shown in Table 8.9. The operations
available on these data types are described in Section 9.9. Dates are counted according to the Gregorian
calendar, even in years before that calendar was introduced (see Section B.6 for more information).
Table 8.9. Date/Time Types
Name
Storage Size
Description
Low Value
High Value
Resolution
timestamp
[ (p) ]
[ without time
zone ]
8 bytes
both date and
time (no time
zone)
4713 BC
294276 AD
1 microsecond
timestamp
[ (p) ]
with time
zone
8 bytes
both date and 4713 BC
time, with time
zone
294276 AD
1 microsecond
date
4 bytes
date (no time
of day)
5874897 AD
1 day
time
[ (p) ]
[ without time
zone ]
8 bytes
time of day (no 00:00:00
date)
24:00:00
1 microsecond
time
[ (p) ]
with time
zone
12 bytes
time of day
00:00:00+1559 24:00:00-1559 1 microsecond
(no date), with
time zone
interval
16 bytes
[ fields ]
[ (p) ]
time interval
4713 BC
-178000000
years
178000000
years
1 microsecond
Note
The SQL standard requires that writing just timestamp be equivalent to timestamp
without time zone, and PostgreSQL honors that behavior. timestamptz is accepted
as an abbreviation for timestamp with time zone; this is a PostgreSQL extension.
time, timestamp, and interval accept an optional precision value p which specifies the number
of fractional digits retained in the seconds field. By default, there is no explicit bound on precision.
The allowed range of p is from 0 to 6.
The interval type has an additional option, which is to restrict the set of stored fields by writing
one of these phrases:
YEAR
MONTH
DAY
160
Data Types
HOUR
MINUTE
SECOND
YEAR TO MONTH
DAY TO HOUR
DAY TO MINUTE
DAY TO SECOND
HOUR TO MINUTE
HOUR TO SECOND
MINUTE TO SECOND
Note that if both fields and p are specified, the fields must include SECOND, since the precision
applies only to the seconds.
The type time with time zone is defined by the SQL standard, but the definition exhibits properties which lead to questionable usefulness. In most cases, a combination of date, time, timestamp without time zone, and timestamp with time zone should provide a complete
range of date/time functionality required by any application.
8.5.1. Date/Time Input
Date and time input is accepted in almost any reasonable format, including ISO 8601, SQL-compatible, traditional POSTGRES, and others. For some formats, ordering of day, month, and year in date
input is ambiguous and there is support for specifying the expected ordering of these fields. Set the
DateStyle parameter to MDY to select month-day-year interpretation, DMY to select day-month-year
interpretation, or YMD to select year-month-day interpretation.
PostgreSQL is more flexible in handling date/time input than the SQL standard requires. See Appendix B for the exact parsing rules of date/time input and for the recognized text fields including months,
days of the week, and time zones.
Remember that any date or time literal input needs to be enclosed in single quotes, like text strings.
Refer to Section 4.1.2.7 for more information. SQL requires the following syntax
type [ (p) ] 'value'
where p is an optional precision specification giving the number of fractional digits in the seconds
field. Precision can be specified for time, timestamp, and interval types, and can range from
0 to 6. If no precision is specified in a constant specification, it defaults to the precision of the literal
value (but not more than 6 digits).
8.5.1.1. Dates
Table 8.10 shows some possible inputs for the date type.
Table 8.10. Date Input
Example
Description
1999-01-08
ISO 8601; January 8 in any mode (recommended format)
January 8, 1999
unambiguous in any datestyle input mode
1/8/1999
January 8 in MDY mode; August 1 in DMY mode
1/18/1999
January 18 in MDY mode; rejected in other modes
01/02/03
January 2, 2003 in MDY mode; February 1, 2003 in DMY mode;
February 3, 2001 in YMD mode
1999-Jan-08
January 8 in any mode
161
Data Types
Example
Description
Jan-08-1999
January 8 in any mode
08-Jan-1999
January 8 in any mode
99-Jan-08
January 8 in YMD mode, else error
08-Jan-99
January 8, except error in YMD mode
Jan-08-99
January 8, except error in YMD mode
19990108
ISO 8601; January 8, 1999 in any mode
990108
ISO 8601; January 8, 1999 in any mode
1999.008
year and day of year
J2451187
Julian date
January 8, 99 BC
year 99 BC
8.5.1.2. Times
The time-of-day types are time [ (p) ] without time zone and time [ (p) ] with
time zone. time alone is equivalent to time without time zone.
Valid input for these types consists of a time of day followed by an optional time zone. (See Table 8.11
and Table 8.12.) If a time zone is specified in the input for time without time zone, it is silently
ignored. You can also specify a date but it will be ignored, except when you use a time zone name
that involves a daylight-savings rule, such as America/New_York. In this case specifying the date
is required in order to determine whether standard or daylight-savings time applies. The appropriate
time zone offset is recorded in the time with time zone value and is output as stored; it is
not adjusted to the active time zone.
Table 8.11. Time Input
Example
Description
04:05:06.789
ISO 8601
04:05:06
ISO 8601
04:05
ISO 8601
040506
ISO 8601
04:05 AM
same as 04:05; AM does not affect
value
04:05 PM
same as 16:05; input hour must be <=
12
04:05:06.789-8
ISO 8601, with time zone as UTC offset
04:05:06-08:00
ISO 8601, with time zone as UTC offset
04:05-08:00
ISO 8601, with time zone as UTC offset
040506-08
ISO 8601, with time zone as UTC offset
040506+0730
ISO 8601, with fractional-hour time
zone as UTC offset
040506+07:30:00
UTC offset specified to seconds (not
allowed in ISO 8601)
04:05:06 PST
time zone specified by abbreviation
162
Data Types
Example
Description
2003-04-12 04:05:06 America/New_York
time zone specified by full name
Table 8.12. Time Zone Input
Example
Description
PST
Abbreviation (for Pacific Standard Time)
America/New_York
Full time zone name
PST8PDT
POSIX-style time zone specification
-8:00:00
UTC offset for PST
-8:00
UTC offset for PST (ISO 8601 extended format)
-800
UTC offset for PST (ISO 8601 basic format)
-8
UTC offset for PST (ISO 8601 basic format)
zulu
Military abbreviation for UTC
z
Short form of zulu (also in ISO 8601)
Refer to Section 8.5.3 for more information on how to specify time zones.
8.5.1.3. Time Stamps
Valid input for the time stamp types consists of the concatenation of a date and a time, followed by
an optional time zone, followed by an optional AD or BC. (Alternatively, AD/BC can appear before the
time zone, but this is not the preferred ordering.) Thus:
1999-01-08 04:05:06
and:
1999-01-08 04:05:06 -8:00
are valid values, which follow the ISO 8601 standard. In addition, the common format:
January 8 04:05:06 1999 PST
is supported.
The SQL standard differentiates timestamp without time zone and timestamp with
time zone literals by the presence of a “+” or “-” symbol and time zone offset after the time. Hence,
according to the standard,
TIMESTAMP '2004-10-19 10:23:54'
is a timestamp without time zone, while
TIMESTAMP '2004-10-19 10:23:54+02'
is a timestamp with time zone. PostgreSQL never examines the content of a literal string
before determining its type, and therefore will treat both of the above as timestamp without
time zone. To ensure that a literal is treated as timestamp with time zone, give it the
correct explicit type:
163
Data Types
TIMESTAMP WITH TIME ZONE '2004-10-19 10:23:54+02'
In a value that has been determined to be timestamp without time zone, PostgreSQL will
silently ignore any time zone indication. That is, the resulting value is derived from the date/time fields
in the input string, and is not adjusted for time zone.
For timestamp with time zone values, an input string that includes an explicit time zone
will be converted to UTC (Universal Coordinated Time) using the appropriate offset for that time
zone. If no time zone is stated in the input string, then it is assumed to be in the time zone indicated
by the system's TimeZone parameter, and is converted to UTC using the offset for the timezone
zone. In either case, the value is stored internally as UTC, and the originally stated or assumed time
zone is not retained.
When a timestamp with time zone value is output, it is always converted from UTC to the
current timezone zone, and displayed as local time in that zone. To see the time in another time
zone, either change timezone or use the AT TIME ZONE construct (see Section 9.9.4).
Conversions between timestamp without time zone and timestamp with time zone
normally assume that the timestamp without time zone value should be taken or given as
timezone local time. A different time zone can be specified for the conversion using AT TIME
ZONE.
8.5.1.4. Special Values
PostgreSQL supports several special date/time input values for convenience, as shown in Table 8.13.
The values infinity and -infinity are specially represented inside the system and will be
displayed unchanged; but the others are simply notational shorthands that will be converted to ordinary
date/time values when read. (In particular, now and related strings are converted to a specific time
value as soon as they are read.) All of these values need to be enclosed in single quotes when used
as constants in SQL commands.
Table 8.13. Special Date/Time Inputs
Input String
Valid Types
Description
epoch
date, timestamp
1970-01-01 00:00:00+00 (Unix
system time zero)
infinity
date, timestamp, interval
later than all other time stamps
-infinity
date, timestamp, interval
earlier than all other time
stamps
now
date, time, timestamp
current transaction's start time
today
date, timestamp
midnight (00:00) today
tomorrow
date, timestamp
midnight (00:00) tomorrow
yesterday
date, timestamp
midnight (00:00) yesterday
allballs
time
00:00:00.00 UTC
The following SQL-compatible functions can also be used to obtain the current time value for the corresponding data type: CURRENT_DATE, CURRENT_TIME, CURRENT_TIMESTAMP, LOCALTIME,
LOCALTIMESTAMP. (See Section 9.9.5.) Note that these are SQL functions and are not recognized
in data input strings.
Caution
While the input strings now, today, tomorrow, and yesterday are fine to use in interactive SQL commands, they can have surprising behavior when the command is saved to be
164
Data Types
executed later, for example in prepared statements, views, and function definitions. The string
can be converted to a specific time value that continues to be used long after it becomes stale.
Use one of the SQL functions instead in such contexts. For example, CURRENT_DATE + 1
is safer than 'tomorrow'::date.
8.5.2. Date/Time Output
The output format of the date/time types can be set to one of the four styles ISO 8601, SQL (Ingres),
traditional POSTGRES (Unix date format), or German. The default is the ISO format. (The SQL
standard requires the use of the ISO 8601 format. The name of the “SQL” output format is a historical
accident.) Table 8.14 shows examples of each output style. The output of the date and time types is
generally only the date or time part in accordance with the given examples. However, the POSTGRES
style outputs date-only values in ISO format.
Table 8.14. Date/Time Output Styles
Style Specification
Description
Example
ISO
ISO 8601, SQL standard
1997-12-17 07:37:16-08
SQL
traditional style
12/17/1997 07:37:16.00 PST
Postgres
original style
Wed Dec 17 07:37:16 1997 PST
German
regional style
17.12.1997 07:37:16.00 PST
Note
ISO 8601 specifies the use of uppercase letter T to separate the date and time. PostgreSQL
accepts that format on input, but on output it uses a space rather than T, as shown above. This
is for readability and for consistency with RFC 33391 as well as some other database systems.
In the SQL and POSTGRES styles, day appears before month if DMY field ordering has been specified, otherwise month appears before day. (See Section 8.5.1 for how this setting also affects interpretation of input values.) Table 8.15 shows examples.
Table 8.15. Date Order Conventions
datestyle Setting
Input Ordering
Example Output
SQL, DMY
day/month/year
17/12/1997 15:37:16.00 CET
SQL, MDY
month/day/year
12/17/1997 07:37:16.00 PST
Postgres, DMY
day/month/year
Wed 17 Dec 07:37:16 1997 PST
In the ISO style, the time zone is always shown as a signed numeric offset from UTC, with positive
sign used for zones east of Greenwich. The offset will be shown as hh (hours only) if it is an integral
number of hours, else as hh:mm if it is an integral number of minutes, else as hh:mm:ss. (The third case
is not possible with any modern time zone standard, but it can appear when working with timestamps
that predate the adoption of standardized time zones.) In the other date styles, the time zone is shown
as an alphabetic abbreviation if one is in common use in the current zone. Otherwise it appears as a
signed numeric offset in ISO 8601 basic format (hh or hhmm).
The date/time style can be selected by the user using the SET datestyle command, the DateStyle
parameter in the postgresql.conf configuration file, or the PGDATESTYLE environment variable on the server or client.
1
https://datatracker.ietf.org/doc/html/rfc3339
165
Data Types
The formatting function to_char (see Section 9.8) is also available as a more flexible way to format
date/time output.
8.5.3. Time Zones
Time zones, and time-zone conventions, are influenced by political decisions, not just earth geometry.
Time zones around the world became somewhat standardized during the 1900s, but continue to be
prone to arbitrary changes, particularly with respect to daylight-savings rules. PostgreSQL uses the
widely-used IANA (Olson) time zone database for information about historical time zone rules. For
times in the future, the assumption is that the latest known rules for a given time zone will continue
to be observed indefinitely far into the future.
PostgreSQL endeavors to be compatible with the SQL standard definitions for typical usage. However,
the SQL standard has an odd mix of date and time types and capabilities. Two obvious problems are:
• Although the date type cannot have an associated time zone, the time type can. Time zones in
the real world have little meaning unless associated with a date as well as a time, since the offset
can vary through the year with daylight-saving time boundaries.
• The default time zone is specified as a constant numeric offset from UTC. It is therefore impossible
to adapt to daylight-saving time when doing date/time arithmetic across DST boundaries.
To address these difficulties, we recommend using date/time types that contain both date and time
when using time zones. We do not recommend using the type time with time zone (though
it is supported by PostgreSQL for legacy applications and for compliance with the SQL standard).
PostgreSQL assumes your local time zone for any type containing only date or time.
All timezone-aware dates and times are stored internally in UTC. They are converted to local time in
the zone specified by the TimeZone configuration parameter before being displayed to the client.
PostgreSQL allows you to specify time zones in three different forms:
• A full time zone name, for example America/New_York. The recognized time zone names are
listed in the pg_timezone_names view (see Section 52.32). PostgreSQL uses the widely-used
IANA time zone data for this purpose, so the same time zone names are also recognized by other
software.
• A time zone abbreviation, for example PST. Such a specification merely defines a particular offset
from UTC, in contrast to full time zone names which can imply a set of daylight savings transition
rules as well. The recognized abbreviations are listed in the pg_timezone_abbrevs view (see
Section 52.31). You cannot set the configuration parameters TimeZone or log_timezone to a time
zone abbreviation, but you can use abbreviations in date/time input values and with the AT TIME
ZONE operator.
• In addition to the timezone names and abbreviations, PostgreSQL will accept POSIX-style time
zone specifications, as described in Section B.5. This option is not normally preferable to using a
named time zone, but it may be necessary if no suitable IANA time zone entry is available.
In short, this is the difference between abbreviations and full names: abbreviations represent a specific
offset from UTC, whereas many of the full names imply a local daylight-savings time rule, and so have
two possible UTC offsets. As an example, 2014-06-04 12:00 America/New_York represents
noon local time in New York, which for this particular date was Eastern Daylight Time (UTC-4). So
2014-06-04 12:00 EDT specifies that same time instant. But 2014-06-04 12:00 EST
specifies noon Eastern Standard Time (UTC-5), regardless of whether daylight savings was nominally
in effect on that date.
To complicate matters, some jurisdictions have used the same timezone abbreviation to mean different
UTC offsets at different times; for example, in Moscow MSK has meant UTC+3 in some years and
UTC+4 in others. PostgreSQL interprets such abbreviations according to whatever they meant (or had
most recently meant) on the specified date; but, as with the EST example above, this is not necessarily
the same as local civil time on that date.
166
Data Types
In all cases, timezone names and abbreviations are recognized case-insensitively. (This is a change
from PostgreSQL versions prior to 8.2, which were case-sensitive in some contexts but not others.)
Neither timezone names nor abbreviations are hard-wired into the server; they are obtained from configuration files stored under .../share/timezone/ and .../share/timezonesets/ of
the installation directory (see Section B.4).
The TimeZone configuration parameter can be set in the file postgresql.conf, or in any of the
other standard ways described in Chapter 19. There are also some special ways to set it:
• The SQL command SET TIME ZONE sets the time zone for the session. This is an alternative
spelling of SET TIMEZONE TO with a more SQL-spec-compatible syntax.
• The PGTZ environment variable is used by libpq clients to send a SET TIME ZONE command
to the server upon connection.
8.5.4. Interval Input
interval values can be written using the following verbose syntax:
[@] quantity unit [quantity unit...] [direction]
where quantity is a number (possibly signed); unit is microsecond, millisecond, second, minute, hour, day, week, month, year, decade, century, millennium, or abbreviations or plurals of these units; direction can be ago or empty. The at sign (@) is optional noise.
The amounts of the different units are implicitly added with appropriate sign accounting. ago negates
all the fields. This syntax is also used for interval output, if IntervalStyle is set to postgres_verbose.
Quantities of days, hours, minutes, and seconds can be specified without explicit unit markings. For
example, '1 12:59:10' is read the same as '1 day 12 hours 59 min 10 sec'. Also,
a combination of years and months can be specified with a dash; for example '200-10' is read the
same as '200 years 10 months'. (These shorter forms are in fact the only ones allowed by the
SQL standard, and are used for output when IntervalStyle is set to sql_standard.)
Interval values can also be written as ISO 8601 time intervals, using either the “format with designators” of the standard's section 4.4.3.2 or the “alternative format” of section 4.4.3.3. The format with
designators looks like this:
P quantity unit [ quantity unit ...] [ T [ quantity unit ...]]
The string must start with a P, and may include a T that introduces the time-of-day units. The available
unit abbreviations are given in Table 8.16. Units may be omitted, and may be specified in any order,
but units smaller than a day must appear after T. In particular, the meaning of M depends on whether
it is before or after T.
Table 8.16. ISO 8601 Interval Unit Abbreviations
Abbreviation
Meaning
Y
Years
M
Months (in the date part)
W
Weeks
D
Days
H
Hours
M
Minutes (in the time part)
S
Seconds
167
Data Types
In the alternative format:
P [ years-months-days ] [ T hours:minutes:seconds ]
the string must begin with P, and a T separates the date and time parts of the interval. The values are
given as numbers similar to ISO 8601 dates.
When writing an interval constant with a fields specification, or when assigning a string to an interval column that was defined with a fields specification, the interpretation of unmarked quantities
depends on the fields. For example INTERVAL '1' YEAR is read as 1 year, whereas INTERVAL '1' means 1 second. Also, field values “to the right” of the least significant field allowed by the
fields specification are silently discarded. For example, writing INTERVAL '1 day 2:03:04'
HOUR TO MINUTE results in dropping the seconds field, but not the day field.
According to the SQL standard all fields of an interval value must have the same sign, so a leading
negative sign applies to all fields; for example the negative sign in the interval literal '-1 2:03:04'
applies to both the days and hour/minute/second parts. PostgreSQL allows the fields to have different
signs, and traditionally treats each field in the textual representation as independently signed, so that
the hour/minute/second part is considered positive in this example. If IntervalStyle is set to
sql_standard then a leading sign is considered to apply to all fields (but only if no additional
signs appear). Otherwise the traditional PostgreSQL interpretation is used. To avoid ambiguity, it's
recommended to attach an explicit sign to each field if any field is negative.
Internally, interval values are stored as three integral fields: months, days, and microseconds.
These fields are kept separate because the number of days in a month varies, while a day can have 23 or
25 hours if a daylight savings time transition is involved. An interval input string that uses other units
is normalized into this format, and then reconstructed in a standardized way for output, for example:
SELECT '2 years 15 months 100 weeks 99 hours 123456789
milliseconds'::interval;
interval
--------------------------------------3 years 3 mons 700 days 133:17:36.789
Here weeks, which are understood as “7 days”, have been kept separate, while the smaller and larger
time units were combined and normalized.
Input field values can have fractional parts, for example '1.5 weeks' or '01:02:03.45'. However, because interval internally stores only integral fields, fractional values must be converted
into smaller units. Fractional parts of units greater than months are rounded to be an integer number
of months, e.g. '1.5 years' becomes '1 year 6 mons'. Fractional parts of weeks and days
are computed to be an integer number of days and microseconds, assuming 30 days per month and
24 hours per day, e.g., '1.75 months' becomes 1 mon 22 days 12:00:00. Only seconds
will ever be shown as fractional on output.
Table 8.17 shows some examples of valid interval input.
Table 8.17. Interval Input
Example
Description
1-2
SQL standard format: 1 year 2 months
3 4:05:06
SQL standard format: 3 days 4 hours 5 minutes 6
seconds
1 year 2 months 3 days 4 hours 5 Traditional Postgres format: 1 year 2 months 3
minutes 6 seconds
days 4 hours 5 minutes 6 seconds
ISO 8601 “format with designators”: same
meaning as above
P1Y2M3DT4H5M6S
168
Data Types
Example
Description
P0001-02-03T04:05:06
ISO 8601 “alternative format”: same meaning as
above
8.5.5. Interval Output
As previously explained, PostgreSQL stores interval values as months, days, and microseconds.
For output, the months field is converted to years and months by dividing by 12. The days field is
shown as-is. The microseconds field is converted to hours, minutes, seconds, and fractional seconds.
Thus months, minutes, and seconds will never be shown as exceeding the ranges 0–11, 0–59, and 0–
59 respectively, while the displayed years, days, and hours fields can be quite large. (The justify_days and justify_hours functions can be used if it is desirable to transpose large days or
hours values into the next higher field.)
The output format of the interval type can be set to one of the four styles sql_standard, postgres, postgres_verbose, or iso_8601, using the command SET intervalstyle. The
default is the postgres format. Table 8.18 shows examples of each output style.
The sql_standard style produces output that conforms to the SQL standard's specification for
interval literal strings, if the interval value meets the standard's restrictions (either year-month only or
day-time only, with no mixing of positive and negative components). Otherwise the output looks like
a standard year-month literal string followed by a day-time literal string, with explicit signs added to
disambiguate mixed-sign intervals.
The output of the postgres style matches the output of PostgreSQL releases prior to 8.4 when the
DateStyle parameter was set to ISO.
The output of the postgres_verbose style matches the output of PostgreSQL releases prior to
8.4 when the DateStyle parameter was set to non-ISO output.
The output of the iso_8601 style matches the “format with designators” described in section 4.4.3.2
of the ISO 8601 standard.
Table 8.18. Interval Output Style Examples
Style Specification
Year-Month Interval
Day-Time Interval
Mixed Interval
sql_standard
1-2
3 4:05:06
-1-2 +3 -4:05:06
postgres
1 year 2 mons
3 days 04:05:06
-1 year -2 mons +3
days -04:05:06
@ 3 days 4 hours 5
mins 6 secs
@ 1 year 2 mons -3
days 4 hours 5 mins 6
secs ago
P3DT4H5M6S
P-1Y-2M3D
T-4H-5M-6S
postgres_verbose @ 1 year 2 mons
iso_8601
P1Y2M
8.6. Boolean Type
PostgreSQL provides the standard SQL type boolean; see Table 8.19. The boolean type can have
several states: “true”, “false”, and a third state, “unknown”, which is represented by the SQL null value.
Table 8.19. Boolean Data Type
Name
Storage Size
Description
boolean
1 byte
state of true or false
Boolean constants can be represented in SQL queries by the SQL key words TRUE, FALSE, and NULL.
169
Data Types
The datatype input function for type boolean accepts these string representations for the “true” state:
true
yes
on
1
and these representations for the “false” state:
false
no
off
0
Unique prefixes of these strings are also accepted, for example t or n. Leading or trailing whitespace
is ignored, and case does not matter.
The datatype output function for type boolean always emits either t or f, as shown in Example 8.2.
Example 8.2. Using the boolean Type
CREATE TABLE test1 (a boolean, b text);
INSERT INTO test1 VALUES (TRUE, 'sic est');
INSERT INTO test1 VALUES (FALSE, 'non est');
SELECT * FROM test1;
a |
b
---+--------t | sic est
f | non est
SELECT * FROM test1 WHERE a;
a |
b
---+--------t | sic est
The key words TRUE and FALSE are the preferred (SQL-compliant) method for writing Boolean
constants in SQL queries. But you can also use the string representations by following the generic
string-literal constant syntax described in Section 4.1.2.7, for example 'yes'::boolean.
Note that the parser automatically understands that TRUE and FALSE are of type boolean, but this
is not so for NULL because that can have any type. So in some contexts you might have to cast NULL
to boolean explicitly, for example NULL::boolean. Conversely, the cast can be omitted from a
string-literal Boolean value in contexts where the parser can deduce that the literal must be of type
boolean.
8.7. Enumerated Types
Enumerated (enum) types are data types that comprise a static, ordered set of values. They are equivalent to the enum types supported in a number of programming languages. An example of an enum
type might be the days of the week, or a set of status values for a piece of data.
8.7.1. Declaration of Enumerated Types
Enum types are created using the CREATE TYPE command, for example:
CREATE TYPE mood AS ENUM ('sad', 'ok', 'happy');
170
Data Types
Once created, the enum type can be used in table and function definitions much like any other type:
CREATE TYPE mood AS ENUM ('sad', 'ok', 'happy');
CREATE TABLE person (
name text,
current_mood mood
);
INSERT INTO person VALUES ('Moe', 'happy');
SELECT * FROM person WHERE current_mood = 'happy';
name | current_mood
------+-------------Moe | happy
(1 row)
8.7.2. Ordering
The ordering of the values in an enum type is the order in which the values were listed when the
type was created. All standard comparison operators and related aggregate functions are supported
for enums. For example:
INSERT INTO person VALUES ('Larry', 'sad');
INSERT INTO person VALUES ('Curly', 'ok');
SELECT * FROM person WHERE current_mood > 'sad';
name | current_mood
-------+-------------Moe
| happy
Curly | ok
(2 rows)
SELECT * FROM person WHERE current_mood > 'sad' ORDER BY
current_mood;
name | current_mood
-------+-------------Curly | ok
Moe
| happy
(2 rows)
SELECT name
FROM person
WHERE current_mood = (SELECT MIN(current_mood) FROM person);
name
------Larry
(1 row)
8.7.3. Type Safety
Each enumerated data type is separate and cannot be compared with other enumerated types. See this
example:
CREATE TYPE happiness AS ENUM ('happy', 'very happy', 'ecstatic');
CREATE TABLE holidays (
num_weeks integer,
happiness happiness
);
171
Data Types
INSERT INTO holidays(num_weeks,happiness) VALUES (4, 'happy');
INSERT INTO holidays(num_weeks,happiness) VALUES (6, 'very happy');
INSERT INTO holidays(num_weeks,happiness) VALUES (8, 'ecstatic');
INSERT INTO holidays(num_weeks,happiness) VALUES (2, 'sad');
ERROR: invalid input value for enum happiness: "sad"
SELECT person.name, holidays.num_weeks FROM person, holidays
WHERE person.current_mood = holidays.happiness;
ERROR: operator does not exist: mood = happiness
If you really need to do something like that, you can either write a custom operator or add explicit
casts to your query:
SELECT person.name, holidays.num_weeks FROM person, holidays
WHERE person.current_mood::text = holidays.happiness::text;
name | num_weeks
------+----------Moe |
4
(1 row)
8.7.4. Implementation Details
Enum labels are case sensitive, so 'happy' is not the same as 'HAPPY'. White space in the labels
is significant too.
Although enum types are primarily intended for static sets of values, there is support for adding new
values to an existing enum type, and for renaming values (see ALTER TYPE). Existing values cannot
be removed from an enum type, nor can the sort ordering of such values be changed, short of dropping
and re-creating the enum type.
An enum value occupies four bytes on disk. The length of an enum value's textual label is limited by
the NAMEDATALEN setting compiled into PostgreSQL; in standard builds this means at most 63 bytes.
The translations from internal enum values to textual labels are kept in the system catalog pg_enum.
Querying this catalog directly can be useful.
8.8. Geometric Types
Geometric data types represent two-dimensional spatial objects. Table 8.20 shows the geometric types
available in PostgreSQL.
Table 8.20. Geometric Types
Name
Storage Size
Description
Representation
point
16 bytes
Point on a plane
(x,y)
line
24 bytes
Infinite line
{A,B,C}
lseg
32 bytes
Finite line segment
[(x1,y1),(x2,y2)]
box
32 bytes
Rectangular box
(x1,y1),(x2,y2)
path
16+16n bytes
Closed path (similar to polygon)
((x1,y1),...)
path
16+16n bytes
Open path
[(x1,y1),...]
polygon
40+16n bytes
Polygon (similar to closed path)
((x1,y1),...)
circle
24 bytes
Circle
<(x,y),r> (center
point and radius)
172
Data Types
In all these types, the individual coordinates are stored as double precision (float8) numbers.
A rich set of functions and operators is available to perform various geometric operations such as
scaling, translation, rotation, and determining intersections. They are explained in Section 9.11.
8.8.1. Points
Points are the fundamental two-dimensional building block for geometric types. Values of type point
are specified using either of the following syntaxes:
( x , y )
x , y
where x and y are the respective coordinates, as floating-point numbers.
Points are output using the first syntax.
8.8.2. Lines
Lines are represented by the linear equation Ax + By + C = 0, where A and B are not both zero. Values
of type line are input and output in the following form:
{ A, B, C }
Alternatively, any of the following forms can be used for input:
[ ( x1 , y1 ) , ( x2 , y2 ) ]
( ( x1 , y1 ) , ( x2 , y2 ) )
( x1 , y1 ) , ( x2 , y2 )
x1 , y1
,
x2 , y2
where (x1,y1) and (x2,y2) are two different points on the line.
8.8.3. Line Segments
Line segments are represented by pairs of points that are the endpoints of the segment. Values of type
lseg are specified using any of the following syntaxes:
[ ( x1 , y1 ) , ( x2 , y2 ) ]
( ( x1 , y1 ) , ( x2 , y2 ) )
( x1 , y1 ) , ( x2 , y2 )
x1 , y1
,
x2 , y2
where (x1,y1) and (x2,y2) are the end points of the line segment.
Line segments are output using the first syntax.
8.8.4. Boxes
Boxes are represented by pairs of points that are opposite corners of the box. Values of type box are
specified using any of the following syntaxes:
( ( x1 , y1 ) , ( x2 , y2 ) )
( x1 , y1 ) , ( x2 , y2 )
x1 , y1
,
x2 , y2
173
Data Types
where (x1,y1) and (x2,y2) are any two opposite corners of the box.
Boxes are output using the second syntax.
Any two opposite corners can be supplied on input, but the values will be reordered as needed to store
the upper right and lower left corners, in that order.
8.8.5. Paths
Paths are represented by lists of connected points. Paths can be open, where the first and last points in
the list are considered not connected, or closed, where the first and last points are considered connected.
Values of type path are specified using any of the following syntaxes:
[ ( x1 , y1 ) , ... , ( xn , yn ) ]
( ( x1 , y1 ) , ... , ( xn , yn ) )
( x1 , y1 ) , ... , ( xn , yn )
( x1 , y1
, ... ,
xn , yn )
x1 , y1
, ... ,
xn , yn
where the points are the end points of the line segments comprising the path. Square brackets ([])
indicate an open path, while parentheses (()) indicate a closed path. When the outermost parentheses
are omitted, as in the third through fifth syntaxes, a closed path is assumed.
Paths are output using the first or second syntax, as appropriate.
8.8.6. Polygons
Polygons are represented by lists of points (the vertices of the polygon). Polygons are very similar
to closed paths; the essential semantic difference is that a polygon is considered to include the area
within it, while a path is not.
An important implementation difference between polygons and paths is that the stored representation
of a polygon includes its smallest bounding box. This speeds up certain search operations, although
computing the bounding box adds overhead while constructing new polygons.
Values of type polygon are specified using any of the following syntaxes:
( ( x1 , y1 ) , ... , ( xn , yn ) )
( x1 , y1 ) , ... , ( xn , yn )
( x1 , y1
, ... ,
xn , yn )
x1 , y1
, ... ,
xn , yn
where the points are the end points of the line segments comprising the boundary of the polygon.
Polygons are output using the first syntax.
8.8.7. Circles
Circles are represented by a center point and radius. Values of type circle are specified using any
of the following syntaxes:
< ( x , y ) , r >
( ( x , y ) , r )
( x , y ) , r
x , y
, r
174
Data Types
where (x,y) is the center point and r is the radius of the circle.
Circles are output using the first syntax.
8.9. Network Address Types
PostgreSQL offers data types to store IPv4, IPv6, and MAC addresses, as shown in Table 8.21. It is
better to use these types instead of plain text types to store network addresses, because these types
offer input error checking and specialized operators and functions (see Section 9.12).
Table 8.21. Network Address Types
Name
Storage Size
Description
cidr
7 or 19 bytes
IPv4 and IPv6 networks
inet
7 or 19 bytes
IPv4 and IPv6 hosts and networks
macaddr
6 bytes
MAC addresses
macaddr8
8 bytes
MAC addresses (EUI-64 format)
When sorting inet or cidr data types, IPv4 addresses will always sort before IPv6 addresses, including IPv4 addresses encapsulated or mapped to IPv6 addresses, such as ::10.2.3.4 or ::ffff:10.4.3.2.
8.9.1. inet
The inet type holds an IPv4 or IPv6 host address, and optionally its subnet, all in one field. The subnet
is represented by the number of network address bits present in the host address (the “netmask”). If
the netmask is 32 and the address is IPv4, then the value does not indicate a subnet, only a single host.
In IPv6, the address length is 128 bits, so 128 bits specify a unique host address. Note that if you want
to accept only networks, you should use the cidr type rather than inet.
The input format for this type is address/y where address is an IPv4 or IPv6 address and y is
the number of bits in the netmask. If the /y portion is omitted, the netmask is taken to be 32 for IPv4
or 128 for IPv6, so the value represents just a single host. On display, the /y portion is suppressed
if the netmask specifies a single host.
8.9.2. cidr
The cidr type holds an IPv4 or IPv6 network specification. Input and output formats follow Classless
Internet Domain Routing conventions. The format for specifying networks is address/y where
address is the network's lowest address represented as an IPv4 or IPv6 address, and y is the number
of bits in the netmask. If y is omitted, it is calculated using assumptions from the older classful network
numbering system, except it will be at least large enough to include all of the octets written in the
input. It is an error to specify a network address that has bits set to the right of the specified netmask.
Table 8.22 shows some examples.
Table 8.22. cidr Type Input Examples
cidr Input
cidr Output
abbrev(cidr)
192.168.100.128/25
192.168.100.128/25
192.168.100.128/25
192.168/24
192.168.0.0/24
192.168.0/24
192.168/25
192.168.0.0/25
192.168.0.0/25
192.168.1
192.168.1.0/24
192.168.1/24
192.168
192.168.0.0/24
192.168.0/24
175
Data Types
cidr Input
cidr Output
abbrev(cidr)
128.1
128.1.0.0/16
128.1/16
128
128.0.0.0/16
128.0/16
128.1.2
128.1.2.0/24
128.1.2/24
10.1.2
10.1.2.0/24
10.1.2/24
10.1
10.1.0.0/16
10.1/16
10
10.0.0.0/8
10/8
10.1.2.3/32
10.1.2.3/32
10.1.2.3/32
2001:4f8:3:ba::/64
2001:4f8:3:ba::/64
2001:4f8:3:ba/64
2001:4f8:3:ba:2e0:81ff:fe22:d1f1/128
2001:4f8:3:ba:2e0:81ff:fe22:d1f1/128
2001:4f8:3:ba:2e0:81ff:fe22:d1f1/128
::ffff:1.2.3.0/120
::ffff:1.2.3.0/120
::ffff:1.2.3/120
::ffff:1.2.3.0/128
::ffff:1.2.3.0/128
::ffff:1.2.3.0/128
8.9.3. inet vs. cidr
The essential difference between inet and cidr data types is that inet accepts values with nonzero
bits to the right of the netmask, whereas cidr does not. For example, 192.168.0.1/24 is valid
for inet but not for cidr.
Tip
If you do not like the output format for inet or cidr values, try the functions host, text,
and abbrev.
8.9.4. macaddr
The macaddr type stores MAC addresses, known for example from Ethernet card hardware addresses
(although MAC addresses are used for other purposes as well). Input is accepted in the following
formats:
'08:00:2b:01:02:03'
'08-00-2b-01-02-03'
'08002b:010203'
'08002b-010203'
'0800.2b01.0203'
'0800-2b01-0203'
'08002b010203'
These examples all specify the same address. Upper and lower case is accepted for the digits a through
f. Output is always in the first of the forms shown.
IEEE Standard 802-2001 specifies the second form shown (with hyphens) as the canonical form for
MAC addresses, and specifies the first form (with colons) as used with bit-reversed, MSB-first notation, so that 08-00-2b-01-02-03 = 10:00:D4:80:40:C0. This convention is widely ignored nowadays,
and it is relevant only for obsolete network protocols (such as Token Ring). PostgreSQL makes no
provisions for bit reversal; all accepted formats use the canonical LSB order.
The remaining five input formats are not part of any standard.
8.9.5. macaddr8
176
Data Types
The macaddr8 type stores MAC addresses in EUI-64 format, known for example from Ethernet
card hardware addresses (although MAC addresses are used for other purposes as well). This type
can accept both 6 and 8 byte length MAC addresses and stores them in 8 byte length format. MAC
addresses given in 6 byte format will be stored in 8 byte length format with the 4th and 5th bytes set
to FF and FE, respectively. Note that IPv6 uses a modified EUI-64 format where the 7th bit should
be set to one after the conversion from EUI-48. The function macaddr8_set7bit is provided to
make this change. Generally speaking, any input which is comprised of pairs of hex digits (on byte
boundaries), optionally separated consistently by one of ':', '-' or '.', is accepted. The number
of hex digits must be either 16 (8 bytes) or 12 (6 bytes). Leading and trailing whitespace is ignored.
The following are examples of input formats that are accepted:
'08:00:2b:01:02:03:04:05'
'08-00-2b-01-02-03-04-05'
'08002b:0102030405'
'08002b-0102030405'
'0800.2b01.0203.0405'
'0800-2b01-0203-0405'
'08002b01:02030405'
'08002b0102030405'
These examples all specify the same address. Upper and lower case is accepted for the digits a through
f. Output is always in the first of the forms shown.
The last six input formats shown above are not part of any standard.
To convert a traditional 48 bit MAC address in EUI-48 format to modified EUI-64 format to be included as the host portion of an IPv6 address, use macaddr8_set7bit as shown:
SELECT macaddr8_set7bit('08:00:2b:01:02:03');
macaddr8_set7bit
------------------------0a:00:2b:ff:fe:01:02:03
(1 row)
8.10. Bit String Types
Bit strings are strings of 1's and 0's. They can be used to store or visualize bit masks. There are two
SQL bit types: bit(n) and bit varying(n), where n is a positive integer.
bit type data must match the length n exactly; it is an error to attempt to store shorter or longer bit
strings. bit varying data is of variable length up to the maximum length n; longer strings will
be rejected. Writing bit without a length is equivalent to bit(1), while bit varying without
a length specification means unlimited length.
Note
If one explicitly casts a bit-string value to bit(n), it will be truncated or zero-padded on the
right to be exactly n bits, without raising an error. Similarly, if one explicitly casts a bit-string
value to bit varying(n), it will be truncated on the right if it is more than n bits.
Refer to Section 4.1.2.5 for information about the syntax of bit string constants. Bit-logical operators
and string manipulation functions are available; see Section 9.6.
177
Data Types
Example 8.3. Using the Bit String Types
CREATE TABLE test (a BIT(3), b BIT VARYING(5));
INSERT INTO test VALUES (B'101', B'00');
INSERT INTO test VALUES (B'10', B'101');
ERROR:
bit string length 2 does not match type bit(3)
INSERT INTO test VALUES (B'10'::bit(3), B'101');
SELECT * FROM test;
a | b
-----+----101 | 00
100 | 101
A bit string value requires 1 byte for each group of 8 bits, plus 5 or 8 bytes overhead depending on
the length of the string (but long values may be compressed or moved out-of-line, as explained in
Section 8.3 for character strings).
8.11. Text Search Types
PostgreSQL provides two data types that are designed to support full text search, which is the activity
of searching through a collection of natural-language documents to locate those that best match a
query. The tsvector type represents a document in a form optimized for text search; the tsquery
type similarly represents a text query. Chapter 12 provides a detailed explanation of this facility, and
Section 9.13 summarizes the related functions and operators.
8.11.1. tsvector
A tsvector value is a sorted list of distinct lexemes, which are words that have been normalized
to merge different variants of the same word (see Chapter 12 for details). Sorting and duplicate-elimination are done automatically during input, as shown in this example:
SELECT 'a fat cat sat on a mat and ate a fat rat'::tsvector;
tsvector
---------------------------------------------------'a' 'and' 'ate' 'cat' 'fat' 'mat' 'on' 'rat' 'sat'
To represent lexemes containing whitespace or punctuation, surround them with quotes:
SELECT $$the lexeme '
' contains spaces$$::tsvector;
tsvector
------------------------------------------'
' 'contains' 'lexeme' 'spaces' 'the'
(We use dollar-quoted string literals in this example and the next one to avoid the confusion of having
to double quote marks within the literals.) Embedded quotes and backslashes must be doubled:
SELECT $$the lexeme 'Joe''s' contains a quote$$::tsvector;
tsvector
-----------------------------------------------'Joe''s' 'a' 'contains' 'lexeme' 'quote' 'the'
178
Data Types
Optionally, integer positions can be attached to lexemes:
SELECT 'a:1 fat:2 cat:3 sat:4 on:5 a:6 mat:7 and:8 ate:9 a:10
fat:11 rat:12'::tsvector;
tsvector
-----------------------------------------------------------------------------'a':1,6,10 'and':8 'ate':9 'cat':3 'fat':2,11 'mat':7 'on':5
'rat':12 'sat':4
A position normally indicates the source word's location in the document. Positional information can
be used for proximity ranking. Position values can range from 1 to 16383; larger numbers are silently
set to 16383. Duplicate positions for the same lexeme are discarded.
Lexemes that have positions can further be labeled with a weight, which can be A, B, C, or D. D is the
default and hence is not shown on output:
SELECT 'a:1A fat:2B,4C cat:5D'::tsvector;
tsvector
---------------------------'a':1A 'cat':5 'fat':2B,4C
Weights are typically used to reflect document structure, for example by marking title words differently from body words. Text search ranking functions can assign different priorities to the different
weight markers.
It is important to understand that the tsvector type itself does not perform any word normalization;
it assumes the words it is given are normalized appropriately for the application. For example,
SELECT 'The Fat Rats'::tsvector;
tsvector
-------------------'Fat' 'Rats' 'The'
For most English-text-searching applications the above words would be considered non-normalized,
but tsvector doesn't care. Raw document text should usually be passed through to_tsvector
to normalize the words appropriately for searching:
SELECT to_tsvector('english', 'The Fat Rats');
to_tsvector
----------------'fat':2 'rat':3
Again, see Chapter 12 for more detail.
8.11.2. tsquery
A tsquery value stores lexemes that are to be searched for, and can combine them using the Boolean
operators & (AND), | (OR), and ! (NOT), as well as the phrase search operator <-> (FOLLOWED
BY). There is also a variant <N> of the FOLLOWED BY operator, where N is an integer constant that
specifies the distance between the two lexemes being searched for. <-> is equivalent to <1>.
Parentheses can be used to enforce grouping of these operators. In the absence of parentheses, ! (NOT)
binds most tightly, <-> (FOLLOWED BY) next most tightly, then & (AND), with | (OR) binding
the least tightly.
179
Data Types
Here are some examples:
SELECT 'fat & rat'::tsquery;
tsquery
--------------'fat' & 'rat'
SELECT 'fat & (rat | cat)'::tsquery;
tsquery
--------------------------'fat' & ( 'rat' | 'cat' )
SELECT 'fat & rat & ! cat'::tsquery;
tsquery
-----------------------'fat' & 'rat' & !'cat'
Optionally, lexemes in a tsquery can be labeled with one or more weight letters, which restricts
them to match only tsvector lexemes with one of those weights:
SELECT 'fat:ab & cat'::tsquery;
tsquery
-----------------'fat':AB & 'cat'
Also, lexemes in a tsquery can be labeled with * to specify prefix matching:
SELECT 'super:*'::tsquery;
tsquery
----------'super':*
This query will match any word in a tsvector that begins with “super”.
Quoting rules for lexemes are the same as described previously for lexemes in tsvector; and, as with
tsvector, any required normalization of words must be done before converting to the tsquery
type. The to_tsquery function is convenient for performing such normalization:
SELECT to_tsquery('Fat:ab & Cats');
to_tsquery
-----------------'fat':AB & 'cat'
Note that to_tsquery will process prefixes in the same way as other words, which means this
comparison returns true:
SELECT to_tsvector( 'postgraduate' ) @@ to_tsquery( 'postgres:*' );
?column?
---------t
because postgres gets stemmed to postgr:
SELECT to_tsvector( 'postgraduate' ), to_tsquery( 'postgres:*' );
to_tsvector | to_tsquery
180
Data Types
---------------+-----------'postgradu':1 | 'postgr':*
which will match the stemmed form of postgraduate.
8.12. UUID Type
The data type uuid stores Universally Unique Identifiers (UUID) as defined by RFC 41222, ISO/
IEC 9834-8:2005, and related standards. (Some systems refer to this data type as a globally unique
identifier, or GUID, instead.) This identifier is a 128-bit quantity that is generated by an algorithm
chosen to make it very unlikely that the same identifier will be generated by anyone else in the known
universe using the same algorithm. Therefore, for distributed systems, these identifiers provide a better
uniqueness guarantee than sequence generators, which are only unique within a single database.
A UUID is written as a sequence of lower-case hexadecimal digits, in several groups separated by
hyphens, specifically a group of 8 digits followed by three groups of 4 digits followed by a group of 12
digits, for a total of 32 digits representing the 128 bits. An example of a UUID in this standard form is:
a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11
PostgreSQL also accepts the following alternative forms for input: use of upper-case digits, the standard format surrounded by braces, omitting some or all hyphens, adding a hyphen after any group of
four digits. Examples are:
A0EEBC99-9C0B-4EF8-BB6D-6BB9BD380A11
{a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11}
a0eebc999c0b4ef8bb6d6bb9bd380a11
a0ee-bc99-9c0b-4ef8-bb6d-6bb9-bd38-0a11
{a0eebc99-9c0b4ef8-bb6d6bb9-bd380a11}
Output is always in the standard form.
See Section 9.14 for how to generate a UUID in PostgreSQL.
8.13. XML Type
The xml data type can be used to store XML data. Its advantage over storing XML data in a text
field is that it checks the input values for well-formedness, and there are support functions to perform
type-safe operations on it; see Section 9.15. Use of this data type requires the installation to have been
built with configure --with-libxml.
The xml type can store well-formed “documents”, as defined by the XML standard, as well as “content” fragments, which are defined by reference to the more permissive “document node”3 of the
XQuery and XPath data model. Roughly, this means that content fragments can have more than one
top-level element or character node. The expression xmlvalue IS DOCUMENT can be used to
evaluate whether a particular xml value is a full document or only a content fragment.
Limits and compatibility notes for the xml data type can be found in Section D.3.
8.13.1. Creating XML Values
To produce a value of type xml from character data, use the function xmlparse:
2
3
https://datatracker.ietf.org/doc/html/rfc4122
https://www.w3.org/TR/2010/REC-xpath-datamodel-20101214/#DocumentNode
181
Data Types
XMLPARSE ( { DOCUMENT | CONTENT } value)
Examples:
XMLPARSE (DOCUMENT '<?xml version="1.0"?><book><title>Manual</
title><chapter>...</chapter></book>')
XMLPARSE (CONTENT 'abc<foo>bar</foo><bar>foo</bar>')
While this is the only way to convert character strings into XML values according to the SQL standard,
the PostgreSQL-specific syntaxes:
xml '<foo>bar</foo>'
'<foo>bar</foo>'::xml
can also be used.
The xml type does not validate input values against a document type declaration (DTD), even when
the input value specifies a DTD. There is also currently no built-in support for validating against other
XML schema languages such as XML Schema.
The inverse operation, producing a character string value from xml, uses the function xmlserialize:
XMLSERIALIZE ( { DOCUMENT | CONTENT } value AS type [ [ NO ]
INDENT ] )
type can be character, character varying, or text (or an alias for one of those). Again,
according to the SQL standard, this is the only way to convert between type xml and character types,
but PostgreSQL also allows you to simply cast the value.
The INDENT option causes the result to be pretty-printed, while NO INDENT (which is the default)
just emits the original input string. Casting to a character type likewise produces the original string.
When a character string value is cast to or from type xml without going through XMLPARSE or XMLSERIALIZE, respectively, the choice of DOCUMENT versus CONTENT is determined by the “XML
option” session configuration parameter, which can be set using the standard command:
SET XML OPTION { DOCUMENT | CONTENT };
or the more PostgreSQL-like syntax
SET xmloption TO { DOCUMENT | CONTENT };
The default is CONTENT, so all forms of XML data are allowed.
8.13.2. Encoding Handling
Care must be taken when dealing with multiple character encodings on the client, server, and in the
XML data passed through them. When using the text mode to pass queries to the server and query
results to the client (which is the normal mode), PostgreSQL converts all character data passed between the client and the server and vice versa to the character encoding of the respective end; see
Section 23.3. This includes string representations of XML values, such as in the above examples. This
would ordinarily mean that encoding declarations contained in XML data can become invalid as the
character data is converted to other encodings while traveling between client and server, because the
embedded encoding declaration is not changed. To cope with this behavior, encoding declarations
contained in character strings presented for input to the xml type are ignored, and content is assumed
182
Data Types
to be in the current server encoding. Consequently, for correct processing, character strings of XML
data must be sent from the client in the current client encoding. It is the responsibility of the client
to either convert documents to the current client encoding before sending them to the server, or to
adjust the client encoding appropriately. On output, values of type xml will not have an encoding
declaration, and clients should assume all data is in the current client encoding.
When using binary mode to pass query parameters to the server and query results back to the client, no
encoding conversion is performed, so the situation is different. In this case, an encoding declaration
in the XML data will be observed, and if it is absent, the data will be assumed to be in UTF-8 (as
required by the XML standard; note that PostgreSQL does not support UTF-16). On output, data will
have an encoding declaration specifying the client encoding, unless the client encoding is UTF-8, in
which case it will be omitted.
Needless to say, processing XML data with PostgreSQL will be less error-prone and more efficient if
the XML data encoding, client encoding, and server encoding are the same. Since XML data is internally processed in UTF-8, computations will be most efficient if the server encoding is also UTF-8.
Caution
Some XML-related functions may not work at all on non-ASCII data when the server encoding
is not UTF-8. This is known to be an issue for xmltable() and xpath() in particular.
8.13.3. Accessing XML Values
The xml data type is unusual in that it does not provide any comparison operators. This is because
there is no well-defined and universally useful comparison algorithm for XML data. One consequence
of this is that you cannot retrieve rows by comparing an xml column against a search value. XML
values should therefore typically be accompanied by a separate key field such as an ID. An alternative
solution for comparing XML values is to convert them to character strings first, but note that character
string comparison has little to do with a useful XML comparison method.
Since there are no comparison operators for the xml data type, it is not possible to create an index
directly on a column of this type. If speedy searches in XML data are desired, possible workarounds
include casting the expression to a character string type and indexing that, or indexing an XPath expression. Of course, the actual query would have to be adjusted to search by the indexed expression.
The text-search functionality in PostgreSQL can also be used to speed up full-document searches of
XML data. The necessary preprocessing support is, however, not yet available in the PostgreSQL
distribution.
8.14. JSON Types
JSON data types are for storing JSON (JavaScript Object Notation) data, as specified in RFC 71594.
Such data can also be stored as text, but the JSON data types have the advantage of enforcing that
each stored value is valid according to the JSON rules. There are also assorted JSON-specific functions
and operators available for data stored in these data types; see Section 9.16.
PostgreSQL offers two types for storing JSON data: json and jsonb. To implement efficient query
mechanisms for these data types, PostgreSQL also provides the jsonpath data type described in
Section 8.14.7.
The json and jsonb data types accept almost identical sets of values as input. The major practical
difference is one of efficiency. The json data type stores an exact copy of the input text, which processing functions must reparse on each execution; while jsonb data is stored in a decomposed binary
format that makes it slightly slower to input due to added conversion overhead, but significantly faster
4
https://datatracker.ietf.org/doc/html/rfc7159
183
Data Types
to process, since no reparsing is needed. jsonb also supports indexing, which can be a significant
advantage.
Because the json type stores an exact copy of the input text, it will preserve semantically-insignificant
white space between tokens, as well as the order of keys within JSON objects. Also, if a JSON object
within the value contains the same key more than once, all the key/value pairs are kept. (The processing
functions consider the last value as the operative one.) By contrast, jsonb does not preserve white
space, does not preserve the order of object keys, and does not keep duplicate object keys. If duplicate
keys are specified in the input, only the last value is kept.
In general, most applications should prefer to store JSON data as jsonb, unless there are quite specialized needs, such as legacy assumptions about ordering of object keys.
RFC 7159 specifies that JSON strings should be encoded in UTF8. It is therefore not possible for the
JSON types to conform rigidly to the JSON specification unless the database encoding is UTF8. Attempts to directly include characters that cannot be represented in the database encoding will fail; conversely, characters that can be represented in the database encoding but not in UTF8 will be allowed.
RFC 7159 permits JSON strings to contain Unicode escape sequences denoted by \uXXXX. In the
input function for the json type, Unicode escapes are allowed regardless of the database encoding,
and are checked only for syntactic correctness (that is, that four hex digits follow \u). However,
the input function for jsonb is stricter: it disallows Unicode escapes for characters that cannot be
represented in the database encoding. The jsonb type also rejects \u0000 (because that cannot
be represented in PostgreSQL's text type), and it insists that any use of Unicode surrogate pairs to
designate characters outside the Unicode Basic Multilingual Plane be correct. Valid Unicode escapes
are converted to the equivalent single character for storage; this includes folding surrogate pairs into
a single character.
Note
Many of the JSON processing functions described in Section 9.16 will convert Unicode escapes to regular characters, and will therefore throw the same types of errors just described
even if their input is of type json not jsonb. The fact that the json input function does not
make these checks may be considered a historical artifact, although it does allow for simple
storage (without processing) of JSON Unicode escapes in a database encoding that does not
support the represented characters.
When converting textual JSON input into jsonb, the primitive types described by RFC 7159 are
effectively mapped onto native PostgreSQL types, as shown in Table 8.23. Therefore, there are some
minor additional constraints on what constitutes valid jsonb data that do not apply to the json type,
nor to JSON in the abstract, corresponding to limits on what can be represented by the underlying data
type. Notably, jsonb will reject numbers that are outside the range of the PostgreSQL numeric
data type, while json will not. Such implementation-defined restrictions are permitted by RFC 7159.
However, in practice such problems are far more likely to occur in other implementations, as it is
common to represent JSON's number primitive type as IEEE 754 double precision floating point
(which RFC 7159 explicitly anticipates and allows for). When using JSON as an interchange format
with such systems, the danger of losing numeric precision compared to data originally stored by PostgreSQL should be considered.
Conversely, as noted in the table there are some minor restrictions on the input format of JSON primitive types that do not apply to the corresponding PostgreSQL types.
Table 8.23. JSON Primitive Types and Corresponding PostgreSQL Types
JSON primitive type
PostgreSQL type
Notes
string
text
\u0000 is disallowed, as are Unicode escapes
representing characters not available in the database encoding
184
Data Types
JSON primitive type
PostgreSQL type
Notes
number
numeric
NaN and infinity values are disallowed
boolean
boolean
Only lowercase true and false spellings are
accepted
null
(none)
SQL NULL is a different concept
8.14.1. JSON Input and Output Syntax
The input/output syntax for the JSON data types is as specified in RFC 7159.
The following are all valid json (or jsonb) expressions:
-- Simple scalar/primitive value
-- Primitive values can be numbers, quoted strings, true, false, or
null
SELECT '5'::json;
-- Array of zero or more elements (elements need not be of same
type)
SELECT '[1, 2, "foo", null]'::json;
-- Object containing pairs of keys and values
-- Note that object keys must always be quoted strings
SELECT '{"bar": "baz", "balance": 7.77, "active": false}'::json;
-- Arrays and objects can be nested arbitrarily
SELECT '{"foo": [true, "bar"], "tags": {"a": 1, "b": null}}'::json;
As previously stated, when a JSON value is input and then printed without any additional processing,
json outputs the same text that was input, while jsonb does not preserve semantically-insignificant
details such as whitespace. For example, note the differences here:
SELECT '{"bar": "baz", "balance": 7.77, "active":false}'::json;
json
------------------------------------------------{"bar": "baz", "balance": 7.77, "active":false}
(1 row)
SELECT '{"bar": "baz", "balance": 7.77, "active":false}'::jsonb;
jsonb
-------------------------------------------------{"bar": "baz", "active": false, "balance": 7.77}
(1 row)
One semantically-insignificant detail worth noting is that in jsonb, numbers will be printed according
to the behavior of the underlying numeric type. In practice this means that numbers entered with E
notation will be printed without it, for example:
SELECT '{"reading": 1.230e-5}'::json, '{"reading":
1.230e-5}'::jsonb;
json
|
jsonb
-----------------------+------------------------{"reading": 1.230e-5} | {"reading": 0.00001230}
(1 row)
185
Data Types
However, jsonb will preserve trailing fractional zeroes, as seen in this example, even though those
are semantically insignificant for purposes such as equality checks.
For the list of built-in functions and operators available for constructing and processing JSON values,
see Section 9.16.
8.14.2. Designing JSON Documents
Representing data as JSON can be considerably more flexible than the traditional relational data model, which is compelling in environments where requirements are fluid. It is quite possible for both
approaches to co-exist and complement each other within the same application. However, even for
applications where maximal flexibility is desired, it is still recommended that JSON documents have
a somewhat fixed structure. The structure is typically unenforced (though enforcing some business
rules declaratively is possible), but having a predictable structure makes it easier to write queries that
usefully summarize a set of “documents” (datums) in a table.
JSON data is subject to the same concurrency-control considerations as any other data type when
stored in a table. Although storing large documents is practicable, keep in mind that any update acquires a row-level lock on the whole row. Consider limiting JSON documents to a manageable size
in order to decrease lock contention among updating transactions. Ideally, JSON documents should
each represent an atomic datum that business rules dictate cannot reasonably be further subdivided
into smaller datums that could be modified independently.
8.14.3. jsonb Containment and Existence
Testing containment is an important capability of jsonb. There is no parallel set of facilities for the
json type. Containment tests whether one jsonb document has contained within it another one.
These examples return true except as noted:
-- Simple scalar/primitive values contain only the identical value:
SELECT '"foo"'::jsonb @> '"foo"'::jsonb;
-- The array on the right side is contained within the one on the
left:
SELECT '[1, 2, 3]'::jsonb @> '[1, 3]'::jsonb;
-- Order of array elements is not significant, so this is also
true:
SELECT '[1, 2, 3]'::jsonb @> '[3, 1]'::jsonb;
-- Duplicate array elements don't matter either:
SELECT '[1, 2, 3]'::jsonb @> '[1, 2, 2]'::jsonb;
-- The object with a single pair on the right side is contained
-- within the object on the left side:
SELECT '{"product": "PostgreSQL", "version": 9.4, "jsonb":
true}'::jsonb @> '{"version": 9.4}'::jsonb;
-- The array on the right side is not considered contained within
the
-- array on the left, even though a similar array is nested within
it:
SELECT '[1, 2, [1, 3]]'::jsonb @> '[1, 3]'::jsonb; -- yields false
-- But with a layer of nesting, it is contained:
SELECT '[1, 2, [1, 3]]'::jsonb @> '[[1, 3]]'::jsonb;
186
Data Types
-- Similarly, containment is not reported here:
SELECT '{"foo": {"bar": "baz"}}'::jsonb @> '{"bar": "baz"}'::jsonb;
-- yields false
-- A top-level key and an empty object is contained:
SELECT '{"foo": {"bar": "baz"}}'::jsonb @> '{"foo": {}}'::jsonb;
The general principle is that the contained object must match the containing object as to structure and
data contents, possibly after discarding some non-matching array elements or object key/value pairs
from the containing object. But remember that the order of array elements is not significant when
doing a containment match, and duplicate array elements are effectively considered only once.
As a special exception to the general principle that the structures must match, an array may contain
a primitive value:
-- This array contains the primitive string value:
SELECT '["foo", "bar"]'::jsonb @> '"bar"'::jsonb;
-- This exception is not reciprocal -- non-containment is reported
here:
SELECT '"bar"'::jsonb @> '["bar"]'::jsonb; -- yields false
jsonb also has an existence operator, which is a variation on the theme of containment: it tests
whether a string (given as a text value) appears as an object key or array element at the top level of
the jsonb value. These examples return true except as noted:
-- String exists as array element:
SELECT '["foo", "bar", "baz"]'::jsonb ? 'bar';
-- String exists as object key:
SELECT '{"foo": "bar"}'::jsonb ? 'foo';
-- Object values are not considered:
SELECT '{"foo": "bar"}'::jsonb ? 'bar';
-- yields false
-- As with containment, existence must match at the top level:
SELECT '{"foo": {"bar": "baz"}}'::jsonb ? 'bar'; -- yields false
-- A string is considered to exist if it matches a primitive JSON
string:
SELECT '"foo"'::jsonb ? 'foo';
JSON objects are better suited than arrays for testing containment or existence when there are many
keys or elements involved, because unlike arrays they are internally optimized for searching, and do
not need to be searched linearly.
Tip
Because JSON containment is nested, an appropriate query can skip explicit selection of subobjects. As an example, suppose that we have a doc column containing objects at the top level,
with most objects containing tags fields that contain arrays of sub-objects. This query finds
entries in which sub-objects containing both "term":"paris" and "term":"food" appear, while ignoring any such keys outside the tags array:
SELECT doc->'site_name' FROM websites
WHERE doc @> '{"tags":[{"term":"paris"}, {"term":"food"}]}';
187
Data Types
One could accomplish the same thing with, say,
SELECT doc->'site_name' FROM websites
WHERE doc->'tags' @> '[{"term":"paris"}, {"term":"food"}]';
but that approach is less flexible, and often less efficient as well.
On the other hand, the JSON existence operator is not nested: it will only look for the specified
key or array element at top level of the JSON value.
The various containment and existence operators, along with all other JSON operators and functions
are documented in Section 9.16.
8.14.4. jsonb Indexing
GIN indexes can be used to efficiently search for keys or key/value pairs occurring within a large
number of jsonb documents (datums). Two GIN “operator classes” are provided, offering different
performance and flexibility trade-offs.
The default GIN operator class for jsonb supports queries with the key-exists operators ?, ?| and
?&, the containment operator @>, and the jsonpath match operators @? and @@. (For details of the
semantics that these operators implement, see Table 9.46.) An example of creating an index with this
operator class is:
CREATE INDEX idxgin ON api USING GIN (jdoc);
The non-default GIN operator class jsonb_path_ops does not support the key-exists operators,
but it does support @>, @? and @@. An example of creating an index with this operator class is:
CREATE INDEX idxginp ON api USING GIN (jdoc jsonb_path_ops);
Consider the example of a table that stores JSON documents retrieved from a third-party web service,
with a documented schema definition. A typical document is:
{
"guid": "9c36adc1-7fb5-4d5b-83b4-90356a46061a",
"name": "Angela Barton",
"is_active": true,
"company": "Magnafone",
"address": "178 Howard Place, Gulf, Washington, 702",
"registered": "2009-11-07T08:53:22 +08:00",
"latitude": 19.793713,
"longitude": 86.513373,
"tags": [
"enim",
"aliquip",
"qui"
]
}
We store these documents in a table named api, in a jsonb column named jdoc. If a GIN index is
created on this column, queries like the following can make use of the index:
-- Find documents in which the key "company" has value "Magnafone"
188
Data Types
SELECT jdoc->'guid', jdoc->'name' FROM api WHERE jdoc @>
'{"company": "Magnafone"}';
However, the index could not be used for queries like the following, because though the operator ? is
indexable, it is not applied directly to the indexed column jdoc:
-- Find documents in which the key "tags" contains key or array
element "qui"
SELECT jdoc->'guid', jdoc->'name' FROM api WHERE jdoc -> 'tags' ?
'qui';
Still, with appropriate use of expression indexes, the above query can use an index. If querying for
particular items within the "tags" key is common, defining an index like this may be worthwhile:
CREATE INDEX idxgintags ON api USING GIN ((jdoc -> 'tags'));
Now, the WHERE clause jdoc -> 'tags' ? 'qui' will be recognized as an application of the
indexable operator ? to the indexed expression jdoc -> 'tags'. (More information on expression
indexes can be found in Section 11.7.)
Another approach to querying is to exploit containment, for example:
-- Find documents in which the key "tags" contains array element
"qui"
SELECT jdoc->'guid', jdoc->'name' FROM api WHERE jdoc @> '{"tags":
["qui"]}';
A simple GIN index on the jdoc column can support this query. But note that such an index will
store copies of every key and value in the jdoc column, whereas the expression index of the previous
example stores only data found under the tags key. While the simple-index approach is far more
flexible (since it supports queries about any key), targeted expression indexes are likely to be smaller
and faster to search than a simple index.
GIN indexes also support the @? and @@ operators, which perform jsonpath matching. Examples
are
SELECT jdoc->'guid', jdoc->'name' FROM api WHERE jdoc @?
'$.tags[*] ? (@ == "qui")';
SELECT jdoc->'guid', jdoc->'name' FROM api WHERE jdoc @@ '$.tags[*]
== "qui"';
For these operators, a GIN index extracts clauses of the form accessors_chain == constant
out of the jsonpath pattern, and does the index search based on the keys and values mentioned
in these clauses. The accessors chain may include .key, [*], and [index] accessors. The jsonb_ops operator class also supports .* and .** accessors, but the jsonb_path_ops operator class
does not.
Although the jsonb_path_ops operator class supports only queries with the @>, @? and @@ operators, it has notable performance advantages over the default operator class jsonb_ops. A jsonb_path_ops index is usually much smaller than a jsonb_ops index over the same data, and the
specificity of searches is better, particularly when queries contain keys that appear frequently in the
data. Therefore search operations typically perform better than with the default operator class.
The technical difference between a jsonb_ops and a jsonb_path_ops GIN index is that the
former creates independent index items for each key and value in the data, while the latter creates
189
Data Types
index items only for each value in the data. 5 Basically, each jsonb_path_ops index item is a
hash of the value and the key(s) leading to it; for example to index {"foo": {"bar": "baz"}},
a single index item would be created incorporating all three of foo, bar, and baz into the hash
value. Thus a containment query looking for this structure would result in an extremely specific index
search; but there is no way at all to find out whether foo appears as a key. On the other hand, a
jsonb_ops index would create three index items representing foo, bar, and baz separately; then
to do the containment query, it would look for rows containing all three of these items. While GIN
indexes can perform such an AND search fairly efficiently, it will still be less specific and slower
than the equivalent jsonb_path_ops search, especially if there are a very large number of rows
containing any single one of the three index items.
A disadvantage of the jsonb_path_ops approach is that it produces no index entries for JSON
structures not containing any values, such as {"a": {}}. If a search for documents containing such
a structure is requested, it will require a full-index scan, which is quite slow. jsonb_path_ops is
therefore ill-suited for applications that often perform such searches.
jsonb also supports btree and hash indexes. These are usually useful only if it's important to
check equality of complete JSON documents. The btree ordering for jsonb datums is seldom of
great interest, but for completeness it is:
Object > Array > Boolean > Number > String > null
Object with n pairs > object with n - 1 pairs
Array with n elements > array with n - 1 elements
with the exception that (for historical reasons) an empty top level array sorts less than null. Objects
with equal numbers of pairs are compared in the order:
key-1, value-1, key-2 ...
Note that object keys are compared in their storage order; in particular, since shorter keys are stored
before longer keys, this can lead to results that might be unintuitive, such as:
{ "aa": 1, "c": 1} > {"b": 1, "d": 1}
Similarly, arrays with equal numbers of elements are compared in the order:
element-1, element-2 ...
Primitive JSON values are compared using the same comparison rules as for the underlying PostgreSQL data type. Strings are compared using the default database collation.
8.14.5. jsonb Subscripting
The jsonb data type supports array-style subscripting expressions to extract and modify elements.
Nested values can be indicated by chaining subscripting expressions, following the same rules as the
path argument in the jsonb_set function. If a jsonb value is an array, numeric subscripts start
at zero, and negative integers count backwards from the last element of the array. Slice expressions
are not supported. The result of a subscripting expression is always of the jsonb data type.
UPDATE statements may use subscripting in the SET clause to modify jsonb values. Subscript paths
must be traversable for all affected values insofar as they exist. For instance, the path val['a']
['b']['c'] can be traversed all the way to c if every val, val['a'], and val['a']['b']
5
For this purpose, the term “value” includes array elements, though JSON terminology sometimes considers array elements distinct from
values within objects.
190
Data Types
is an object. If any val['a'] or val['a']['b'] is not defined, it will be created as an empty
object and filled as necessary. However, if any val itself or one of the intermediary values is defined
as a non-object such as a string, number, or jsonb null, traversal cannot proceed so an error is
raised and the transaction aborted.
An example of subscripting syntax:
-- Extract object value by key
SELECT ('{"a": 1}'::jsonb)['a'];
-- Extract nested object value by key path
SELECT ('{"a": {"b": {"c": 1}}}'::jsonb)['a']['b']['c'];
-- Extract array element by index
SELECT ('[1, "2", null]'::jsonb)[1];
-- Update object value by key. Note the quotes around '1': the
assigned
-- value must be of the jsonb type as well
UPDATE table_name SET jsonb_field['key'] = '1';
-- This will raise an error if any record's jsonb_field['a']['b']
is something
-- other than an object. For example, the value {"a": 1} has a
numeric value
-- of the key 'a'.
UPDATE table_name SET jsonb_field['a']['b']['c'] = '1';
-- Filter records using a WHERE clause with subscripting. Since the
result of
-- subscripting is jsonb, the value we compare it against must also
be jsonb.
-- The double quotes make "value" also a valid jsonb string.
SELECT * FROM table_name WHERE jsonb_field['key'] = '"value"';
jsonb assignment via subscripting handles a few edge cases differently from jsonb_set. When a
source jsonb value is NULL, assignment via subscripting will proceed as if it was an empty JSON
value of the type (object or array) implied by the subscript key:
-- Where jsonb_field was NULL, it is now {"a": 1}
UPDATE table_name SET jsonb_field['a'] = '1';
-- Where jsonb_field was NULL, it is now [1]
UPDATE table_name SET jsonb_field[0] = '1';
If an index is specified for an array containing too few elements, NULL elements will be appended
until the index is reachable and the value can be set.
-- Where jsonb_field was [], it is now [null, null, 2];
-- where jsonb_field was [0], it is now [0, null, 2]
UPDATE table_name SET jsonb_field[2] = '2';
A jsonb value will accept assignments to nonexistent subscript paths as long as the last existing
element to be traversed is an object or array, as implied by the corresponding subscript (the element
indicated by the last subscript in the path is not traversed and may be anything). Nested array and
191
Data Types
object structures will be created, and in the former case null-padded, as specified by the subscript
path until the assigned value can be placed.
-- Where jsonb_field was {}, it is now {"a": [{"b": 1}]}
UPDATE table_name SET jsonb_field['a'][0]['b'] = '1';
-- Where jsonb_field was [], it is now [null, {"a": 1}]
UPDATE table_name SET jsonb_field[1]['a'] = '1';
8.14.6. Transforms
Additional extensions are available that implement transforms for the jsonb type for different procedural languages.
The extensions for PL/Perl are called jsonb_plperl and jsonb_plperlu. If you use them,
jsonb values are mapped to Perl arrays, hashes, and scalars, as appropriate.
The extension for PL/Python is called jsonb_plpython3u. If you use it, jsonb values are mapped
to Python dictionaries, lists, and scalars, as appropriate.
Of these extensions, jsonb_plperl is considered “trusted”, that is, it can be installed by nonsuperusers who have CREATE privilege on the current database. The rest require superuser privilege
to install.
8.14.7. jsonpath Type
The jsonpath type implements support for the SQL/JSON path language in PostgreSQL to efficiently query JSON data. It provides a binary representation of the parsed SQL/JSON path expression
that specifies the items to be retrieved by the path engine from the JSON data for further processing
with the SQL/JSON query functions.
The semantics of SQL/JSON path predicates and operators generally follow SQL. At the same time,
to provide a natural way of working with JSON data, SQL/JSON path syntax uses some JavaScript
conventions:
• Dot (.) is used for member access.
• Square brackets ([]) are used for array access.
• SQL/JSON arrays are 0-relative, unlike regular SQL arrays that start from 1.
Numeric literals in SQL/JSON path expressions follow JavaScript rules, which are different from both
SQL and JSON in some minor details. For example, SQL/JSON path allows .1 and 1., which are
invalid in JSON. Non-decimal integer literals and underscore separators are supported, for example,
1_000_000, 0x1EEE_FFFF, 0o273, 0b100101. In SQL/JSON path (and in JavaScript, but not
in SQL proper), there must not be an underscore separator directly after the radix prefix.
An SQL/JSON path expression is typically written in an SQL query as an SQL character string literal,
so it must be enclosed in single quotes, and any single quotes desired within the value must be doubled
(see Section 4.1.2.1). Some forms of path expressions require string literals within them. These embedded string literals follow JavaScript/ECMAScript conventions: they must be surrounded by double
quotes, and backslash escapes may be used within them to represent otherwise-hard-to-type characters. In particular, the way to write a double quote within an embedded string literal is \", and to write
a backslash itself, you must write \\. Other special backslash sequences include those recognized in
JavaScript strings: \b, \f, \n, \r, \t, \v for various ASCII control characters, \xNN for a character
code written with only two hex digits, \uNNNN for a Unicode character identified by its 4-hex-digit
code point, and \u{N...} for a Unicode character code point written with 1 to 6 hex digits.
A path expression consists of a sequence of path elements, which can be any of the following:
192
Data Types
• Path literals of JSON primitive types: Unicode text, numeric, true, false, or null.
• Path variables listed in Table 8.24.
• Accessor operators listed in Table 8.25.
• jsonpath operators and methods listed in Section 9.16.2.3.
• Parentheses, which can be used to provide filter expressions or define the order of path evaluation.
For details on using jsonpath expressions with SQL/JSON query functions, see Section 9.16.2.
Table 8.24. jsonpath Variables
Variable
Description
$
A variable representing the JSON value being queried (the context item).
$varname
A named variable. Its value can be set by the parameter vars of
several JSON processing functions; see Table 9.49 for details.
@
A variable representing the result of path evaluation in filter expressions.
Table 8.25. jsonpath Accessors
Accessor Operator
Description
.key
Member accessor that returns an object member with the specified key. If the key name matches some named variable starting
with $ or does not meet the JavaScript rules for an identifier, it
must be enclosed in double quotes to make it a string literal.
."$varname"
.*
Wildcard member accessor that returns the values of all members
located at the top level of the current object.
.**
Recursive wildcard member accessor that processes all levels of
the JSON hierarchy of the current object and returns all the member values, regardless of their nesting level. This is a PostgreSQL
extension of the SQL/JSON standard.
.**{level}
Like .**, but selects only the specified levels of the JSON hierarchy. Nesting levels are specified as integers. Level zero corresponds to the current object. To access the lowest nesting level,
you can use the last keyword. This is a PostgreSQL extension
of the SQL/JSON standard.
.**{start_level to
end_level}
[subscript, ...]
Array element accessor. subscript can be given in two forms:
index or start_index to end_index. The first form returns a single array element by its index. The second form returns
an array slice by the range of indexes, including the elements that
correspond to the provided start_index and end_index.
The specified index can be an integer, as well as an expression
returning a single numeric value, which is automatically cast to
integer. Index zero corresponds to the first array element. You
can also use the last keyword to denote the last array element,
which is useful for handling arrays of unknown length.
[*]
Wildcard array element accessor that returns all array elements.
8.15. Arrays
193
Data Types
PostgreSQL allows columns of a table to be defined as variable-length multidimensional arrays. Arrays
of any built-in or user-defined base type, enum type, composite type, range type, or domain can be
created.
8.15.1. Declaration of Array Types
To illustrate the use of array types, we create this table:
CREATE TABLE sal_emp (
name
text,
pay_by_quarter integer[],
schedule
text[][]
);
As shown, an array data type is named by appending square brackets ([]) to the data type name of
the array elements. The above command will create a table named sal_emp with a column of type
text (name), a one-dimensional array of type integer (pay_by_quarter), which represents
the employee's salary by quarter, and a two-dimensional array of text (schedule), which represents the employee's weekly schedule.
The syntax for CREATE TABLE allows the exact size of arrays to be specified, for example:
CREATE TABLE tictactoe (
squares
integer[3][3]
);
However, the current implementation ignores any supplied array size limits, i.e., the behavior is the
same as for arrays of unspecified length.
The current implementation does not enforce the declared number of dimensions either. Arrays of
a particular element type are all considered to be of the same type, regardless of size or number of
dimensions. So, declaring the array size or number of dimensions in CREATE TABLE is simply
documentation; it does not affect run-time behavior.
An alternative syntax, which conforms to the SQL standard by using the keyword ARRAY, can be used
for one-dimensional arrays. pay_by_quarter could have been defined as:
pay_by_quarter
integer ARRAY[4],
Or, if no array size is to be specified:
pay_by_quarter
integer ARRAY,
As before, however, PostgreSQL does not enforce the size restriction in any case.
8.15.2. Array Value Input
To write an array value as a literal constant, enclose the element values within curly braces and separate
them by commas. (If you know C, this is not unlike the C syntax for initializing structures.) You can
put double quotes around any element value, and must do so if it contains commas or curly braces.
(More details appear below.) Thus, the general format of an array constant is the following:
'{ val1 delim val2 delim ... }'
where delim is the delimiter character for the type, as recorded in its pg_type entry. Among the
standard data types provided in the PostgreSQL distribution, all use a comma (,), except for type box
194
Data Types
which uses a semicolon (;). Each val is either a constant of the array element type, or a subarray.
An example of an array constant is:
'{{1,2,3},{4,5,6},{7,8,9}}'
This constant is a two-dimensional, 3-by-3 array consisting of three subarrays of integers.
To set an element of an array constant to NULL, write NULL for the element value. (Any upper- or
lower-case variant of NULL will do.) If you want an actual string value “NULL”, you must put double
quotes around it.
(These kinds of array constants are actually only a special case of the generic type constants discussed
in Section 4.1.2.7. The constant is initially treated as a string and passed to the array input conversion
routine. An explicit type specification might be necessary.)
Now we can show some INSERT statements:
INSERT INTO sal_emp
VALUES ('Bill',
'{10000, 10000, 10000, 10000}',
'{{"meeting", "lunch"}, {"training", "presentation"}}');
INSERT INTO sal_emp
VALUES ('Carol',
'{20000, 25000, 25000, 25000}',
'{{"breakfast", "consulting"}, {"meeting", "lunch"}}');
The result of the previous two inserts looks like this:
SELECT * FROM sal_emp;
name |
pay_by_quarter
|
schedule
-------+--------------------------+------------------------------------------Bill | {10000,10000,10000,10000} | {{meeting,lunch},
{training,presentation}}
Carol | {20000,25000,25000,25000} | {{breakfast,consulting},
{meeting,lunch}}
(2 rows)
Multidimensional arrays must have matching extents for each dimension. A mismatch causes an error,
for example:
INSERT INTO sal_emp
VALUES ('Bill',
'{10000, 10000, 10000, 10000}',
'{{"meeting", "lunch"}, {"meeting"}}');
ERROR: malformed array literal: "{{"meeting", "lunch"},
{"meeting"}}"
DETAIL: Multidimensional arrays must have sub-arrays with matching
dimensions.
The ARRAY constructor syntax can also be used:
INSERT INTO sal_emp
VALUES ('Bill',
195
Data Types
ARRAY[10000, 10000, 10000, 10000],
ARRAY[['meeting', 'lunch'], ['training', 'presentation']]);
INSERT INTO sal_emp
VALUES ('Carol',
ARRAY[20000, 25000, 25000, 25000],
ARRAY[['breakfast', 'consulting'], ['meeting', 'lunch']]);
Notice that the array elements are ordinary SQL constants or expressions; for instance, string literals
are single quoted, instead of double quoted as they would be in an array literal. The ARRAY constructor
syntax is discussed in more detail in Section 4.2.12.
8.15.3. Accessing Arrays
Now, we can run some queries on the table. First, we show how to access a single element of an array.
This query retrieves the names of the employees whose pay changed in the second quarter:
SELECT name FROM sal_emp WHERE pay_by_quarter[1] <>
pay_by_quarter[2];
name
------Carol
(1 row)
The array subscript numbers are written within square brackets. By default PostgreSQL uses a onebased numbering convention for arrays, that is, an array of n elements starts with array[1] and
ends with array[n].
This query retrieves the third quarter pay of all employees:
SELECT pay_by_quarter[3] FROM sal_emp;
pay_by_quarter
---------------10000
25000
(2 rows)
We can also access arbitrary rectangular slices of an array, or subarrays. An array slice is denoted by
writing lower-bound:upper-bound for one or more array dimensions. For example, this query
retrieves the first item on Bill's schedule for the first two days of the week:
SELECT schedule[1:2][1:1] FROM sal_emp WHERE name = 'Bill';
schedule
-----------------------{{meeting},{training}}
(1 row)
If any dimension is written as a slice, i.e., contains a colon, then all dimensions are treated as slices.
Any dimension that has only a single number (no colon) is treated as being from 1 to the number
specified. For example, [2] is treated as [1:2], as in this example:
SELECT schedule[1:2][2] FROM sal_emp WHERE name = 'Bill';
196
Data Types
schedule
------------------------------------------{{meeting,lunch},{training,presentation}}
(1 row)
To avoid confusion with the non-slice case, it's best to use slice syntax for all dimensions, e.g., [1:2]
[1:1], not [2][1:1].
It is possible to omit the lower-bound and/or upper-bound of a slice specifier; the missing
bound is replaced by the lower or upper limit of the array's subscripts. For example:
SELECT schedule[:2][2:] FROM sal_emp WHERE name = 'Bill';
schedule
-----------------------{{lunch},{presentation}}
(1 row)
SELECT schedule[:][1:1] FROM sal_emp WHERE name = 'Bill';
schedule
-----------------------{{meeting},{training}}
(1 row)
An array subscript expression will return null if either the array itself or any of the subscript expressions
are null. Also, null is returned if a subscript is outside the array bounds (this case does not raise
an error). For example, if schedule currently has the dimensions [1:3][1:2] then referencing
schedule[3][3] yields NULL. Similarly, an array reference with the wrong number of subscripts
yields a null rather than an error.
An array slice expression likewise yields null if the array itself or any of the subscript expressions are
null. However, in other cases such as selecting an array slice that is completely outside the current array
bounds, a slice expression yields an empty (zero-dimensional) array instead of null. (This does not
match non-slice behavior and is done for historical reasons.) If the requested slice partially overlaps
the array bounds, then it is silently reduced to just the overlapping region instead of returning null.
The current dimensions of any array value can be retrieved with the array_dims function:
SELECT array_dims(schedule) FROM sal_emp WHERE name = 'Carol';
array_dims
-----------[1:2][1:2]
(1 row)
array_dims produces a text result, which is convenient for people to read but perhaps inconvenient for programs. Dimensions can also be retrieved with array_upper and array_lower,
which return the upper and lower bound of a specified array dimension, respectively:
SELECT array_upper(schedule, 1) FROM sal_emp WHERE name = 'Carol';
array_upper
------------2
(1 row)
197
Data Types
array_length will return the length of a specified array dimension:
SELECT array_length(schedule, 1) FROM sal_emp WHERE name = 'Carol';
array_length
-------------2
(1 row)
cardinality returns the total number of elements in an array across all dimensions. It is effectively
the number of rows a call to unnest would yield:
SELECT cardinality(schedule) FROM sal_emp WHERE name = 'Carol';
cardinality
------------4
(1 row)
8.15.4. Modifying Arrays
An array value can be replaced completely:
UPDATE sal_emp SET pay_by_quarter = '{25000,25000,27000,27000}'
WHERE name = 'Carol';
or using the ARRAY expression syntax:
UPDATE sal_emp SET pay_by_quarter = ARRAY[25000,25000,27000,27000]
WHERE name = 'Carol';
An array can also be updated at a single element:
UPDATE sal_emp SET pay_by_quarter[4] = 15000
WHERE name = 'Bill';
or updated in a slice:
UPDATE sal_emp SET pay_by_quarter[1:2] = '{27000,27000}'
WHERE name = 'Carol';
The slice syntaxes with omitted lower-bound and/or upper-bound can be used too, but only
when updating an array value that is not NULL or zero-dimensional (otherwise, there is no existing
subscript limit to substitute).
A stored array value can be enlarged by assigning to elements not already present. Any positions between those previously present and the newly assigned elements will be filled with nulls. For example, if array myarray currently has 4 elements, it will have six elements after an update that assigns
to myarray[6]; myarray[5] will contain null. Currently, enlargement in this fashion is only allowed for one-dimensional arrays, not multidimensional arrays.
Subscripted assignment allows creation of arrays that do not use one-based subscripts. For example
one might assign to myarray[-2:7] to create an array with subscript values from -2 to 7.
New array values can also be constructed using the concatenation operator, ||:
198
Data Types
SELECT ARRAY[1,2] || ARRAY[3,4];
?column?
----------{1,2,3,4}
(1 row)
SELECT ARRAY[5,6] || ARRAY[[1,2],[3,4]];
?column?
--------------------{{5,6},{1,2},{3,4}}
(1 row)
The concatenation operator allows a single element to be pushed onto the beginning or end of a onedimensional array. It also accepts two N-dimensional arrays, or an N-dimensional and an N+1-dimensional array.
When a single element is pushed onto either the beginning or end of a one-dimensional array, the
result is an array with the same lower bound subscript as the array operand. For example:
SELECT array_dims(1 || '[0:1]={2,3}'::int[]);
array_dims
-----------[0:2]
(1 row)
SELECT array_dims(ARRAY[1,2] || 3);
array_dims
-----------[1:3]
(1 row)
When two arrays with an equal number of dimensions are concatenated, the result retains the lower
bound subscript of the left-hand operand's outer dimension. The result is an array comprising every
element of the left-hand operand followed by every element of the right-hand operand. For example:
SELECT array_dims(ARRAY[1,2] || ARRAY[3,4,5]);
array_dims
-----------[1:5]
(1 row)
SELECT array_dims(ARRAY[[1,2],[3,4]] || ARRAY[[5,6],[7,8],[9,0]]);
array_dims
-----------[1:5][1:2]
(1 row)
When an N-dimensional array is pushed onto the beginning or end of an N+1-dimensional array, the
result is analogous to the element-array case above. Each N-dimensional sub-array is essentially an
element of the N+1-dimensional array's outer dimension. For example:
SELECT array_dims(ARRAY[1,2] || ARRAY[[3,4],[5,6]]);
array_dims
-----------[1:3][1:2]
199
Data Types
(1 row)
An array can also be constructed by using the functions array_prepend, array_append, or
array_cat. The first two only support one-dimensional arrays, but array_cat supports multidimensional arrays. Some examples:
SELECT array_prepend(1, ARRAY[2,3]);
array_prepend
--------------{1,2,3}
(1 row)
SELECT array_append(ARRAY[1,2], 3);
array_append
-------------{1,2,3}
(1 row)
SELECT array_cat(ARRAY[1,2], ARRAY[3,4]);
array_cat
----------{1,2,3,4}
(1 row)
SELECT array_cat(ARRAY[[1,2],[3,4]], ARRAY[5,6]);
array_cat
--------------------{{1,2},{3,4},{5,6}}
(1 row)
SELECT array_cat(ARRAY[5,6], ARRAY[[1,2],[3,4]]);
array_cat
--------------------{{5,6},{1,2},{3,4}}
In simple cases, the concatenation operator discussed above is preferred over direct use of these functions. However, because the concatenation operator is overloaded to serve all three cases, there are
situations where use of one of the functions is helpful to avoid ambiguity. For example consider:
SELECT ARRAY[1, 2] || '{3, 4}';
an array
?column?
----------{1,2,3,4}
-- the untyped literal is taken as
SELECT ARRAY[1, 2] || '7';
ERROR: malformed array literal: "7"
-- so is this one
SELECT ARRAY[1, 2] || NULL;
NULL
?column?
---------{1,2}
(1 row)
-- so is an undecorated
SELECT array_append(ARRAY[1, 2], NULL);
meant
-- this might have been
200
Data Types
array_append
-------------{1,2,NULL}
In the examples above, the parser sees an integer array on one side of the concatenation operator,
and a constant of undetermined type on the other. The heuristic it uses to resolve the constant's type
is to assume it's of the same type as the operator's other input — in this case, integer array. So the
concatenation operator is presumed to represent array_cat, not array_append. When that's the
wrong choice, it could be fixed by casting the constant to the array's element type; but explicit use of
array_append might be a preferable solution.
8.15.5. Searching in Arrays
To search for a value in an array, each value must be checked. This can be done manually, if you know
the size of the array. For example:
SELECT * FROM sal_emp WHERE pay_by_quarter[1] = 10000 OR
pay_by_quarter[2] = 10000 OR
pay_by_quarter[3] = 10000 OR
pay_by_quarter[4] = 10000;
However, this quickly becomes tedious for large arrays, and is not helpful if the size of the array is
unknown. An alternative method is described in Section 9.25. The above query could be replaced by:
SELECT * FROM sal_emp WHERE 10000 = ANY (pay_by_quarter);
In addition, you can find rows where the array has all values equal to 10000 with:
SELECT * FROM sal_emp WHERE 10000 = ALL (pay_by_quarter);
Alternatively, the generate_subscripts function can be used. For example:
SELECT * FROM
(SELECT pay_by_quarter,
generate_subscripts(pay_by_quarter, 1) AS s
FROM sal_emp) AS foo
WHERE pay_by_quarter[s] = 10000;
This function is described in Table 9.68.
You can also search an array using the && operator, which checks whether the left operand overlaps
with the right operand. For instance:
SELECT * FROM sal_emp WHERE pay_by_quarter && ARRAY[10000];
This and other array operators are further described in Section 9.19. It can be accelerated by an appropriate index, as described in Section 11.2.
You can also search for specific values in an array using the array_position and array_positions functions. The former returns the subscript of the first occurrence of a value in an array;
the latter returns an array with the subscripts of all occurrences of the value in the array. For example:
SELECT
array_position(ARRAY['sun','mon','tue','wed','thu','fri','sat'],
'mon');
201
Data Types
array_position
---------------2
(1 row)
SELECT array_positions(ARRAY[1, 4, 3, 1, 3, 4, 2, 1], 1);
array_positions
----------------{1,4,8}
(1 row)
Tip
Arrays are not sets; searching for specific array elements can be a sign of database misdesign.
Consider using a separate table with a row for each item that would be an array element. This
will be easier to search, and is likely to scale better for a large number of elements.
8.15.6. Array Input and Output Syntax
The external text representation of an array value consists of items that are interpreted according to
the I/O conversion rules for the array's element type, plus decoration that indicates the array structure.
The decoration consists of curly braces ({ and }) around the array value plus delimiter characters
between adjacent items. The delimiter character is usually a comma (,) but can be something else: it
is determined by the typdelim setting for the array's element type. Among the standard data types
provided in the PostgreSQL distribution, all use a comma, except for type box, which uses a semicolon
(;). In a multidimensional array, each dimension (row, plane, cube, etc.) gets its own level of curly
braces, and delimiters must be written between adjacent curly-braced entities of the same level.
The array output routine will put double quotes around element values if they are empty strings, contain curly braces, delimiter characters, double quotes, backslashes, or white space, or match the word
NULL. Double quotes and backslashes embedded in element values will be backslash-escaped. For
numeric data types it is safe to assume that double quotes will never appear, but for textual data types
one should be prepared to cope with either the presence or absence of quotes.
By default, the lower bound index value of an array's dimensions is set to one. To represent arrays
with other lower bounds, the array subscript ranges can be specified explicitly before writing the array
contents. This decoration consists of square brackets ([]) around each array dimension's lower and
upper bounds, with a colon (:) delimiter character in between. The array dimension decoration is
followed by an equal sign (=). For example:
SELECT f1[1][-2][3] AS e1, f1[1][-1][5] AS e2
FROM (SELECT '[1:1][-2:-1][3:5]={{{1,2,3},{4,5,6}}}'::int[] AS f1)
AS ss;
e1 | e2
----+---1 | 6
(1 row)
The array output routine will include explicit dimensions in its result only when there are one or more
lower bounds different from one.
If the value written for an element is NULL (in any case variant), the element is taken to be NULL.
The presence of any quotes or backslashes disables this and allows the literal string value “NULL”
to be entered. Also, for backward compatibility with pre-8.2 versions of PostgreSQL, the array_nulls
configuration parameter can be turned off to suppress recognition of NULL as a NULL.
202
Data Types
As shown previously, when writing an array value you can use double quotes around any individual
array element. You must do so if the element value would otherwise confuse the array-value parser.
For example, elements containing curly braces, commas (or the data type's delimiter character), double quotes, backslashes, or leading or trailing whitespace must be double-quoted. Empty strings and
strings matching the word NULL must be quoted, too. To put a double quote or backslash in a quoted
array element value, precede it with a backslash. Alternatively, you can avoid quotes and use backslash-escaping to protect all data characters that would otherwise be taken as array syntax.
You can add whitespace before a left brace or after a right brace. You can also add whitespace before
or after any individual item string. In all of these cases the whitespace will be ignored. However,
whitespace within double-quoted elements, or surrounded on both sides by non-whitespace characters
of an element, is not ignored.
Tip
The ARRAY constructor syntax (see Section 4.2.12) is often easier to work with than the array-literal syntax when writing array values in SQL commands. In ARRAY, individual element
values are written the same way they would be written when not members of an array.
8.16. Composite Types
A composite type represents the structure of a row or record; it is essentially just a list of field names
and their data types. PostgreSQL allows composite types to be used in many of the same ways that
simple types can be used. For example, a column of a table can be declared to be of a composite type.
8.16.1. Declaration of Composite Types
Here are two simple examples of defining composite types:
CREATE TYPE complex AS (
r
double precision,
i
double precision
);
CREATE TYPE inventory_item AS (
name
text,
supplier_id
integer,
price
numeric
);
The syntax is comparable to CREATE TABLE, except that only field names and types can be specified;
no constraints (such as NOT NULL) can presently be included. Note that the AS keyword is essential;
without it, the system will think a different kind of CREATE TYPE command is meant, and you will
get odd syntax errors.
Having defined the types, we can use them to create tables:
CREATE TABLE on_hand (
item
inventory_item,
count
integer
);
INSERT INTO on_hand VALUES (ROW('fuzzy dice', 42, 1.99), 1000);
203
Data Types
or functions:
CREATE FUNCTION price_extension(inventory_item, integer) RETURNS
numeric
AS 'SELECT $1.price * $2' LANGUAGE SQL;
SELECT price_extension(item, 10) FROM on_hand;
Whenever you create a table, a composite type is also automatically created, with the same name as
the table, to represent the table's row type. For example, had we said:
CREATE TABLE inventory_item (
name
text,
supplier_id
integer REFERENCES suppliers,
price
numeric CHECK (price > 0)
);
then the same inventory_item composite type shown above would come into being as a byproduct, and could be used just as above. Note however an important restriction of the current implementation: since no constraints are associated with a composite type, the constraints shown in the table
definition do not apply to values of the composite type outside the table. (To work around this, create a domain over the composite type, and apply the desired constraints as CHECK constraints of the
domain.)
8.16.2. Constructing Composite Values
To write a composite value as a literal constant, enclose the field values within parentheses and separate
them by commas. You can put double quotes around any field value, and must do so if it contains
commas or parentheses. (More details appear below.) Thus, the general format of a composite constant
is the following:
'( val1 , val2 , ... )'
An example is:
'("fuzzy dice",42,1.99)'
which would be a valid value of the inventory_item type defined above. To make a field be
NULL, write no characters at all in its position in the list. For example, this constant specifies a NULL
third field:
'("fuzzy dice",42,)'
If you want an empty string rather than NULL, write double quotes:
'("",42,)'
Here the first field is a non-NULL empty string, the third is NULL.
(These constants are actually only a special case of the generic type constants discussed in Section 4.1.2.7. The constant is initially treated as a string and passed to the composite-type input conversion routine. An explicit type specification might be necessary to tell which type to convert the
constant to.)
204
Data Types
The ROW expression syntax can also be used to construct composite values. In most cases this is
considerably simpler to use than the string-literal syntax since you don't have to worry about multiple
layers of quoting. We already used this method above:
ROW('fuzzy dice', 42, 1.99)
ROW('', 42, NULL)
The ROW keyword is actually optional as long as you have more than one field in the expression,
so these can be simplified to:
('fuzzy dice', 42, 1.99)
('', 42, NULL)
The ROW expression syntax is discussed in more detail in Section 4.2.13.
8.16.3. Accessing Composite Types
To access a field of a composite column, one writes a dot and the field name, much like selecting a
field from a table name. In fact, it's so much like selecting from a table name that you often have to use
parentheses to keep from confusing the parser. For example, you might try to select some subfields
from our on_hand example table with something like:
SELECT item.name FROM on_hand WHERE item.price > 9.99;
This will not work since the name item is taken to be a table name, not a column name of on_hand,
per SQL syntax rules. You must write it like this:
SELECT (item).name FROM on_hand WHERE (item).price > 9.99;
or if you need to use the table name as well (for instance in a multitable query), like this:
SELECT (on_hand.item).name FROM on_hand WHERE (on_hand.item).price
> 9.99;
Now the parenthesized object is correctly interpreted as a reference to the item column, and then the
subfield can be selected from it.
Similar syntactic issues apply whenever you select a field from a composite value. For instance, to
select just one field from the result of a function that returns a composite value, you'd need to write
something like:
SELECT (my_func(...)).field FROM ...
Without the extra parentheses, this will generate a syntax error.
The special field name * means “all fields”, as further explained in Section 8.16.5.
8.16.4. Modifying Composite Types
Here are some examples of the proper syntax for inserting and updating composite columns. First,
inserting or updating a whole column:
INSERT INTO mytab (complex_col) VALUES((1.1,2.2));
205
Data Types
UPDATE mytab SET complex_col = ROW(1.1,2.2) WHERE ...;
The first example omits ROW, the second uses it; we could have done it either way.
We can update an individual subfield of a composite column:
UPDATE mytab SET complex_col.r = (complex_col).r + 1 WHERE ...;
Notice here that we don't need to (and indeed cannot) put parentheses around the column name appearing just after SET, but we do need parentheses when referencing the same column in the expression to the right of the equal sign.
And we can specify subfields as targets for INSERT, too:
INSERT INTO mytab (complex_col.r, complex_col.i) VALUES(1.1, 2.2);
Had we not supplied values for all the subfields of the column, the remaining subfields would have
been filled with null values.
8.16.5. Using Composite Types in Queries
There are various special syntax rules and behaviors associated with composite types in queries. These
rules provide useful shortcuts, but can be confusing if you don't know the logic behind them.
In PostgreSQL, a reference to a table name (or alias) in a query is effectively a reference to the composite value of the table's current row. For example, if we had a table inventory_item as shown
above, we could write:
SELECT c FROM inventory_item c;
This query produces a single composite-valued column, so we might get output like:
c
-----------------------("fuzzy dice",42,1.99)
(1 row)
Note however that simple names are matched to column names before table names, so this example
works only because there is no column named c in the query's tables.
The ordinary qualified-column-name syntax table_name.column_name can be understood as
applying field selection to the composite value of the table's current row. (For efficiency reasons, it's
not actually implemented that way.)
When we write
SELECT c.* FROM inventory_item c;
then, according to the SQL standard, we should get the contents of the table expanded into separate
columns:
name
| supplier_id | price
------------+-------------+-------
206
Data Types
fuzzy dice |
(1 row)
42 |
1.99
as if the query were
SELECT c.name, c.supplier_id, c.price FROM inventory_item c;
PostgreSQL will apply this expansion behavior to any composite-valued expression, although as
shown above, you need to write parentheses around the value that .* is applied to whenever it's not a
simple table name. For example, if myfunc() is a function returning a composite type with columns
a, b, and c, then these two queries have the same result:
SELECT (myfunc(x)).* FROM some_table;
SELECT (myfunc(x)).a, (myfunc(x)).b, (myfunc(x)).c FROM some_table;
Tip
PostgreSQL handles column expansion by actually transforming the first form into the second.
So, in this example, myfunc() would get invoked three times per row with either syntax. If
it's an expensive function you may wish to avoid that, which you can do with a query like:
SELECT m.* FROM some_table, LATERAL myfunc(x) AS m;
Placing the function in a LATERAL FROM item keeps it from being invoked more than once per
row. m.* is still expanded into m.a, m.b, m.c, but now those variables are just references
to the output of the FROM item. (The LATERAL keyword is optional here, but we show it to
clarify that the function is getting x from some_table.)
The composite_value.* syntax results in column expansion of this kind when it appears at the
top level of a SELECT output list, a RETURNING list in INSERT/UPDATE/DELETE/MERGE, a VALUES clause, or a row constructor. In all other contexts (including when nested inside one of those
constructs), attaching .* to a composite value does not change the value, since it means “all columns”
and so the same composite value is produced again. For example, if somefunc() accepts a composite-valued argument, these queries are the same:
SELECT somefunc(c.*) FROM inventory_item c;
SELECT somefunc(c) FROM inventory_item c;
In both cases, the current row of inventory_item is passed to the function as a single composite-valued argument. Even though .* does nothing in such cases, using it is good style, since it makes
clear that a composite value is intended. In particular, the parser will consider c in c.* to refer to a
table name or alias, not to a column name, so that there is no ambiguity; whereas without .*, it is not
clear whether c means a table name or a column name, and in fact the column-name interpretation
will be preferred if there is a column named c.
Another example demonstrating these concepts is that all these queries mean the same thing:
SELECT * FROM inventory_item c ORDER BY c;
SELECT * FROM inventory_item c ORDER BY c.*;
SELECT * FROM inventory_item c ORDER BY ROW(c.*);
All of these ORDER BY clauses specify the row's composite value, resulting in sorting the rows according to the rules described in Section 9.25.6. However, if inventory_item contained a column
207
Data Types
named c, the first case would be different from the others, as it would mean to sort by that column
only. Given the column names previously shown, these queries are also equivalent to those above:
SELECT * FROM inventory_item c ORDER BY ROW(c.name, c.supplier_id,
c.price);
SELECT * FROM inventory_item c ORDER BY (c.name, c.supplier_id,
c.price);
(The last case uses a row constructor with the key word ROW omitted.)
Another special syntactical behavior associated with composite values is that we can use functional
notation for extracting a field of a composite value. The simple way to explain this is that the notations
field(table) and table.field are interchangeable. For example, these queries are equivalent:
SELECT c.name FROM inventory_item c WHERE c.price > 1000;
SELECT name(c) FROM inventory_item c WHERE price(c) > 1000;
Moreover, if we have a function that accepts a single argument of a composite type, we can call it
with either notation. These queries are all equivalent:
SELECT somefunc(c) FROM inventory_item c;
SELECT somefunc(c.*) FROM inventory_item c;
SELECT c.somefunc FROM inventory_item c;
This equivalence between functional notation and field notation makes it possible to use functions on
composite types to implement “computed fields”. An application using the last query above wouldn't
need to be directly aware that somefunc isn't a real column of the table.
Tip
Because of this behavior, it's unwise to give a function that takes a single composite-type
argument the same name as any of the fields of that composite type. If there is ambiguity, the
field-name interpretation will be chosen if field-name syntax is used, while the function will
be chosen if function-call syntax is used. However, PostgreSQL versions before 11 always
chose the field-name interpretation, unless the syntax of the call required it to be a function
call. One way to force the function interpretation in older versions is to schema-qualify the
function name, that is, write schema.func(compositevalue).
8.16.6. Composite Type Input and Output Syntax
The external text representation of a composite value consists of items that are interpreted according
to the I/O conversion rules for the individual field types, plus decoration that indicates the composite
structure. The decoration consists of parentheses (( and )) around the whole value, plus commas (,)
between adjacent items. Whitespace outside the parentheses is ignored, but within the parentheses it
is considered part of the field value, and might or might not be significant depending on the input
conversion rules for the field data type. For example, in:
'(
42)'
the whitespace will be ignored if the field type is integer, but not if it is text.
As shown previously, when writing a composite value you can write double quotes around any individual field value. You must do so if the field value would otherwise confuse the composite-value
208
Data Types
parser. In particular, fields containing parentheses, commas, double quotes, or backslashes must be
double-quoted. To put a double quote or backslash in a quoted composite field value, precede it with
a backslash. (Also, a pair of double quotes within a double-quoted field value is taken to represent a
double quote character, analogously to the rules for single quotes in SQL literal strings.) Alternatively,
you can avoid quoting and use backslash-escaping to protect all data characters that would otherwise
be taken as composite syntax.
A completely empty field value (no characters at all between the commas or parentheses) represents
a NULL. To write a value that is an empty string rather than NULL, write "".
The composite output routine will put double quotes around field values if they are empty strings or
contain parentheses, commas, double quotes, backslashes, or white space. (Doing so for white space
is not essential, but aids legibility.) Double quotes and backslashes embedded in field values will be
doubled.
Note
Remember that what you write in an SQL command will first be interpreted as a string literal,
and then as a composite. This doubles the number of backslashes you need (assuming escape
string syntax is used). For example, to insert a text field containing a double quote and a
backslash in a composite value, you'd need to write:
INSERT ... VALUES ('("\"\\")');
The string-literal processor removes one level of backslashes, so that what arrives at the composite-value parser looks like ("\"\\"). In turn, the string fed to the text data type's input
routine becomes "\. (If we were working with a data type whose input routine also treated
backslashes specially, bytea for example, we might need as many as eight backslashes in
the command to get one backslash into the stored composite field.) Dollar quoting (see Section 4.1.2.4) can be used to avoid the need to double backslashes.
Tip
The ROW constructor syntax is usually easier to work with than the composite-literal syntax
when writing composite values in SQL commands. In ROW, individual field values are written
the same way they would be written when not members of a composite.
8.17. Range Types
Range types are data types representing a range of values of some element type (called the range's
subtype). For instance, ranges of timestamp might be used to represent the ranges of time that a
meeting room is reserved. In this case the data type is tsrange (short for “timestamp range”), and
timestamp is the subtype. The subtype must have a total order so that it is well-defined whether
element values are within, before, or after a range of values.
Range types are useful because they represent many element values in a single range value, and because concepts such as overlapping ranges can be expressed clearly. The use of time and date ranges
for scheduling purposes is the clearest example; but price ranges, measurement ranges from an instrument, and so forth can also be useful.
Every range type has a corresponding multirange type. A multirange is an ordered list of non-contiguous, non-empty, non-null ranges. Most range operators also work on multiranges, and they have a few
functions of their own.
209
Data Types
8.17.1. Built-in Range and Multirange Types
PostgreSQL comes with the following built-in range types:
• int4range — Range of integer, int4multirange — corresponding Multirange
• int8range — Range of bigint, int8multirange — corresponding Multirange
• numrange — Range of numeric, nummultirange — corresponding Multirange
• tsrange — Range of timestamp without time zone, tsmultirange — corresponding Multirange
• tstzrange — Range of timestamp with time zone, tstzmultirange — corresponding Multirange
• daterange — Range of date, datemultirange — corresponding Multirange
In addition, you can define your own range types; see CREATE TYPE for more information.
8.17.2. Examples
CREATE TABLE reservation (room int, during tsrange);
INSERT INTO reservation VALUES
(1108, '[2010-01-01 14:30, 2010-01-01 15:30)');
-- Containment
SELECT int4range(10, 20) @> 3;
-- Overlaps
SELECT numrange(11.1, 22.2) && numrange(20.0, 30.0);
-- Extract the upper bound
SELECT upper(int8range(15, 25));
-- Compute the intersection
SELECT int4range(10, 20) * int4range(15, 25);
-- Is the range empty?
SELECT isempty(numrange(1, 5));
See Table 9.56 and Table 9.58 for complete lists of operators and functions on range types.
8.17.3. Inclusive and Exclusive Bounds
Every non-empty range has two bounds, the lower bound and the upper bound. All points between
these values are included in the range. An inclusive bound means that the boundary point itself is
included in the range as well, while an exclusive bound means that the boundary point is not included
in the range.
In the text form of a range, an inclusive lower bound is represented by “[” while an exclusive lower
bound is represented by “(”. Likewise, an inclusive upper bound is represented by “]”, while an
exclusive upper bound is represented by “)”. (See Section 8.17.5 for more details.)
The functions lower_inc and upper_inc test the inclusivity of the lower and upper bounds of
a range value, respectively.
210
Data Types
8.17.4. Infinite (Unbounded) Ranges
The lower bound of a range can be omitted, meaning that all values less than the upper bound are
included in the range, e.g., (,3]. Likewise, if the upper bound of the range is omitted, then all values
greater than the lower bound are included in the range. If both lower and upper bounds are omitted, all
values of the element type are considered to be in the range. Specifying a missing bound as inclusive
is automatically converted to exclusive, e.g., [,] is converted to (,). You can think of these missing
values as +/-infinity, but they are special range type values and are considered to be beyond any range
element type's +/-infinity values.
Element types that have the notion of “infinity” can use them as explicit bound values. For example,
with timestamp ranges, [today,infinity) excludes the special timestamp value infinity,
while [today,infinity] include it, as does [today,) and [today,].
The functions lower_inf and upper_inf test for infinite lower and upper bounds of a range,
respectively.
8.17.5. Range Input/Output
The input for a range value must follow one of the following patterns:
(lower-bound,upper-bound)
(lower-bound,upper-bound]
[lower-bound,upper-bound)
[lower-bound,upper-bound]
empty
The parentheses or brackets indicate whether the lower and upper bounds are exclusive or inclusive,
as described previously. Notice that the final pattern is empty, which represents an empty range (a
range that contains no points).
The lower-bound may be either a string that is valid input for the subtype, or empty to indicate
no lower bound. Likewise, upper-bound may be either a string that is valid input for the subtype,
or empty to indicate no upper bound.
Each bound value can be quoted using " (double quote) characters. This is necessary if the bound
value contains parentheses, brackets, commas, double quotes, or backslashes, since these characters
would otherwise be taken as part of the range syntax. To put a double quote or backslash in a quoted
bound value, precede it with a backslash. (Also, a pair of double quotes within a double-quoted bound
value is taken to represent a double quote character, analogously to the rules for single quotes in SQL
literal strings.) Alternatively, you can avoid quoting and use backslash-escaping to protect all data
characters that would otherwise be taken as range syntax. Also, to write a bound value that is an empty
string, write "", since writing nothing means an infinite bound.
Whitespace is allowed before and after the range value, but any whitespace between the parentheses
or brackets is taken as part of the lower or upper bound value. (Depending on the element type, it
might or might not be significant.)
Note
These rules are very similar to those for writing field values in composite-type literals. See
Section 8.16.6 for additional commentary.
Examples:
211
Data Types
-- includes 3, does not include 7, and does include all points in
between
SELECT '[3,7)'::int4range;
-- does not include either 3 or 7, but includes all points in
between
SELECT '(3,7)'::int4range;
-- includes only the single point 4
SELECT '[4,4]'::int4range;
-- includes no points (and will be normalized to 'empty')
SELECT '[4,4)'::int4range;
The input for a multirange is curly brackets ({ and }) containing zero or more valid ranges, separated
by commas. Whitespace is permitted around the brackets and commas. This is intended to be reminiscent of array syntax, although multiranges are much simpler: they have just one dimension and there
is no need to quote their contents. (The bounds of their ranges may be quoted as above however.)
Examples:
SELECT '{}'::int4multirange;
SELECT '{[3,7)}'::int4multirange;
SELECT '{[3,7), [8,9)}'::int4multirange;
8.17.6. Constructing Ranges and Multiranges
Each range type has a constructor function with the same name as the range type. Using the constructor
function is frequently more convenient than writing a range literal constant, since it avoids the need
for extra quoting of the bound values. The constructor function accepts two or three arguments. The
two-argument form constructs a range in standard form (lower bound inclusive, upper bound exclusive), while the three-argument form constructs a range with bounds of the form specified by the third
argument. The third argument must be one of the strings “()”, “(]”, “[)”, or “[]”. For example:
-- The full form is: lower bound, upper bound, and text argument
indicating
-- inclusivity/exclusivity of bounds.
SELECT numrange(1.0, 14.0, '(]');
-- If the third argument is omitted, '[)' is assumed.
SELECT numrange(1.0, 14.0);
-- Although '(]' is specified here, on display the value will be
converted to
-- canonical form, since int8range is a discrete range type (see
below).
SELECT int8range(1, 14, '(]');
-- Using NULL for either bound causes the range to be unbounded on
that side.
SELECT numrange(NULL, 2.2);
Each range type also has a multirange constructor with the same name as the multirange type. The
constructor function takes zero or more arguments which are all ranges of the appropriate type. For
example:
212
Data Types
SELECT nummultirange();
SELECT nummultirange(numrange(1.0, 14.0));
SELECT nummultirange(numrange(1.0, 14.0), numrange(20.0, 25.0));
8.17.7. Discrete Range Types
A discrete range is one whose element type has a well-defined “step”, such as integer or date.
In these types two elements can be said to be adjacent, when there are no valid values between them.
This contrasts with continuous ranges, where it's always (or almost always) possible to identify other
element values between two given values. For example, a range over the numeric type is continuous, as is a range over timestamp. (Even though timestamp has limited precision, and so could
theoretically be treated as discrete, it's better to consider it continuous since the step size is normally
not of interest.)
Another way to think about a discrete range type is that there is a clear idea of a “next” or “previous”
value for each element value. Knowing that, it is possible to convert between inclusive and exclusive
representations of a range's bounds, by choosing the next or previous element value instead of the one
originally given. For example, in an integer range type [4,8] and (3,9) denote the same set of
values; but this would not be so for a range over numeric.
A discrete range type should have a canonicalization function that is aware of the desired step size for
the element type. The canonicalization function is charged with converting equivalent values of the
range type to have identical representations, in particular consistently inclusive or exclusive bounds.
If a canonicalization function is not specified, then ranges with different formatting will always be
treated as unequal, even though they might represent the same set of values in reality.
The built-in range types int4range, int8range, and daterange all use a canonical form that
includes the lower bound and excludes the upper bound; that is, [). User-defined range types can use
other conventions, however.
8.17.8. Defining New Range Types
Users can define their own range types. The most common reason to do this is to use ranges over
subtypes not provided among the built-in range types. For example, to define a new range type of
subtype float8:
CREATE TYPE floatrange AS RANGE (
subtype = float8,
subtype_diff = float8mi
);
SELECT '[1.234, 5.678]'::floatrange;
Because float8 has no meaningful “step”, we do not define a canonicalization function in this example.
When you define your own range you automatically get a corresponding multirange type.
Defining your own range type also allows you to specify a different subtype B-tree operator class or
collation to use, so as to change the sort ordering that determines which values fall into a given range.
If the subtype is considered to have discrete rather than continuous values, the CREATE TYPE command should specify a canonical function. The canonicalization function takes an input range value, and must return an equivalent range value that may have different bounds and formatting. The
canonical output for two ranges that represent the same set of values, for example the integer ranges
[1, 7] and [1, 8), must be identical. It doesn't matter which representation you choose to be the
canonical one, so long as two equivalent values with different formattings are always mapped to the
same value with the same formatting. In addition to adjusting the inclusive/exclusive bounds format, a
213
Data Types
canonicalization function might round off boundary values, in case the desired step size is larger than
what the subtype is capable of storing. For instance, a range type over timestamp could be defined
to have a step size of an hour, in which case the canonicalization function would need to round off
bounds that weren't a multiple of an hour, or perhaps throw an error instead.
In addition, any range type that is meant to be used with GiST or SP-GiST indexes should define a subtype difference, or subtype_diff, function. (The index will still work without subtype_diff,
but it is likely to be considerably less efficient than if a difference function is provided.) The subtype
difference function takes two input values of the subtype, and returns their difference (i.e., X minus
Y) represented as a float8 value. In our example above, the function float8mi that underlies the
regular float8 minus operator can be used; but for any other subtype, some type conversion would
be necessary. Some creative thought about how to represent differences as numbers might be needed,
too. To the greatest extent possible, the subtype_diff function should agree with the sort ordering
implied by the selected operator class and collation; that is, its result should be positive whenever its
first argument is greater than its second according to the sort ordering.
A less-oversimplified example of a subtype_diff function is:
CREATE FUNCTION time_subtype_diff(x time, y time) RETURNS float8 AS
'SELECT EXTRACT(EPOCH FROM (x - y))' LANGUAGE sql STRICT IMMUTABLE;
CREATE TYPE timerange AS RANGE (
subtype = time,
subtype_diff = time_subtype_diff
);
SELECT '[11:10, 23:00]'::timerange;
See CREATE TYPE for more information about creating range types.
8.17.9. Indexing
GiST and SP-GiST indexes can be created for table columns of range types. GiST indexes can be also
created for table columns of multirange types. For instance, to create a GiST index:
CREATE INDEX reservation_idx ON reservation USING GIST (during);
A GiST or SP-GiST index on ranges can accelerate queries involving these range operators: =, &&,
<@, @>, <<, >>, -|-, &<, and &>. A GiST index on multiranges can accelerate queries involving the
same set of multirange operators. A GiST index on ranges and GiST index on multiranges can also
accelerate queries involving these cross-type range to multirange and multirange to range operators
correspondingly: &&, <@, @>, <<, >>, -|-, &<, and &>. See Table 9.56 for more information.
In addition, B-tree and hash indexes can be created for table columns of range types. For these index
types, basically the only useful range operation is equality. There is a B-tree sort ordering defined for
range values, with corresponding < and > operators, but the ordering is rather arbitrary and not usually
useful in the real world. Range types' B-tree and hash support is primarily meant to allow sorting and
hashing internally in queries, rather than creation of actual indexes.
8.17.10. Constraints on Ranges
While UNIQUE is a natural constraint for scalar values, it is usually unsuitable for range types. Instead, an exclusion constraint is often more appropriate (see CREATE TABLE ... CONSTRAINT ...
EXCLUDE). Exclusion constraints allow the specification of constraints such as “non-overlapping”
on a range type. For example:
214
Data Types
CREATE TABLE reservation (
during tsrange,
EXCLUDE USING GIST (during WITH &&)
);
That constraint will prevent any overlapping values from existing in the table at the same time:
INSERT INTO reservation VALUES
('[2010-01-01 11:30, 2010-01-01 15:00)');
INSERT 0 1
INSERT INTO reservation VALUES
('[2010-01-01 14:45, 2010-01-01 15:45)');
ERROR: conflicting key value violates exclusion constraint
"reservation_during_excl"
DETAIL: Key (during)=(["2010-01-01 14:45:00","2010-01-01
15:45:00")) conflicts
with existing key (during)=(["2010-01-01 11:30:00","2010-01-01
15:00:00")).
You can use the btree_gist extension to define exclusion constraints on plain scalar data types,
which can then be combined with range exclusions for maximum flexibility. For example, after
btree_gist is installed, the following constraint will reject overlapping ranges only if the meeting
room numbers are equal:
CREATE EXTENSION btree_gist;
CREATE TABLE room_reservation (
room text,
during tsrange,
EXCLUDE USING GIST (room WITH =, during WITH &&)
);
INSERT INTO room_reservation VALUES
('123A', '[2010-01-01 14:00, 2010-01-01 15:00)');
INSERT 0 1
INSERT INTO room_reservation VALUES
('123A', '[2010-01-01 14:30, 2010-01-01 15:30)');
ERROR: conflicting key value violates exclusion constraint
"room_reservation_room_during_excl"
DETAIL: Key (room, during)=(123A, ["2010-01-01
14:30:00","2010-01-01 15:30:00")) conflicts
with existing key (room, during)=(123A, ["2010-01-01
14:00:00","2010-01-01 15:00:00")).
INSERT INTO room_reservation VALUES
('123B', '[2010-01-01 14:30, 2010-01-01 15:30)');
INSERT 0 1
8.18. Domain Types
A domain is a user-defined data type that is based on another underlying type. Optionally, it can have
constraints that restrict its valid values to a subset of what the underlying type would allow. Otherwise
it behaves like the underlying type — for example, any operator or function that can be applied to the
underlying type will work on the domain type. The underlying type can be any built-in or user-defined
base type, enum type, array type, composite type, range type, or another domain.
215
Data Types
For example, we could create a domain over integers that accepts only positive integers:
CREATE DOMAIN posint AS integer CHECK (VALUE > 0);
CREATE TABLE mytable (id posint);
INSERT INTO mytable VALUES(1);
-- works
INSERT INTO mytable VALUES(-1); -- fails
When an operator or function of the underlying type is applied to a domain value, the domain is
automatically down-cast to the underlying type. Thus, for example, the result of mytable.id - 1 is
considered to be of type integer not posint. We could write (mytable.id - 1)::posint
to cast the result back to posint, causing the domain's constraints to be rechecked. In this case, that
would result in an error if the expression had been applied to an id value of 1. Assigning a value of
the underlying type to a field or variable of the domain type is allowed without writing an explicit
cast, but the domain's constraints will be checked.
For additional information see CREATE DOMAIN.
8.19. Object Identifier Types
Object identifiers (OIDs) are used internally by PostgreSQL as primary keys for various system tables.
Type oid represents an object identifier. There are also several alias types for oid, each named
regsomething. Table 8.26 shows an overview.
The oid type is currently implemented as an unsigned four-byte integer. Therefore, it is not large
enough to provide database-wide uniqueness in large databases, or even in large individual tables.
The oid type itself has few operations beyond comparison. It can be cast to integer, however, and
then manipulated using the standard integer operators. (Beware of possible signed-versus-unsigned
confusion if you do this.)
The OID alias types have no operations of their own except for specialized input and output routines.
These routines are able to accept and display symbolic names for system objects, rather than the raw
numeric value that type oid would use. The alias types allow simplified lookup of OID values for
objects. For example, to examine the pg_attribute rows related to a table mytable, one could
write:
SELECT * FROM pg_attribute WHERE attrelid = 'mytable'::regclass;
rather than:
SELECT * FROM pg_attribute
WHERE attrelid = (SELECT oid FROM pg_class WHERE relname =
'mytable');
While that doesn't look all that bad by itself, it's still oversimplified. A far more complicated subselect would be needed to select the right OID if there are multiple tables named mytable in different
schemas. The regclass input converter handles the table lookup according to the schema path
setting, and so it does the “right thing” automatically. Similarly, casting a table's OID to regclass
is handy for symbolic display of a numeric OID.
Table 8.26. Object Identifier Types
Name
References
Description
oid
any
numeric object identifi- 564182
er
216
Value Example
Data Types
Name
References
Description
Value Example
regclass
pg_class
relation name
pg_type
regcollation
pg_collation
collation name
"POSIX"
regconfig
pg_ts_config
text search configuration
english
regdictionary
pg_ts_dict
text search dictionary
simple
regnamespace
pg_namespace
namespace name
pg_catalog
regoper
pg_operator
operator name
+
regoperator
pg_operator
operator with argument *(integer,intetypes
ger) or -(NONE,
integer)
regproc
pg_proc
function name
regprocedure
pg_proc
function with argument sum(int4)
types
regrole
pg_authid
role name
smithee
regtype
pg_type
data type name
integer
sum
All of the OID alias types for objects that are grouped by namespace accept schema-qualified names,
and will display schema-qualified names on output if the object would not be found in the current
search path without being qualified. For example, myschema.mytable is acceptable input for
regclass (if there is such a table). That value might be output as myschema.mytable, or just
mytable, depending on the current search path. The regproc and regoper alias types will only accept input names that are unique (not overloaded), so they are of limited use; for most uses
regprocedure or regoperator are more appropriate. For regoperator, unary operators are
identified by writing NONE for the unused operand.
The input functions for these types allow whitespace between tokens, and will fold upper-case letters
to lower case, except within double quotes; this is done to make the syntax rules similar to the way
object names are written in SQL. Conversely, the output functions will use double quotes if needed
to make the output be a valid SQL identifier. For example, the OID of a function named Foo (with
upper case F) taking two integer arguments could be entered as ' "Foo" ( int, integer )
'::regprocedure. The output would look like "Foo"(integer,integer). Both the function name and the argument type names could be schema-qualified, too.
Many built-in PostgreSQL functions accept the OID of a table, or another kind of database object, and
for convenience are declared as taking regclass (or the appropriate OID alias type). This means
you do not have to look up the object's OID by hand, but can just enter its name as a string literal.
For example, the nextval(regclass) function takes a sequence relation's OID, so you could call
it like this:
nextval('foo')
nextval('FOO')
nextval('"Foo"')
nextval('myschema.foo')
nextval('"myschema".foo')
nextval('foo')
operates on sequence foo
same as above
operates on sequence Foo
operates on myschema.foo
same as above
searches search path for foo
Note
When you write the argument of such a function as an unadorned literal string, it becomes
a constant of type regclass (or the appropriate type). Since this is really just an OID, it
will track the originally identified object despite later renaming, schema reassignment, etc.
This “early binding” behavior is usually desirable for object references in column defaults and
217
Data Types
views. But sometimes you might want “late binding” where the object reference is resolved
at run time. To get late-binding behavior, force the constant to be stored as a text constant
instead of regclass:
nextval('foo'::text)
foo is looked up at runtime
The to_regclass() function and its siblings can also be used to perform run-time lookups.
See Table 9.74.
Another practical example of use of regclass is to look up the OID of a table listed in the information_schema views, which don't supply such OIDs directly. One might for example wish to call
the pg_relation_size() function, which requires the table OID. Taking the above rules into
account, the correct way to do that is
SELECT table_schema, table_name,
pg_relation_size((quote_ident(table_schema) || '.' ||
quote_ident(table_name))::regclass)
FROM information_schema.tables
WHERE ...
The quote_ident() function will take care of double-quoting the identifiers where needed. The
seemingly easier
SELECT pg_relation_size(table_name)
FROM information_schema.tables
WHERE ...
is not recommended, because it will fail for tables that are outside your search path or have names
that require quoting.
An additional property of most of the OID alias types is the creation of dependencies. If a constant
of one of these types appears in a stored expression (such as a column default expression or view),
it creates a dependency on the referenced object. For example, if a column has a default expression nextval('my_seq'::regclass), PostgreSQL understands that the default expression depends on the sequence my_seq, so the system will not let the sequence be dropped without first removing the default expression. The alternative of nextval('my_seq'::text) does not create
a dependency. (regrole is an exception to this property. Constants of this type are not allowed in
stored expressions.)
Another identifier type used by the system is xid, or transaction (abbreviated xact) identifier. This
is the data type of the system columns xmin and xmax. Transaction identifiers are 32-bit quantities.
In some contexts, a 64-bit variant xid8 is used. Unlike xid values, xid8 values increase strictly
monotonically and cannot be reused in the lifetime of a database cluster. See Section 66.1 for more
details.
A third identifier type used by the system is cid, or command identifier. This is the data type of the
system columns cmin and cmax. Command identifiers are also 32-bit quantities.
A final identifier type used by the system is tid, or tuple identifier (row identifier). This is the data
type of the system column ctid. A tuple ID is a pair (block number, tuple index within block) that
identifies the physical location of the row within its table.
(The system columns are further explained in Section 5.6.)
8.20. pg_lsn Type
218
Data Types
The pg_lsn data type can be used to store LSN (Log Sequence Number) data which is a pointer to
a location in the WAL. This type is a representation of XLogRecPtr and an internal system type
of PostgreSQL.
Internally, an LSN is a 64-bit integer, representing a byte position in the write-ahead log stream. It
is printed as two hexadecimal numbers of up to 8 digits each, separated by a slash; for example,
16/B374D848. The pg_lsn type supports the standard comparison operators, like = and >. Two
LSNs can be subtracted using the - operator; the result is the number of bytes separating those writeahead log locations. Also the number of bytes can be added into and subtracted from LSN using the
+(pg_lsn,numeric) and -(pg_lsn,numeric) operators, respectively. Note that the calculated LSN should be in the range of pg_lsn type, i.e., between 0/0 and FFFFFFFF/FFFFFFFF.
8.21. Pseudo-Types
The PostgreSQL type system contains a number of special-purpose entries that are collectively called
pseudo-types. A pseudo-type cannot be used as a column data type, but it can be used to declare a
function's argument or result type. Each of the available pseudo-types is useful in situations where a
function's behavior does not correspond to simply taking or returning a value of a specific SQL data
type. Table 8.27 lists the existing pseudo-types.
Table 8.27. Pseudo-Types
Name
Description
any
Indicates that a function accepts any input data type.
anyelement
Indicates that a function accepts any data type (see Section 36.2.5).
anyarray
Indicates that a function accepts any array data type (see
Section 36.2.5).
anynonarray
Indicates that a function accepts any non-array data type
(see Section 36.2.5).
anyenum
Indicates that a function accepts any enum data type (see
Section 36.2.5 and Section 8.7).
anyrange
Indicates that a function accepts any range data type (see
Section 36.2.5 and Section 8.17).
anymultirange
Indicates that a function accepts any multirange data type
(see Section 36.2.5 and Section 8.17).
anycompatible
Indicates that a function accepts any data type, with automatic promotion of multiple arguments to a common data
type (see Section 36.2.5).
anycompatiblearray
Indicates that a function accepts any array data type, with
automatic promotion of multiple arguments to a common
data type (see Section 36.2.5).
anycompatiblenonarray
Indicates that a function accepts any non-array data type,
with automatic promotion of multiple arguments to a common data type (see Section 36.2.5).
anycompatiblerange
Indicates that a function accepts any range data type, with
automatic promotion of multiple arguments to a common
data type (see Section 36.2.5 and Section 8.17).
anycompatiblemultirange
Indicates that a function accepts any multirange data type,
with automatic promotion of multiple arguments to a common data type (see Section 36.2.5 and Section 8.17).
cstring
Indicates that a function accepts or returns a null-terminated C string.
219
Data Types
Name
Description
internal
Indicates that a function accepts or returns a server-internal
data type.
language_handler
A procedural language call handler is declared to return
language_handler.
fdw_handler
A foreign-data wrapper handler is declared to return fdw_handler.
table_am_handler
A table access method handler is declared to return table_am_handler.
index_am_handler
An index access method handler is declared to return index_am_handler.
tsm_handler
A tablesample method handler is declared to return
tsm_handler.
record
Identifies a function taking or returning an unspecified row
type.
trigger
A trigger function is declared to return trigger.
event_trigger
An event trigger function is declared to return event_trigger.
pg_ddl_command
Identifies a representation of DDL commands that is available to event triggers.
void
Indicates that a function returns no value.
unknown
Identifies a not-yet-resolved type, e.g., of an undecorated
string literal.
Functions coded in C (whether built-in or dynamically loaded) can be declared to accept or return any
of these pseudo-types. It is up to the function author to ensure that the function will behave safely
when a pseudo-type is used as an argument type.
Functions coded in procedural languages can use pseudo-types only as allowed by their implementation languages. At present most procedural languages forbid use of a pseudo-type as an argument
type, and allow only void and record as a result type (plus trigger or event_trigger when
the function is used as a trigger or event trigger). Some also support polymorphic functions using the
polymorphic pseudo-types, which are shown above and discussed in detail in Section 36.2.5.
The internal pseudo-type is used to declare functions that are meant only to be called internally
by the database system, and not by direct invocation in an SQL query. If a function has at least one
internal-type argument then it cannot be called from SQL. To preserve the type safety of this
restriction it is important to follow this coding rule: do not create any function that is declared to return
internal unless it has at least one internal argument.
220
Chapter 9. Functions and Operators
PostgreSQL provides a large number of functions and operators for the built-in data types. This chapter
describes most of them, although additional special-purpose functions appear in relevant sections of
the manual. Users can also define their own functions and operators, as described in Part V. The psql
commands \df and \do can be used to list all available functions and operators, respectively.
The notation used throughout this chapter to describe the argument and result data types of a function
or operator is like this:
repeat ( text, integer ) → text
which says that the function repeat takes one text and one integer argument and returns a result of
type text. The right arrow is also used to indicate the result of an example, thus:
repeat('Pg', 4) → PgPgPgPg
If you are concerned about portability then note that most of the functions and operators described
in this chapter, with the exception of the most trivial arithmetic and comparison operators and some
explicitly marked functions, are not specified by the SQL standard. Some of this extended functionality is present in other SQL database management systems, and in many cases this functionality is
compatible and consistent between the various implementations.
9.1. Logical Operators
The usual logical operators are available:
boolean AND boolean → boolean
boolean OR boolean → boolean
NOT boolean → boolean
SQL uses a three-valued logic system with true, false, and null, which represents “unknown”. Observe the following truth tables:
a
b
a AND b
a OR b
TRUE
TRUE
TRUE
TRUE
TRUE
FALSE
FALSE
TRUE
TRUE
NULL
NULL
TRUE
FALSE
FALSE
FALSE
FALSE
FALSE
NULL
FALSE
NULL
NULL
NULL
NULL
NULL
a
NOT a
TRUE
FALSE
FALSE
TRUE
NULL
NULL
The operators AND and OR are commutative, that is, you can switch the left and right operands without
affecting the result. (However, it is not guaranteed that the left operand is evaluated before the right
operand. See Section 4.2.14 for more information about the order of evaluation of subexpressions.)
221
Functions and Operators
9.2. Comparison Functions and Operators
The usual comparison operators are available, as shown in Table 9.1.
Table 9.1. Comparison Operators
Operator
Description
datatype < datatype → boolean
Less than
datatype > datatype → boolean
Greater than
datatype <= datatype → boolean
Less than or equal to
datatype >= datatype → boolean
Greater than or equal to
datatype = datatype → boolean
Equal
datatype <> datatype → boolean
Not equal
datatype != datatype → boolean
Not equal
Note
<> is the standard SQL notation for “not equal”. != is an alias, which is converted to <> at
a very early stage of parsing. Hence, it is not possible to implement != and <> operators that
do different things.
These comparison operators are available for all built-in data types that have a natural ordering, including numeric, string, and date/time types. In addition, arrays, composite types, and ranges can be
compared if their component data types are comparable.
It is usually possible to compare values of related data types as well; for example integer > bigint
will work. Some cases of this sort are implemented directly by “cross-type” comparison operators, but
if no such operator is available, the parser will coerce the less-general type to the more-general type
and apply the latter's comparison operator.
As shown above, all comparison operators are binary operators that return values of type boolean.
Thus, expressions like 1 < 2 < 3 are not valid (because there is no < operator to compare a Boolean
value with 3). Use the BETWEEN predicates shown below to perform range tests.
There are also some comparison predicates, as shown in Table 9.2. These behave much like operators,
but have special syntax mandated by the SQL standard.
Table 9.2. Comparison Predicates
Predicate
Description
Example(s)
datatype BETWEEN datatype AND datatype → boolean
Between (inclusive of the range endpoints).
2 BETWEEN 1 AND 3 → t
2 BETWEEN 3 AND 1 → f
datatype NOT BETWEEN datatype AND datatype → boolean
Not between (the negation of BETWEEN).
2 NOT BETWEEN 1 AND 3 → f
222
Functions and Operators
Predicate
Description
Example(s)
datatype BETWEEN SYMMETRIC datatype AND datatype → boolean
Between, after sorting the two endpoint values.
2 BETWEEN SYMMETRIC 3 AND 1 → t
datatype NOT BETWEEN SYMMETRIC datatype AND datatype → boolean
Not between, after sorting the two endpoint values.
2 NOT BETWEEN SYMMETRIC 3 AND 1 → f
datatype IS DISTINCT FROM datatype → boolean
Not equal, treating null as a comparable value.
1 IS DISTINCT FROM NULL → t (rather than NULL)
NULL IS DISTINCT FROM NULL → f (rather than NULL)
datatype IS NOT DISTINCT FROM datatype → boolean
Equal, treating null as a comparable value.
1 IS NOT DISTINCT FROM NULL → f (rather than NULL)
NULL IS NOT DISTINCT FROM NULL → t (rather than NULL)
datatype IS NULL → boolean
Test whether value is null.
1.5 IS NULL → f
datatype IS NOT NULL → boolean
Test whether value is not null.
'null' IS NOT NULL → t
datatype ISNULL → boolean
Test whether value is null (nonstandard syntax).
datatype NOTNULL → boolean
Test whether value is not null (nonstandard syntax).
boolean IS TRUE → boolean
Test whether boolean expression yields true.
true IS TRUE → t
NULL::boolean IS TRUE → f (rather than NULL)
boolean IS NOT TRUE → boolean
Test whether boolean expression yields false or unknown.
true IS NOT TRUE → f
NULL::boolean IS NOT TRUE → t (rather than NULL)
boolean IS FALSE → boolean
Test whether boolean expression yields false.
true IS FALSE → f
NULL::boolean IS FALSE → f (rather than NULL)
boolean IS NOT FALSE → boolean
Test whether boolean expression yields true or unknown.
true IS NOT FALSE → t
NULL::boolean IS NOT FALSE → t (rather than NULL)
223
Functions and Operators
Predicate
Description
Example(s)
boolean IS UNKNOWN → boolean
Test whether boolean expression yields unknown.
true IS UNKNOWN → f
NULL::boolean IS UNKNOWN → t (rather than NULL)
boolean IS NOT UNKNOWN → boolean
Test whether boolean expression yields true or false.
true IS NOT UNKNOWN → t
NULL::boolean IS NOT UNKNOWN → f (rather than NULL)
The BETWEEN predicate simplifies range tests:
a BETWEEN x AND y
is equivalent to
a >= x AND a <= y
Notice that BETWEEN treats the endpoint values as included in the range. BETWEEN SYMMETRIC
is like BETWEEN except there is no requirement that the argument to the left of AND be less than or
equal to the argument on the right. If it is not, those two arguments are automatically swapped, so that
a nonempty range is always implied.
The various variants of BETWEEN are implemented in terms of the ordinary comparison operators,
and therefore will work for any data type(s) that can be compared.
Note
The use of AND in the BETWEEN syntax creates an ambiguity with the use of AND as a logical operator. To resolve this, only a limited set of expression types are allowed as the second
argument of a BETWEEN clause. If you need to write a more complex sub-expression in BETWEEN, write parentheses around the sub-expression.
Ordinary comparison operators yield null (signifying “unknown”), not true or false, when either input
is null. For example, 7 = NULL yields null, as does 7 <> NULL. When this behavior is not suitable,
use the IS [ NOT ] DISTINCT FROM predicates:
a IS DISTINCT FROM b
a IS NOT DISTINCT FROM b
For non-null inputs, IS DISTINCT FROM is the same as the <> operator. However, if both inputs
are null it returns false, and if only one input is null it returns true. Similarly, IS NOT DISTINCT
FROM is identical to = for non-null inputs, but it returns true when both inputs are null, and false when
only one input is null. Thus, these predicates effectively act as though null were a normal data value,
rather than “unknown”.
To check whether a value is or is not null, use the predicates:
expression IS NULL
224
Functions and Operators
expression IS NOT NULL
or the equivalent, but nonstandard, predicates:
expression ISNULL
expression NOTNULL
Do not write expression = NULL because NULL is not “equal to” NULL. (The null value represents an unknown value, and it is not known whether two unknown values are equal.)
Tip
Some applications might expect that expression = NULL returns true if expression
evaluates to the null value. It is highly recommended that these applications be modified to
comply with the SQL standard. However, if that cannot be done the transform_null_equals
configuration variable is available. If it is enabled, PostgreSQL will convert x = NULL
clauses to x IS NULL.
If the expression is row-valued, then IS NULL is true when the row expression itself is null or
when all the row's fields are null, while IS NOT NULL is true when the row expression itself is nonnull and all the row's fields are non-null. Because of this behavior, IS NULL and IS NOT NULL do
not always return inverse results for row-valued expressions; in particular, a row-valued expression
that contains both null and non-null fields will return false for both tests. For example:
SELECT ROW(1,2.5,'this is a test') = ROW(1, 3, 'not the same');
SELECT ROW(table.*) IS NULL FROM table;
-- detect all-null rows
SELECT ROW(table.*) IS NOT NULL FROM table;
rows
-- detect all-non-null
SELECT NOT(ROW(table.*) IS NOT NULL) FROM TABLE; -- detect at least
one null in rows
In some cases, it may be preferable to write row IS DISTINCT FROM NULL or row IS NOT
DISTINCT FROM NULL, which will simply check whether the overall row value is null without
any additional tests on the row fields.
Boolean values can also be tested using the predicates
boolean_expression IS TRUE
boolean_expression IS NOT TRUE
boolean_expression IS FALSE
boolean_expression IS NOT FALSE
boolean_expression IS UNKNOWN
boolean_expression IS NOT UNKNOWN
These will always return true or false, never a null value, even when the operand is null. A null input
is treated as the logical value “unknown”. Notice that IS UNKNOWN and IS NOT UNKNOWN are
effectively the same as IS NULL and IS NOT NULL, respectively, except that the input expression
must be of Boolean type.
Some comparison-related functions are also available, as shown in Table 9.3.
225
Functions and Operators
Table 9.3. Comparison Functions
Function
Description
Example(s)
num_nonnulls ( VARIADIC "any" ) → integer
Returns the number of non-null arguments.
num_nonnulls(1, NULL, 2) → 2
num_nulls ( VARIADIC "any" ) → integer
Returns the number of null arguments.
num_nulls(1, NULL, 2) → 1
9.3. Mathematical Functions and Operators
Mathematical operators are provided for many PostgreSQL types. For types without standard mathematical conventions (e.g., date/time types) we describe the actual behavior in subsequent sections.
Table 9.4 shows the mathematical operators that are available for the standard numeric types. Unless otherwise noted, operators shown as accepting numeric_type are available for all the types
smallint, integer, bigint, numeric, real, and double precision. Operators shown
as accepting integral_type are available for the types smallint, integer, and bigint.
Except where noted, each form of an operator returns the same data type as its argument(s). Calls
involving multiple argument data types, such as integer + numeric, are resolved by using the
type appearing later in these lists.
Table 9.4. Mathematical Operators
Operator
Description
Example(s)
numeric_type + numeric_type → numeric_type
Addition
2 + 3→5
+ numeric_type → numeric_type
Unary plus (no operation)
+ 3.5 → 3.5
numeric_type - numeric_type → numeric_type
Subtraction
2 - 3 → -1
- numeric_type → numeric_type
Negation
- (-4) → 4
numeric_type * numeric_type → numeric_type
Multiplication
2 * 3→6
numeric_type / numeric_type → numeric_type
Division (for integral types, division truncates the result towards zero)
5.0 / 2 → 2.5000000000000000
5 / 2→2
226
Functions and Operators
Operator
Description
Example(s)
(-5) / 2 → -2
numeric_type % numeric_type → numeric_type
Modulo (remainder); available for smallint, integer, bigint, and numeric
5 % 4→1
numeric ^ numeric → numeric
double precision ^ double precision → double precision
Exponentiation
2 ^ 3→8
Unlike typical mathematical practice, multiple uses of ^ will associate left to right by default:
2 ^ 3 ^ 3 → 512
2 ^ (3 ^ 3) → 134217728
|/ double precision → double precision
Square root
|/ 25.0 → 5
||/ double precision → double precision
Cube root
||/ 64.0 → 4
@ numeric_type → numeric_type
Absolute value
@ -5.0 → 5.0
integral_type & integral_type → integral_type
Bitwise AND
91 & 15 → 11
integral_type | integral_type → integral_type
Bitwise OR
32 | 3 → 35
integral_type # integral_type → integral_type
Bitwise exclusive OR
17 # 5 → 20
~ integral_type → integral_type
Bitwise NOT
~1 → -2
integral_type << integer → integral_type
Bitwise shift left
1 << 4 → 16
integral_type >> integer → integral_type
Bitwise shift right
8 >> 2 → 2
Table 9.5 shows the available mathematical functions. Many of these functions are provided in multiple forms with different argument types. Except where noted, any given form of a function returns the
227
Functions and Operators
same data type as its argument(s); cross-type cases are resolved in the same way as explained above
for operators. The functions working with double precision data are mostly implemented on top
of the host system's C library; accuracy and behavior in boundary cases can therefore vary depending
on the host system.
Table 9.5. Mathematical Functions
Function
Description
Example(s)
abs ( numeric_type ) → numeric_type
Absolute value
abs(-17.4) → 17.4
cbrt ( double precision ) → double precision
Cube root
cbrt(64.0) → 4
ceil ( numeric ) → numeric
ceil ( double precision ) → double precision
Nearest integer greater than or equal to argument
ceil(42.2) → 43
ceil(-42.8) → -42
ceiling ( numeric ) → numeric
ceiling ( double precision ) → double precision
Nearest integer greater than or equal to argument (same as ceil)
ceiling(95.3) → 96
degrees ( double precision ) → double precision
Converts radians to degrees
degrees(0.5) → 28.64788975654116
div ( y numeric, x numeric ) → numeric
Integer quotient of y/x (truncates towards zero)
div(9, 4) → 2
erf ( double precision ) → double precision
Error function
erf(1.0) → 0.8427007929497149
erfc ( double precision ) → double precision
Complementary error function (1 - erf(x), without loss of precision for large inputs)
erfc(1.0) → 0.15729920705028513
exp ( numeric ) → numeric
exp ( double precision ) → double precision
Exponential (e raised to the given power)
exp(1.0) → 2.7182818284590452
factorial ( bigint ) → numeric
Factorial
factorial(5) → 120
floor ( numeric ) → numeric
228
Functions and Operators
Function
Description
Example(s)
floor ( double precision ) → double precision
Nearest integer less than or equal to argument
floor(42.8) → 42
floor(-42.8) → -43
gcd ( numeric_type, numeric_type ) → numeric_type
Greatest common divisor (the largest positive number that divides both inputs with no remainder); returns 0 if both inputs are zero; available for integer, bigint, and numeric
gcd(1071, 462) → 21
lcm ( numeric_type, numeric_type ) → numeric_type
Least common multiple (the smallest strictly positive number that is an integral multiple
of both inputs); returns 0 if either input is zero; available for integer, bigint, and
numeric
lcm(1071, 462) → 23562
ln ( numeric ) → numeric
ln ( double precision ) → double precision
Natural logarithm
ln(2.0) → 0.6931471805599453
log ( numeric ) → numeric
log ( double precision ) → double precision
Base 10 logarithm
log(100) → 2
log10 ( numeric ) → numeric
log10 ( double precision ) → double precision
Base 10 logarithm (same as log)
log10(1000) → 3
log ( b numeric, x numeric ) → numeric
Logarithm of x to base b
log(2.0, 64.0) → 6.0000000000000000
min_scale ( numeric ) → integer
Minimum scale (number of fractional decimal digits) needed to represent the supplied
value precisely
min_scale(8.4100) → 2
mod ( y numeric_type, x numeric_type ) → numeric_type
Remainder of y/x; available for smallint, integer, bigint, and numeric
mod(9, 4) → 1
pi ( ) → double precision
Approximate value of π
pi() → 3.141592653589793
power ( a numeric, b numeric ) → numeric
power ( a double precision, b double precision ) → double precision
229
Functions and Operators
Function
Description
Example(s)
a raised to the power of b
power(9, 3) → 729
radians ( double precision ) → double precision
Converts degrees to radians
radians(45.0) → 0.7853981633974483
round ( numeric ) → numeric
round ( double precision ) → double precision
Rounds to nearest integer. For numeric, ties are broken by rounding away from zero.
For double precision, the tie-breaking behavior is platform dependent, but “round
to nearest even” is the most common rule.
round(42.4) → 42
round ( v numeric, s integer ) → numeric
Rounds v to s decimal places. Ties are broken by rounding away from zero.
round(42.4382, 2) → 42.44
round(1234.56, -1) → 1230
scale ( numeric ) → integer
Scale of the argument (the number of decimal digits in the fractional part)
scale(8.4100) → 4
sign ( numeric ) → numeric
sign ( double precision ) → double precision
Sign of the argument (-1, 0, or +1)
sign(-8.4) → -1
sqrt ( numeric ) → numeric
sqrt ( double precision ) → double precision
Square root
sqrt(2) → 1.4142135623730951
trim_scale ( numeric ) → numeric
Reduces the value's scale (number of fractional decimal digits) by removing trailing zeroes
trim_scale(8.4100) → 8.41
trunc ( numeric ) → numeric
trunc ( double precision ) → double precision
Truncates to integer (towards zero)
trunc(42.8) → 42
trunc(-42.8) → -42
trunc ( v numeric, s integer ) → numeric
Truncates v to s decimal places
trunc(42.4382, 2) → 42.43
width_bucket ( operand numeric, low numeric, high numeric, count integer
) → integer
230
Functions and Operators
Function
Description
Example(s)
width_bucket ( operand double precision, low double precision, high
double precision, count integer ) → integer
Returns the number of the bucket in which operand falls in a histogram having count
equal-width buckets spanning the range low to high. Returns 0 or count+1 for an input outside that range.
width_bucket(5.35, 0.024, 10.06, 5) → 3
width_bucket ( operand anycompatible, thresholds anycompatiblearray )
→ integer
Returns the number of the bucket in which operand falls given an array listing the
lower bounds of the buckets. Returns 0 for an input less than the first lower bound.
operand and the array elements can be of any type having standard comparison operators. The thresholds array must be sorted, smallest first, or unexpected results will be
obtained.
width_bucket(now(), array['yesterday', 'today', 'tomorrow']::timestamptz[]) → 2
Table 9.6 shows functions for generating random numbers.
Table 9.6. Random Functions
Function
Description
Example(s)
random ( ) → double precision
Returns a random value in the range 0.0 <= x < 1.0
random() → 0.897124072839091
random ( min integer, max integer ) → integer
random ( min bigint, max bigint ) → bigint
random ( min numeric, max numeric ) → numeric
Returns a random value in the range min <= x <= max. For type numeric, the result
will have the same number of fractional decimal digits as min or max, whichever has
more.
random(1, 10) → 7
random(-0.499, 0.499) → 0.347
random_normal ( [ mean double precision [, stddev double precision ]] ) →
double precision
Returns a random value from the normal distribution with the given parameters; mean
defaults to 0.0 and stddev defaults to 1.0
random_normal(0.0, 1.0) → 0.051285419
setseed ( double precision ) → void
Sets the seed for subsequent random() and random_normal() calls; argument must
be between -1.0 and 1.0, inclusive
setseed(0.12345)
The random() and random_normal() functions listed in Table 9.6 use a deterministic pseudo-random number generator. It is fast but not suitable for cryptographic applications; see the pgcrypto module for a more secure alternative. If setseed() is called, the series of results of subsequent
calls to these functions in the current session can be repeated by re-issuing setseed() with the
231
Functions and Operators
same argument. Without any prior setseed() call in the same session, the first call to any of these
functions obtains a seed from a platform-dependent source of random bits.
Table 9.7 shows the available trigonometric functions. Each of these functions comes in two variants,
one that measures angles in radians and one that measures angles in degrees.
Table 9.7. Trigonometric Functions
Function
Description
Example(s)
acos ( double precision ) → double precision
Inverse cosine, result in radians
acos(1) → 0
acosd ( double precision ) → double precision
Inverse cosine, result in degrees
acosd(0.5) → 60
asin ( double precision ) → double precision
Inverse sine, result in radians
asin(1) → 1.5707963267948966
asind ( double precision ) → double precision
Inverse sine, result in degrees
asind(0.5) → 30
atan ( double precision ) → double precision
Inverse tangent, result in radians
atan(1) → 0.7853981633974483
atand ( double precision ) → double precision
Inverse tangent, result in degrees
atand(1) → 45
atan2 ( y double precision, x double precision ) → double precision
Inverse tangent of y/x, result in radians
atan2(1, 0) → 1.5707963267948966
atan2d ( y double precision, x double precision ) → double precision
Inverse tangent of y/x, result in degrees
atan2d(1, 0) → 90
cos ( double precision ) → double precision
Cosine, argument in radians
cos(0) → 1
cosd ( double precision ) → double precision
Cosine, argument in degrees
cosd(60) → 0.5
cot ( double precision ) → double precision
Cotangent, argument in radians
cot(0.5) → 1.830487721712452
cotd ( double precision ) → double precision
Cotangent, argument in degrees
232
Functions and Operators
Function
Description
Example(s)
cotd(45) → 1
sin ( double precision ) → double precision
Sine, argument in radians
sin(1) → 0.8414709848078965
sind ( double precision ) → double precision
Sine, argument in degrees
sind(30) → 0.5
tan ( double precision ) → double precision
Tangent, argument in radians
tan(1) → 1.5574077246549023
tand ( double precision ) → double precision
Tangent, argument in degrees
tand(45) → 1
Note
Another way to work with angles measured in degrees is to use the unit transformation functions radians() and degrees() shown earlier. However, using the degree-based trigonometric functions is preferred, as that way avoids round-off error for special cases such as
sind(30).
Table 9.8 shows the available hyperbolic functions.
Table 9.8. Hyperbolic Functions
Function
Description
Example(s)
sinh ( double precision ) → double precision
Hyperbolic sine
sinh(1) → 1.1752011936438014
cosh ( double precision ) → double precision
Hyperbolic cosine
cosh(0) → 1
tanh ( double precision ) → double precision
Hyperbolic tangent
tanh(1) → 0.7615941559557649
asinh ( double precision ) → double precision
Inverse hyperbolic sine
asinh(1) → 0.881373587019543
acosh ( double precision ) → double precision
Inverse hyperbolic cosine
acosh(1) → 0
233
Functions and Operators
Function
Description
Example(s)
atanh ( double precision ) → double precision
Inverse hyperbolic tangent
atanh(0.5) → 0.5493061443340548
9.4. String Functions and Operators
This section describes functions and operators for examining and manipulating string values. Strings
in this context include values of the types character, character varying, and text. Except
where noted, these functions and operators are declared to accept and return type text. They will
interchangeably accept character varying arguments. Values of type character will be
converted to text before the function or operator is applied, resulting in stripping any trailing spaces
in the character value.
SQL defines some string functions that use key words, rather than commas, to separate arguments.
Details are in Table 9.9. PostgreSQL also provides versions of these functions that use the regular
function invocation syntax (see Table 9.10).
Note
The string concatenation operator (||) will accept non-string input, so long as at least one
input is of string type, as shown in Table 9.9. For other cases, inserting an explicit coercion to
text can be used to have non-string input accepted.
Table 9.9. SQL String Functions and Operators
Function/Operator
Description
Example(s)
text || text → text
Concatenates the two strings.
'Post' || 'greSQL' → PostgreSQL
text || anynonarray → text
anynonarray || text → text
Converts the non-string input to text, then concatenates the two strings. (The non-string
input cannot be of an array type, because that would create ambiguity with the array ||
operators. If you want to concatenate an array's text equivalent, cast it to text explicitly.)
'Value: ' || 42 → Value: 42
btrim ( string text [, characters text ] ) → text
Removes the longest string containing only characters in characters (a space by default) from the start and end of string.
btrim('xyxtrimyyx', 'xyz') → trim
text IS [NOT] [form] NORMALIZED → boolean
Checks whether the string is in the specified Unicode normalization form. The optional form key word specifies the form: NFC (the default), NFD, NFKC, or NFKD. This expression can only be used when the server encoding is UTF8. Note that checking for normalization using this expression is often faster than normalizing possibly already normalized strings.
234
Functions and Operators
Function/Operator
Description
Example(s)
U&'\0061\0308bc' IS NFD NORMALIZED → t
bit_length ( text ) → integer
Returns number of bits in the string (8 times the octet_length).
bit_length('jose') → 32
char_length ( text ) → integer
character_length ( text ) → integer
Returns number of characters in the string.
char_length('josé') → 4
lower ( text ) → text
Converts the string to all lower case, according to the rules of the database's locale.
lower('TOM') → tom
lpad ( string text, length integer [, fill text ] ) → text
Extends the string to length length by prepending the characters fill (a space by
default). If the string is already longer than length then it is truncated (on the right).
lpad('hi', 5, 'xy') → xyxhi
ltrim ( string text [, characters text ] ) → text
Removes the longest string containing only characters in characters (a space by default) from the start of string.
ltrim('zzzytest', 'xyz') → test
normalize ( text [, form ] ) → text
Converts the string to the specified Unicode normalization form. The optional form key
word specifies the form: NFC (the default), NFD, NFKC, or NFKD. This function can only
be used when the server encoding is UTF8.
normalize(U&'\0061\0308bc', NFC) → U&'\00E4bc'
octet_length ( text ) → integer
Returns number of bytes in the string.
octet_length('josé') → 5 (if server encoding is UTF8)
octet_length ( character ) → integer
Returns number of bytes in the string. Since this version of the function accepts type
character directly, it will not strip trailing spaces.
octet_length('abc '::character(4)) → 4
overlay ( string text PLACING newsubstring text FROM start integer [ FOR
count integer ] ) → text
Replaces the substring of string that starts at the start'th character and extends for
count characters with newsubstring. If count is omitted, it defaults to the length
of newsubstring.
overlay('Txxxxas' placing 'hom' from 2 for 4) → Thomas
position ( substring text IN string text ) → integer
Returns first starting index of the specified substring within string, or zero if it's
not present.
position('om' in 'Thomas') → 3
rpad ( string text, length integer [, fill text ] ) → text
235
Functions and Operators
Function/Operator
Description
Example(s)
Extends the string to length length by appending the characters fill (a space by
default). If the string is already longer than length then it is truncated.
rpad('hi', 5, 'xy') → hixyx
rtrim ( string text [, characters text ] ) → text
Removes the longest string containing only characters in characters (a space by default) from the end of string.
rtrim('testxxzx', 'xyz') → test
substring ( string text [ FROM start integer ] [ FOR count integer ] ) →
text
Extracts the substring of string starting at the start'th character if that is specified,
and stopping after count characters if that is specified. Provide at least one of start
and count.
substring('Thomas' from 2 for 3) → hom
substring('Thomas' from 3) → omas
substring('Thomas' for 2) → Th
substring ( string text FROM pattern text ) → text
Extracts the first substring matching POSIX regular expression; see Section 9.7.3.
substring('Thomas' from '...$') → mas
substring ( string text SIMILAR pattern text ESCAPE escape text ) → text
substring ( string text FROM pattern text FOR escape text ) → text
Extracts the first substring matching SQL regular expression; see Section 9.7.2. The first
form has been specified since SQL:2003; the second form was only in SQL:1999 and
should be considered obsolete.
substring('Thomas' similar '%#"o_a#"_' escape '#') → oma
trim ( [ LEADING | TRAILING | BOTH ] [ characters text ] FROM string text ) →
text
Removes the longest string containing only characters in characters (a space by default) from the start, end, or both ends (BOTH is the default) of string.
trim(both 'xyz' from 'yxTomxx') → Tom
trim ( [ LEADING | TRAILING | BOTH ] [ FROM ] string text [, characters text ] )
→ text
This is a non-standard syntax for trim().
trim(both from 'yxTomxx', 'xyz') → Tom
unicode_assigned ( text ) → boolean
Returns true if all characters in the string are assigned Unicode codepoints; false
otherwise. This function can only be used when the server encoding is UTF8.
upper ( text ) → text
Converts the string to all upper case, according to the rules of the database's locale.
upper('tom') → TOM
Additional string manipulation functions and operators are available and are listed in Table 9.10.
(Some of these are used internally to implement the SQL-standard string functions listed in Table 9.9.)
There are also pattern-matching operators, which are described in Section 9.7, and operators for fulltext search, which are described in Chapter 12.
236
Functions and Operators
Table 9.10. Other String Functions and Operators
Function/Operator
Description
Example(s)
text ^@ text → boolean
Returns true if the first string starts with the second string (equivalent to the starts_with() function).
'alphabet' ^@ 'alph' → t
ascii ( text ) → integer
Returns the numeric code of the first character of the argument. In UTF8 encoding, returns the Unicode code point of the character. In other multibyte encodings, the argument
must be an ASCII character.
ascii('x') → 120
chr ( integer ) → text
Returns the character with the given code. In UTF8 encoding the argument is treated as a
Unicode code point. In other multibyte encodings the argument must designate an ASCII
character. chr(0) is disallowed because text data types cannot store that character.
chr(65) → A
concat ( val1 "any" [, val2 "any" [, ...] ] ) → text
Concatenates the text representations of all the arguments. NULL arguments are ignored.
concat('abcde', 2, NULL, 22) → abcde222
concat_ws ( sep text, val1 "any" [, val2 "any" [, ...] ] ) → text
Concatenates all but the first argument, with separators. The first argument is used as the
separator string, and should not be NULL. Other NULL arguments are ignored.
concat_ws(',', 'abcde', 2, NULL, 22) → abcde,2,22
format ( formatstr text [, formatarg "any" [, ...] ] ) → text
Formats arguments according to a format string; see Section 9.4.1. This function is similar to the C function sprintf.
format('Hello %s, %1$s', 'World') → Hello World, World
initcap ( text ) → text
Converts the first letter of each word to upper case and the rest to lower case. Words are
sequences of alphanumeric characters separated by non-alphanumeric characters.
initcap('hi THOMAS') → Hi Thomas
left ( string text, n integer ) → text
Returns first n characters in the string, or when n is negative, returns all but last |n| characters.
left('abcde', 2) → ab
length ( text ) → integer
Returns the number of characters in the string.
length('jose') → 4
md5 ( text ) → text
Computes the MD5 hash of the argument, with the result written in hexadecimal.
md5('abc') → 900150983cd24fb0d6963f7d28e17f72
parse_ident ( qualified_identifier text [, strict_mode boolean DEFAULT
true ] ) → text[]
237
Functions and Operators
Function/Operator
Description
Example(s)
Splits qualified_identifier into an array of identifiers, removing any quoting of
individual identifiers. By default, extra characters after the last identifier are considered
an error; but if the second parameter is false, then such extra characters are ignored.
(This behavior is useful for parsing names for objects like functions.) Note that this function does not truncate over-length identifiers. If you want truncation you can cast the result to name[].
parse_ident('"SomeSchema".someTable') →
{SomeSchema,sometable}
pg_client_encoding ( ) → name
Returns current client encoding name.
pg_client_encoding() → UTF8
quote_ident ( text ) → text
Returns the given string suitably quoted to be used as an identifier in an SQL statement
string. Quotes are added only if necessary (i.e., if the string contains non-identifier characters or would be case-folded). Embedded quotes are properly doubled. See also Example 41.1.
quote_ident('Foo bar') → "Foo bar"
quote_literal ( text ) → text
Returns the given string suitably quoted to be used as a string literal in an SQL statement string. Embedded single-quotes and backslashes are properly doubled. Note
that quote_literal returns null on null input; if the argument might be null,
quote_nullable is often more suitable. See also Example 41.1.
quote_literal(E'O\'Reilly') → 'O''Reilly'
quote_literal ( anyelement ) → text
Converts the given value to text and then quotes it as a literal. Embedded single-quotes
and backslashes are properly doubled.
quote_literal(42.5) → '42.5'
quote_nullable ( text ) → text
Returns the given string suitably quoted to be used as a string literal in an SQL statement
string; or, if the argument is null, returns NULL. Embedded single-quotes and backslashes are properly doubled. See also Example 41.1.
quote_nullable(NULL) → NULL
quote_nullable ( anyelement ) → text
Converts the given value to text and then quotes it as a literal; or, if the argument is null,
returns NULL. Embedded single-quotes and backslashes are properly doubled.
quote_nullable(42.5) → '42.5'
regexp_count ( string text, pattern text [, start integer [, flags text ] ] )
→ integer
Returns the number of times the POSIX regular expression pattern matches in the
string; see Section 9.7.3.
regexp_count('123456789012', '\d\d\d', 2) → 3
regexp_instr ( string text, pattern text [, start integer [, N integer [,
endoption integer [, flags text [, subexpr integer ] ] ] ] ] ) → integer
Returns the position within string where the N'th match of the POSIX regular expression pattern occurs, or zero if there is no such match; see Section 9.7.3.
regexp_instr('ABCDEF', 'c(.)(..)', 1, 1, 0, 'i') → 3
238
Functions and Operators
Function/Operator
Description
Example(s)
regexp_instr('ABCDEF', 'c(.)(..)', 1, 1, 0, 'i', 2) → 5
regexp_like ( string text, pattern text [, flags text ] ) → boolean
Checks whether a match of the POSIX regular expression pattern occurs within
string; see Section 9.7.3.
regexp_like('Hello World', 'world$', 'i') → t
regexp_match ( string text, pattern text [, flags text ] ) → text[]
Returns substrings within the first match of the POSIX regular expression pattern to
the string; see Section 9.7.3.
regexp_match('foobarbequebaz', '(bar)(beque)') → {bar,beque}
regexp_matches ( string text, pattern text [, flags text ] ) → setof
text[]
Returns substrings within the first match of the POSIX regular expression pattern
to the string, or substrings within all such matches if the g flag is used; see Section 9.7.3.
regexp_matches('foobarbequebaz', 'ba.', 'g') →
{bar}
{baz}
regexp_replace ( string text, pattern text, replacement text [, start integer ] [, flags text ] ) → text
Replaces the substring that is the first match to the POSIX regular expression pattern,
or all such matches if the g flag is used; see Section 9.7.3.
regexp_replace('Thomas', '.[mN]a.', 'M') → ThM
regexp_replace ( string text, pattern text, replacement text, start integer, N integer [, flags text ] ) → text
Replaces the substring that is the N'th match to the POSIX regular expression pattern,
or all such matches if N is zero; see Section 9.7.3.
regexp_replace('Thomas', '.', 'X', 3, 2) → ThoXas
regexp_split_to_array ( string text, pattern text [, flags text ] ) →
text[]
Splits string using a POSIX regular expression as the delimiter, producing an array of
results; see Section 9.7.3.
regexp_split_to_array('hello world', '\s+') → {hello,world}
regexp_split_to_table ( string text, pattern text [, flags text ] ) →
setof text
Splits string using a POSIX regular expression as the delimiter, producing a set of results; see Section 9.7.3.
regexp_split_to_table('hello world', '\s+') →
hello
world
regexp_substr ( string text, pattern text [, start integer [, N integer [,
flags text [, subexpr integer ] ] ] ] ) → text
239
Functions and Operators
Function/Operator
Description
Example(s)
Returns the substring within string that matches the N'th occurrence of the POSIX
regular expression pattern, or NULL if there is no such match; see Section 9.7.3.
regexp_substr('ABCDEF', 'c(.)(..)', 1, 1, 'i') → CDEF
regexp_substr('ABCDEF', 'c(.)(..)', 1, 1, 'i', 2) → EF
repeat ( string text, number integer ) → text
Repeats string the specified number of times.
repeat('Pg', 4) → PgPgPgPg
replace ( string text, from text, to text ) → text
Replaces all occurrences in string of substring from with substring to.
replace('abcdefabcdef', 'cd', 'XX') → abXXefabXXef
reverse ( text ) → text
Reverses the order of the characters in the string.
reverse('abcde') → edcba
right ( string text, n integer ) → text
Returns last n characters in the string, or when n is negative, returns all but first |n| characters.
right('abcde', 2) → de
split_part ( string text, delimiter text, n integer ) → text
Splits string at occurrences of delimiter and returns the n'th field (counting from
one), or when n is negative, returns the |n|'th-from-last field.
split_part('abc~@~def~@~ghi', '~@~', 2) → def
split_part('abc,def,ghi,jkl', ',', -2) → ghi
starts_with ( string text, prefix text ) → boolean
Returns true if string starts with prefix.
starts_with('alphabet', 'alph') → t
string_to_array ( string text, delimiter text [, null_string text ] ) →
text[]
Splits the string at occurrences of delimiter and forms the resulting fields into a
text array. If delimiter is NULL, each character in the string will become a separate element in the array. If delimiter is an empty string, then the string is treated as a single field. If null_string is supplied and is not NULL, fields matching that
string are replaced by NULL. See also array_to_string.
string_to_array('xx~~yy~~zz', '~~', 'yy') → {xx,NULL,zz}
string_to_table ( string text, delimiter text [, null_string text ] ) →
setof text
Splits the string at occurrences of delimiter and returns the resulting fields as a
set of text rows. If delimiter is NULL, each character in the string will become
a separate row of the result. If delimiter is an empty string, then the string is treated as a single field. If null_string is supplied and is not NULL, fields matching that
string are replaced by NULL.
string_to_table('xx~^~yy~^~zz', '~^~', 'yy') →
xx
NULL
240
Functions and Operators
Function/Operator
Description
Example(s)
zz
strpos ( string text, substring text ) → integer
Returns first starting index of the specified substring within string, or zero if it's
not present. (Same as position(substring in string), but note the reversed
argument order.)
strpos('high', 'ig') → 2
substr ( string text, start integer [, count integer ] ) → text
Extracts the substring of string starting at the start'th character, and extending for
count characters if that is specified. (Same as substring(string from start
for count).)
substr('alphabet', 3) → phabet
substr('alphabet', 3, 2) → ph
to_ascii ( string text ) → text
to_ascii ( string text, encoding name ) → text
to_ascii ( string text, encoding integer ) → text
Converts string to ASCII from another encoding, which may be identified by name or
number. If encoding is omitted the database encoding is assumed (which in practice is
the only useful case). The conversion consists primarily of dropping accents. Conversion
is only supported from LATIN1, LATIN2, LATIN9, and WIN1250 encodings. (See the
unaccent module for another, more flexible solution.)
to_ascii('Karél') → Karel
to_bin ( integer ) → text
to_bin ( bigint ) → text
Converts the number to its equivalent two's complement binary representation.
to_bin(2147483647) → 1111111111111111111111111111111
to_bin(-1234) → 11111111111111111111101100101110
to_hex ( integer ) → text
to_hex ( bigint ) → text
Converts the number to its equivalent two's complement hexadecimal representation.
to_hex(2147483647) → 7fffffff
to_hex(-1234) → fffffb2e
to_oct ( integer ) → text
to_oct ( bigint ) → text
Converts the number to its equivalent two's complement octal representation.
to_oct(2147483647) → 17777777777
to_oct(-1234) → 37777775456
translate ( string text, from text, to text ) → text
Replaces each character in string that matches a character in the from set with the
corresponding character in the to set. If from is longer than to, occurrences of the extra characters in from are deleted.
translate('12345', '143', 'ax') → a2x5
unistr ( text ) → text
241
Functions and Operators
Function/Operator
Description
Example(s)
Evaluate escaped Unicode characters in the argument. Unicode characters can be specified as \XXXX (4 hexadecimal digits), \+XXXXXX (6 hexadecimal digits), \uXXXX (4
hexadecimal digits), or \UXXXXXXXX (8 hexadecimal digits). To specify a backslash,
write two backslashes. All other characters are taken literally.
If the server encoding is not UTF-8, the Unicode code point identified by one of these escape sequences is converted to the actual server encoding; an error is reported if that's not
possible.
This function provides a (non-standard) alternative to string constants with Unicode escapes (see Section 4.1.2.3).
unistr('d\0061t\+000061') → data
unistr('d\u0061t\U00000061') → data
The concat, concat_ws and format functions are variadic, so it is possible to pass the values to
be concatenated or formatted as an array marked with the VARIADIC keyword (see Section 36.5.6).
The array's elements are treated as if they were separate ordinary arguments to the function. If the
variadic array argument is NULL, concat and concat_ws return NULL, but format treats a
NULL as a zero-element array.
See also the aggregate function string_agg in Section 9.21, and the functions for converting between strings and the bytea type in Table 9.13.
9.4.1. format
The function format produces output formatted according to a format string, in a style similar to
the C function sprintf.
format(formatstr text [, formatarg "any" [, ...] ])
formatstr is a format string that specifies how the result should be formatted. Text in the format
string is copied directly to the result, except where format specifiers are used. Format specifiers act
as placeholders in the string, defining how subsequent function arguments should be formatted and
inserted into the result. Each formatarg argument is converted to text according to the usual output
rules for its data type, and then formatted and inserted into the result string according to the format
specifier(s).
Format specifiers are introduced by a % character and have the form
%[position][flags][width]type
where the component fields are:
position (optional)
A string of the form n$ where n is the index of the argument to print. Index 1 means the first
argument after formatstr. If the position is omitted, the default is to use the next argument
in sequence.
flags (optional)
Additional options controlling how the format specifier's output is formatted. Currently the only
supported flag is a minus sign (-) which will cause the format specifier's output to be left-justified.
This has no effect unless the width field is also specified.
242
Functions and Operators
width (optional)
Specifies the minimum number of characters to use to display the format specifier's output. The
output is padded on the left or right (depending on the - flag) with spaces as needed to fill the
width. A too-small width does not cause truncation of the output, but is simply ignored. The width
may be specified using any of the following: a positive integer; an asterisk (*) to use the next
function argument as the width; or a string of the form *n$ to use the nth function argument
as the width.
If the width comes from a function argument, that argument is consumed before the argument that
is used for the format specifier's value. If the width argument is negative, the result is left aligned
(as if the - flag had been specified) within a field of length abs(width).
type (required)
The type of format conversion to use to produce the format specifier's output. The following types
are supported:
• s formats the argument value as a simple string. A null value is treated as an empty string.
• I treats the argument value as an SQL identifier, double-quoting it if necessary. It is an error
for the value to be null (equivalent to quote_ident).
• L quotes the argument value as an SQL literal. A null value is displayed as the string NULL,
without quotes (equivalent to quote_nullable).
In addition to the format specifiers described above, the special sequence %% may be used to output
a literal % character.
Here are some examples of the basic format conversions:
SELECT format('Hello %s', 'World');
Result: Hello World
SELECT format('Testing %s, %s, %s, %%', 'one', 'two', 'three');
Result: Testing one, two, three, %
SELECT format('INSERT INTO %I VALUES(%L)', 'Foo bar', E'O
\'Reilly');
Result: INSERT INTO "Foo bar" VALUES('O''Reilly')
SELECT format('INSERT INTO %I VALUES(%L)', 'locations', 'C:\Program
Files');
Result: INSERT INTO locations VALUES('C:\Program Files')
Here are examples using width fields and the - flag:
SELECT format('|%10s|', 'foo');
Result: |
foo|
SELECT format('|%-10s|', 'foo');
Result: |foo
|
SELECT format('|%*s|', 10, 'foo');
Result: |
foo|
SELECT format('|%*s|', -10, 'foo');
243
Functions and Operators
Result: |foo
|
SELECT format('|%-*s|', 10, 'foo');
Result: |foo
|
SELECT format('|%-*s|', -10, 'foo');
Result: |foo
|
These examples show use of position fields:
SELECT format('Testing %3$s, %2$s, %1$s', 'one', 'two', 'three');
Result: Testing three, two, one
SELECT format('|%*2$s|', 'foo', 10, 'bar');
Result: |
bar|
SELECT format('|%1$*2$s|', 'foo', 10, 'bar');
Result: |
foo|
Unlike the standard C function sprintf, PostgreSQL's format function allows format specifiers
with and without position fields to be mixed in the same format string. A format specifier without
a position field always uses the next argument after the last argument consumed. In addition, the
format function does not require all function arguments to be used in the format string. For example:
SELECT format('Testing %3$s, %2$s, %s', 'one', 'two', 'three');
Result: Testing three, two, three
The %I and %L format specifiers are particularly useful for safely constructing dynamic SQL statements. See Example 41.1.
9.5. Binary String Functions and Operators
This section describes functions and operators for examining and manipulating binary strings, that is
values of type bytea. Many of these are equivalent, in purpose and syntax, to the text-string functions
described in the previous section.
SQL defines some string functions that use key words, rather than commas, to separate arguments.
Details are in Table 9.11. PostgreSQL also provides versions of these functions that use the regular
function invocation syntax (see Table 9.12).
Table 9.11. SQL Binary String Functions and Operators
Function/Operator
Description
Example(s)
bytea || bytea → bytea
Concatenates the two binary strings.
'\x123456'::bytea || '\x789a00bcde'::bytea →
\x123456789a00bcde
bit_length ( bytea ) → integer
Returns number of bits in the binary string (8 times the octet_length).
bit_length('\x123456'::bytea) → 24
btrim ( bytes bytea, bytesremoved bytea ) → bytea
244
Functions and Operators
Function/Operator
Description
Example(s)
Removes the longest string containing only bytes appearing in bytesremoved from
the start and end of bytes.
btrim('\x1234567890'::bytea, '\x9012'::bytea) → \x345678
ltrim ( bytes bytea, bytesremoved bytea ) → bytea
Removes the longest string containing only bytes appearing in bytesremoved from
the start of bytes.
ltrim('\x1234567890'::bytea, '\x9012'::bytea) → \x34567890
octet_length ( bytea ) → integer
Returns number of bytes in the binary string.
octet_length('\x123456'::bytea) → 3
overlay ( bytes bytea PLACING newsubstring bytea FROM start integer [
FOR count integer ] ) → bytea
Replaces the substring of bytes that starts at the start'th byte and extends for count
bytes with newsubstring. If count is omitted, it defaults to the length of newsubstring.
overlay('\x1234567890'::bytea placing '\002\003'::bytea
from 2 for 3) → \x12020390
position ( substring bytea IN bytes bytea ) → integer
Returns first starting index of the specified substring within bytes, or zero if it's
not present.
position('\x5678'::bytea in '\x1234567890'::bytea) → 3
rtrim ( bytes bytea, bytesremoved bytea ) → bytea
Removes the longest string containing only bytes appearing in bytesremoved from
the end of bytes.
rtrim('\x1234567890'::bytea, '\x9012'::bytea) → \x12345678
substring ( bytes bytea [ FROM start integer ] [ FOR count integer ] ) →
bytea
Extracts the substring of bytes starting at the start'th byte if that is specified, and
stopping after count bytes if that is specified. Provide at least one of start and
count.
substring('\x1234567890'::bytea from 3 for 2) → \x5678
trim ( [ LEADING | TRAILING | BOTH ] bytesremoved bytea FROM bytes bytea ) →
bytea
Removes the longest string containing only bytes appearing in bytesremoved from
the start, end, or both ends (BOTH is the default) of bytes.
trim('\x9012'::bytea from '\x1234567890'::bytea) → \x345678
trim ( [ LEADING | TRAILING | BOTH ] [ FROM ] bytes bytea, bytesremoved bytea )
→ bytea
This is a non-standard syntax for trim().
trim(both from '\x1234567890'::bytea, '\x9012'::bytea) →
\x345678
Additional binary string manipulation functions are available and are listed in Table 9.12. Some of
them are used internally to implement the SQL-standard string functions listed in Table 9.11.
245
Functions and Operators
Table 9.12. Other Binary String Functions
Function
Description
Example(s)
bit_count ( bytes bytea ) → bigint
Returns the number of bits set in the binary string (also known as “popcount”).
bit_count('\x1234567890'::bytea) → 15
get_bit ( bytes bytea, n bigint ) → integer
Extracts n'th bit from binary string.
get_bit('\x1234567890'::bytea, 30) → 1
get_byte ( bytes bytea, n integer ) → integer
Extracts n'th byte from binary string.
get_byte('\x1234567890'::bytea, 4) → 144
length ( bytea ) → integer
Returns the number of bytes in the binary string.
length('\x1234567890'::bytea) → 5
length ( bytes bytea, encoding name ) → integer
Returns the number of characters in the binary string, assuming that it is text in the given
encoding.
length('jose'::bytea, 'UTF8') → 4
md5 ( bytea ) → text
Computes the MD5 hash of the binary string, with the result written in hexadecimal.
md5('Th\000omas'::bytea) → 8ab2d3c9689aaf18b4958c334c82d8b1
set_bit ( bytes bytea, n bigint, newvalue integer ) → bytea
Sets n'th bit in binary string to newvalue.
set_bit('\x1234567890'::bytea, 30, 0) → \x1234563890
set_byte ( bytes bytea, n integer, newvalue integer ) → bytea
Sets n'th byte in binary string to newvalue.
set_byte('\x1234567890'::bytea, 4, 64) → \x1234567840
sha224 ( bytea ) → bytea
Computes the SHA-224 hash of the binary string.
sha224('abc'::bytea) → \x23097d223405d8228642a477bda2
55b32aadbce4bda0b3f7e36c9da7
sha256 ( bytea ) → bytea
Computes the SHA-256 hash of the binary string.
sha256('abc'::bytea) → \xba7816bf8f01cfea414140de5dae2223
b00361a396177a9cb410ff61f20015ad
sha384 ( bytea ) → bytea
Computes the SHA-384 hash of the binary string.
sha384('abc'::bytea) → \xcb00753f45a35e8bb5a03d699ac65007
272c32ab0eded1631a8b605a43ff5bed8086072ba1e7cc2358baeca134c825a7
sha512 ( bytea ) → bytea
Computes the SHA-512 hash of the binary string.
246
Functions and Operators
Function
Description
Example(s)
sha512('abc'::bytea) → \xddaf35a193617abacc417349ae20413112e6fa4e89a97ea20a9eeee64b55d39a
2192992a274fc1a836ba3c23a3feebbd
454d4423643ce80e2a9ac94fa54ca49f
substr ( bytes bytea, start integer [, count integer ] ) → bytea
Extracts the substring of bytes starting at the start'th byte, and extending for
count bytes if that is specified. (Same as substring(bytes from start for
count).)
substr('\x1234567890'::bytea, 3, 2) → \x5678
Functions get_byte and set_byte number the first byte of a binary string as byte 0. Functions
get_bit and set_bit number bits from the right within each byte; for example bit 0 is the least
significant bit of the first byte, and bit 15 is the most significant bit of the second byte.
For historical reasons, the function md5 returns a hex-encoded value of type text whereas the SHA-2
functions return type bytea. Use the functions encode and decode to convert between the two.
For example write encode(sha256('abc'), 'hex') to get a hex-encoded text representation,
or decode(md5('abc'), 'hex') to get a bytea value.
Functions for converting strings between different character sets (encodings), and for representing
arbitrary binary data in textual form, are shown in Table 9.13. For these functions, an argument or
result of type text is expressed in the database's default encoding, while arguments or results of type
bytea are in an encoding named by another argument.
Table 9.13. Text/Binary String Conversion Functions
Function
Description
Example(s)
convert ( bytes bytea, src_encoding name, dest_encoding name ) → bytea
Converts a binary string representing text in encoding src_encoding to a binary
string in encoding dest_encoding (see Section 23.3.4 for available conversions).
convert('text_in_utf8', 'UTF8', 'LATIN1') →
\x746578745f696e5f75746638
convert_from ( bytes bytea, src_encoding name ) → text
Converts a binary string representing text in encoding src_encoding to text in the
database encoding (see Section 23.3.4 for available conversions).
convert_from('text_in_utf8', 'UTF8') → text_in_utf8
convert_to ( string text, dest_encoding name ) → bytea
Converts a text string (in the database encoding) to a binary string encoded in encoding
dest_encoding (see Section 23.3.4 for available conversions).
convert_to('some_text', 'UTF8') → \x736f6d655f74657874
encode ( bytes bytea, format text ) → text
Encodes binary data into a textual representation; supported format values are:
base64, escape, hex.
encode('123\000\001', 'base64') → MTIzAAE=
decode ( string text, format text ) → bytea
Decodes binary data from a textual representation; supported format values are the
same as for encode.
247
Functions and Operators
Function
Description
Example(s)
decode('MTIzAAE=', 'base64') → \x3132330001
The encode and decode functions support the following textual formats:
base64
The base64 format is that of RFC 2045 Section 6.81. As per the RFC, encoded lines are broken
at 76 characters. However instead of the MIME CRLF end-of-line marker, only a newline is used
for end-of-line. The decode function ignores carriage-return, newline, space, and tab characters.
Otherwise, an error is raised when decode is supplied invalid base64 data — including when
trailing padding is incorrect.
escape
The escape format converts zero bytes and bytes with the high bit set into octal escape sequences
(\nnn), and it doubles backslashes. Other byte values are represented literally. The decode
function will raise an error if a backslash is not followed by either a second backslash or three
octal digits; it accepts other byte values unchanged.
hex
The hex format represents each 4 bits of data as one hexadecimal digit, 0 through f, writing the
higher-order digit of each byte first. The encode function outputs the a-f hex digits in lower
case. Because the smallest unit of data is 8 bits, there are always an even number of characters
returned by encode. The decode function accepts the a-f characters in either upper or lower
case. An error is raised when decode is given invalid hex data — including when given an odd
number of characters.
See also the aggregate function string_agg in Section 9.21 and the large object functions in Section 33.4.
9.6. Bit String Functions and Operators
This section describes functions and operators for examining and manipulating bit strings, that is
values of the types bit and bit varying. (While only type bit is mentioned in these tables,
values of type bit varying can be used interchangeably.) Bit strings support the usual comparison
operators shown in Table 9.1, as well as the operators shown in Table 9.14.
Table 9.14. Bit String Operators
Operator
Description
Example(s)
bit || bit → bit
Concatenation
B'10001' || B'011' → 10001011
bit & bit → bit
Bitwise AND (inputs must be of equal length)
B'10001' & B'01101' → 00001
bit | bit → bit
Bitwise OR (inputs must be of equal length)
1
https://datatracker.ietf.org/doc/html/rfc2045#section-6.8
248
Functions and Operators
Operator
Description
Example(s)
B'10001' | B'01101' → 11101
bit # bit → bit
Bitwise exclusive OR (inputs must be of equal length)
B'10001' # B'01101' → 11100
~ bit → bit
Bitwise NOT
~ B'10001' → 01110
bit << integer → bit
Bitwise shift left (string length is preserved)
B'10001' << 3 → 01000
bit >> integer → bit
Bitwise shift right (string length is preserved)
B'10001' >> 2 → 00100
Some of the functions available for binary strings are also available for bit strings, as shown in Table 9.15.
Table 9.15. Bit String Functions
Function
Description
Example(s)
bit_count ( bit ) → bigint
Returns the number of bits set in the bit string (also known as “popcount”).
bit_count(B'10111') → 4
bit_length ( bit ) → integer
Returns number of bits in the bit string.
bit_length(B'10111') → 5
length ( bit ) → integer
Returns number of bits in the bit string.
length(B'10111') → 5
octet_length ( bit ) → integer
Returns number of bytes in the bit string.
octet_length(B'1011111011') → 2
overlay ( bits bit PLACING newsubstring bit FROM start integer [ FOR
count integer ] ) → bit
Replaces the substring of bits that starts at the start'th bit and extends for count
bits with newsubstring. If count is omitted, it defaults to the length of newsubstring.
overlay(B'01010101010101010' placing B'11111' from 2 for 3)
→ 0111110101010101010
position ( substring bit IN bits bit ) → integer
Returns first starting index of the specified substring within bits, or zero if it's not
present.
249
Functions and Operators
Function
Description
Example(s)
position(B'010' in B'000001101011') → 8
substring ( bits bit [ FROM start integer ] [ FOR count integer ] ) → bit
Extracts the substring of bits starting at the start'th bit if that is specified, and stopping after count bits if that is specified. Provide at least one of start and count.
substring(B'110010111111' from 3 for 2) → 00
get_bit ( bits bit, n integer ) → integer
Extracts n'th bit from bit string; the first (leftmost) bit is bit 0.
get_bit(B'101010101010101010', 6) → 1
set_bit ( bits bit, n integer, newvalue integer ) → bit
Sets n'th bit in bit string to newvalue; the first (leftmost) bit is bit 0.
set_bit(B'101010101010101010', 6, 0) → 101010001010101010
In addition, it is possible to cast integral values to and from type bit. Casting an integer to bit(n)
copies the rightmost n bits. Casting an integer to a bit string width wider than the integer itself will
sign-extend on the left. Some examples:
44::bit(10)
44::bit(3)
cast(-44 as bit(12))
'1110'::bit(4)::integer
0000101100
100
111111010100
14
Note that casting to just “bit” means casting to bit(1), and so will deliver only the least significant
bit of the integer.
9.7. Pattern Matching
There are three separate approaches to pattern matching provided by PostgreSQL: the traditional SQL
LIKE operator, the more recent SIMILAR TO operator (added in SQL:1999), and POSIX-style regular expressions. Aside from the basic “does this string match this pattern?” operators, functions are
available to extract or replace matching substrings and to split a string at matching locations.
Tip
If you have pattern matching needs that go beyond this, consider writing a user-defined function in Perl or Tcl.
Caution
While most regular-expression searches can be executed very quickly, regular expressions can
be contrived that take arbitrary amounts of time and memory to process. Be wary of accepting
regular-expression search patterns from hostile sources. If you must do so, it is advisable to
impose a statement timeout.
Searches using SIMILAR TO patterns have the same security hazards, since SIMILAR TO
provides many of the same capabilities as POSIX-style regular expressions.
LIKE searches, being much simpler than the other two options, are safer to use with possibly-hostile pattern sources.
250
Functions and Operators
The pattern matching operators of all three kinds do not support nondeterministic collations. If required, apply a different collation to the expression to work around this limitation.
9.7.1. LIKE
string LIKE pattern [ESCAPE escape-character]
string NOT LIKE pattern [ESCAPE escape-character]
The LIKE expression returns true if the string matches the supplied pattern. (As expected, the
NOT LIKE expression returns false if LIKE returns true, and vice versa. An equivalent expression
is NOT (string LIKE pattern).)
If pattern does not contain percent signs or underscores, then the pattern only represents the string
itself; in that case LIKE acts like the equals operator. An underscore (_) in pattern stands for
(matches) any single character; a percent sign (%) matches any sequence of zero or more characters.
Some examples:
'abc' LIKE 'abc'
'abc' LIKE 'a%'
'abc' LIKE '_b_'
'abc' LIKE 'c'
true
true
true
false
LIKE pattern matching always covers the entire string. Therefore, if it's desired to match a sequence
anywhere within a string, the pattern must start and end with a percent sign.
To match a literal underscore or percent sign without matching other characters, the respective character in pattern must be preceded by the escape character. The default escape character is the backslash but a different one can be selected by using the ESCAPE clause. To match the escape character
itself, write two escape characters.
Note
If you have standard_conforming_strings turned off, any backslashes you write in literal string
constants will need to be doubled. See Section 4.1.2.1 for more information.
It's also possible to select no escape character by writing ESCAPE ''. This effectively disables
the escape mechanism, which makes it impossible to turn off the special meaning of underscore and
percent signs in the pattern.
According to the SQL standard, omitting ESCAPE means there is no escape character (rather than
defaulting to a backslash), and a zero-length ESCAPE value is disallowed. PostgreSQL's behavior in
this regard is therefore slightly nonstandard.
The key word ILIKE can be used instead of LIKE to make the match case-insensitive according to
the active locale. This is not in the SQL standard but is a PostgreSQL extension.
The operator ~~ is equivalent to LIKE, and ~~* corresponds to ILIKE. There are also !~~ and !
~~* operators that represent NOT LIKE and NOT ILIKE, respectively. All of these operators are
PostgreSQL-specific. You may see these operator names in EXPLAIN output and similar places, since
the parser actually translates LIKE et al. to these operators.
The phrases LIKE, ILIKE, NOT LIKE, and NOT ILIKE are generally treated as operators in
PostgreSQL syntax; for example they can be used in expression operator ANY (subquery)
constructs, although an ESCAPE clause cannot be included there. In some obscure cases it may be
necessary to use the underlying operator names instead.
251
Functions and Operators
Also see the starts-with operator ^@ and the corresponding starts_with() function, which are
useful in cases where simply matching the beginning of a string is needed.
9.7.2. SIMILAR TO Regular Expressions
string SIMILAR TO pattern [ESCAPE escape-character]
string NOT SIMILAR TO pattern [ESCAPE escape-character]
The SIMILAR TO operator returns true or false depending on whether its pattern matches the given
string. It is similar to LIKE, except that it interprets the pattern using the SQL standard's definition of a
regular expression. SQL regular expressions are a curious cross between LIKE notation and common
(POSIX) regular expression notation.
Like LIKE, the SIMILAR TO operator succeeds only if its pattern matches the entire string; this is
unlike common regular expression behavior where the pattern can match any part of the string. Also
like LIKE, SIMILAR TO uses _ and % as wildcard characters denoting any single character and any
string, respectively (these are comparable to . and .* in POSIX regular expressions).
In addition to these facilities borrowed from LIKE, SIMILAR TO supports these pattern-matching
metacharacters borrowed from POSIX regular expressions:
• | denotes alternation (either of two alternatives).
• * denotes repetition of the previous item zero or more times.
• + denotes repetition of the previous item one or more times.
• ? denotes repetition of the previous item zero or one time.
• {m} denotes repetition of the previous item exactly m times.
• {m,} denotes repetition of the previous item m or more times.
• {m,n} denotes repetition of the previous item at least m and not more than n times.
• Parentheses () can be used to group items into a single logical item.
• A bracket expression [...] specifies a character class, just as in POSIX regular expressions.
Notice that the period (.) is not a metacharacter for SIMILAR TO.
As with LIKE, a backslash disables the special meaning of any of these metacharacters. A different
escape character can be specified with ESCAPE, or the escape capability can be disabled by writing
ESCAPE ''.
According to the SQL standard, omitting ESCAPE means there is no escape character (rather than
defaulting to a backslash), and a zero-length ESCAPE value is disallowed. PostgreSQL's behavior in
this regard is therefore slightly nonstandard.
Another nonstandard extension is that following the escape character with a letter or digit provides
access to the escape sequences defined for POSIX regular expressions; see Table 9.20, Table 9.21,
and Table 9.22 below.
Some examples:
'abc' SIMILAR TO 'abc'
'abc' SIMILAR TO 'a'
'abc' SIMILAR TO '%(b|d)%'
'abc' SIMILAR TO '(b|c)%'
'-abc-' SIMILAR TO '%\mabc\M%'
252
true
false
true
false
true
Functions and Operators
'xabcy' SIMILAR TO '%\mabc\M%'
false
The substring function with three parameters provides extraction of a substring that matches an
SQL regular expression pattern. The function can be written according to standard SQL syntax:
substring(string similar pattern escape escape-character)
or using the now obsolete SQL:1999 syntax:
substring(string from pattern for escape-character)
or as a plain three-argument function:
substring(string, pattern, escape-character)
As with SIMILAR TO, the specified pattern must match the entire data string, or else the function
fails and returns null. To indicate the part of the pattern for which the matching data sub-string is
of interest, the pattern should contain two occurrences of the escape character followed by a double
quote ("). The text matching the portion of the pattern between these separators is returned when the
match is successful.
The escape-double-quote separators actually divide substring's pattern into three independent regular expressions; for example, a vertical bar (|) in any of the three sections affects only that section.
Also, the first and third of these regular expressions are defined to match the smallest possible amount
of text, not the largest, when there is any ambiguity about how much of the data string matches which
pattern. (In POSIX parlance, the first and third regular expressions are forced to be non-greedy.)
As an extension to the SQL standard, PostgreSQL allows there to be just one escape-double-quote
separator, in which case the third regular expression is taken as empty; or no separators, in which case
the first and third regular expressions are taken as empty.
Some examples, with #" delimiting the return string:
substring('foobar' similar '%#"o_b#"%' escape '#')
substring('foobar' similar '#"o_b#"%' escape '#')
oob
NULL
9.7.3. POSIX Regular Expressions
Table 9.16 lists the available operators for pattern matching using POSIX regular expressions.
Table 9.16. Regular Expression Match Operators
Operator
Description
Example(s)
text ~ text → boolean
String matches regular expression, case sensitively
'thomas' ~ 't.*ma' → t
text ~* text → boolean
String matches regular expression, case-insensitively
'thomas' ~* 'T.*ma' → t
text !~ text → boolean
String does not match regular expression, case sensitively
253
Functions and Operators
Operator
Description
Example(s)
'thomas' !~ 't.*max' → t
text !~* text → boolean
String does not match regular expression, case-insensitively
'thomas' !~* 'T.*ma' → f
POSIX regular expressions provide a more powerful means for pattern matching than the LIKE and
SIMILAR TO operators. Many Unix tools such as egrep, sed, or awk use a pattern matching
language that is similar to the one described here.
A regular expression is a character sequence that is an abbreviated definition of a set of strings (a
regular set). A string is said to match a regular expression if it is a member of the regular set described
by the regular expression. As with LIKE, pattern characters match string characters exactly unless
they are special characters in the regular expression language — but regular expressions use different
special characters than LIKE does. Unlike LIKE patterns, a regular expression is allowed to match
anywhere within a string, unless the regular expression is explicitly anchored to the beginning or end
of the string.
Some examples:
'abcd' ~ 'bc'
true
'abcd' ~ 'a.c'
true — dot matches any character
'abcd' ~ 'a.*d'
true — * repeats the preceding pattern item
'abcd' ~ '(b|x)' true — | means OR, parentheses group
'abcd' ~ '^a'
true — ^ anchors to start of string
'abcd' ~ '^(b|c)' false — would match except for anchoring
The POSIX pattern language is described in much greater detail below.
The substring function with two parameters, substring(string from pattern), provides extraction of a substring that matches a POSIX regular expression pattern. It returns null if there
is no match, otherwise the first portion of the text that matched the pattern. But if the pattern contains
any parentheses, the portion of the text that matched the first parenthesized subexpression (the one
whose left parenthesis comes first) is returned. You can put parentheses around the whole expression
if you want to use parentheses within it without triggering this exception. If you need parentheses in
the pattern before the subexpression you want to extract, see the non-capturing parentheses described
below.
Some examples:
substring('foobar' from 'o.b')
substring('foobar' from 'o(.)b')
oob
o
The regexp_count function counts the number of places where a POSIX regular expression pattern
matches a string. It has the syntax regexp_count(string, pattern [, start [, flags ]]).
pattern is searched for in string, normally from the beginning of the string, but if the start
parameter is provided then beginning from that character index. The flags parameter is an optional
text string containing zero or more single-letter flags that change the function's behavior. For example,
including i in flags specifies case-insensitive matching. Supported flags are described in Table 9.24.
Some examples:
regexp_count('ABCABCAXYaxy', 'A.')
regexp_count('ABCABCAXYaxy', 'A.', 1, 'i')
254
3
4
Functions and Operators
The regexp_instr function returns the starting or ending position of the N'th match of a POSIX
regular expression pattern to a string, or zero if there is no such match. It has the syntax regexp_instr(string, pattern [, start [, N [, endoption [, flags [, subexpr ]]]]]). pattern is
searched for in string, normally from the beginning of the string, but if the start parameter is
provided then beginning from that character index. If N is specified then the N'th match of the pattern
is located, otherwise the first match is located. If the endoption parameter is omitted or specified
as zero, the function returns the position of the first character of the match. Otherwise, endoption
must be one, and the function returns the position of the character following the match. The flags
parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. Supported flags are described in Table 9.24. For a pattern containing parenthesized
subexpressions, subexpr is an integer indicating which subexpression is of interest: the result identifies the position of the substring matching that subexpression. Subexpressions are numbered in the
order of their leading parentheses. When subexpr is omitted or zero, the result identifies the position
of the whole match regardless of parenthesized subexpressions.
Some examples:
regexp_instr('number of your street, town zip, FR', '[^,]+', 1, 2)
23
regexp_instr('ABCDEFGHI', '(c..)(...)', 1, 1, 0, 'i', 2)
6
The regexp_like function checks whether a match of a POSIX regular expression pattern occurs
within a string, returning boolean true or false. It has the syntax regexp_like(string, pattern
[, flags ]). The flags parameter is an optional text string containing zero or more single-letter
flags that change the function's behavior. Supported flags are described in Table 9.24. This function
has the same results as the ~ operator if no flags are specified. If only the i flag is specified, it has
the same results as the ~* operator.
Some examples:
regexp_like('Hello World', 'world')
regexp_like('Hello World', 'world', 'i')
false
true
The regexp_match function returns a text array of matching substring(s) within the first match of
a POSIX regular expression pattern to a string. It has the syntax regexp_match(string, pattern [, flags ]). If there is no match, the result is NULL. If a match is found, and the pattern
contains no parenthesized subexpressions, then the result is a single-element text array containing the
substring matching the whole pattern. If a match is found, and the pattern contains parenthesized
subexpressions, then the result is a text array whose n'th element is the substring matching the n'th
parenthesized subexpression of the pattern (not counting “non-capturing” parentheses; see below
for details). The flags parameter is an optional text string containing zero or more single-letter flags
that change the function's behavior. Supported flags are described in Table 9.24.
Some examples:
SELECT regexp_match('foobarbequebaz', 'bar.*que');
regexp_match
-------------{barbeque}
(1 row)
SELECT regexp_match('foobarbequebaz', '(bar)(beque)');
regexp_match
-------------{bar,beque}
(1 row)
255
Functions and Operators
Tip
In the common case where you just want the whole matching substring or NULL for no match,
the best solution is to use regexp_substr(). However, regexp_substr() only exists
in PostgreSQL version 15 and up. When working in older versions, you can extract the first
element of regexp_match()'s result, for example:
SELECT (regexp_match('foobarbequebaz', 'bar.*que'))[1];
regexp_match
-------------barbeque
(1 row)
The regexp_matches function returns a set of text arrays of matching substring(s) within matches
of a POSIX regular expression pattern to a string. It has the same syntax as regexp_match. This
function returns no rows if there is no match, one row if there is a match and the g flag is not given, or
N rows if there are N matches and the g flag is given. Each returned row is a text array containing the
whole matched substring or the substrings matching parenthesized subexpressions of the pattern,
just as described above for regexp_match. regexp_matches accepts all the flags shown in
Table 9.24, plus the g flag which commands it to return all matches, not just the first one.
Some examples:
SELECT regexp_matches('foo', 'not there');
regexp_matches
---------------(0 rows)
SELECT regexp_matches('foobarbequebazilbarfbonk', '(b[^b]+)
(b[^b]+)', 'g');
regexp_matches
---------------{bar,beque}
{bazil,barf}
(2 rows)
Tip
In most cases regexp_matches() should be used with the g flag, since if you only want
the first match, it's easier and more efficient to use regexp_match(). However, regexp_match() only exists in PostgreSQL version 10 and up. When working in older versions,
a common trick is to place a regexp_matches() call in a sub-select, for example:
SELECT col1, (SELECT regexp_matches(col2, '(bar)(beque)'))
FROM tab;
This produces a text array if there's a match, or NULL if not, the same as regexp_match()
would do. Without the sub-select, this query would produce no output at all for table rows
without a match, which is typically not the desired behavior.
The regexp_replace function provides substitution of new text for substrings that match POSIX
regular expression patterns. It has the syntax regexp_replace(source, pattern, replacement [, start [, N ]] [, flags ]). (Notice that N cannot be specified unless start is, but flags
can be given in any case.) The source string is returned unchanged if there is no match to the pat-
256
Functions and Operators
tern. If there is a match, the source string is returned with the replacement string substituted
for the matching substring. The replacement string can contain \n, where n is 1 through 9, to
indicate that the source substring matching the n'th parenthesized subexpression of the pattern should
be inserted, and it can contain \& to indicate that the substring matching the entire pattern should be
inserted. Write \\ if you need to put a literal backslash in the replacement text. pattern is searched
for in string, normally from the beginning of the string, but if the start parameter is provided
then beginning from that character index. By default, only the first match of the pattern is replaced.
If N is specified and is greater than zero, then the N'th match of the pattern is replaced. If the g flag
is given, or if N is specified and is zero, then all matches at or after the start position are replaced.
(The g flag is ignored when N is specified.) The flags parameter is an optional text string containing
zero or more single-letter flags that change the function's behavior. Supported flags (though not g)
are described in Table 9.24.
Some examples:
regexp_replace('foobarbaz', 'b..', 'X')
fooXbaz
regexp_replace('foobarbaz', 'b..', 'X', 'g')
fooXX
regexp_replace('foobarbaz', 'b(..)', 'X\1Y', 'g')
fooXarYXazY
regexp_replace('A PostgreSQL function', 'a|e|i|o|u', 'X', 1, 0,
'i')
X PXstgrXSQL fXnctXXn
regexp_replace('A PostgreSQL function', 'a|e|i|o|u', 'X', 1, 3,
'i')
A PostgrXSQL function
The regexp_split_to_table function splits a string using a POSIX regular expression pattern
as a delimiter. It has the syntax regexp_split_to_table(string, pattern [, flags ]). If
there is no match to the pattern, the function returns the string. If there is at least one match,
for each match it returns the text from the end of the last match (or the beginning of the string) to
the beginning of the match. When there are no more matches, it returns the text from the end of the
last match to the end of the string. The flags parameter is an optional text string containing zero or
more single-letter flags that change the function's behavior. regexp_split_to_table supports
the flags described in Table 9.24.
The regexp_split_to_array function behaves the same as regexp_split_to_table,
except that regexp_split_to_array returns its result as an array of text. It has the syntax
regexp_split_to_array(string, pattern [, flags ]). The parameters are the same as for
regexp_split_to_table.
Some examples:
SELECT foo FROM regexp_split_to_table('the quick brown fox jumps
over the lazy dog', '\s+') AS foo;
foo
------the
quick
brown
fox
jumps
over
the
lazy
dog
(9 rows)
257
Functions and Operators
SELECT regexp_split_to_array('the quick brown fox jumps over the
lazy dog', '\s+');
regexp_split_to_array
----------------------------------------------{the,quick,brown,fox,jumps,over,the,lazy,dog}
(1 row)
SELECT foo FROM regexp_split_to_table('the quick brown fox', '\s*')
AS foo;
foo
----t
h
e
q
u
i
c
k
b
r
o
w
n
f
o
x
(16 rows)
As the last example demonstrates, the regexp split functions ignore zero-length matches that occur
at the start or end of the string or immediately after a previous match. This is contrary to the strict
definition of regexp matching that is implemented by the other regexp functions, but is usually the
most convenient behavior in practice. Other software systems such as Perl use similar definitions.
The regexp_substr function returns the substring that matches a POSIX regular expression pattern, or NULL if there is no match. It has the syntax regexp_substr(string, pattern [, start
[, N [, flags [, subexpr ]]]]). pattern is searched for in string, normally from the beginning
of the string, but if the start parameter is provided then beginning from that character index. If N
is specified then the N'th match of the pattern is returned, otherwise the first match is returned. The
flags parameter is an optional text string containing zero or more single-letter flags that change
the function's behavior. Supported flags are described in Table 9.24. For a pattern containing parenthesized subexpressions, subexpr is an integer indicating which subexpression is of interest: the
result is the substring matching that subexpression. Subexpressions are numbered in the order of their
leading parentheses. When subexpr is omitted or zero, the result is the whole match regardless of
parenthesized subexpressions.
Some examples:
regexp_substr('number of your street, town zip, FR', '[^,]+', 1, 2)
town zip
regexp_substr('ABCDEFGHI', '(c..)(...)', 1, 1, 'i', 2)
FGH
9.7.3.1. Regular Expression Details
PostgreSQL's regular expressions are implemented using a software package written by Henry
Spencer. Much of the description of regular expressions below is copied verbatim from his manual.
258
Functions and Operators
Regular expressions (REs), as defined in POSIX 1003.2, come in two forms: extended REs or EREs
(roughly those of egrep), and basic REs or BREs (roughly those of ed). PostgreSQL supports both
forms, and also implements some extensions that are not in the POSIX standard, but have become
widely used due to their availability in programming languages such as Perl and Tcl. REs using these
non-POSIX extensions are called advanced REs or AREs in this documentation. AREs are almost an
exact superset of EREs, but BREs have several notational incompatibilities (as well as being much
more limited). We first describe the ARE and ERE forms, noting features that apply only to AREs,
and then describe how BREs differ.
Note
PostgreSQL always initially presumes that a regular expression follows the ARE rules. However, the more limited ERE or BRE rules can be chosen by prepending an embedded option
to the RE pattern, as described in Section 9.7.3.4. This can be useful for compatibility with
applications that expect exactly the POSIX 1003.2 rules.
A regular expression is defined as one or more branches, separated by |. It matches anything that
matches one of the branches.
A branch is zero or more quantified atoms or constraints, concatenated. It matches a match for the
first, followed by a match for the second, etc.; an empty branch matches the empty string.
A quantified atom is an atom possibly followed by a single quantifier. Without a quantifier, it matches
a match for the atom. With a quantifier, it can match some number of matches of the atom. An atom
can be any of the possibilities shown in Table 9.17. The possible quantifiers and their meanings are
shown in Table 9.18.
A constraint matches an empty string, but matches only when specific conditions are met. A constraint
can be used where an atom could be used, except it cannot be followed by a quantifier. The simple
constraints are shown in Table 9.19; some more constraints are described later.
Table 9.17. Regular Expression Atoms
Atom
Description
(re)
(where re is any regular expression) matches a
match for re, with the match noted for possible
reporting
(?:re)
as above, but the match is not noted for reporting
(a “non-capturing” set of parentheses) (AREs
only)
.
matches any single character
[chars]
a bracket expression, matching any one of the
chars (see Section 9.7.3.2 for more detail)
\k
(where k is a non-alphanumeric character)
matches that character taken as an ordinary character, e.g., \\ matches a backslash character
\c
where c is alphanumeric (possibly followed
by other characters) is an escape, see Section 9.7.3.3 (AREs only; in EREs and BREs, this
matches c)
{
when followed by a character other than a digit, matches the left-brace character {; when followed by a digit, it is the beginning of a bound
(see below)
259
Functions and Operators
Atom
Description
x
where x is a single character with no other significance, matches that character
An RE cannot end with a backslash (\).
Note
If you have standard_conforming_strings turned off, any backslashes you write in literal string
constants will need to be doubled. See Section 4.1.2.1 for more information.
Table 9.18. Regular Expression Quantifiers
Quantifier
Matches
*
a sequence of 0 or more matches of the atom
+
a sequence of 1 or more matches of the atom
?
a sequence of 0 or 1 matches of the atom
{m}
a sequence of exactly m matches of the atom
{m,}
a sequence of m or more matches of the atom
{m,n}
a sequence of m through n (inclusive) matches of
the atom; m cannot exceed n
*?
non-greedy version of *
+?
non-greedy version of +
??
non-greedy version of ?
{m}?
non-greedy version of {m}
{m,}?
non-greedy version of {m,}
{m,n}?
non-greedy version of {m,n}
The forms using {...} are known as bounds. The numbers m and n within a bound are unsigned
decimal integers with permissible values from 0 to 255 inclusive.
Non-greedy quantifiers (available in AREs only) match the same possibilities as their corresponding
normal (greedy) counterparts, but prefer the smallest number rather than the largest number of matches.
See Section 9.7.3.5 for more detail.
Note
A quantifier cannot immediately follow another quantifier, e.g., ** is invalid. A quantifier
cannot begin an expression or subexpression or follow ^ or |.
Table 9.19. Regular Expression Constraints
Constraint
Description
^
matches at the beginning of the string
$
matches at the end of the string
(?=re)
positive lookahead matches at any point where a
substring matching re begins (AREs only)
(?!re)
negative lookahead matches at any point where
no substring matching re begins (AREs only)
260
Functions and Operators
Constraint
Description
(?<=re)
positive lookbehind matches at any point where a
substring matching re ends (AREs only)
(?<!re)
negative lookbehind matches at any point where
no substring matching re ends (AREs only)
Lookahead and lookbehind constraints cannot contain back references (see Section 9.7.3.3), and all
parentheses within them are considered non-capturing.
9.7.3.2. Bracket Expressions
A bracket expression is a list of characters enclosed in []. It normally matches any single character
from the list (but see below). If the list begins with ^, it matches any single character not from the
rest of the list. If two characters in the list are separated by -, this is shorthand for the full range of
characters between those two (inclusive) in the collating sequence, e.g., [0-9] in ASCII matches
any decimal digit. It is illegal for two ranges to share an endpoint, e.g., a-c-e. Ranges are very
collating-sequence-dependent, so portable programs should avoid relying on them.
To include a literal ] in the list, make it the first character (after ^, if that is used). To include a
literal -, make it the first or last character, or the second endpoint of a range. To use a literal - as
the first endpoint of a range, enclose it in [. and .] to make it a collating element (see below).
With the exception of these characters, some combinations using [ (see next paragraphs), and escapes
(AREs only), all other special characters lose their special significance within a bracket expression.
In particular, \ is not special when following ERE or BRE rules, though it is special (as introducing
an escape) in AREs.
Within a bracket expression, a collating element (a character, a multiple-character sequence that collates as if it were a single character, or a collating-sequence name for either) enclosed in [. and .]
stands for the sequence of characters of that collating element. The sequence is treated as a single
element of the bracket expression's list. This allows a bracket expression containing a multiple-character collating element to match more than one character, e.g., if the collating sequence includes a ch
collating element, then the RE [[.ch.]]*c matches the first five characters of chchcc.
Note
PostgreSQL currently does not support multi-character collating elements. This information
describes possible future behavior.
Within a bracket expression, a collating element enclosed in [= and =] is an equivalence class, standing for the sequences of characters of all collating elements equivalent to that one, including itself. (If
there are no other equivalent collating elements, the treatment is as if the enclosing delimiters were [.
and .].) For example, if o and ^ are the members of an equivalence class, then [[=o=]], [[=^=]],
and [o^] are all synonymous. An equivalence class cannot be an endpoint of a range.
Within a bracket expression, the name of a character class enclosed in [: and :] stands for the list of
all characters belonging to that class. A character class cannot be used as an endpoint of a range. The
POSIX standard defines these character class names: alnum (letters and numeric digits), alpha (letters), blank (space and tab), cntrl (control characters), digit (numeric digits), graph (printable
characters except space), lower (lower-case letters), print (printable characters including space),
punct (punctuation), space (any white space), upper (upper-case letters), and xdigit (hexadecimal digits). The behavior of these standard character classes is generally consistent across platforms
for characters in the 7-bit ASCII set. Whether a given non-ASCII character is considered to belong to
one of these classes depends on the collation that is used for the regular-expression function or operator (see Section 23.2), or by default on the database's LC_CTYPE locale setting (see Section 23.1).
The classification of non-ASCII characters can vary across platforms even in similarly-named locales.
(But the C locale never considers any non-ASCII characters to belong to any of these classes.) In
addition to these standard character classes, PostgreSQL defines the word character class, which is
261
Functions and Operators
the same as alnum plus the underscore (_) character, and the ascii character class, which contains
exactly the 7-bit ASCII set.
There are two special cases of bracket expressions: the bracket expressions [[:<:]] and [[:>:]]
are constraints, matching empty strings at the beginning and end of a word respectively. A word is
defined as a sequence of word characters that is neither preceded nor followed by word characters.
A word character is any character belonging to the word character class, that is, any letter, digit, or
underscore. This is an extension, compatible with but not specified by POSIX 1003.2, and should
be used with caution in software intended to be portable to other systems. The constraint escapes
described below are usually preferable; they are no more standard, but are easier to type.
9.7.3.3. Regular Expression Escapes
Escapes are special sequences beginning with \ followed by an alphanumeric character. Escapes come
in several varieties: character entry, class shorthands, constraint escapes, and back references. A \
followed by an alphanumeric character but not constituting a valid escape is illegal in AREs. In EREs,
there are no escapes: outside a bracket expression, a \ followed by an alphanumeric character merely
stands for that character as an ordinary character, and inside a bracket expression, \ is an ordinary
character. (The latter is the one actual incompatibility between EREs and AREs.)
Character-entry escapes exist to make it easier to specify non-printing and other inconvenient characters in REs. They are shown in Table 9.20.
Class-shorthand escapes provide shorthands for certain commonly-used character classes. They are
shown in Table 9.21.
A constraint escape is a constraint, matching the empty string if specific conditions are met, written
as an escape. They are shown in Table 9.22.
A back reference (\n) matches the same string matched by the previous parenthesized subexpression
specified by the number n (see Table 9.23). For example, ([bc])\1 matches bb or cc but not bc
or cb. The subexpression must entirely precede the back reference in the RE. Subexpressions are
numbered in the order of their leading parentheses. Non-capturing parentheses do not define subexpressions. The back reference considers only the string characters matched by the referenced subexpression, not any constraints contained in it. For example, (^\d)\1 will match 22.
Table 9.20. Regular Expression Character-Entry Escapes
Escape
Description
\a
alert (bell) character, as in C
\b
backspace, as in C
\B
synonym for backslash (\) to help reduce the
need for backslash doubling
\cX
(where X is any character) the character whose
low-order 5 bits are the same as those of X, and
whose other bits are all zero
\e
the character whose collating-sequence name is
ESC, or failing that, the character with octal value 033
\f
form feed, as in C
\n
newline, as in C
\r
carriage return, as in C
\t
horizontal tab, as in C
\uwxyz
(where wxyz is exactly four hexadecimal digits) the character whose hexadecimal value is
0xwxyz
262
Functions and Operators
Escape
Description
\Ustuvwxyz
(where stuvwxyz is exactly eight hexadecimal
digits) the character whose hexadecimal value is
0xstuvwxyz
\v
vertical tab, as in C
\xhhh
(where hhh is any sequence of hexadecimal digits) the character whose hexadecimal value is
0xhhh (a single character no matter how many
hexadecimal digits are used)
\0
the character whose value is 0 (the null byte)
\xy
(where xy is exactly two octal digits, and is not
a back reference) the character whose octal value is 0xy
\xyz
(where xyz is exactly three octal digits, and is
not a back reference) the character whose octal
value is 0xyz
Hexadecimal digits are 0-9, a-f, and A-F. Octal digits are 0-7.
Numeric character-entry escapes specifying values outside the ASCII range (0–127) have meanings
dependent on the database encoding. When the encoding is UTF-8, escape values are equivalent to
Unicode code points, for example \u1234 means the character U+1234. For other multibyte encodings, character-entry escapes usually just specify the concatenation of the byte values for the character.
If the escape value does not correspond to any legal character in the database encoding, no error will
be raised, but it will never match any data.
The character-entry escapes are always taken as ordinary characters. For example, \135 is ] in ASCII,
but \135 does not terminate a bracket expression.
Table 9.21. Regular Expression Class-Shorthand Escapes
Escape
Description
\d
matches any digit, like [[:digit:]]
\s
matches any whitespace character, like
[[:space:]]
\w
matches any word character, like [[:word:]]
\D
matches any non-digit, like [^[:digit:]]
\S
matches any non-whitespace character, like
[^[:space:]]
\W
matches any non-word character, like
[^[:word:]]
The class-shorthand escapes also work within bracket expressions, although the definitions shown
above are not quite syntactically valid in that context. For example, [a-c\d] is equivalent to [ac[:digit:]].
Table 9.22. Regular Expression Constraint Escapes
Escape
Description
\A
matches only at the beginning of the string (see
Section 9.7.3.5 for how this differs from ^)
\m
matches only at the beginning of a word
\M
matches only at the end of a word
263
Functions and Operators
Escape
Description
\y
matches only at the beginning or end of a word
\Y
matches only at a point that is not the beginning
or end of a word
\Z
matches only at the end of the string (see Section 9.7.3.5 for how this differs from $)
A word is defined as in the specification of [[:<:]] and [[:>:]] above. Constraint escapes are
illegal within bracket expressions.
Table 9.23. Regular Expression Back References
Escape
Description
\m
(where m is a nonzero digit) a back reference to
the m'th subexpression
\mnn
(where m is a nonzero digit, and nn is some
more digits, and the decimal value mnn is not
greater than the number of closing capturing
parentheses seen so far) a back reference to the
mnn'th subexpression
Note
There is an inherent ambiguity between octal character-entry escapes and back references,
which is resolved by the following heuristics, as hinted at above. A leading zero always indicates an octal escape. A single non-zero digit, not followed by another digit, is always taken as
a back reference. A multi-digit sequence not starting with a zero is taken as a back reference
if it comes after a suitable subexpression (i.e., the number is in the legal range for a back reference), and otherwise is taken as octal.
9.7.3.4. Regular Expression Metasyntax
In addition to the main syntax described above, there are some special forms and miscellaneous syntactic facilities available.
An RE can begin with one of two special director prefixes. If an RE begins with ***:, the rest of
the RE is taken as an ARE. (This normally has no effect in PostgreSQL, since REs are assumed to be
AREs; but it does have an effect if ERE or BRE mode had been specified by the flags parameter
to a regex function.) If an RE begins with ***=, the rest of the RE is taken to be a literal string, with
all characters considered ordinary characters.
An ARE can begin with embedded options: a sequence (?xyz) (where xyz is one or more alphabetic characters) specifies options affecting the rest of the RE. These options override any previously
determined options — in particular, they can override the case-sensitivity behavior implied by a regex
operator, or the flags parameter to a regex function. The available option letters are shown in Table 9.24. Note that these same option letters are used in the flags parameters of regex functions.
Table 9.24. ARE Embedded-Option Letters
Option
Description
b
rest of RE is a BRE
c
case-sensitive matching (overrides operator
type)
e
rest of RE is an ERE
264
Functions and Operators
Option
Description
i
case-insensitive matching (see Section 9.7.3.5)
(overrides operator type)
m
historical synonym for n
n
newline-sensitive matching (see Section 9.7.3.5)
p
partial newline-sensitive matching (see Section 9.7.3.5)
q
rest of RE is a literal (“quoted”) string, all ordinary characters
s
non-newline-sensitive matching (default)
t
tight syntax (default; see below)
w
inverse partial newline-sensitive (“weird”)
matching (see Section 9.7.3.5)
x
expanded syntax (see below)
Embedded options take effect at the ) terminating the sequence. They can appear only at the start of
an ARE (after the ***: director if any).
In addition to the usual (tight) RE syntax, in which all characters are significant, there is an expanded
syntax, available by specifying the embedded x option. In the expanded syntax, white-space characters
in the RE are ignored, as are all characters between a # and the following newline (or the end of the
RE). This permits paragraphing and commenting a complex RE. There are three exceptions to that
basic rule:
• a white-space character or # preceded by \ is retained
• white space or # within a bracket expression is retained
• white space and comments cannot appear within multi-character symbols, such as (?:
For this purpose, white-space characters are blank, tab, newline, and any character that belongs to the
space character class.
Finally, in an ARE, outside bracket expressions, the sequence (?#ttt) (where ttt is any text not
containing a )) is a comment, completely ignored. Again, this is not allowed between the characters of
multi-character symbols, like (?:. Such comments are more a historical artifact than a useful facility,
and their use is deprecated; use the expanded syntax instead.
None of these metasyntax extensions is available if an initial ***= director has specified that the user's
input be treated as a literal string rather than as an RE.
9.7.3.5. Regular Expression Matching Rules
In the event that an RE could match more than one substring of a given string, the RE matches the
one starting earliest in the string. If the RE could match more than one substring starting at that point,
either the longest possible match or the shortest possible match will be taken, depending on whether
the RE is greedy or non-greedy.
Whether an RE is greedy or not is determined by the following rules:
• Most atoms, and all constraints, have no greediness attribute (because they cannot match variable
amounts of text anyway).
• Adding parentheses around an RE does not change its greediness.
• A quantified atom with a fixed-repetition quantifier ({m} or {m}?) has the same greediness (possibly none) as the atom itself.
265
Functions and Operators
• A quantified atom with other normal quantifiers (including {m,n} with m equal to n) is greedy
(prefers longest match).
• A quantified atom with a non-greedy quantifier (including {m,n}? with m equal to n) is non-greedy
(prefers shortest match).
• A branch — that is, an RE that has no top-level | operator — has the same greediness as the first
quantified atom in it that has a greediness attribute.
• An RE consisting of two or more branches connected by the | operator is always greedy.
The above rules associate greediness attributes not only with individual quantified atoms, but with
branches and entire REs that contain quantified atoms. What that means is that the matching is done in
such a way that the branch, or whole RE, matches the longest or shortest possible substring as a whole.
Once the length of the entire match is determined, the part of it that matches any particular subexpression is determined on the basis of the greediness attribute of that subexpression, with subexpressions
starting earlier in the RE taking priority over ones starting later.
An example of what this means:
SELECT SUBSTRING('XY1234Z', 'Y*([0-9]{1,3})');
Result: 123
SELECT SUBSTRING('XY1234Z', 'Y*?([0-9]{1,3})');
Result: 1
In the first case, the RE as a whole is greedy because Y* is greedy. It can match beginning at the Y,
and it matches the longest possible string starting there, i.e., Y123. The output is the parenthesized
part of that, or 123. In the second case, the RE as a whole is non-greedy because Y*? is non-greedy.
It can match beginning at the Y, and it matches the shortest possible string starting there, i.e., Y1.
The subexpression [0-9]{1,3} is greedy but it cannot change the decision as to the overall match
length; so it is forced to match just 1.
In short, when an RE contains both greedy and non-greedy subexpressions, the total match length is
either as long as possible or as short as possible, according to the attribute assigned to the whole RE.
The attributes assigned to the subexpressions only affect how much of that match they are allowed
to “eat” relative to each other.
The quantifiers {1,1} and {1,1}? can be used to force greediness or non-greediness, respectively,
on a subexpression or a whole RE. This is useful when you need the whole RE to have a greediness
attribute different from what's deduced from its elements. As an example, suppose that we are trying
to separate a string containing some digits into the digits and the parts before and after them. We might
try to do that like this:
SELECT regexp_match('abc01234xyz', '(.*)(\d+)(.*)');
Result: {abc0123,4,xyz}
That didn't work: the first .* is greedy so it “eats” as much as it can, leaving the \d+ to match at the
last possible place, the last digit. We might try to fix that by making it non-greedy:
SELECT regexp_match('abc01234xyz', '(.*?)(\d+)(.*)');
Result: {abc,0,""}
That didn't work either, because now the RE as a whole is non-greedy and so it ends the overall match
as soon as possible. We can get what we want by forcing the RE as a whole to be greedy:
SELECT regexp_match('abc01234xyz', '(?:(.*?)(\d+)(.*)){1,1}');
Result: {abc,01234,xyz}
266
Functions and Operators
Controlling the RE's overall greediness separately from its components' greediness allows great flexibility in handling variable-length patterns.
When deciding what is a longer or shorter match, match lengths are measured in characters, not collating elements. An empty string is considered longer than no match at all. For example: bb* matches the
three middle characters of abbbc; (week|wee)(night|knights) matches all ten characters
of weeknights; when (.*).* is matched against abc the parenthesized subexpression matches
all three characters; and when (a*)* is matched against bc both the whole RE and the parenthesized
subexpression match an empty string.
If case-independent matching is specified, the effect is much as if all case distinctions had vanished
from the alphabet. When an alphabetic that exists in multiple cases appears as an ordinary character
outside a bracket expression, it is effectively transformed into a bracket expression containing both
cases, e.g., x becomes [xX]. When it appears inside a bracket expression, all case counterparts of it
are added to the bracket expression, e.g., [x] becomes [xX] and [^x] becomes [^xX].
If newline-sensitive matching is specified, . and bracket expressions using ^ will never match the
newline character (so that matches will not cross lines unless the RE explicitly includes a newline) and
^ and $ will match the empty string after and before a newline respectively, in addition to matching at
beginning and end of string respectively. But the ARE escapes \A and \Z continue to match beginning
or end of string only. Also, the character class shorthands \D and \W will match a newline regardless
of this mode. (Before PostgreSQL 14, they did not match newlines when in newline-sensitive mode.
Write [^[:digit:]] or [^[:word:]] to get the old behavior.)
If partial newline-sensitive matching is specified, this affects . and bracket expressions as with newline-sensitive matching, but not ^ and $.
If inverse partial newline-sensitive matching is specified, this affects ^ and $ as with newline-sensitive
matching, but not . and bracket expressions. This isn't very useful but is provided for symmetry.
9.7.3.6. Limits and Compatibility
No particular limit is imposed on the length of REs in this implementation. However, programs intended to be highly portable should not employ REs longer than 256 bytes, as a POSIX-compliant
implementation can refuse to accept such REs.
The only feature of AREs that is actually incompatible with POSIX EREs is that \ does not lose its
special significance inside bracket expressions. All other ARE features use syntax which is illegal or
has undefined or unspecified effects in POSIX EREs; the *** syntax of directors likewise is outside
the POSIX syntax for both BREs and EREs.
Many of the ARE extensions are borrowed from Perl, but some have been changed to clean them up,
and a few Perl extensions are not present. Incompatibilities of note include \b, \B, the lack of special treatment for a trailing newline, the addition of complemented bracket expressions to the things
affected by newline-sensitive matching, the restrictions on parentheses and back references in lookahead/lookbehind constraints, and the longest/shortest-match (rather than first-match) matching semantics.
9.7.3.7. Basic Regular Expressions
BREs differ from EREs in several respects. In BREs, |, +, and ? are ordinary characters and there
is no equivalent for their functionality. The delimiters for bounds are \{ and \}, with { and } by
themselves ordinary characters. The parentheses for nested subexpressions are \( and \), with ( and
) by themselves ordinary characters. ^ is an ordinary character except at the beginning of the RE or
the beginning of a parenthesized subexpression, $ is an ordinary character except at the end of the
RE or the end of a parenthesized subexpression, and * is an ordinary character if it appears at the
beginning of the RE or the beginning of a parenthesized subexpression (after a possible leading ^).
Finally, single-digit back references are available, and \< and \> are synonyms for [[:<:]] and
[[:>:]] respectively; no other escapes are available in BREs.
267
Functions and Operators
9.7.3.8. Differences from SQL Standard and XQuery
Since SQL:2008, the SQL standard includes regular expression operators and functions that performs
pattern matching according to the XQuery regular expression standard:
• LIKE_REGEX
• OCCURRENCES_REGEX
• POSITION_REGEX
• SUBSTRING_REGEX
• TRANSLATE_REGEX
PostgreSQL does not currently implement these operators and functions. You can get approximately
equivalent functionality in each case as shown in Table 9.25. (Various optional clauses on both sides
have been omitted in this table.)
Table 9.25. Regular Expression Functions Equivalencies
SQL standard
PostgreSQL
string LIKE_REGEX pattern
regexp_like(string, pattern) or
string ~ pattern
OCCURRENCES_REGEX(pattern IN
string)
regexp_count(string, pattern)
POSITION_REGEX(pattern IN
string)
regexp_instr(string, pattern)
SUBSTRING_REGEX(pattern IN
string)
regexp_substr(string, pattern)
TRANSLATE_REGEX(pattern IN
string WITH replacement)
regexp_replace(string, pattern,
replacement)
Regular expression functions similar to those provided by PostgreSQL are also available in a number
of other SQL implementations, whereas the SQL-standard functions are not as widely implemented.
Some of the details of the regular expression syntax will likely differ in each implementation.
The SQL-standard operators and functions use XQuery regular expressions, which are quite close to
the ARE syntax described above. Notable differences between the existing POSIX-based regular-expression feature and XQuery regular expressions include:
• XQuery character class subtraction is not supported. An example of this feature is using the following to match only English consonants: [a-z-[aeiou]].
• XQuery character class shorthands \c, \C, \i, and \I are not supported.
• XQuery character class elements using \p{UnicodeProperty} or the inverse \P{UnicodeProperty} are not supported.
• POSIX interprets character classes such as \w (see Table 9.21) according to the prevailing locale
(which you can control by attaching a COLLATE clause to the operator or function). XQuery specifies these classes by reference to Unicode character properties, so equivalent behavior is obtained
only with a locale that follows the Unicode rules.
• The SQL standard (not XQuery itself) attempts to cater for more variants of “newline” than POSIX
does. The newline-sensitive matching options described above consider only ASCII NL (\n) to be
a newline, but SQL would have us treat CR (\r), CRLF (\r\n) (a Windows-style newline), and
some Unicode-only characters like LINE SEPARATOR (U+2028) as newlines as well. Notably, .
and \s should count \r\n as one character not two according to SQL.
• Of the character-entry escapes described in Table 9.20, XQuery supports only \n, \r, and \t.
268
Functions and Operators
• XQuery does not support the [:name:] syntax for character classes within bracket expressions.
• XQuery does not have lookahead or lookbehind constraints, nor any of the constraint escapes described in Table 9.22.
• The metasyntax forms described in Section 9.7.3.4 do not exist in XQuery.
• The regular expression flag letters defined by XQuery are related to but not the same as the option
letters for POSIX (Table 9.24). While the i and q options behave the same, others do not:
• XQuery's s (allow dot to match newline) and m (allow ^ and $ to match at newlines) flags provide
access to the same behaviors as POSIX's n, p and w flags, but they do not match the behavior
of POSIX's s and m flags. Note in particular that dot-matches-newline is the default behavior in
POSIX but not XQuery.
• XQuery's x (ignore whitespace in pattern) flag is noticeably different from POSIX's expanded-mode flag. POSIX's x flag also allows # to begin a comment in the pattern, and POSIX will
not ignore a whitespace character after a backslash.
9.8. Data Type Formatting Functions
The PostgreSQL formatting functions provide a powerful set of tools for converting various data types
(date/time, integer, floating point, numeric) to formatted strings and for converting from formatted
strings to specific data types. Table 9.26 lists them. These functions all follow a common calling
convention: the first argument is the value to be formatted and the second argument is a template that
defines the output or input format.
Table 9.26. Formatting Functions
Function
Description
Example(s)
to_char ( timestamp, text ) → text
to_char ( timestamp with time zone, text ) → text
Converts time stamp to string according to the given format.
to_char(timestamp '2002-04-20 17:31:12.66', 'HH12:MI:SS') →
05:31:12
to_char ( interval, text ) → text
Converts interval to string according to the given format.
to_char(interval '15h 2m 12s', 'HH24:MI:SS') → 15:02:12
to_char ( numeric_type, text ) → text
Converts number to string according to the given format; available for integer, bigint, numeric, real, double precision.
to_char(125, '999') → 125
to_char(125.8::real, '999D9') → 125.8
to_char(-125.8, '999D99S') → 125.80to_date ( text, text ) → date
Converts string to date according to the given format.
to_date('05 Dec 2000', 'DD Mon YYYY') → 2000-12-05
to_number ( text, text ) → numeric
Converts string to numeric according to the given format.
to_number('12,454.8-', '99G999D9S') → -12454.8
269
Functions and Operators
Function
Description
Example(s)
to_timestamp ( text, text ) → timestamp with time zone
Converts string to time stamp according to the given format. (See also to_timestamp(double precision) in Table 9.33.)
to_timestamp('05 Dec 2000', 'DD Mon YYYY') → 2000-12-05
00:00:00-05
Tip
to_timestamp and to_date exist to handle input formats that cannot be converted by
simple casting. For most standard date/time formats, simply casting the source string to the
required data type works, and is much easier. Similarly, to_number is unnecessary for standard numeric representations.
In a to_char output template string, there are certain patterns that are recognized and replaced with
appropriately-formatted data based on the given value. Any text that is not a template pattern is simply
copied verbatim. Similarly, in an input template string (for the other functions), template patterns
identify the values to be supplied by the input data string. If there are characters in the template string
that are not template patterns, the corresponding characters in the input data string are simply skipped
over (whether or not they are equal to the template string characters).
Table 9.27 shows the template patterns available for formatting date and time values.
Table 9.27. Template Patterns for Date/Time Formatting
Pattern
Description
HH
hour of day (01–12)
HH12
hour of day (01–12)
HH24
hour of day (00–23)
MI
minute (00–59)
SS
second (00–59)
MS
millisecond (000–999)
US
microsecond (000000–999999)
FF1
tenth of second (0–9)
FF2
hundredth of second (00–99)
FF3
millisecond (000–999)
FF4
tenth of a millisecond (0000–9999)
FF5
hundredth of a millisecond (00000–99999)
FF6
microsecond (000000–999999)
SSSS, SSSSS
seconds past midnight (0–86399)
AM, am, PM or pm
meridiem indicator (without periods)
A.M., a.m., P.M. or p.m.
meridiem indicator (with periods)
Y,YYY
year (4 or more digits) with comma
YYYY
year (4 or more digits)
YYY
last 3 digits of year
YY
last 2 digits of year
270
Functions and Operators
Pattern
Description
Y
last digit of year
IYYY
ISO 8601 week-numbering year (4 or more digits)
IYY
last 3 digits of ISO 8601 week-numbering year
IY
last 2 digits of ISO 8601 week-numbering year
I
last digit of ISO 8601 week-numbering year
BC, bc, AD or ad
era indicator (without periods)
B.C., b.c., A.D. or a.d.
era indicator (with periods)
MONTH
full upper case month name (blank-padded to 9
chars)
Month
full capitalized month name (blank-padded to 9
chars)
month
full lower case month name (blank-padded to 9
chars)
MON
abbreviated upper case month name (3 chars in
English, localized lengths vary)
Mon
abbreviated capitalized month name (3 chars in
English, localized lengths vary)
mon
abbreviated lower case month name (3 chars in
English, localized lengths vary)
MM
month number (01–12)
DAY
full upper case day name (blank-padded to 9
chars)
Day
full capitalized day name (blank-padded to 9
chars)
day
full lower case day name (blank-padded to 9
chars)
DY
abbreviated upper case day name (3 chars in
English, localized lengths vary)
Dy
abbreviated capitalized day name (3 chars in
English, localized lengths vary)
dy
abbreviated lower case day name (3 chars in
English, localized lengths vary)
DDD
day of year (001–366)
IDDD
day of ISO 8601 week-numbering year (001–
371; day 1 of the year is Monday of the first ISO
week)
DD
day of month (01–31)
D
day of the week, Sunday (1) to Saturday (7)
ID
ISO 8601 day of the week, Monday (1) to Sunday (7)
W
week of month (1–5) (the first week starts on the
first day of the month)
WW
week number of year (1–53) (the first week
starts on the first day of the year)
271
Functions and Operators
Pattern
Description
IW
week number of ISO 8601 week-numbering year
(01–53; the first Thursday of the year is in week
1)
CC
century (2 digits) (the twenty-first century starts
on 2001-01-01)
J
Julian Date (integer days since November 24,
4714 BC at local midnight; see Section B.7)
Q
quarter
RM
month in upper case Roman numerals (I–XII;
I=January)
rm
month in lower case Roman numerals (i–xii;
i=January)
TZ
upper case time-zone abbreviation
tz
lower case time-zone abbreviation
TZH
time-zone hours
TZM
time-zone minutes
OF
time-zone offset from UTC (HH or HH:MM)
Modifiers can be applied to any template pattern to alter its behavior. For example, FMMonth is the
Month pattern with the FM modifier. Table 9.28 shows the modifier patterns for date/time formatting.
Table 9.28. Template Pattern Modifiers for Date/Time Formatting
Modifier
Description
Example
FM prefix
fill mode (suppress leading zeroes and padding blanks)
FMMonth
TH suffix
upper case ordinal number suffix
DDTH, e.g., 12TH
th suffix
lower case ordinal number suffix
DDth, e.g., 12th
FX prefix
fixed format global option (see
usage notes)
FX Month DD Day
TM prefix
translation mode (use localized TMMonth
day and month names based on
lc_time)
SP suffix
spell mode (not implemented)
DDSP
Usage notes for date/time formatting:
• FM suppresses leading zeroes and trailing blanks that would otherwise be added to make the output
of a pattern be fixed-width. In PostgreSQL, FM modifies only the next specification, while in Oracle
FM affects all subsequent specifications, and repeated FM modifiers toggle fill mode on and off.
• TM suppresses trailing blanks whether or not FM is specified.
• to_timestamp and to_date ignore letter case in the input; so for example MON, Mon, and mon
all accept the same strings. When using the TM modifier, case-folding is done according to the rules
of the function's input collation (see Section 23.2).
• to_timestamp and to_date skip multiple blank spaces at the beginning of the input string
and around date and time values unless the FX option is used. For example, to_timestamp(' 2000
JUN', 'YYYY MON') and to_timestamp('2000 - JUN', 'YYYY-
272
Functions and Operators
MON') work, but to_timestamp('2000
JUN', 'FXYYYY MON') returns an error
because to_timestamp expects only a single space. FX must be specified as the first item in
the template.
• A separator (a space or non-letter/non-digit character) in the template string of to_timestamp
and to_date matches any single separator in the input string or is skipped, unless the FX option is
used. For example, to_timestamp('2000JUN', 'YYYY///MON') and to_timestamp('2000/JUN', 'YYYY MON') work, but to_timestamp('2000//JUN', 'YYYY/
MON') returns an error because the number of separators in the input string exceeds the number
of separators in the template.
If FX is specified, a separator in the template string matches exactly one character in the input
string. But note that the input string character is not required to be the same as the separator from
the template string. For example, to_timestamp('2000/JUN', 'FXYYYY MON') works,
but to_timestamp('2000/JUN', 'FXYYYY MON') returns an error because the second
space in the template string consumes the letter J from the input string.
• A TZH template pattern can match a signed number. Without the FX option, minus signs may be ambiguous, and could be interpreted as a separator. This ambiguity is resolved as follows: If the number of separators before TZH in the template string is less than the number of separators before the
minus sign in the input string, the minus sign is interpreted as part of TZH. Otherwise, the minus sign
is considered to be a separator between values. For example, to_timestamp('2000 -10',
'YYYY TZH') matches -10 to TZH, but to_timestamp('2000 -10', 'YYYY TZH')
matches 10 to TZH.
• Ordinary text is allowed in to_char templates and will be output literally. You can put a substring
in double quotes to force it to be interpreted as literal text even if it contains template patterns.
For example, in '"Hello Year "YYYY', the YYYY will be replaced by the year data, but
the single Y in Year will not be. In to_date, to_number, and to_timestamp, literal text
and double-quoted strings result in skipping the number of characters contained in the string; for
example "XX" skips two input characters (whether or not they are XX).
Tip
Prior to PostgreSQL 12, it was possible to skip arbitrary text in the input string using nonletter or non-digit characters. For example, to_timestamp('2000y6m1d', 'yyyyMM-DD') used to work. Now you can only use letter characters for this purpose. For example, to_timestamp('2000y6m1d', 'yyyytMMtDDt') and to_timestamp('2000y6m1d', 'yyyy"y"MM"m"DD"d"') skip y, m, and d.
• If you want to have a double quote in the output you must precede it with a backslash, for example
'\"YYYY Month\"'. Backslashes are not otherwise special outside of double-quoted strings.
Within a double-quoted string, a backslash causes the next character to be taken literally, whatever
it is (but this has no special effect unless the next character is a double quote or another backslash).
• In to_timestamp and to_date, if the year format specification is less than four digits, e.g.,
YYY, and the supplied year is less than four digits, the year will be adjusted to be nearest to the year
2020, e.g., 95 becomes 1995.
• In to_timestamp and to_date, negative years are treated as signifying BC. If you write both
a negative year and an explicit BC field, you get AD again. An input of year zero is treated as 1 BC.
• In to_timestamp and to_date, the YYYY conversion has a restriction when processing years with more than 4 digits. You must use some non-digit character or template after
YYYY, otherwise the year is always interpreted as 4 digits. For example (with the year 20000):
to_date('200001130', 'YYYYMMDD') will be interpreted as a 4-digit year; instead use
a non-digit separator after the year, like to_date('20000-1130', 'YYYY-MMDD') or
to_date('20000Nov30', 'YYYYMonDD').
273
Functions and Operators
• In to_timestamp and to_date, the CC (century) field is accepted but ignored if there is a
YYY, YYYY or Y,YYY field. If CC is used with YY or Y then the result is computed as that year
in the specified century. If the century is specified but the year is not, the first year of the century
is assumed.
• In to_timestamp and to_date, weekday names or numbers (DAY, D, and related field types)
are accepted but are ignored for purposes of computing the result. The same is true for quarter (Q)
fields.
• In to_timestamp and to_date, an ISO 8601 week-numbering date (as distinct from a Gregorian date) can be specified in one of two ways:
• Year, week number, and weekday: for example to_date('2006-42-4', 'IYYY-IWID') returns the date 2006-10-19. If you omit the weekday it is assumed to be 1 (Monday).
• Year and day of year: for example to_date('2006-291', 'IYYY-IDDD') also returns
2006-10-19.
Attempting to enter a date using a mixture of ISO 8601 week-numbering fields and Gregorian date
fields is nonsensical, and will cause an error. In the context of an ISO 8601 week-numbering year,
the concept of a “month” or “day of month” has no meaning. In the context of a Gregorian year,
the ISO week has no meaning.
Caution
While to_date will reject a mixture of Gregorian and ISO week-numbering date fields,
to_char will not, since output format specifications like YYYY-MM-DD (IYYY-IDDD)
can be useful. But avoid writing something like IYYY-MM-DD; that would yield surprising
results near the start of the year. (See Section 9.9.1 for more information.)
• In to_timestamp, millisecond (MS) or microsecond (US) fields are used as the seconds digits
after the decimal point. For example to_timestamp('12.3', 'SS.MS') is not 3 milliseconds, but 300, because the conversion treats it as 12 + 0.3 seconds. So, for the format SS.MS, the
input values 12.3, 12.30, and 12.300 specify the same number of milliseconds. To get three
milliseconds, one must write 12.003, which the conversion treats as 12 + 0.003 = 12.003 seconds.
Here is a more complex example: to_timestamp('15:12:02.020.001230',
'HH24:MI:SS.MS.US') is 15 hours, 12 minutes, and 2 seconds + 20 milliseconds + 1230 microseconds = 2.021230 seconds.
• to_char(..., 'ID')'s day of the week numbering matches the extract(isodow
from ...) function, but to_char(..., 'D')'s does not match extract(dow
from ...)'s day numbering.
• to_char(interval) formats HH and HH12 as shown on a 12-hour clock, for example zero
hours and 36 hours both output as 12, while HH24 outputs the full hour value, which can exceed
23 in an interval value.
Table 9.29 shows the template patterns available for formatting numeric values.
Table 9.29. Template Patterns for Numeric Formatting
Pattern
Description
9
digit position (can be dropped if insignificant)
0
digit position (will not be dropped, even if insignificant)
. (period)
decimal point
274
Functions and Operators
Pattern
Description
, (comma)
group (thousands) separator
PR
negative value in angle brackets
S
sign anchored to number (uses locale)
L
currency symbol (uses locale)
D
decimal point (uses locale)
G
group separator (uses locale)
MI
minus sign in specified position (if number < 0)
PL
plus sign in specified position (if number > 0)
SG
plus/minus sign in specified position
RN
Roman numeral (input between 1 and 3999)
TH or th
ordinal number suffix
V
shift specified number of digits (see notes)
EEEE
exponent for scientific notation
Usage notes for numeric formatting:
• 0 specifies a digit position that will always be printed, even if it contains a leading/trailing zero. 9
also specifies a digit position, but if it is a leading zero then it will be replaced by a space, while
if it is a trailing zero and fill mode is specified then it will be deleted. (For to_number(), these
two pattern characters are equivalent.)
• If the format provides fewer fractional digits than the number being formatted, to_char() will
round the number to the specified number of fractional digits.
• The pattern characters S, L, D, and G represent the sign, currency symbol, decimal point, and thousands separator characters defined by the current locale (see lc_monetary and lc_numeric). The pattern characters period and comma represent those exact characters, with the meanings of decimal
point and thousands separator, regardless of locale.
• If no explicit provision is made for a sign in to_char()'s pattern, one column will be reserved
for the sign, and it will be anchored to (appear just left of) the number. If S appears just left of some
9's, it will likewise be anchored to the number.
• A sign formatted using SG, PL, or MI is not anchored to the number; for example, to_char(-12,
'MI9999') produces '- 12' but to_char(-12, 'S9999') produces ' -12'. (The
Oracle implementation does not allow the use of MI before 9, but rather requires that 9 precede MI.)
• TH does not convert values less than zero and does not convert fractional numbers.
• PL, SG, and TH are PostgreSQL extensions.
• In to_number, if non-data template patterns such as L or TH are used, the corresponding number
of input characters are skipped, whether or not they match the template pattern, unless they are data
characters (that is, digits, sign, decimal point, or comma). For example, TH would skip two nondata characters.
• V with to_char multiplies the input values by 10^n, where n is the number of digits following
V. V with to_number divides in a similar manner. to_char and to_number do not support
the use of V combined with a decimal point (e.g., 99.9V99 is not allowed).
• EEEE (scientific notation) cannot be used in combination with any of the other formatting patterns
or modifiers other than digit and decimal point patterns, and must be at the end of the format string
(e.g., 9.99EEEE is a valid pattern).
275
Functions and Operators
Certain modifiers can be applied to any template pattern to alter its behavior. For example, FM99.99
is the 99.99 pattern with the FM modifier. Table 9.30 shows the modifier patterns for numeric formatting.
Table 9.30. Template Pattern Modifiers for Numeric Formatting
Modifier
Description
Example
FM prefix
fill mode (suppress trailing zeroes and padding blanks)
FM99.99
TH suffix
upper case ordinal number suffix
999TH
th suffix
lower case ordinal number suffix
999th
Table 9.31 shows some examples of the use of the to_char function.
Table 9.31. to_char Examples
Expression
Result
to_char(current_timestamp,
'Day, DD HH12:MI:SS')
'Tuesday
to_char(current_timestamp, 'FMDay, FMDD HH12:MI:SS')
'Tuesday, 6
to_char(current_timestamp AT
TIME ZONE 'UTC', 'YYYY-MMDD"T"HH24:MI:SS"Z"')
'2022-12-06T05:39:18Z', ISO 8601 extended format
to_char(-0.1, '99.99')
'
to_char(-0.1, 'FM9.99')
'-.1'
to_char(-0.1, 'FM90.99')
'-0.1'
to_char(0.1, '0.9')
' 0.1'
to_char(12, '9990999.9')
'
to_char(12, 'FM9990999.9')
'0012.'
to_char(485, '999')
' 485'
to_char(-485, '999')
'-485'
to_char(485, '9 9 9')
' 4 8 5'
to_char(1485, '9,999')
' 1,485'
to_char(1485, '9G999')
' 1 485'
to_char(148.5, '999.999')
' 148.500'
to_char(148.5, 'FM999.999')
'148.5'
to_char(148.5, 'FM999.990')
'148.500'
to_char(148.5, '999D999')
' 148,500'
to_char(3148.5, '9G999D999')
' 3 148,500'
to_char(-485, '999S')
'485-'
to_char(-485, '999MI')
'485-'
to_char(485, '999MI')
'485 '
to_char(485, 'FM999MI')
'485'
to_char(485, 'PL999')
'+485'
to_char(485, 'SG999')
'+485'
276
, 06
-.10'
0012.0'
05:39:18'
05:39:18'
Functions and Operators
Expression
Result
to_char(-485, 'SG999')
'-485'
to_char(-485, '9SG99')
'4-85'
to_char(-485, '999PR')
'<485>'
to_char(485, 'L999')
'DM 485'
to_char(485, 'RN')
'
to_char(485, 'FMRN')
'CDLXXXV'
to_char(5.2, 'FMRN')
'V'
to_char(482, '999th')
' 482nd'
to_char(485, '"Good number:"999')
'Good number: 485'
to_char(485.8,
'"Pre:"999" Post:" .999')
'Pre: 485 Post: .800'
to_char(12, '99V999')
' 12000'
to_char(12.4, '99V999')
' 12400'
to_char(12.45, '99V9')
' 125'
to_char(0.0004859, '9.99EEEE')
' 4.86e-04'
CDLXXXV'
9.9. Date/Time Functions and Operators
Table 9.33 shows the available functions for date/time value processing, with details appearing in
the following subsections. Table 9.32 illustrates the behaviors of the basic arithmetic operators (+,
*, etc.). For formatting functions, refer to Section 9.8. You should be familiar with the background
information on date/time data types from Section 8.5.
In addition, the usual comparison operators shown in Table 9.1 are available for the date/time types.
Dates and timestamps (with or without time zone) are all comparable, while times (with or without
time zone) and intervals can only be compared to other values of the same data type. When comparing
a timestamp without time zone to a timestamp with time zone, the former value is assumed to be
given in the time zone specified by the TimeZone configuration parameter, and is rotated to UTC for
comparison to the latter value (which is already in UTC internally). Similarly, a date value is assumed
to represent midnight in the TimeZone zone when comparing it to a timestamp.
All the functions and operators described below that take time or timestamp inputs actually come
in two variants: one that takes time with time zone or timestamp with time zone, and
one that takes time without time zone or timestamp without time zone. For brevity,
these variants are not shown separately. Also, the + and * operators come in commutative pairs (for
example both date + integer and integer + date); we show only one of each such pair.
Table 9.32. Date/Time Operators
Operator
Description
Example(s)
date + integer → date
Add a number of days to a date
date '2001-09-28' + 7 → 2001-10-05
date + interval → timestamp
Add an interval to a date
date '2001-09-28' + interval '1 hour' → 2001-09-28 01:00:00
277
Functions and Operators
Operator
Description
Example(s)
date + time → timestamp
Add a time-of-day to a date
date '2001-09-28' + time '03:00' → 2001-09-28 03:00:00
interval + interval → interval
Add intervals
interval '1 day' + interval '1 hour' → 1 day 01:00:00
timestamp + interval → timestamp
Add an interval to a timestamp
timestamp '2001-09-28 01:00' + interval '23 hours' →
2001-09-29 00:00:00
time + interval → time
Add an interval to a time
time '01:00' + interval '3 hours' → 04:00:00
- interval → interval
Negate an interval
- interval '23 hours' → -23:00:00
date - date → integer
Subtract dates, producing the number of days elapsed
date '2001-10-01' - date '2001-09-28' → 3
date - integer → date
Subtract a number of days from a date
date '2001-10-01' - 7 → 2001-09-24
date - interval → timestamp
Subtract an interval from a date
date '2001-09-28' - interval '1 hour' → 2001-09-27 23:00:00
time - time → interval
Subtract times
time '05:00' - time '03:00' → 02:00:00
time - interval → time
Subtract an interval from a time
time '05:00' - interval '2 hours' → 03:00:00
timestamp - interval → timestamp
Subtract an interval from a timestamp
timestamp '2001-09-28 23:00' - interval '23 hours' →
2001-09-28 00:00:00
interval - interval → interval
Subtract intervals
interval '1 day' - interval '1 hour' → 1 day -01:00:00
timestamp - timestamp → interval
Subtract timestamps (converting 24-hour intervals into days, similarly to justify_hours())
278
Functions and Operators
Operator
Description
Example(s)
timestamp '2001-09-29 03:00' - timestamp '2001-07-27 12:00'
→ 63 days 15:00:00
interval * double precision → interval
Multiply an interval by a scalar
interval '1 second' * 900 → 00:15:00
interval '1 day' * 21 → 21 days
interval '1 hour' * 3.5 → 03:30:00
interval / double precision → interval
Divide an interval by a scalar
interval '1 hour' / 1.5 → 00:40:00
Table 9.33. Date/Time Functions
Function
Description
Example(s)
age ( timestamp, timestamp ) → interval
Subtract arguments, producing a “symbolic” result that uses years and months, rather
than just days
age(timestamp '2001-04-10', timestamp '1957-06-13') → 43
years 9 mons 27 days
age ( timestamp ) → interval
Subtract argument from current_date (at midnight)
age(timestamp '1957-06-13') → 62 years 6 mons 10 days
clock_timestamp ( ) → timestamp with time zone
Current date and time (changes during statement execution); see Section 9.9.5
clock_timestamp() → 2019-12-23 14:39:53.662522-05
current_date → date
Current date; see Section 9.9.5
current_date → 2019-12-23
current_time → time with time zone
Current time of day; see Section 9.9.5
current_time → 14:39:53.662522-05
current_time ( integer ) → time with time zone
Current time of day, with limited precision; see Section 9.9.5
current_time(2) → 14:39:53.66-05
current_timestamp → timestamp with time zone
Current date and time (start of current transaction); see Section 9.9.5
current_timestamp → 2019-12-23 14:39:53.662522-05
current_timestamp ( integer ) → timestamp with time zone
Current date and time (start of current transaction), with limited precision; see Section 9.9.5
current_timestamp(0) → 2019-12-23 14:39:53-05
279
Functions and Operators
Function
Description
Example(s)
date_add ( timestamp with time zone, interval [, text ] ) → timestamp
with time zone
Add an interval to a timestamp with time zone, computing times of day
and daylight-savings adjustments according to the time zone named by the third argument, or the current TimeZone setting if that is omitted. The form with two arguments is
equivalent to the timestamp with time zone + interval operator.
date_add('2021-10-31 00:00:00+02'::timestamptz, '1
day'::interval, 'Europe/Warsaw') → 2021-10-31 23:00:00+00
date_bin ( interval, timestamp, timestamp ) → timestamp
Bin input into specified interval aligned with specified origin; see Section 9.9.3
date_bin('15 minutes', timestamp '2001-02-16 20:38:40',
timestamp '2001-02-16 20:05:00') → 2001-02-16 20:35:00
date_part ( text, timestamp ) → double precision
Get timestamp subfield (equivalent to extract); see Section 9.9.1
date_part('hour', timestamp '2001-02-16 20:38:40') → 20
date_part ( text, interval ) → double precision
Get interval subfield (equivalent to extract); see Section 9.9.1
date_part('month', interval '2 years 3 months') → 3
date_subtract ( timestamp with time zone, interval [, text ] ) → timestamp with time zone
Subtract an interval from a timestamp with time zone, computing times of
day and daylight-savings adjustments according to the time zone named by the third argument, or the current TimeZone setting if that is omitted. The form with two arguments
is equivalent to the timestamp with time zone - interval operator.
date_subtract('2021-11-01 00:00:00+01'::timestamptz, '1
day'::interval, 'Europe/Warsaw') → 2021-10-30 22:00:00+00
date_trunc ( text, timestamp ) → timestamp
Truncate to specified precision; see Section 9.9.2
date_trunc('hour', timestamp '2001-02-16 20:38:40') →
2001-02-16 20:00:00
date_trunc ( text, timestamp with time zone, text ) → timestamp with
time zone
Truncate to specified precision in the specified time zone; see Section 9.9.2
date_trunc('day', timestamptz '2001-02-16 20:38:40+00',
'Australia/Sydney') → 2001-02-16 13:00:00+00
date_trunc ( text, interval ) → interval
Truncate to specified precision; see Section 9.9.2
date_trunc('hour', interval '2 days 3 hours 40 minutes') → 2
days 03:00:00
extract ( field from timestamp ) → numeric
Get timestamp subfield; see Section 9.9.1
extract(hour from timestamp '2001-02-16 20:38:40') → 20
extract ( field from interval ) → numeric
Get interval subfield; see Section 9.9.1
280
Functions and Operators
Function
Description
Example(s)
extract(month from interval '2 years 3 months') → 3
isfinite ( date ) → boolean
Test for finite date (not +/-infinity)
isfinite(date '2001-02-16') → true
isfinite ( timestamp ) → boolean
Test for finite timestamp (not +/-infinity)
isfinite(timestamp 'infinity') → false
isfinite ( interval ) → boolean
Test for finite interval (not +/-infinity)
isfinite(interval '4 hours') → true
justify_days ( interval ) → interval
Adjust interval, converting 30-day time periods to months
justify_days(interval '1 year 65 days') → 1 year 2 mons 5
days
justify_hours ( interval ) → interval
Adjust interval, converting 24-hour time periods to days
justify_hours(interval '50 hours 10 minutes') → 2 days
02:10:00
justify_interval ( interval ) → interval
Adjust interval using justify_days and justify_hours, with additional sign adjustments
justify_interval(interval '1 mon -1 hour') → 29 days
23:00:00
localtime → time
Current time of day; see Section 9.9.5
localtime → 14:39:53.662522
localtime ( integer ) → time
Current time of day, with limited precision; see Section 9.9.5
localtime(0) → 14:39:53
localtimestamp → timestamp
Current date and time (start of current transaction); see Section 9.9.5
localtimestamp → 2019-12-23 14:39:53.662522
localtimestamp ( integer ) → timestamp
Current date and time (start of current transaction), with limited precision; see Section 9.9.5
localtimestamp(2) → 2019-12-23 14:39:53.66
make_date ( year int, month int, day int ) → date
Create date from year, month and day fields (negative years signify BC)
make_date(2013, 7, 15) → 2013-07-15
make_interval ( [ years int [, months int [, weeks int [, days int [, hours int
[, mins int [, secs double precision ]]]]]]] ) → interval
281
Functions and Operators
Function
Description
Example(s)
Create interval from years, months, weeks, days, hours, minutes and seconds fields, each
of which can default to zero
make_interval(days => 10) → 10 days
make_time ( hour int, min int, sec double precision ) → time
Create time from hour, minute and seconds fields
make_time(8, 15, 23.5) → 08:15:23.5
make_timestamp ( year int, month int, day int, hour int, min int, sec double
precision ) → timestamp
Create timestamp from year, month, day, hour, minute and seconds fields (negative years
signify BC)
make_timestamp(2013, 7, 15, 8, 15, 23.5) → 2013-07-15
08:15:23.5
make_timestamptz ( year int, month int, day int, hour int, min int, sec double precision [, timezone text ] ) → timestamp with time zone
Create timestamp with time zone from year, month, day, hour, minute and seconds fields
(negative years signify BC). If timezone is not specified, the current time zone is used;
the examples assume the session time zone is Europe/London
make_timestamptz(2013, 7, 15, 8, 15, 23.5) → 2013-07-15
08:15:23.5+01
make_timestamptz(2013, 7, 15, 8, 15, 23.5, 'America/New_York') → 2013-07-15 13:15:23.5+01
now ( ) → timestamp with time zone
Current date and time (start of current transaction); see Section 9.9.5
now() → 2019-12-23 14:39:53.662522-05
statement_timestamp ( ) → timestamp with time zone
Current date and time (start of current statement); see Section 9.9.5
statement_timestamp() → 2019-12-23 14:39:53.662522-05
timeofday ( ) → text
Current date and time (like clock_timestamp, but as a text string); see Section 9.9.5
timeofday() → Mon Dec 23 14:39:53.662522 2019 EST
transaction_timestamp ( ) → timestamp with time zone
Current date and time (start of current transaction); see Section 9.9.5
transaction_timestamp() → 2019-12-23 14:39:53.662522-05
to_timestamp ( double precision ) → timestamp with time zone
Convert Unix epoch (seconds since 1970-01-01 00:00:00+00) to timestamp with time
zone
to_timestamp(1284352323) → 2010-09-13 04:32:03+00
In addition to these functions, the SQL OVERLAPS operator is supported:
(start1, end1) OVERLAPS (start2, end2)
(start1, length1) OVERLAPS (start2, length2)
282
Functions and Operators
This expression yields true when two time periods (defined by their endpoints) overlap, false when
they do not overlap. The endpoints can be specified as pairs of dates, times, or time stamps; or as a
date, time, or time stamp followed by an interval. When a pair of values is provided, either the start or
the end can be written first; OVERLAPS automatically takes the earlier value of the pair as the start.
Each time period is considered to represent the half-open interval start <= time < end, unless
start and end are equal in which case it represents that single time instant. This means for instance
that two time periods with only an endpoint in common do not overlap.
SELECT (DATE '2001-02-16', DATE '2001-12-21') OVERLAPS
(DATE '2001-10-30', DATE '2002-10-30');
Result: true
SELECT (DATE '2001-02-16', INTERVAL '100 days') OVERLAPS
(DATE '2001-10-30', DATE '2002-10-30');
Result: false
SELECT (DATE '2001-10-29', DATE '2001-10-30') OVERLAPS
(DATE '2001-10-30', DATE '2001-10-31');
Result: false
SELECT (DATE '2001-10-30', DATE '2001-10-30') OVERLAPS
(DATE '2001-10-30', DATE '2001-10-31');
Result: true
When adding an interval value to (or subtracting an interval value from) a timestamp or
timestamp with time zone value, the months, days, and microseconds fields of the interval value are handled in turn. First, a nonzero months field advances or decrements the date of the
timestamp by the indicated number of months, keeping the day of month the same unless it would be
past the end of the new month, in which case the last day of that month is used. (For example, March
31 plus 1 month becomes April 30, but March 31 plus 2 months becomes May 31.) Then the days field
advances or decrements the date of the timestamp by the indicated number of days. In both these steps
the local time of day is kept the same. Finally, if there is a nonzero microseconds field, it is added
or subtracted literally. When doing arithmetic on a timestamp with time zone value in a
time zone that recognizes DST, this means that adding or subtracting (say) interval '1 day'
does not necessarily have the same result as adding or subtracting interval '24 hours'. For
example, with the session time zone set to America/Denver:
SELECT timestamp with time zone '2005-04-02 12:00:00-07' + interval
'1 day';
Result: 2005-04-03 12:00:00-06
SELECT timestamp with time zone '2005-04-02 12:00:00-07' + interval
'24 hours';
Result: 2005-04-03 13:00:00-06
This happens because an hour was skipped due to a change in daylight saving time at 2005-04-03
02:00:00 in time zone America/Denver.
Note there can be ambiguity in the months field returned by age because different months have
different numbers of days. PostgreSQL's approach uses the month from the earlier of the two dates
when calculating partial months. For example, age('2004-06-01', '2004-04-30') uses
April to yield 1 mon 1 day, while using May would yield 1 mon 2 days because May has
31 days, while April has only 30.
Subtraction of dates and timestamps can also be complex. One conceptually simple way to perform
subtraction is to convert each value to a number of seconds using EXTRACT(EPOCH FROM ...),
then subtract the results; this produces the number of seconds between the two values. This will adjust
for the number of days in each month, timezone changes, and daylight saving time adjustments. Subtraction of date or timestamp values with the “-” operator returns the number of days (24-hours) and
hours/minutes/seconds between the values, making the same adjustments. The age function returns
years, months, days, and hours/minutes/seconds, performing field-by-field subtraction and then ad-
283
Functions and Operators
justing for negative field values. The following queries illustrate the differences in these approaches.
The sample results were produced with timezone = 'US/Eastern'; there is a daylight saving
time change between the two dates used:
SELECT EXTRACT(EPOCH FROM timestamptz '2013-07-01 12:00:00') EXTRACT(EPOCH FROM timestamptz '2013-03-01 12:00:00');
Result: 10537200.000000
SELECT (EXTRACT(EPOCH FROM timestamptz '2013-07-01 12:00:00') EXTRACT(EPOCH FROM timestamptz '2013-03-01 12:00:00'))
/ 60 / 60 / 24;
Result: 121.9583333333333333
SELECT timestamptz '2013-07-01 12:00:00' - timestamptz '2013-03-01
12:00:00';
Result: 121 days 23:00:00
SELECT age(timestamptz '2013-07-01 12:00:00', timestamptz
'2013-03-01 12:00:00');
Result: 4 mons
9.9.1. EXTRACT, date_part
EXTRACT(field FROM source)
The extract function retrieves subfields such as year or hour from date/time values. source must
be a value expression of type timestamp, date, time, or interval. (Timestamps and times can
be with or without time zone.) field is an identifier or string that selects what field to extract from
the source value. Not all fields are valid for every input data type; for example, fields smaller than a
day cannot be extracted from a date, while fields of a day or more cannot be extracted from a time.
The extract function returns values of type numeric.
The following are valid field names:
century
The century; for interval values, the year field divided by 100
SELECT EXTRACT(CENTURY FROM TIMESTAMP '2000-12-16 12:21:13');
Result: 20
SELECT EXTRACT(CENTURY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 21
SELECT EXTRACT(CENTURY FROM DATE '0001-01-01 AD');
Result: 1
SELECT EXTRACT(CENTURY FROM DATE '0001-12-31 BC');
Result: -1
SELECT EXTRACT(CENTURY FROM INTERVAL '2001 years');
Result: 20
day
The day of the month (1–31); for interval values, the number of days
SELECT EXTRACT(DAY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 16
SELECT EXTRACT(DAY FROM INTERVAL '40 days 1 minute');
Result: 40
284
Functions and Operators
decade
The year field divided by 10
SELECT EXTRACT(DECADE FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 200
dow
The day of the week as Sunday (0) to Saturday (6)
SELECT EXTRACT(DOW FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 5
Note that extract's day of the week numbering differs from that of the to_char(..., 'D')
function.
doy
The day of the year (1–365/366)
SELECT EXTRACT(DOY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 47
epoch
For timestamp with time zone values, the number of seconds since 1970-01-01 00:00:00
UTC (negative for timestamps before that); for date and timestamp values, the nominal number of seconds since 1970-01-01 00:00:00, without regard to timezone or daylight-savings rules;
for interval values, the total number of seconds in the interval
SELECT EXTRACT(EPOCH FROM TIMESTAMP WITH TIME ZONE '2001-02-16
20:38:40.12-08');
Result: 982384720.120000
SELECT EXTRACT(EPOCH FROM TIMESTAMP '2001-02-16 20:38:40.12');
Result: 982355920.120000
SELECT EXTRACT(EPOCH FROM INTERVAL '5 days 3 hours');
Result: 442800.000000
You can convert an epoch value back to a timestamp with time zone with to_timestamp:
SELECT to_timestamp(982384720.12);
Result: 2001-02-17 04:38:40.12+00
Beware that applying to_timestamp to an epoch extracted from a date or timestamp value
could produce a misleading result: the result will effectively assume that the original value had
been given in UTC, which might not be the case.
hour
The hour field (0–23 in timestamps, unrestricted in intervals)
SELECT EXTRACT(HOUR FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 20
285
Functions and Operators
isodow
The day of the week as Monday (1) to Sunday (7)
SELECT EXTRACT(ISODOW FROM TIMESTAMP '2001-02-18 20:38:40');
Result: 7
This is identical to dow except for Sunday. This matches the ISO 8601 day of the week numbering.
isoyear
The ISO 8601 week-numbering year that the date falls in
SELECT EXTRACT(ISOYEAR FROM DATE '2006-01-01');
Result: 2005
SELECT EXTRACT(ISOYEAR FROM DATE '2006-01-02');
Result: 2006
Each ISO 8601 week-numbering year begins with the Monday of the week containing the 4th of
January, so in early January or late December the ISO year may be different from the Gregorian
year. See the week field for more information.
julian
The Julian Date corresponding to the date or timestamp. Timestamps that are not local midnight
result in a fractional value. See Section B.7 for more information.
SELECT EXTRACT(JULIAN FROM DATE '2006-01-01');
Result: 2453737
SELECT EXTRACT(JULIAN FROM TIMESTAMP '2006-01-01 12:00');
Result: 2453737.50000000000000000000
microseconds
The seconds field, including fractional parts, multiplied by 1 000 000; note that this includes full
seconds
SELECT EXTRACT(MICROSECONDS FROM TIME '17:12:28.5');
Result: 28500000
millennium
The millennium; for interval values, the year field divided by 1000
SELECT EXTRACT(MILLENNIUM FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 3
SELECT EXTRACT(MILLENNIUM FROM INTERVAL '2001 years');
Result: 2
Years in the 1900s are in the second millennium. The third millennium started January 1, 2001.
milliseconds
The seconds field, including fractional parts, multiplied by 1000. Note that this includes full seconds.
286
Functions and Operators
SELECT EXTRACT(MILLISECONDS FROM TIME '17:12:28.5');
Result: 28500.000
minute
The minutes field (0–59)
SELECT EXTRACT(MINUTE FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 38
month
The number of the month within the year (1–12); for interval values, the number of months
modulo 12 (0–11)
SELECT EXTRACT(MONTH FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2
SELECT EXTRACT(MONTH FROM INTERVAL '2 years 3 months');
Result: 3
SELECT EXTRACT(MONTH FROM INTERVAL '2 years 13 months');
Result: 1
quarter
The quarter of the year (1–4) that the date is in
SELECT EXTRACT(QUARTER FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 1
second
The seconds field, including any fractional seconds
SELECT EXTRACT(SECOND FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 40.000000
SELECT EXTRACT(SECOND FROM TIME '17:12:28.5');
Result: 28.500000
timezone
The time zone offset from UTC, measured in seconds. Positive values correspond to time zones
east of UTC, negative values to zones west of UTC. (Technically, PostgreSQL does not use UTC
because leap seconds are not handled.)
timezone_hour
The hour component of the time zone offset
timezone_minute
The minute component of the time zone offset
week
The number of the ISO 8601 week-numbering week of the year. By definition, ISO weeks start
on Mondays and the first week of a year contains January 4 of that year. In other words, the first
Thursday of a year is in week 1 of that year.
287
Functions and Operators
In the ISO week-numbering system, it is possible for early-January dates to be part of the 52nd
or 53rd week of the previous year, and for late-December dates to be part of the first week of the
next year. For example, 2005-01-01 is part of the 53rd week of year 2004, and 2006-01-01
is part of the 52nd week of year 2005, while 2012-12-31 is part of the first week of 2013. It's
recommended to use the isoyear field together with week to get consistent results.
SELECT EXTRACT(WEEK FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 7
year
The year field. Keep in mind there is no 0 AD, so subtracting BC years from AD years should
be done with care.
SELECT EXTRACT(YEAR FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2001
When processing an interval value, the extract function produces field values that match the
interpretation used by the interval output function. This can produce surprising results if one starts
with a non-normalized interval representation, for example:
SELECT INTERVAL '80 minutes';
Result: 01:20:00
SELECT EXTRACT(MINUTES FROM INTERVAL '80 minutes');
Result: 20
Note
When the input value is +/-Infinity, extract returns +/-Infinity for monotonically-increasing fields (epoch, julian, year, isoyear, decade, century, and millennium for
timestamp inputs; epoch, hour, day, year, decade, century, and millennium
for interval inputs). For other fields, NULL is returned. PostgreSQL versions before 9.6
returned zero for all cases of infinite input.
The extract function is primarily intended for computational processing. For formatting date/time
values for display, see Section 9.8.
The date_part function is modeled on the traditional Ingres equivalent to the SQL-standard function extract:
date_part('field', source)
Note that here the field parameter needs to be a string value, not a name. The valid field names for
date_part are the same as for extract. For historical reasons, the date_part function returns
values of type double precision. This can result in a loss of precision in certain uses. Using
extract is recommended instead.
SELECT date_part('day', TIMESTAMP '2001-02-16 20:38:40');
Result: 16
SELECT date_part('hour', INTERVAL '4 hours 3 minutes');
Result: 4
9.9.2. date_trunc
288
Functions and Operators
The function date_trunc is conceptually similar to the trunc function for numbers.
date_trunc(field, source [, time_zone ])
source is a value expression of type timestamp, timestamp with time zone, or interval. (Values of type date and time are cast automatically to timestamp or interval, respectively.) field selects to which precision to truncate the input value. The return value is likewise of
type timestamp, timestamp with time zone, or interval, and it has all fields that are
less significant than the selected one set to zero (or one, for day and month).
Valid values for field are:
microseconds
milliseconds
second
minute
hour
day
week
month
quarter
year
decade
century
millennium
When the input value is of type timestamp with time zone, the truncation is performed with
respect to a particular time zone; for example, truncation to day produces a value that is midnight in
that zone. By default, truncation is done with respect to the current TimeZone setting, but the optional
time_zone argument can be provided to specify a different time zone. The time zone name can be
specified in any of the ways described in Section 8.5.3.
A time zone cannot be specified when processing timestamp without time zone or interval inputs. These are always taken at face value.
Examples (assuming the local time zone is America/New_York):
SELECT date_trunc('hour', TIMESTAMP '2001-02-16 20:38:40');
Result: 2001-02-16 20:00:00
SELECT date_trunc('year', TIMESTAMP '2001-02-16 20:38:40');
Result: 2001-01-01 00:00:00
SELECT date_trunc('day', TIMESTAMP WITH TIME ZONE '2001-02-16
20:38:40+00');
Result: 2001-02-16 00:00:00-05
SELECT date_trunc('day', TIMESTAMP WITH TIME ZONE '2001-02-16
20:38:40+00', 'Australia/Sydney');
Result: 2001-02-16 08:00:00-05
SELECT date_trunc('hour', INTERVAL '3 days 02:47:33');
Result: 3 days 02:00:00
9.9.3. date_bin
The function date_bin “bins” the input timestamp into the specified interval (the stride) aligned
with a specified origin.
289
Functions and Operators
date_bin(stride, source, origin)
source is a value expression of type timestamp or timestamp with time zone. (Values of
type date are cast automatically to timestamp.) stride is a value expression of type interval.
The return value is likewise of type timestamp or timestamp with time zone, and it marks
the beginning of the bin into which the source is placed.
Examples:
SELECT date_bin('15 minutes', TIMESTAMP '2020-02-11 15:44:17',
TIMESTAMP '2001-01-01');
Result: 2020-02-11 15:30:00
SELECT date_bin('15 minutes', TIMESTAMP '2020-02-11 15:44:17',
TIMESTAMP '2001-01-01 00:02:30');
Result: 2020-02-11 15:32:30
In the case of full units (1 minute, 1 hour, etc.), it gives the same result as the analogous date_trunc
call, but the difference is that date_bin can truncate to an arbitrary interval.
The stride interval must be greater than zero and cannot contain units of month or larger.
9.9.4. AT TIME ZONE and AT LOCAL
The AT TIME ZONE operator converts time stamp without time zone to/from time stamp with time
zone, and time with time zone values to different time zones. Table 9.34 shows its variants.
Table 9.34. AT TIME ZONE and AT LOCAL Variants
Operator
Description
Example(s)
timestamp without time zone AT TIME ZONE zone → timestamp with time
zone
Converts given time stamp without time zone to time stamp with time zone, assuming the
given value is in the named time zone.
timestamp '2001-02-16 20:38:40' at time zone 'America/Denver' → 2001-02-17 03:38:40+00
timestamp without time zone AT LOCAL → timestamp with time zone
Converts given time stamp without time zone to time stamp with the session's TimeZone value as time zone.
timestamp '2001-02-16 20:38:40' at local → 2001-02-17
03:38:40+00
timestamp with time zone AT TIME ZONE zone → timestamp without time
zone
Converts given time stamp with time zone to time stamp without time zone, as the time
would appear in that zone.
timestamp with time zone '2001-02-16 20:38:40-05' at time
zone 'America/Denver' → 2001-02-16 18:38:40
timestamp with time zone AT LOCAL → timestamp without time zone
Converts given time stamp with time zone to time stamp without time zone, as the time
would appear with the session's TimeZone value as time zone.
timestamp with time zone '2001-02-16 20:38:40-05' at local
→ 2001-02-16 18:38:40
290
Functions and Operators
Operator
Description
Example(s)
time with time zone AT TIME ZONE zone → time with time zone
Converts given time with time zone to a new time zone. Since no date is supplied, this
uses the currently active UTC offset for the named destination zone.
time with time zone '05:34:17-05' at time zone 'UTC' →
10:34:17+00
time with time zone AT LOCAL → time with time zone
Converts given time with time zone to a new time zone. Since no date is supplied, this
uses the currently active UTC offset for the session's TimeZone value.
Assuming the session's TimeZone is set to UTC:
time with time zone '05:34:17-05' at local → 10:34:17+00
In these expressions, the desired time zone zone can be specified either as a text value (e.g., 'America/Los_Angeles') or as an interval (e.g., INTERVAL '-08:00'). In the text case, a time
zone name can be specified in any of the ways described in Section 8.5.3. The interval case is only
useful for zones that have fixed offsets from UTC, so it is not very common in practice.
The syntax AT LOCAL may be used as shorthand for AT TIME ZONE local, where local is
the session's TimeZone value.
Examples (assuming the current TimeZone setting is America/Los_Angeles):
SELECT TIMESTAMP '2001-02-16 20:38:40' AT TIME ZONE 'America/
Denver';
Result: 2001-02-16 19:38:40-08
SELECT TIMESTAMP WITH TIME ZONE '2001-02-16 20:38:40-05' AT TIME
ZONE 'America/Denver';
Result: 2001-02-16 18:38:40
SELECT TIMESTAMP '2001-02-16 20:38:40' AT TIME ZONE 'Asia/Tokyo' AT
TIME ZONE 'America/Chicago';
Result: 2001-02-16 05:38:40
SELECT TIMESTAMP WITH TIME ZONE '2001-02-16 20:38:40-05' AT LOCAL;
Result: 2001-02-16 17:38:40
SELECT TIME WITH TIME ZONE '20:38:40-05' AT LOCAL;
Result: 17:38:40
The first example adds a time zone to a value that lacks it, and displays the value using the current
TimeZone setting. The second example shifts the time stamp with time zone value to the specified
time zone, and returns the value without a time zone. This allows storage and display of values different
from the current TimeZone setting. The third example converts Tokyo time to Chicago time. The
fourth example shifts the time stamp with time zone value to the time zone currently specified by the
TimeZone setting and returns the value without a time zone.
The fifth example is a cautionary tale. Due to the fact that there is no date associated with the input
value, the conversion is made using the current date of the session. Therefore, this static example may
show a wrong result depending on the time of the year it is viewed because 'America/Los_Angeles' observes Daylight Savings Time.
The function timezone(zone, timestamp) is equivalent to the SQL-conforming construct
timestamp AT TIME ZONE zone.
The function timezone(zone, time) is equivalent to the SQL-conforming construct time AT
TIME ZONE zone.
291
Functions and Operators
The function timezone(timestamp) is equivalent to the SQL-conforming construct timestamp AT LOCAL.
The function timezone(time) is equivalent to the SQL-conforming construct time AT LOCAL.
9.9.5. Current Date/Time
PostgreSQL provides a number of functions that return values related to the current date and time.
These SQL-standard functions all return values based on the start time of the current transaction:
CURRENT_DATE
CURRENT_TIME
CURRENT_TIMESTAMP
CURRENT_TIME(precision)
CURRENT_TIMESTAMP(precision)
LOCALTIME
LOCALTIMESTAMP
LOCALTIME(precision)
LOCALTIMESTAMP(precision)
CURRENT_TIME and CURRENT_TIMESTAMP deliver values with time zone; LOCALTIME and LOCALTIMESTAMP deliver values without time zone.
CURRENT_TIME, CURRENT_TIMESTAMP, LOCALTIME, and LOCALTIMESTAMP can optionally
take a precision parameter, which causes the result to be rounded to that many fractional digits in the
seconds field. Without a precision parameter, the result is given to the full available precision.
Some examples:
SELECT CURRENT_TIME;
Result: 14:39:53.662522-05
SELECT CURRENT_DATE;
Result: 2019-12-23
SELECT CURRENT_TIMESTAMP;
Result: 2019-12-23 14:39:53.662522-05
SELECT CURRENT_TIMESTAMP(2);
Result: 2019-12-23 14:39:53.66-05
SELECT LOCALTIMESTAMP;
Result: 2019-12-23 14:39:53.662522
Since these functions return the start time of the current transaction, their values do not change during
the transaction. This is considered a feature: the intent is to allow a single transaction to have a consistent notion of the “current” time, so that multiple modifications within the same transaction bear
the same time stamp.
Note
Other database systems might advance these values more frequently.
PostgreSQL also provides functions that return the start time of the current statement, as well as the
actual current time at the instant the function is called. The complete list of non-SQL-standard time
functions is:
transaction_timestamp()
292
Functions and Operators
statement_timestamp()
clock_timestamp()
timeofday()
now()
transaction_timestamp() is equivalent to CURRENT_TIMESTAMP, but is named to clearly reflect what it returns. statement_timestamp() returns the start time of the current statement (more specifically, the time of receipt of the latest command message from the client). statement_timestamp() and transaction_timestamp() return the same value during the first
command of a transaction, but might differ during subsequent commands. clock_timestamp()
returns the actual current time, and therefore its value changes even within a single SQL command.
timeofday() is a historical PostgreSQL function. Like clock_timestamp(), it returns the actual current time, but as a formatted text string rather than a timestamp with time zone
value. now() is a traditional PostgreSQL equivalent to transaction_timestamp().
All the date/time data types also accept the special literal value now to specify the current date and time
(again, interpreted as the transaction start time). Thus, the following three all return the same result:
SELECT CURRENT_TIMESTAMP;
SELECT now();
SELECT TIMESTAMP 'now'; -- but see tip below
Tip
Do not use the third form when specifying a value to be evaluated later, for example in a
DEFAULT clause for a table column. The system will convert now to a timestamp as soon
as the constant is parsed, so that when the default value is needed, the time of the table creation
would be used! The first two forms will not be evaluated until the default value is used, because
they are function calls. Thus they will give the desired behavior of defaulting to the time of
row insertion. (See also Section 8.5.1.4.)
9.9.6. Delaying Execution
The following functions are available to delay execution of the server process:
pg_sleep ( double precision )
pg_sleep_for ( interval )
pg_sleep_until ( timestamp with time zone )
pg_sleep makes the current session's process sleep until the given number of seconds have elapsed.
Fractional-second delays can be specified. pg_sleep_for is a convenience function to allow the
sleep time to be specified as an interval. pg_sleep_until is a convenience function for when
a specific wake-up time is desired. For example:
SELECT pg_sleep(1.5);
SELECT pg_sleep_for('5 minutes');
SELECT pg_sleep_until('tomorrow 03:00');
Note
The effective resolution of the sleep interval is platform-specific; 0.01 seconds is a common
value. The sleep delay will be at least as long as specified. It might be longer depending on
293
Functions and Operators
factors such as server load. In particular, pg_sleep_until is not guaranteed to wake up
exactly at the specified time, but it will not wake up any earlier.
Warning
Make sure that your session does not hold more locks than necessary when calling pg_sleep
or its variants. Otherwise other sessions might have to wait for your sleeping process, slowing
down the entire system.
9.10. Enum Support Functions
For enum types (described in Section 8.7), there are several functions that allow cleaner programming
without hard-coding particular values of an enum type. These are listed in Table 9.35. The examples
assume an enum type created as:
CREATE TYPE rainbow AS ENUM ('red', 'orange', 'yellow', 'green',
'blue', 'purple');
Table 9.35. Enum Support Functions
Function
Description
Example(s)
enum_first ( anyenum ) → anyenum
Returns the first value of the input enum type.
enum_first(null::rainbow) → red
enum_last ( anyenum ) → anyenum
Returns the last value of the input enum type.
enum_last(null::rainbow) → purple
enum_range ( anyenum ) → anyarray
Returns all values of the input enum type in an ordered array.
enum_range(null::rainbow) → {red,orange,yellow,
green,blue,purple}
enum_range ( anyenum, anyenum ) → anyarray
Returns the range between the two given enum values, as an ordered array. The values
must be from the same enum type. If the first parameter is null, the result will start with
the first value of the enum type. If the second parameter is null, the result will end with
the last value of the enum type.
enum_range('orange'::rainbow, 'green'::rainbow) → {orange,yellow,green}
enum_range(NULL, 'green'::rainbow) → {red,orange,yellow,green}
enum_range('orange'::rainbow, NULL) → {orange,yellow,green,
blue,purple}
Notice that except for the two-argument form of enum_range, these functions disregard the specific
value passed to them; they care only about its declared data type. Either null or a specific value of the
type can be passed, with the same result. It is more common to apply these functions to a table column
or function argument than to a hardwired type name as used in the examples.
294
Functions and Operators
9.11. Geometric Functions and Operators
The geometric types point, box, lseg, line, path, polygon, and circle have a large set of
native support functions and operators, shown in Table 9.36, Table 9.37, and Table 9.38.
Table 9.36. Geometric Operators
Operator
Description
Example(s)
geometric_type + point → geometric_type
Adds the coordinates of the second point to those of each point of the first argument,
thus performing translation. Available for point, box, path, circle.
box '(1,1),(0,0)' + point '(2,0)' → (3,1),(2,0)
path + path → path
Concatenates two open paths (returns NULL if either path is closed).
path '[(0,0),(1,1)]' + path '[(2,2),(3,3),(4,4)]' → [(0,0),
(1,1),(2,2),(3,3),(4,4)]
geometric_type - point → geometric_type
Subtracts the coordinates of the second point from those of each point of the first argument, thus performing translation. Available for point, box, path, circle.
box '(1,1),(0,0)' - point '(2,0)' → (-1,1),(-2,0)
geometric_type * point → geometric_type
Multiplies each point of the first argument by the second point (treating a point as being a complex number represented by real and imaginary parts, and performing standard
complex multiplication). If one interprets the second point as a vector, this is equivalent to scaling the object's size and distance from the origin by the length of the vector,
and rotating it counterclockwise around the origin by the vector's angle from the x axis.
Available for point, box,a path, circle.
path '((0,0),(1,0),(1,1))' * point '(3.0,0)' → ((0,0),(3,0),
(3,3))
path '((0,0),(1,0),(1,1))' * point(cosd(45), sind(45))
→ ((0,0),(0.7071067811865475,0.7071067811865475),
(0,1.414213562373095))
geometric_type / point → geometric_type
Divides each point of the first argument by the second point (treating a point as being a
complex number represented by real and imaginary parts, and performing standard complex division). If one interprets the second point as a vector, this is equivalent to scaling the object's size and distance from the origin down by the length of the vector, and
rotating it clockwise around the origin by the vector's angle from the x axis. Available
for point, box,a path, circle.
path '((0,0),(1,0),(1,1))' / point '(2.0,0)' → ((0,0),
(0.5,0),(0.5,0.5))
path '((0,0),(1,0),(1,1))' / point(cosd(45), sind(45))
→ ((0,0),(0.7071067811865476,-0.7071067811865476),
(1.4142135623730951,0))
@-@ geometric_type → double precision
Computes the total length. Available for lseg, path.
@-@ path '[(0,0),(1,0),(1,1)]' → 2
@@ geometric_type → point
295
Functions and Operators
Operator
Description
Example(s)
Computes the center point. Available for box, lseg, polygon, circle.
@@ box '(2,2),(0,0)' → (1,1)
# geometric_type → integer
Returns the number of points. Available for path, polygon.
# path '((1,0),(0,1),(-1,0))' → 3
geometric_type # geometric_type → point
Computes the point of intersection, or NULL if there is none. Available for lseg,
line.
lseg '[(0,0),(1,1)]' # lseg '[(1,0),(0,1)]' → (0.5,0.5)
box # box → box
Computes the intersection of two boxes, or NULL if there is none.
box '(2,2),(-1,-1)' # box '(1,1),(-2,-2)' → (1,1),(-1,-1)
geometric_type ## geometric_type → point
Computes the closest point to the first object on the second object. Available for these
pairs of types: (point, box), (point, lseg), (point, line), (lseg, box), (lseg,
lseg), (line, lseg).
point '(0,0)' ## lseg '[(2,0),(0,2)]' → (1,1)
geometric_type <-> geometric_type → double precision
Computes the distance between the objects. Available for all seven geometric types, for
all combinations of point with another geometric type, and for these additional pairs of
types: (box, lseg), (lseg, line), (polygon, circle) (and the commutator cases).
circle '<(0,0),1>' <-> circle '<(5,0),1>' → 3
geometric_type @> geometric_type → boolean
Does first object contain second? Available for these pairs of types: (box, point),
(box, box), (path, point), (polygon, point), (polygon, polygon), (circle,
point), (circle, circle).
circle '<(0,0),2>' @> point '(1,1)' → t
geometric_type <@ geometric_type → boolean
Is first object contained in or on second? Available for these pairs of types: (point,
box), (point, lseg), (point, line), (point, path), (point, polygon),
(point, circle), (box, box), (lseg, box), (lseg, line), (polygon, polygon),
(circle, circle).
point '(1,1)' <@ circle '<(0,0),2>' → t
geometric_type && geometric_type → boolean
Do these objects overlap? (One point in common makes this true.) Available for box,
polygon, circle.
box '(1,1),(0,0)' && box '(2,2),(0,0)' → t
geometric_type << geometric_type → boolean
Is first object strictly left of second? Available for point, box, polygon, circle.
circle '<(0,0),1>' << circle '<(5,0),1>' → t
geometric_type >> geometric_type → boolean
Is first object strictly right of second? Available for point, box, polygon, circle.
circle '<(5,0),1>' >> circle '<(0,0),1>' → t
296
Functions and Operators
Operator
Description
Example(s)
geometric_type &< geometric_type → boolean
Does first object not extend to the right of second? Available for box, polygon, circle.
box '(1,1),(0,0)' &< box '(2,2),(0,0)' → t
geometric_type &> geometric_type → boolean
Does first object not extend to the left of second? Available for box, polygon, circle.
box '(3,3),(0,0)' &> box '(2,2),(0,0)' → t
geometric_type <<| geometric_type → boolean
Is first object strictly below second? Available for point, box, polygon, circle.
box '(3,3),(0,0)' <<| box '(5,5),(3,4)' → t
geometric_type |>> geometric_type → boolean
Is first object strictly above second? Available for point, box, polygon, circle.
box '(5,5),(3,4)' |>> box '(3,3),(0,0)' → t
geometric_type &<| geometric_type → boolean
Does first object not extend above second? Available for box, polygon, circle.
box '(1,1),(0,0)' &<| box '(2,2),(0,0)' → t
geometric_type |&> geometric_type → boolean
Does first object not extend below second? Available for box, polygon, circle.
box '(3,3),(0,0)' |&> box '(2,2),(0,0)' → t
box <^ box → boolean
Is first object below second (allows edges to touch)?
box '((1,1),(0,0))' <^ box '((2,2),(1,1))' → t
box >^ box → boolean
Is first object above second (allows edges to touch)?
box '((2,2),(1,1))' >^ box '((1,1),(0,0))' → t
geometric_type ?# geometric_type → boolean
Do these objects intersect? Available for these pairs of types: (box, box), (lseg, box),
(lseg, lseg), (lseg, line), (line, box), (line, line), (path, path).
lseg '[(-1,0),(1,0)]' ?# box '(2,2),(-2,-2)' → t
?- line → boolean
?- lseg → boolean
Is line horizontal?
?- lseg '[(-1,0),(1,0)]' → t
point ?- point → boolean
Are points horizontally aligned (that is, have same y coordinate)?
point '(1,0)' ?- point '(0,0)' → t
?| line → boolean
?| lseg → boolean
Is line vertical?
?| lseg '[(-1,0),(1,0)]' → f
297
Functions and Operators
Operator
Description
Example(s)
point ?| point → boolean
Are points vertically aligned (that is, have same x coordinate)?
point '(0,1)' ?| point '(0,0)' → t
line ?-| line → boolean
lseg ?-| lseg → boolean
Are lines perpendicular?
lseg '[(0,0),(0,1)]' ?-| lseg '[(0,0),(1,0)]' → t
line ?|| line → boolean
lseg ?|| lseg → boolean
Are lines parallel?
lseg '[(-1,0),(1,0)]' ?|| lseg '[(-1,2),(1,2)]' → t
geometric_type ~= geometric_type → boolean
Are these objects the same? Available for point, box, polygon, circle.
polygon '((0,0),(1,1))' ~= polygon '((1,1),(0,0))' → t
a
“Rotating” a box with these operators only moves its corner points: the box is still considered to have sides parallel to the axes.
Hence the box's size is not preserved, as a true rotation would do.
Caution
Note that the “same as” operator, ~=, represents the usual notion of equality for the point,
box, polygon, and circle types. Some of the geometric types also have an = operator,
but = compares for equal areas only. The other scalar comparison operators (<= and so on),
where available for these types, likewise compare areas.
Note
Before PostgreSQL 14, the point is strictly below/above comparison operators point <<|
point and point |>> point were respectively called <^ and >^. These names are still
available, but are deprecated and will eventually be removed.
Table 9.37. Geometric Functions
Function
Description
Example(s)
area ( geometric_type ) → double precision
Computes area. Available for box, path, circle. A path input must be closed, else
NULL is returned. Also, if the path is self-intersecting, the result may be meaningless.
area(box '(2,2),(0,0)') → 4
center ( geometric_type ) → point
Computes center point. Available for box, circle.
center(box '(1,2),(0,0)') → (0.5,1)
diagonal ( box ) → lseg
Extracts box's diagonal as a line segment (same as lseg(box)).
298
Functions and Operators
Function
Description
Example(s)
diagonal(box '(1,2),(0,0)') → [(1,2),(0,0)]
diameter ( circle ) → double precision
Computes diameter of circle.
diameter(circle '<(0,0),2>') → 4
height ( box ) → double precision
Computes vertical size of box.
height(box '(1,2),(0,0)') → 2
isclosed ( path ) → boolean
Is path closed?
isclosed(path '((0,0),(1,1),(2,0))') → t
isopen ( path ) → boolean
Is path open?
isopen(path '[(0,0),(1,1),(2,0)]') → t
length ( geometric_type ) → double precision
Computes the total length. Available for lseg, path.
length(path '((-1,0),(1,0))') → 4
npoints ( geometric_type ) → integer
Returns the number of points. Available for path, polygon.
npoints(path '[(0,0),(1,1),(2,0)]') → 3
pclose ( path ) → path
Converts path to closed form.
pclose(path '[(0,0),(1,1),(2,0)]') → ((0,0),(1,1),(2,0))
popen ( path ) → path
Converts path to open form.
popen(path '((0,0),(1,1),(2,0))') → [(0,0),(1,1),(2,0)]
radius ( circle ) → double precision
Computes radius of circle.
radius(circle '<(0,0),2>') → 2
slope ( point, point ) → double precision
Computes slope of a line drawn through the two points.
slope(point '(0,0)', point '(2,1)') → 0.5
width ( box ) → double precision
Computes horizontal size of box.
width(box '(1,2),(0,0)') → 1
Table 9.38. Geometric Type Conversion Functions
Function
Description
Example(s)
box ( circle ) → box
Computes box inscribed within the circle.
299
Functions and Operators
Function
Description
Example(s)
box(circle '<(0,0),2>') →
(1.414213562373095,1.414213562373095),
(-1.414213562373095,-1.414213562373095)
box ( point ) → box
Converts point to empty box.
box(point '(1,0)') → (1,0),(1,0)
box ( point, point ) → box
Converts any two corner points to box.
box(point '(0,1)', point '(1,0)') → (1,1),(0,0)
box ( polygon ) → box
Computes bounding box of polygon.
box(polygon '((0,0),(1,1),(2,0))') → (2,1),(0,0)
bound_box ( box, box ) → box
Computes bounding box of two boxes.
bound_box(box '(1,1),(0,0)', box '(4,4),(3,3)') → (4,4),
(0,0)
circle ( box ) → circle
Computes smallest circle enclosing box.
circle(box '(1,1),(0,0)') → <(0.5,0.5),0.7071067811865476>
circle ( point, double precision ) → circle
Constructs circle from center and radius.
circle(point '(0,0)', 2.0) → <(0,0),2>
circle ( polygon ) → circle
Converts polygon to circle. The circle's center is the mean of the positions of the polygon's points, and the radius is the average distance of the polygon's points from that center.
circle(polygon '((0,0),(1,3),(2,0))') →
<(1,1),1.6094757082487299>
line ( point, point ) → line
Converts two points to the line through them.
line(point '(-1,0)', point '(1,0)') → {0,-1,0}
lseg ( box ) → lseg
Extracts box's diagonal as a line segment.
lseg(box '(1,0),(-1,0)') → [(1,0),(-1,0)]
lseg ( point, point ) → lseg
Constructs line segment from two endpoints.
lseg(point '(-1,0)', point '(1,0)') → [(-1,0),(1,0)]
path ( polygon ) → path
Converts polygon to a closed path with the same list of points.
path(polygon '((0,0),(1,1),(2,0))') → ((0,0),(1,1),(2,0))
point ( double precision, double precision ) → point
Constructs point from its coordinates.
300
Functions and Operators
Function
Description
Example(s)
point(23.4, -44.5) → (23.4,-44.5)
point ( box ) → point
Computes center of box.
point(box '(1,0),(-1,0)') → (0,0)
point ( circle ) → point
Computes center of circle.
point(circle '<(0,0),2>') → (0,0)
point ( lseg ) → point
Computes center of line segment.
point(lseg '[(-1,0),(1,0)]') → (0,0)
point ( polygon ) → point
Computes center of polygon (the mean of the positions of the polygon's points).
point(polygon '((0,0),(1,1),(2,0))') →
(1,0.3333333333333333)
polygon ( box ) → polygon
Converts box to a 4-point polygon.
polygon(box '(1,1),(0,0)') → ((0,0),(0,1),(1,1),(1,0))
polygon ( circle ) → polygon
Converts circle to a 12-point polygon.
polygon(circle '<(0,0),2>') → ((-2,0),
(-1.7320508075688774,0.9999999999999999),
(-1.0000000000000002,1.7320508075688772),
(-1.2246063538223773e-16,2),
(0.9999999999999996,1.7320508075688774),
(1.732050807568877,1.0000000000000007),
(2,2.4492127076447545e-16),
(1.7320508075688776,-0.9999999999999994),
(1.0000000000000009,-1.7320508075688767),
(3.673819061467132e-16,-2),
(-0.9999999999999987,-1.732050807568878),
(-1.7320508075688767,-1.0000000000000009))
polygon ( integer, circle ) → polygon
Converts circle to an n-point polygon.
polygon(4, circle '<(3,0),1>') → ((2,0),(3,1),
(4,1.2246063538223773e-16),(3,-1))
polygon ( path ) → polygon
Converts closed path to a polygon with the same list of points.
polygon(path '((0,0),(1,1),(2,0))') → ((0,0),(1,1),(2,0))
It is possible to access the two component numbers of a point as though the point were an array with
indexes 0 and 1. For example, if t.p is a point column then SELECT p[0] FROM t retrieves
the X coordinate and UPDATE t SET p[1] = ... changes the Y coordinate. In the same way,
a value of type box or lseg can be treated as an array of two point values.
301
Functions and Operators
9.12. Network Address Functions and Operators
The IP network address types, cidr and inet, support the usual comparison operators shown in
Table 9.1 as well as the specialized operators and functions shown in Table 9.39 and Table 9.40.
Any cidr value can be cast to inet implicitly; therefore, the operators and functions shown below
as operating on inet also work on cidr values. (Where there are separate functions for inet and
cidr, it is because the behavior should be different for the two cases.) Also, it is permitted to cast
an inet value to cidr. When this is done, any bits to the right of the netmask are silently zeroed
to create a valid cidr value.
Table 9.39. IP Address Operators
Operator
Description
Example(s)
inet << inet → boolean
Is subnet strictly contained by subnet? This operator, and the next four, test for subnet inclusion. They consider only the network parts of the two addresses (ignoring any bits to
the right of the netmasks) and determine whether one network is identical to or a subnet
of the other.
inet '192.168.1.5' << inet '192.168.1/24' → t
inet '192.168.0.5' << inet '192.168.1/24' → f
inet '192.168.1/24' << inet '192.168.1/24' → f
inet <<= inet → boolean
Is subnet contained by or equal to subnet?
inet '192.168.1/24' <<= inet '192.168.1/24' → t
inet >> inet → boolean
Does subnet strictly contain subnet?
inet '192.168.1/24' >> inet '192.168.1.5' → t
inet >>= inet → boolean
Does subnet contain or equal subnet?
inet '192.168.1/24' >>= inet '192.168.1/24' → t
inet && inet → boolean
Does either subnet contain or equal the other?
inet '192.168.1/24' && inet '192.168.1.80/28' → t
inet '192.168.1/24' && inet '192.168.2.0/28' → f
~ inet → inet
Computes bitwise NOT.
~ inet '192.168.1.6' → 63.87.254.249
inet & inet → inet
Computes bitwise AND.
inet '192.168.1.6' & inet '0.0.0.255' → 0.0.0.6
inet | inet → inet
Computes bitwise OR.
inet '192.168.1.6' | inet '0.0.0.255' → 192.168.1.255
302
Functions and Operators
Operator
Description
Example(s)
inet + bigint → inet
Adds an offset to an address.
inet '192.168.1.6' + 25 → 192.168.1.31
bigint + inet → inet
Adds an offset to an address.
200 + inet '::ffff:fff0:1' → ::ffff:255.240.0.201
inet - bigint → inet
Subtracts an offset from an address.
inet '192.168.1.43' - 36 → 192.168.1.7
inet - inet → bigint
Computes the difference of two addresses.
inet '192.168.1.43' - inet '192.168.1.19' → 24
inet '::1' - inet '::ffff:1' → -4294901760
Table 9.40. IP Address Functions
Function
Description
Example(s)
abbrev ( inet ) → text
Creates an abbreviated display format as text. (The result is the same as the inet output
function produces; it is “abbreviated” only in comparison to the result of an explicit cast
to text, which for historical reasons will never suppress the netmask part.)
abbrev(inet '10.1.0.0/32') → 10.1.0.0
abbrev ( cidr ) → text
Creates an abbreviated display format as text. (The abbreviation consists of dropping allzero octets to the right of the netmask; more examples are in Table 8.22.)
abbrev(cidr '10.1.0.0/16') → 10.1/16
broadcast ( inet ) → inet
Computes the broadcast address for the address's network.
broadcast(inet '192.168.1.5/24') → 192.168.1.255/24
family ( inet ) → integer
Returns the address's family: 4 for IPv4, 6 for IPv6.
family(inet '::1') → 6
host ( inet ) → text
Returns the IP address as text, ignoring the netmask.
host(inet '192.168.1.0/24') → 192.168.1.0
hostmask ( inet ) → inet
Computes the host mask for the address's network.
hostmask(inet '192.168.23.20/30') → 0.0.0.3
inet_merge ( inet, inet ) → cidr
Computes the smallest network that includes both of the given networks.
303
Functions and Operators
Function
Description
Example(s)
inet_merge(inet '192.168.1.5/24', inet '192.168.2.5/24') →
192.168.0.0/22
inet_same_family ( inet, inet ) → boolean
Tests whether the addresses belong to the same IP family.
inet_same_family(inet '192.168.1.5/24', inet '::1') → f
masklen ( inet ) → integer
Returns the netmask length in bits.
masklen(inet '192.168.1.5/24') → 24
netmask ( inet ) → inet
Computes the network mask for the address's network.
netmask(inet '192.168.1.5/24') → 255.255.255.0
network ( inet ) → cidr
Returns the network part of the address, zeroing out whatever is to the right of the netmask. (This is equivalent to casting the value to cidr.)
network(inet '192.168.1.5/24') → 192.168.1.0/24
set_masklen ( inet, integer ) → inet
Sets the netmask length for an inet value. The address part does not change.
set_masklen(inet '192.168.1.5/24', 16) → 192.168.1.5/16
set_masklen ( cidr, integer ) → cidr
Sets the netmask length for a cidr value. Address bits to the right of the new netmask
are set to zero.
set_masklen(cidr '192.168.1.0/24', 16) → 192.168.0.0/16
text ( inet ) → text
Returns the unabbreviated IP address and netmask length as text. (This has the same result as an explicit cast to text.)
text(inet '192.168.1.5') → 192.168.1.5/32
Tip
The abbrev, host, and text functions are primarily intended to offer alternative display
formats for IP addresses.
The MAC address types, macaddr and macaddr8, support the usual comparison operators shown
in Table 9.1 as well as the specialized functions shown in Table 9.41. In addition, they support the
bitwise logical operators ~, & and | (NOT, AND and OR), just as shown above for IP addresses.
Table 9.41. MAC Address Functions
Function
Description
Example(s)
trunc ( macaddr ) → macaddr
Sets the last 3 bytes of the address to zero. The remaining prefix can be associated with a
particular manufacturer (using data not included in PostgreSQL).
304
Functions and Operators
Function
Description
Example(s)
trunc(macaddr '12:34:56:78:90:ab') → 12:34:56:00:00:00
trunc ( macaddr8 ) → macaddr8
Sets the last 5 bytes of the address to zero. The remaining prefix can be associated with a
particular manufacturer (using data not included in PostgreSQL).
trunc(macaddr8 '12:34:56:78:90:ab:cd:ef') →
12:34:56:00:00:00:00:00
macaddr8_set7bit ( macaddr8 ) → macaddr8
Sets the 7th bit of the address to one, creating what is known as modified EUI-64, for inclusion in an IPv6 address.
macaddr8_set7bit(macaddr8 '00:34:56:ab:cd:ef') →
02:34:56:ff:fe:ab:cd:ef
9.13. Text Search Functions and Operators
Table 9.42, Table 9.43 and Table 9.44 summarize the functions and operators that are provided for
full text searching. See Chapter 12 for a detailed explanation of PostgreSQL's text search facility.
Table 9.42. Text Search Operators
Operator
Description
Example(s)
tsvector @@ tsquery → boolean
tsquery @@ tsvector → boolean
Does tsvector match tsquery? (The arguments can be given in either order.)
to_tsvector('fat cats ate rats') @@ to_tsquery('cat & rat')
→t
text @@ tsquery → boolean
Does text string, after implicit invocation of to_tsvector(), match tsquery?
'fat cats ate rats' @@ to_tsquery('cat & rat') → t
tsvector || tsvector → tsvector
Concatenates two tsvectors. If both inputs contain lexeme positions, the second input's positions are adjusted accordingly.
'a:1 b:2'::tsvector || 'c:1 d:2 b:3'::tsvector → 'a':1
'b':2,5 'c':3 'd':4
tsquery && tsquery → tsquery
ANDs two tsquerys together, producing a query that matches documents that match
both input queries.
'fat | rat'::tsquery && 'cat'::tsquery → ( 'fat' | 'rat' ) &
'cat'
tsquery || tsquery → tsquery
ORs two tsquerys together, producing a query that matches documents that match either input query.
'fat | rat'::tsquery || 'cat'::tsquery → 'fat' | 'rat' |
'cat'
305
Functions and Operators
Operator
Description
Example(s)
!! tsquery → tsquery
Negates a tsquery, producing a query that matches documents that do not match the
input query.
!! 'cat'::tsquery → !'cat'
tsquery <-> tsquery → tsquery
Constructs a phrase query, which matches if the two input queries match at successive
lexemes.
to_tsquery('fat') <-> to_tsquery('rat') → 'fat' <-> 'rat'
tsquery @> tsquery → boolean
Does first tsquery contain the second? (This considers only whether all the lexemes
appearing in one query appear in the other, ignoring the combining operators.)
'cat'::tsquery @> 'cat & rat'::tsquery → f
tsquery <@ tsquery → boolean
Is first tsquery contained in the second? (This considers only whether all the lexemes
appearing in one query appear in the other, ignoring the combining operators.)
'cat'::tsquery <@ 'cat & rat'::tsquery → t
'cat'::tsquery <@ '!cat & rat'::tsquery → t
In addition to these specialized operators, the usual comparison operators shown in Table 9.1 are
available for types tsvector and tsquery. These are not very useful for text searching but allow,
for example, unique indexes to be built on columns of these types.
Table 9.43. Text Search Functions
Function
Description
Example(s)
array_to_tsvector ( text[] ) → tsvector
Converts an array of text strings to a tsvector. The given strings are used as lexemes
as-is, without further processing. Array elements must not be empty strings or NULL.
array_to_tsvector('{fat,cat,rat}'::text[]) → 'cat' 'fat'
'rat'
get_current_ts_config ( ) → regconfig
Returns the OID of the current default text search configuration (as set by default_text_search_config).
get_current_ts_config() → english
length ( tsvector ) → integer
Returns the number of lexemes in the tsvector.
length('fat:2,4 cat:3 rat:5A'::tsvector) → 3
numnode ( tsquery ) → integer
Returns the number of lexemes plus operators in the tsquery.
numnode('(fat & rat) | cat'::tsquery) → 5
plainto_tsquery ( [ config regconfig, ] query text ) → tsquery
Converts text to a tsquery, normalizing words according to the specified or default
configuration. Any punctuation in the string is ignored (it does not determine query operators). The resulting query matches documents containing all non-stopwords in the text.
306
Functions and Operators
Function
Description
Example(s)
plainto_tsquery('english', 'The Fat Rats') → 'fat' & 'rat'
phraseto_tsquery ( [ config regconfig, ] query text ) → tsquery
Converts text to a tsquery, normalizing words according to the specified or default
configuration. Any punctuation in the string is ignored (it does not determine query operators). The resulting query matches phrases containing all non-stopwords in the text.
phraseto_tsquery('english', 'The Fat Rats') → 'fat' <->
'rat'
phraseto_tsquery('english', 'The Cat and Rats') → 'cat' <2>
'rat'
websearch_to_tsquery ( [ config regconfig, ] query text ) → tsquery
Converts text to a tsquery, normalizing words according to the specified or default
configuration. Quoted word sequences are converted to phrase tests. The word “or” is understood as producing an OR operator, and a dash produces a NOT operator; other punctuation is ignored. This approximates the behavior of some common web search tools.
websearch_to_tsquery('english', '"fat rat" or cat dog') →
'fat' <-> 'rat' | 'cat' & 'dog'
querytree ( tsquery ) → text
Produces a representation of the indexable portion of a tsquery. A result that is empty
or just T indicates a non-indexable query.
querytree('foo & ! bar'::tsquery) → 'foo'
setweight ( vector tsvector, weight "char" ) → tsvector
Assigns the specified weight to each element of the vector.
setweight('fat:2,4 cat:3 rat:5B'::tsvector, 'A') → 'cat':3A
'fat':2A,4A 'rat':5A
setweight ( vector tsvector, weight "char", lexemes text[] ) → tsvector
Assigns the specified weight to elements of the vector that are listed in lexemes.
The strings in lexemes are taken as lexemes as-is, without further processing. Strings
that do not match any lexeme in vector are ignored.
setweight('fat:2,4 cat:3 rat:5,6B'::tsvector, 'A',
'{cat,rat}') → 'cat':3A 'fat':2,4 'rat':5A,6A
strip ( tsvector ) → tsvector
Removes positions and weights from the tsvector.
strip('fat:2,4 cat:3 rat:5A'::tsvector) → 'cat' 'fat' 'rat'
to_tsquery ( [ config regconfig, ] query text ) → tsquery
Converts text to a tsquery, normalizing words according to the specified or default
configuration. The words must be combined by valid tsquery operators.
to_tsquery('english', 'The & Fat & Rats') → 'fat' & 'rat'
to_tsvector ( [ config regconfig, ] document text ) → tsvector
Converts text to a tsvector, normalizing words according to the specified or default
configuration. Position information is included in the result.
to_tsvector('english', 'The Fat Rats') → 'fat':2 'rat':3
to_tsvector ( [ config regconfig, ] document json ) → tsvector
to_tsvector ( [ config regconfig, ] document jsonb ) → tsvector
307
Functions and Operators
Function
Description
Example(s)
Converts each string value in the JSON document to a tsvector, normalizing words
according to the specified or default configuration. The results are then concatenated in
document order to produce the output. Position information is generated as though one
stopword exists between each pair of string values. (Beware that “document order” of the
fields of a JSON object is implementation-dependent when the input is jsonb; observe
the difference in the examples.)
to_tsvector('english', '{"aa": "The Fat Rats", "b":
"dog"}'::json) → 'dog':5 'fat':2 'rat':3
to_tsvector('english', '{"aa": "The Fat Rats", "b":
"dog"}'::jsonb) → 'dog':1 'fat':4 'rat':5
json_to_tsvector ( [ config regconfig, ] document json, filter jsonb ) →
tsvector
jsonb_to_tsvector ( [ config regconfig, ] document jsonb, filter jsonb )
→ tsvector
Selects each item in the JSON document that is requested by the filter and converts
each one to a tsvector, normalizing words according to the specified or default configuration. The results are then concatenated in document order to produce the output.
Position information is generated as though one stopword exists between each pair of selected items. (Beware that “document order” of the fields of a JSON object is implementation-dependent when the input is jsonb.) The filter must be a jsonb array containing zero or more of these keywords: "string" (to include all string values), "numeric" (to include all numeric values), "boolean" (to include all boolean values),
"key" (to include all keys), or "all" (to include all the above). As a special case, the
filter can also be a simple JSON value that is one of these keywords.
json_to_tsvector('english', '{"a": "The Fat Rats", "b":
123}'::json, '["string", "numeric"]') → '123':5 'fat':2
'rat':3
json_to_tsvector('english', '{"cat": "The Fat Rats", "dog":
123}'::json, '"all"') → '123':9 'cat':1 'dog':7 'fat':4
'rat':5
ts_delete ( vector tsvector, lexeme text ) → tsvector
Removes any occurrence of the given lexeme from the vector. The lexeme string is
treated as a lexeme as-is, without further processing.
ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, 'fat') → 'cat':3
'rat':5A
ts_delete ( vector tsvector, lexemes text[] ) → tsvector
Removes any occurrences of the lexemes in lexemes from the vector. The strings
in lexemes are taken as lexemes as-is, without further processing. Strings that do not
match any lexeme in vector are ignored.
ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, ARRAY['fat','rat']) → 'cat':3
ts_filter ( vector tsvector, weights "char"[] ) → tsvector
Selects only elements with the given weights from the vector.
ts_filter('fat:2,4 cat:3b,7c rat:5A'::tsvector, '{a,b}') →
'cat':3B 'rat':5A
ts_headline ( [ config regconfig, ] document text, query tsquery [, options
text ] ) → text
308
Functions and Operators
Function
Description
Example(s)
Displays, in an abbreviated form, the match(es) for the query in the document, which
must be raw text not a tsvector. Words in the document are normalized according to
the specified or default configuration before matching to the query. Use of this function
is discussed in Section 12.3.4, which also describes the available options.
ts_headline('The fat cat ate the rat.', 'cat') → The fat
<b>cat</b> ate the rat.
ts_headline ( [ config regconfig, ] document json, query tsquery [, options
text ] ) → text
ts_headline ( [ config regconfig, ] document jsonb, query tsquery [, options text ] ) → text
Displays, in an abbreviated form, match(es) for the query that occur in string values
within the JSON document. See Section 12.3.4 for more details.
ts_headline('{"cat":"raining cats and dogs"}'::jsonb,
'cat') → {"cat": "raining <b>cats</b> and dogs"}
ts_rank ( [ weights real[], ] vector tsvector, query tsquery [, normalization integer ] ) → real
Computes a score showing how well the vector matches the query. See Section 12.3.3 for details.
ts_rank(to_tsvector('raining cats and dogs'), 'cat') →
0.06079271
ts_rank_cd ( [ weights real[], ] vector tsvector, query tsquery [, normalization integer ] ) → real
Computes a score showing how well the vector matches the query, using a cover
density algorithm. See Section 12.3.3 for details.
ts_rank_cd(to_tsvector('raining cats and dogs'), 'cat') →
0.1
ts_rewrite ( query tsquery, target tsquery, substitute tsquery ) → tsquery
Replaces occurrences of target with substitute within the query. See Section 12.4.2.1 for details.
ts_rewrite('a & b'::tsquery, 'a'::tsquery, 'foo|bar'::tsquery) → 'b' & ( 'foo' | 'bar' )
ts_rewrite ( query tsquery, select text ) → tsquery
Replaces portions of the query according to target(s) and substitute(s) obtained by executing a SELECT command. See Section 12.4.2.1 for details.
SELECT ts_rewrite('a & b'::tsquery, 'SELECT t,s FROM aliases') → 'b' & ( 'foo' | 'bar' )
tsquery_phrase ( query1 tsquery, query2 tsquery ) → tsquery
Constructs a phrase query that searches for matches of query1 and query2 at successive lexemes (same as <-> operator).
tsquery_phrase(to_tsquery('fat'), to_tsquery('cat')) → 'fat'
<-> 'cat'
tsquery_phrase ( query1 tsquery, query2 tsquery, distance integer ) →
tsquery
Constructs a phrase query that searches for matches of query1 and query2 that occur
exactly distance lexemes apart.
309
Functions and Operators
Function
Description
Example(s)
tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'), 10) →
'fat' <10> 'cat'
tsvector_to_array ( tsvector ) → text[]
Converts a tsvector to an array of lexemes.
tsvector_to_array('fat:2,4 cat:3 rat:5A'::tsvector) →
{cat,fat,rat}
unnest ( tsvector ) → setof record ( lexeme text, positions smallint[],
weights text )
Expands a tsvector into a set of rows, one per lexeme.
select * from unnest('cat:3 fat:2,4 rat:5A'::tsvector) →
lexeme | positions | weights
--------+-----------+--------cat
| {3}
| {D}
fat
| {2,4}
| {D,D}
rat
| {5}
| {A}
Note
All the text search functions that accept an optional regconfig argument will use the configuration specified by default_text_search_config when that argument is omitted.
The functions in Table 9.44 are listed separately because they are not usually used in everyday text
searching operations. They are primarily helpful for development and debugging of new text search
configurations.
Table 9.44. Text Search Debugging Functions
Function
Description
Example(s)
ts_debug ( [ config regconfig, ] document text ) → setof record ( alias
text, description text, token text, dictionaries regdictionary[],
dictionary regdictionary, lexemes text[] )
Extracts and normalizes tokens from the document according to the specified or default
text search configuration, and returns information about how each token was processed.
See Section 12.8.1 for details.
ts_debug('english', 'The Brightest supernovaes') →
(asciiword,"Word, all ASCII",The,{english_stem},english_stem,{}) ...
ts_lexize ( dict regdictionary, token text ) → text[]
Returns an array of replacement lexemes if the input token is known to the dictionary, or
an empty array if the token is known to the dictionary but it is a stop word, or NULL if it
is not a known word. See Section 12.8.3 for details.
ts_lexize('english_stem', 'stars') → {star}
ts_parse ( parser_name text, document text ) → setof record ( tokid integer, token text )
310
Functions and Operators
Function
Description
Example(s)
Extracts tokens from the document using the named parser. See Section 12.8.2 for details.
ts_parse('default', 'foo - bar') → (1,foo) ...
ts_parse ( parser_oid oid, document text ) → setof record ( tokid integer, token text )
Extracts tokens from the document using a parser specified by OID. See Section 12.8.2
for details.
ts_parse(3722, 'foo - bar') → (1,foo) ...
ts_token_type ( parser_name text ) → setof record ( tokid integer, alias
text, description text )
Returns a table that describes each type of token the named parser can recognize. See
Section 12.8.2 for details.
ts_token_type('default') → (1,asciiword,"Word, all
ASCII") ...
ts_token_type ( parser_oid oid ) → setof record ( tokid integer, alias
text, description text )
Returns a table that describes each type of token a parser specified by OID can recognize. See Section 12.8.2 for details.
ts_token_type(3722) → (1,asciiword,"Word, all ASCII") ...
ts_stat ( sqlquery text [, weights text ] ) → setof record ( word text, ndoc
integer, nentry integer )
Executes the sqlquery, which must return a single tsvector column, and returns
statistics about each distinct lexeme contained in the data. See Section 12.4.4 for details.
ts_stat('SELECT vector FROM apod') → (foo,10,15) ...
9.14. UUID Functions
PostgreSQL includes one function to generate a UUID:
gen_random_uuid () → uuid
This function returns a version 4 (random) UUID. This is the most commonly used type of UUID and
is appropriate for most applications.
The uuid-ossp module provides additional functions that implement other standard algorithms for
generating UUIDs.
There are also functions to extract data from UUIDs:
uuid_extract_timestamp (uuid) → timestamp with time zone
This function extracts a timestamp with time zone from UUID version 1. For other versions,
this function returns null. Note that the extracted timestamp is not necessarily exactly equal to the time
the UUID was generated; this depends on the implementation that generated the UUID.
uuid_extract_version (uuid) → smallint
311
Functions and Operators
This function extracts the version from a UUID of the variant described by RFC 41222. For other
variants, this function returns null. For example, for a UUID generated by gen_random_uuid, this
function will return 4.
PostgreSQL also provides the usual comparison operators shown in Table 9.1 for UUIDs.
9.15. XML Functions
The functions and function-like expressions described in this section operate on values of type xml.
See Section 8.13 for information about the xml type. The function-like expressions xmlparse and
xmlserialize for converting to and from type xml are documented there, not in this section.
Use of most of these functions requires PostgreSQL to have been built with configure --withlibxml.
9.15.1. Producing XML Content
A set of functions and function-like expressions is available for producing XML content from SQL
data. As such, they are particularly suitable for formatting query results into XML documents for
processing in client applications.
9.15.1.1. xmltext
xmltext ( text ) → xml
The function xmltext returns an XML value with a single text node containing the input argument
as its content. Predefined entities like ampersand (&), left and right angle brackets (< >), and quotation
marks ("") are escaped.
Example:
SELECT xmltext('< foo & bar >');
xmltext
------------------------&lt; foo &amp; bar &gt;
9.15.1.2. xmlcomment
xmlcomment ( text ) → xml
The function xmlcomment creates an XML value containing an XML comment with the specified
text as content. The text cannot contain “--” or end with a “-”, otherwise the resulting construct
would not be a valid XML comment. If the argument is null, the result is null.
Example:
SELECT xmlcomment('hello');
xmlcomment
-------------<!--hello-->
9.15.1.3. xmlconcat
2
https://datatracker.ietf.org/doc/html/rfc4122
312
Functions and Operators
xmlconcat ( xml [, ...] ) → xml
The function xmlconcat concatenates a list of individual XML values to create a single value containing an XML content fragment. Null values are omitted; the result is only null if there are no nonnull arguments.
Example:
SELECT xmlconcat('<abc/>', '<bar>foo</bar>');
xmlconcat
---------------------<abc/><bar>foo</bar>
XML declarations, if present, are combined as follows. If all argument values have the same XML
version declaration, that version is used in the result, else no version is used. If all argument values
have the standalone declaration value “yes”, then that value is used in the result. If all argument values
have a standalone declaration value and at least one is “no”, then that is used in the result. Else the
result will have no standalone declaration. If the result is determined to require a standalone declaration
but no version declaration, a version declaration with version 1.0 will be used because XML requires
an XML declaration to contain a version declaration. Encoding declarations are ignored and removed
in all cases.
Example:
SELECT xmlconcat('<?xml version="1.1"?><foo/>', '<?xml
version="1.1" standalone="no"?><bar/>');
xmlconcat
----------------------------------<?xml version="1.1"?><foo/><bar/>
9.15.1.4. xmlelement
xmlelement ( NAME name [, XMLATTRIBUTES ( attvalue [ AS attname ]
[, ...] ) ] [, content [, ...]] ) → xml
The xmlelement expression produces an XML element with the given name, attributes, and content.
The name and attname items shown in the syntax are simple identifiers, not values. The attvalue and content items are expressions, which can yield any PostgreSQL data type. The argument(s)
within XMLATTRIBUTES generate attributes of the XML element; the content value(s) are concatenated to form its content.
Examples:
SELECT xmlelement(name foo);
xmlelement
-----------<foo/>
SELECT xmlelement(name foo, xmlattributes('xyz' as bar));
xmlelement
313
Functions and Operators
-----------------<foo bar="xyz"/>
SELECT xmlelement(name foo, xmlattributes(current_date as bar),
'cont', 'ent');
xmlelement
------------------------------------<foo bar="2007-01-26">content</foo>
Element and attribute names that are not valid XML names are escaped by replacing the offending
characters by the sequence _xHHHH_, where HHHH is the character's Unicode codepoint in hexadecimal notation. For example:
SELECT xmlelement(name "foo$bar", xmlattributes('xyz' as "a&b"));
xmlelement
---------------------------------<foo_x0024_bar a_x0026_b="xyz"/>
An explicit attribute name need not be specified if the attribute value is a column reference, in which
case the column's name will be used as the attribute name by default. In other cases, the attribute must
be given an explicit name. So this example is valid:
CREATE TABLE test (a xml, b xml);
SELECT xmlelement(name test, xmlattributes(a, b)) FROM test;
But these are not:
SELECT xmlelement(name test, xmlattributes('constant'), a, b) FROM
test;
SELECT xmlelement(name test, xmlattributes(func(a, b))) FROM test;
Element content, if specified, will be formatted according to its data type. If the content is itself of
type xml, complex XML documents can be constructed. For example:
SELECT xmlelement(name foo, xmlattributes('xyz' as bar),
xmlelement(name abc),
xmlcomment('test'),
xmlelement(name xyz));
xmlelement
---------------------------------------------<foo bar="xyz"><abc/><!--test--><xyz/></foo>
Content of other types will be formatted into valid XML character data. This means in particular
that the characters <, >, and & will be converted to entities. Binary data (data type bytea) will
be represented in base64 or hex encoding, depending on the setting of the configuration parameter
xmlbinary. The particular behavior for individual data types is expected to evolve in order to align the
PostgreSQL mappings with those specified in SQL:2006 and later, as discussed in Section D.3.1.3.
9.15.1.5. xmlforest
xmlforest ( content [ AS name ] [, ...] ) → xml
314
Functions and Operators
The xmlforest expression produces an XML forest (sequence) of elements using the given names
and content. As for xmlelement, each name must be a simple identifier, while the content expressions can have any data type.
Examples:
SELECT xmlforest('abc' AS foo, 123 AS bar);
xmlforest
-----------------------------<foo>abc</foo><bar>123</bar>
SELECT xmlforest(table_name, column_name)
FROM information_schema.columns
WHERE table_schema = 'pg_catalog';
xmlforest
---------------------------------------------------------------------<table_name>pg_authid</table_name><column_name>rolname</
column_name>
<table_name>pg_authid</table_name><column_name>rolsuper</
column_name>
...
As seen in the second example, the element name can be omitted if the content value is a column
reference, in which case the column name is used by default. Otherwise, a name must be specified.
Element names that are not valid XML names are escaped as shown for xmlelement above. Similarly, content data is escaped to make valid XML content, unless it is already of type xml.
Note that XML forests are not valid XML documents if they consist of more than one element, so it
might be useful to wrap xmlforest expressions in xmlelement.
9.15.1.6. xmlpi
xmlpi ( NAME name [, content ] ) → xml
The xmlpi expression creates an XML processing instruction. As for xmlelement, the name must
be a simple identifier, while the content expression can have any data type. The content, if
present, must not contain the character sequence ?>.
Example:
SELECT xmlpi(name php, 'echo "hello world";');
xmlpi
----------------------------<?php echo "hello world";?>
9.15.1.7. xmlroot
xmlroot ( xml, VERSION {text|NO VALUE} [, STANDALONE {YES|NO|NO
VALUE} ] ) → xml
315
Functions and Operators
The xmlroot expression alters the properties of the root node of an XML value. If a version is
specified, it replaces the value in the root node's version declaration; if a standalone setting is specified,
it replaces the value in the root node's standalone declaration.
SELECT xmlroot(xmlparse(document '<?xml version="1.1"?
><content>abc</content>'),
version '1.0', standalone yes);
xmlroot
---------------------------------------<?xml version="1.0" standalone="yes"?>
<content>abc</content>
9.15.1.8. xmlagg
xmlagg ( xml ) → xml
The function xmlagg is, unlike the other functions described here, an aggregate function. It concatenates the input values to the aggregate function call, much like xmlconcat does, except that concatenation occurs across rows rather than across expressions in a single row. See Section 9.21 for
additional information about aggregate functions.
Example:
CREATE TABLE test (y int, x xml);
INSERT INTO test VALUES (1, '<foo>abc</foo>');
INSERT INTO test VALUES (2, '<bar/>');
SELECT xmlagg(x) FROM test;
xmlagg
---------------------<foo>abc</foo><bar/>
To determine the order of the concatenation, an ORDER BY clause may be added to the aggregate call
as described in Section 4.2.7. For example:
SELECT xmlagg(x ORDER BY y DESC) FROM test;
xmlagg
---------------------<bar/><foo>abc</foo>
The following non-standard approach used to be recommended in previous versions, and may still be
useful in specific cases:
SELECT xmlagg(x) FROM (SELECT * FROM test ORDER BY y DESC) AS tab;
xmlagg
---------------------<bar/><foo>abc</foo>
9.15.2. XML Predicates
The expressions described in this section check properties of xml values.
9.15.2.1. IS DOCUMENT
316
Functions and Operators
xml IS DOCUMENT → boolean
The expression IS DOCUMENT returns true if the argument XML value is a proper XML document,
false if it is not (that is, it is a content fragment), or null if the argument is null. See Section 8.13 about
the difference between documents and content fragments.
9.15.2.2. IS NOT DOCUMENT
xml IS NOT DOCUMENT → boolean
The expression IS NOT DOCUMENT returns false if the argument XML value is a proper XML
document, true if it is not (that is, it is a content fragment), or null if the argument is null.
9.15.2.3. XMLEXISTS
XMLEXISTS ( text PASSING [BY {REF|VALUE}] xml [BY
{REF|VALUE}] ) → boolean
The function xmlexists evaluates an XPath 1.0 expression (the first argument), with the passed
XML value as its context item. The function returns false if the result of that evaluation yields an
empty node-set, true if it yields any other value. The function returns null if any argument is null. A
nonnull value passed as the context item must be an XML document, not a content fragment or any
non-XML value.
Example:
SELECT xmlexists('//town[text() = ''Toronto'']' PASSING BY VALUE
'<towns><town>Toronto</town><town>Ottawa</town></towns>');
xmlexists
-----------t
(1 row)
The BY REF and BY VALUE clauses are accepted in PostgreSQL, but are ignored, as discussed in
Section D.3.2.
In the SQL standard, the xmlexists function evaluates an expression in the XML Query language,
but PostgreSQL allows only an XPath 1.0 expression, as discussed in Section D.3.1.
9.15.2.4. xml_is_well_formed
xml_is_well_formed ( text ) → boolean
xml_is_well_formed_document ( text ) → boolean
xml_is_well_formed_content ( text ) → boolean
These functions check whether a text string represents well-formed XML, returning a Boolean
result. xml_is_well_formed_document checks for a well-formed document, while xml_is_well_formed_content checks for well-formed content. xml_is_well_formed does
the former if the xmloption configuration parameter is set to DOCUMENT, or the latter if it is set to
CONTENT. This means that xml_is_well_formed is useful for seeing whether a simple cast to
type xml will succeed, whereas the other two functions are useful for seeing whether the corresponding variants of XMLPARSE will succeed.
Examples:
317
Functions and Operators
SET xmloption TO DOCUMENT;
SELECT xml_is_well_formed('<>');
xml_is_well_formed
-------------------f
(1 row)
SELECT xml_is_well_formed('<abc/>');
xml_is_well_formed
-------------------t
(1 row)
SET xmloption TO CONTENT;
SELECT xml_is_well_formed('abc');
xml_is_well_formed
-------------------t
(1 row)
SELECT xml_is_well_formed_document('<pg:foo xmlns:pg="http://
postgresql.org/stuff">bar</pg:foo>');
xml_is_well_formed_document
----------------------------t
(1 row)
SELECT xml_is_well_formed_document('<pg:foo xmlns:pg="http://
postgresql.org/stuff">bar</my:foo>');
xml_is_well_formed_document
----------------------------f
(1 row)
The last example shows that the checks include whether namespaces are correctly matched.
9.15.3. Processing XML
To process values of data type xml, PostgreSQL offers the functions xpath and xpath_exists,
which evaluate XPath 1.0 expressions, and the XMLTABLE table function.
9.15.3.1. xpath
xpath ( xpath text, xml xml [, nsarray text[] ] ) → xml[]
The function xpath evaluates the XPath 1.0 expression xpath (given as text) against the XML
value xml. It returns an array of XML values corresponding to the node-set produced by the XPath
expression. If the XPath expression returns a scalar value rather than a node-set, a single-element array
is returned.
The second argument must be a well formed XML document. In particular, it must have a single root
node element.
The optional third argument of the function is an array of namespace mappings. This array should be
a two-dimensional text array with the length of the second axis being equal to 2 (i.e., it should be
an array of arrays, each of which consists of exactly 2 elements). The first element of each array entry
318
Functions and Operators
is the namespace name (alias), the second the namespace URI. It is not required that aliases provided
in this array be the same as those being used in the XML document itself (in other words, both in the
XML document and in the xpath function context, aliases are local).
Example:
SELECT xpath('/my:a/text()', '<my:a xmlns:my="http://
example.com">test</my:a>',
ARRAY[ARRAY['my', 'http://example.com']]);
xpath
-------{test}
(1 row)
To deal with default (anonymous) namespaces, do something like this:
SELECT xpath('//mydefns:b/text()', '<a xmlns="http://
example.com"><b>test</b></a>',
ARRAY[ARRAY['mydefns', 'http://example.com']]);
xpath
-------{test}
(1 row)
9.15.3.2. xpath_exists
xpath_exists ( xpath text, xml xml [, nsarray text[] ] ) → boolean
The function xpath_exists is a specialized form of the xpath function. Instead of returning the
individual XML values that satisfy the XPath 1.0 expression, this function returns a Boolean indicating
whether the query was satisfied or not (specifically, whether it produced any value other than an empty
node-set). This function is equivalent to the XMLEXISTS predicate, except that it also offers support
for a namespace mapping argument.
Example:
SELECT xpath_exists('/my:a/text()', '<my:a xmlns:my="http://
example.com">test</my:a>',
ARRAY[ARRAY['my', 'http://example.com']]);
xpath_exists
-------------t
(1 row)
9.15.3.3. xmltable
XMLTABLE (
[ XMLNAMESPACES ( namespace_uri AS namespace_name [, ...] ), ]
row_expression PASSING [BY {REF|VALUE}] document_expression [BY
{REF|VALUE}]
COLUMNS name { type [PATH column_expression]
[DEFAULT default_expression] [NOT NULL | NULL]
319
Functions and Operators
| FOR ORDINALITY }
[, ...]
) → setof record
The xmltable expression produces a table based on an XML value, an XPath filter to extract rows,
and a set of column definitions. Although it syntactically resembles a function, it can only appear as
a table in a query's FROM clause.
The optional XMLNAMESPACES clause gives a comma-separated list of namespace definitions, where
each namespace_uri is a text expression and each namespace_name is a simple identifier.
It specifies the XML namespaces used in the document and their aliases. A default namespace specification is not currently supported.
The required row_expression argument is an XPath 1.0 expression (given as text) that is evaluated, passing the XML value document_expression as its context item, to obtain a set of XML
nodes. These nodes are what xmltable transforms into output rows. No rows will be produced if
the document_expression is null, nor if the row_expression produces an empty node-set
or any value other than a node-set.
document_expression provides the context item for the row_expression. It must be a wellformed XML document; fragments/forests are not accepted. The BY REF and BY VALUE clauses
are accepted but ignored, as discussed in Section D.3.2.
In the SQL standard, the xmltable function evaluates expressions in the XML Query language, but
PostgreSQL allows only XPath 1.0 expressions, as discussed in Section D.3.1.
The required COLUMNS clause specifies the column(s) that will be produced in the output table. See
the syntax summary above for the format. A name is required for each column, as is a data type (unless
FOR ORDINALITY is specified, in which case type integer is implicit). The path, default and
nullability clauses are optional.
A column marked FOR ORDINALITY will be populated with row numbers, starting with 1, in the
order of nodes retrieved from the row_expression's result node-set. At most one column may be
marked FOR ORDINALITY.
Note
XPath 1.0 does not specify an order for nodes in a node-set, so code that relies on a particular
order of the results will be implementation-dependent. Details can be found in Section D.3.1.2.
The column_expression for a column is an XPath 1.0 expression that is evaluated for each row,
with the current node from the row_expression result as its context item, to find the value of the
column. If no column_expression is given, then the column name is used as an implicit path.
If a column's XPath expression returns a non-XML value (which is limited to string, boolean, or double
in XPath 1.0) and the column has a PostgreSQL type other than xml, the column will be set as if
by assigning the value's string representation to the PostgreSQL type. (If the value is a boolean, its
string representation is taken to be 1 or 0 if the output column's type category is numeric, otherwise
true or false.)
If a column's XPath expression returns a non-empty set of XML nodes and the column's PostgreSQL
type is xml, the column will be assigned the expression result exactly, if it is of document or content
form. 3
A non-XML result assigned to an xml output column produces content, a single text node with the
string value of the result. An XML result assigned to a column of any other type may not have more
3
A result containing more than one element node at the top level, or non-whitespace text outside of an element, is an example of content form.
An XPath result can be of neither form, for example if it returns an attribute node selected from the element that contains it. Such a result will
be put into content form with each such disallowed node replaced by its string value, as defined for the XPath 1.0 string function.
320
Functions and Operators
than one node, or an error is raised. If there is exactly one node, the column will be set as if by assigning
the node's string value (as defined for the XPath 1.0 string function) to the PostgreSQL type.
The string value of an XML element is the concatenation, in document order, of all text nodes contained
in that element and its descendants. The string value of an element with no descendant text nodes
is an empty string (not NULL). Any xsi:nil attributes are ignored. Note that the whitespace-only
text() node between two non-text elements is preserved, and that leading whitespace on a text()
node is not flattened. The XPath 1.0 string function may be consulted for the rules defining the
string value of other XML node types and non-XML values.
The conversion rules presented here are not exactly those of the SQL standard, as discussed in Section D.3.1.3.
If the path expression returns an empty node-set (typically, when it does not match) for a given row, the
column will be set to NULL, unless a default_expression is specified; then the value resulting
from evaluating that expression is used.
A default_expression, rather than being evaluated immediately when xmltable is called,
is evaluated each time a default is needed for the column. If the expression qualifies as stable or immutable, the repeat evaluation may be skipped. This means that you can usefully use volatile functions
like nextval in default_expression.
Columns may be marked NOT NULL. If the column_expression for a NOT NULL column does
not match anything and there is no DEFAULT or the default_expression also evaluates to null,
an error is reported.
Examples:
CREATE TABLE xmldata AS SELECT
xml $$
<ROWS>
<ROW id="1">
<COUNTRY_ID>AU</COUNTRY_ID>
<COUNTRY_NAME>Australia</COUNTRY_NAME>
</ROW>
<ROW id="5">
<COUNTRY_ID>JP</COUNTRY_ID>
<COUNTRY_NAME>Japan</COUNTRY_NAME>
<PREMIER_NAME>Shinzo Abe</PREMIER_NAME>
<SIZE unit="sq_mi">145935</SIZE>
</ROW>
<ROW id="6">
<COUNTRY_ID>SG</COUNTRY_ID>
<COUNTRY_NAME>Singapore</COUNTRY_NAME>
<SIZE unit="sq_km">697</SIZE>
</ROW>
</ROWS>
$$ AS data;
SELECT xmltable.*
FROM xmldata,
XMLTABLE('//ROWS/ROW'
PASSING data
COLUMNS id int PATH '@id',
ordinality FOR ORDINALITY,
"COUNTRY_NAME" text,
country_id text PATH 'COUNTRY_ID',
size_sq_km float PATH 'SIZE[@unit =
"sq_km"]',
321
Functions and Operators
size_other text PATH
'concat(SIZE[@unit!="sq_km"], " ",
SIZE[@unit!="sq_km"]/@unit)',
premier_name text PATH 'PREMIER_NAME'
DEFAULT 'not specified');
id | ordinality | COUNTRY_NAME | country_id | size_sq_km |
size_other | premier_name
----+------------+--------------+------------+-----------+--------------+--------------1 |
1 | Australia
| AU
|
|
| not specified
5 |
2 | Japan
| JP
|
| 145935
sq_mi | Shinzo Abe
6 |
3 | Singapore
| SG
|
697 |
| not specified
The following example shows concatenation of multiple text() nodes, usage of the column name as
XPath filter, and the treatment of whitespace, XML comments and processing instructions:
CREATE TABLE xmlelements AS SELECT
xml $$
<root>
<element> Hello<!-- xyxxz -->2a2<?aaaaa?> <!--x-->
x>CC </element>
</root>
$$ AS data;
bbb<x>xxx</
SELECT xmltable.*
FROM xmlelements, XMLTABLE('/root' PASSING data COLUMNS element
text);
element
------------------------Hello2a2
bbbxxxCC
The following example illustrates how the XMLNAMESPACES clause can be used to specify a list of
namespaces used in the XML document as well as in the XPath expressions:
WITH xmldata(data) AS (VALUES ('
<example xmlns="http://example.com/myns" xmlns:B="http://
example.com/b">
<item foo="1" B:bar="2"/>
<item foo="3" B:bar="4"/>
<item foo="4" B:bar="5"/>
</example>'::xml)
)
SELECT xmltable.*
FROM XMLTABLE(XMLNAMESPACES('http://example.com/myns' AS x,
'http://example.com/b' AS "B"),
'/x:example/x:item'
PASSING (SELECT data FROM xmldata)
COLUMNS foo int PATH '@foo',
bar int PATH '@B:bar');
foo | bar
-----+----1 |
2
3 |
4
322
Functions and Operators
4 |
5
(3 rows)
9.15.4. Mapping Tables to XML
The following functions map the contents of relational tables to XML values. They can be thought
of as XML export functionality:
table_to_xml ( table regclass, nulls boolean,
tableforest boolean, targetns text ) → xml
query_to_xml ( query text, nulls boolean,
tableforest boolean, targetns text ) → xml
cursor_to_xml ( cursor refcursor, count integer, nulls boolean,
tableforest boolean, targetns text ) → xml
table_to_xml maps the content of the named table, passed as parameter table. The regclass
type accepts strings identifying tables using the usual notation, including optional schema qualification
and double quotes (see Section 8.19 for details). query_to_xml executes the query whose text is
passed as parameter query and maps the result set. cursor_to_xml fetches the indicated number
of rows from the cursor specified by the parameter cursor. This variant is recommended if large
tables have to be mapped, because the result value is built up in memory by each function.
If tableforest is false, then the resulting XML document looks like this:
<tablename>
<row>
<columnname1>data</columnname1>
<columnname2>data</columnname2>
</row>
<row>
...
</row>
...
</tablename>
If tableforest is true, the result is an XML content fragment that looks like this:
<tablename>
<columnname1>data</columnname1>
<columnname2>data</columnname2>
</tablename>
<tablename>
...
</tablename>
...
If no table name is available, that is, when mapping a query or a cursor, the string table is used in
the first format, row in the second format.
The choice between these formats is up to the user. The first format is a proper XML document,
which will be important in many applications. The second format tends to be more useful in the cur-
323
Functions and Operators
sor_to_xml function if the result values are to be reassembled into one document later on. The
functions for producing XML content discussed above, in particular xmlelement, can be used to
alter the results to taste.
The data values are mapped in the same way as described for the function xmlelement above.
The parameter nulls determines whether null values should be included in the output. If true, null
values in columns are represented as:
<columnname xsi:nil="true"/>
where xsi is the XML namespace prefix for XML Schema Instance. An appropriate namespace declaration will be added to the result value. If false, columns containing null values are simply omitted
from the output.
The parameter targetns specifies the desired XML namespace of the result. If no particular namespace is wanted, an empty string should be passed.
The following functions return XML Schema documents describing the mappings performed by the
corresponding functions above:
table_to_xmlschema ( table regclass, nulls boolean,
tableforest boolean, targetns text ) → xml
query_to_xmlschema ( query text, nulls boolean,
tableforest boolean, targetns text ) → xml
cursor_to_xmlschema ( cursor refcursor, nulls boolean,
tableforest boolean, targetns text ) → xml
It is essential that the same parameters are passed in order to obtain matching XML data mappings
and XML Schema documents.
The following functions produce XML data mappings and the corresponding XML Schema in one
document (or forest), linked together. They can be useful where self-contained and self-describing
results are wanted:
table_to_xml_and_xmlschema ( table regclass, nulls boolean,
tableforest boolean, targetns text
) → xml
query_to_xml_and_xmlschema ( query text, nulls boolean,
tableforest boolean, targetns text
) → xml
In addition, the following functions are available to produce analogous mappings of entire schemas
or the entire current database:
schema_to_xml ( schema name, nulls boolean,
tableforest boolean, targetns text ) → xml
schema_to_xmlschema ( schema name, nulls boolean,
tableforest boolean, targetns text ) → xml
schema_to_xml_and_xmlschema ( schema name, nulls boolean,
tableforest boolean, targetns text
) → xml
database_to_xml ( nulls boolean,
324
Functions and Operators
tableforest boolean, targetns text ) → xml
database_to_xmlschema ( nulls boolean,
tableforest boolean, targetns text ) → xml
database_to_xml_and_xmlschema ( nulls boolean,
tableforest boolean, targetns text
) → xml
These functions ignore tables that are not readable by the current user. The database-wide functions
additionally ignore schemas that the current user does not have USAGE (lookup) privilege for.
Note that these potentially produce a lot of data, which needs to be built up in memory. When requesting content mappings of large schemas or databases, it might be worthwhile to consider mapping the
tables separately instead, possibly even through a cursor.
The result of a schema content mapping looks like this:
<schemaname>
table1-mapping
table2-mapping
...
</schemaname>
where the format of a table mapping depends on the tableforest parameter as explained above.
The result of a database content mapping looks like this:
<dbname>
<schema1name>
...
</schema1name>
<schema2name>
...
</schema2name>
...
</dbname>
where the schema mapping is as above.
As an example of using the output produced by these functions, Example 9.1 shows an XSLT
stylesheet that converts the output of table_to_xml_and_xmlschema to an HTML document
containing a tabular rendition of the table data. In a similar manner, the results from these functions
can be converted into other XML-based formats.
Example 9.1. XSLT Stylesheet for Converting SQL/XML Output to HTML
<?xml version="1.0"?>
<xsl:stylesheet version="1.0"
xmlns:xsl="http://www.w3.org/1999/XSL/Transform"
325
Functions and Operators
xmlns:xsd="http://www.w3.org/2001/XMLSchema"
xmlns="http://www.w3.org/1999/xhtml"
>
<xsl:output method="xml"
doctype-system="http://www.w3.org/TR/xhtml1/DTD/xhtml1strict.dtd"
doctype-public="-//W3C/DTD XHTML 1.0 Strict//EN"
indent="yes"/>
<xsl:template match="/*">
<xsl:variable name="schema" select="//xsd:schema"/>
<xsl:variable name="tabletypename"
select="$schema/
xsd:element[@name=name(current())]/@type"/>
<xsl:variable name="rowtypename"
select="$schema/xsd:complexType[@name=
$tabletypename]/xsd:sequence/xsd:element[@name='row']/@type"/>
<html>
<head>
<title><xsl:value-of select="name(current())"/></title>
</head>
<body>
<table>
<tr>
<xsl:for-each select="$schema/xsd:complexType[@name=
$rowtypename]/xsd:sequence/xsd:element/@name">
<th><xsl:value-of select="."/></th>
</xsl:for-each>
</tr>
<xsl:for-each select="row">
<tr>
<xsl:for-each select="*">
<td><xsl:value-of select="."/></td>
</xsl:for-each>
</tr>
</xsl:for-each>
</table>
</body>
</html>
</xsl:template>
</xsl:stylesheet>
9.16. JSON Functions and Operators
This section describes:
• functions and operators for processing and creating JSON data
• the SQL/JSON path language
• the SQL/JSON query functions
To provide native support for JSON data types within the SQL environment, PostgreSQL implements
the SQL/JSON data model. This model comprises sequences of items. Each item can hold SQL scalar
326
Functions and Operators
values, with an additional SQL/JSON null value, and composite data structures that use JSON arrays
and objects. The model is a formalization of the implied data model in the JSON specification RFC
71594.
SQL/JSON allows you to handle JSON data alongside regular SQL data, with transaction support,
including:
• Uploading JSON data into the database and storing it in regular SQL columns as character or binary
strings.
• Generating JSON objects and arrays from relational data.
• Querying JSON data using SQL/JSON query functions and SQL/JSON path language expressions.
To learn more about the SQL/JSON standard, see [sqltr-19075-6]. For details on JSON types supported
in PostgreSQL, see Section 8.14.
9.16.1. Processing and Creating JSON Data
Table 9.45 shows the operators that are available for use with JSON data types (see Section 8.14).
In addition, the usual comparison operators shown in Table 9.1 are available for jsonb, though not
for json. The comparison operators follow the ordering rules for B-tree operations outlined in Section 8.14.4. See also Section 9.21 for the aggregate function json_agg which aggregates record
values as JSON, the aggregate function json_object_agg which aggregates pairs of values into
a JSON object, and their jsonb equivalents, jsonb_agg and jsonb_object_agg.
Table 9.45. json and jsonb Operators
Operator
Description
Example(s)
json -> integer → json
jsonb -> integer → jsonb
Extracts n'th element of JSON array (array elements are indexed from zero, but negative
integers count from the end).
'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json -> 2 →
{"c":"baz"}
'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json -> -3 →
{"a":"foo"}
json -> text → json
jsonb -> text → jsonb
Extracts JSON object field with the given key.
'{"a": {"b":"foo"}}'::json -> 'a' → {"b":"foo"}
json ->> integer → text
jsonb ->> integer → text
Extracts n'th element of JSON array, as text.
'[1,2,3]'::json ->> 2 → 3
json ->> text → text
jsonb ->> text → text
Extracts JSON object field with the given key, as text.
'{"a":1,"b":2}'::json ->> 'b' → 2
4
https://datatracker.ietf.org/doc/html/rfc7159
327
Functions and Operators
Operator
Description
Example(s)
json #> text[] → json
jsonb #> text[] → jsonb
Extracts JSON sub-object at the specified path, where path elements can be either field
keys or array indexes.
'{"a": {"b": ["foo","bar"]}}'::json #> '{a,b,1}' → "bar"
json #>> text[] → text
jsonb #>> text[] → text
Extracts JSON sub-object at the specified path as text.
'{"a": {"b": ["foo","bar"]}}'::json #>> '{a,b,1}' → bar
Note
The field/element/path extraction operators return NULL, rather than failing, if the JSON input
does not have the right structure to match the request; for example if no such key or array
element exists.
Some further operators exist only for jsonb, as shown in Table 9.46. Section 8.14.4 describes how
these operators can be used to effectively search indexed jsonb data.
Table 9.46. Additional jsonb Operators
Operator
Description
Example(s)
jsonb @> jsonb → boolean
Does the first JSON value contain the second? (See Section 8.14.3 for details about containment.)
'{"a":1, "b":2}'::jsonb @> '{"b":2}'::jsonb → t
jsonb <@ jsonb → boolean
Is the first JSON value contained in the second?
'{"b":2}'::jsonb <@ '{"a":1, "b":2}'::jsonb → t
jsonb ? text → boolean
Does the text string exist as a top-level key or array element within the JSON value?
'{"a":1, "b":2}'::jsonb ? 'b' → t
'["a", "b", "c"]'::jsonb ? 'b' → t
jsonb ?| text[] → boolean
Do any of the strings in the text array exist as top-level keys or array elements?
'{"a":1, "b":2, "c":3}'::jsonb ?| array['b', 'd'] → t
jsonb ?& text[] → boolean
Do all of the strings in the text array exist as top-level keys or array elements?
'["a", "b", "c"]'::jsonb ?& array['a', 'b'] → t
jsonb || jsonb → jsonb
Concatenates two jsonb values. Concatenating two arrays generates an array containing
all the elements of each input. Concatenating two objects generates an object containing
328
Functions and Operators
Operator
Description
Example(s)
the union of their keys, taking the second object's value when there are duplicate keys.
All other cases are treated by converting a non-array input into a single-element array,
and then proceeding as for two arrays. Does not operate recursively: only the top-level
array or object structure is merged.
'["a", "b"]'::jsonb || '["a", "d"]'::jsonb → ["a", "b", "a",
"d"]
'{"a": "b"}'::jsonb || '{"c": "d"}'::jsonb → {"a": "b", "c":
"d"}
'[1, 2]'::jsonb || '3'::jsonb → [1, 2, 3]
'{"a": "b"}'::jsonb || '42'::jsonb → [{"a": "b"}, 42]
To append an array to another array as a single entry, wrap it in an additional layer of array, for example:
'[1, 2]'::jsonb || jsonb_build_array('[3, 4]'::jsonb) → [1,
2, [3, 4]]
jsonb - text → jsonb
Deletes a key (and its value) from a JSON object, or matching string value(s) from a
JSON array.
'{"a": "b", "c": "d"}'::jsonb - 'a' → {"c": "d"}
'["a", "b", "c", "b"]'::jsonb - 'b' → ["a", "c"]
jsonb - text[] → jsonb
Deletes all matching keys or array elements from the left operand.
'{"a": "b", "c": "d"}'::jsonb - '{a,c}'::text[] → {}
jsonb - integer → jsonb
Deletes the array element with specified index (negative integers count from the end).
Throws an error if JSON value is not an array.
'["a", "b"]'::jsonb - 1 → ["a"]
jsonb #- text[] → jsonb
Deletes the field or array element at the specified path, where path elements can be either
field keys or array indexes.
'["a", {"b":1}]'::jsonb #- '{1,b}' → ["a", {}]
jsonb @? jsonpath → boolean
Does JSON path return any item for the specified JSON value? (This is useful only with
SQL-standard JSON path expressions, not predicate check expressions, since those always return a value.)
'{"a":[1,2,3,4,5]}'::jsonb @? '$.a[*] ? (@ > 2)' → t
jsonb @@ jsonpath → boolean
Returns the result of a JSON path predicate check for the specified JSON value. (This is
useful only with predicate check expressions, not SQL-standard JSON path expressions,
since it will return NULL if the path result is not a single boolean value.)
'{"a":[1,2,3,4,5]}'::jsonb @@ '$.a[*] > 2' → t
Note
The jsonpath operators @? and @@ suppress the following errors: missing object field or
array element, unexpected JSON item type, datetime and numeric errors. The jsonpath-
329
Functions and Operators
related functions described below can also be told to suppress these types of errors. This behavior might be helpful when searching JSON document collections of varying structure.
Table 9.47 shows the functions that are available for constructing json and jsonb values. Some
functions in this table have a RETURNING clause, which specifies the data type returned. It must be
one of json, jsonb, bytea, a character string type (text, char, or varchar), or a type that can
be cast to json. By default, the json type is returned.
Table 9.47. JSON Creation Functions
Function
Description
Example(s)
to_json ( anyelement ) → json
to_jsonb ( anyelement ) → jsonb
Converts any SQL value to json or jsonb. Arrays and composites are converted recursively to arrays and objects (multidimensional arrays become arrays of arrays in JSON).
Otherwise, if there is a cast from the SQL data type to json, the cast function will be
used to perform the conversion;a otherwise, a scalar JSON value is produced. For any
scalar other than a number, a Boolean, or a null value, the text representation will be
used, with escaping as necessary to make it a valid JSON string value.
to_json('Fred said "Hi."'::text) → "Fred said \"Hi.\""
to_jsonb(row(42, 'Fred said "Hi."'::text)) → {"f1": 42,
"f2": "Fred said \"Hi.\""}
array_to_json ( anyarray [, boolean ] ) → json
Converts an SQL array to a JSON array. The behavior is the same as to_json except
that line feeds will be added between top-level array elements if the optional boolean parameter is true.
array_to_json('{{1,5},{99,100}}'::int[]) → [[1,5],[99,100]]
json_array ( [ { value_expression [ FORMAT JSON ] } [, ...] ] [ { NULL | ABSENT }
ON NULL ] [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ])
json_array ( [ query_expression ] [ RETURNING data_type [ FORMAT JSON [
ENCODING UTF8 ] ] ])
Constructs a JSON array from either a series of value_expression parameters or
from the results of query_expression, which must be a SELECT query returning a
single column. If ABSENT ON NULL is specified, NULL values are ignored. This is always the case if a query_expression is used.
json_array(1,true,json '{"a":null}') → [1, true, {"a":null}]
json_array(SELECT * FROM (VALUES(1),(2)) t) → [1, 2]
row_to_json ( record [, boolean ] ) → json
Converts an SQL composite value to a JSON object. The behavior is the same as to_json except that line feeds will be added between top-level elements if the optional
boolean parameter is true.
row_to_json(row(1,'foo')) → {"f1":1,"f2":"foo"}
json_build_array ( VARIADIC "any" ) → json
jsonb_build_array ( VARIADIC "any" ) → jsonb
Builds a possibly-heterogeneously-typed JSON array out of a variadic argument list.
Each argument is converted as per to_json or to_jsonb.
json_build_array(1, 2, 'foo', 4, 5) → [1, 2, "foo", 4, 5]
json_build_object ( VARIADIC "any" ) → json
330
Functions and Operators
Function
Description
Example(s)
jsonb_build_object ( VARIADIC "any" ) → jsonb
Builds a JSON object out of a variadic argument list. By convention, the argument list
consists of alternating keys and values. Key arguments are coerced to text; value arguments are converted as per to_json or to_jsonb.
json_build_object('foo', 1, 2, row(3,'bar')) → {"foo" : 1,
"2" : {"f1":3,"f2":"bar"}}
json_object ( [ { key_expression { VALUE | ':' } value_expression [ FORMAT
JSON [ ENCODING UTF8 ] ] }[, ...] ] [ { NULL | ABSENT } ON NULL ] [ { WITH |
WITHOUT } UNIQUE [ KEYS ] ] [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ])
Constructs a JSON object of all the key/value pairs given, or an empty object if none are
given. key_expression is a scalar expression defining the JSON key, which is converted to the text type. It cannot be NULL nor can it belong to a type that has a cast to
the json type. If WITH UNIQUE KEYS is specified, there must not be any duplicate
key_expression. Any pair for which the value_expression evaluates to NULL
is omitted from the output if ABSENT ON NULL is specified; if NULL ON NULL is
specified or the clause omitted, the key is included with value NULL.
json_object('code' VALUE 'P123', 'title': 'Jaws') →
{"code" : "P123", "title" : "Jaws"}
json_object ( text[] ) → json
jsonb_object ( text[] ) → jsonb
Builds a JSON object out of a text array. The array must have either exactly one dimension with an even number of members, in which case they are taken as alternating
key/value pairs, or two dimensions such that each inner array has exactly two elements,
which are taken as a key/value pair. All values are converted to JSON strings.
json_object('{a, 1, b, "def", c, 3.5}') → {"a" : "1", "b" :
"def", "c" : "3.5"}
json_object('{{a, 1}, {b, "def"}, {c, 3.5}}') → {"a" : "1",
"b" : "def", "c" : "3.5"}
json_object ( keys text[], values text[] ) → json
jsonb_object ( keys text[], values text[] ) → jsonb
This form of json_object takes keys and values pairwise from separate text arrays.
Otherwise it is identical to the one-argument form.
json_object('{a,b}', '{1,2}') → {"a": "1", "b": "2"}
json ( expression [ FORMAT JSON [ ENCODING UTF8 ]] [ { WITH | WITHOUT }
UNIQUE [ KEYS ]] ) → json
Converts a given expression specified as text or bytea string (in UTF8 encoding)
into a JSON value. If expression is NULL, an SQL null value is returned. If WITH
UNIQUE is specified, the expression must not contain any duplicate object keys.
json('{"a":123, "b":[true,"foo"], "a":"bar"}') → {"a":123,
"b":[true,"foo"], "a":"bar"}
json_scalar ( expression )
Converts a given SQL scalar value into a JSON scalar value. If the input is NULL, an
SQL null is returned. If the input is number or a boolean value, a corresponding JSON
number or boolean value is returned. For any other value, a JSON string is returned.
json_scalar(123.45) → 123.45
331
Functions and Operators
Function
Description
Example(s)
json_scalar(CURRENT_TIMESTAMP) →
"2022-05-10T10:51:04.62128-04:00"
json_serialize ( expression [ FORMAT JSON [ ENCODING UTF8 ] ] [ RETURNING
data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ] )
Converts an SQL/JSON expression into a character or binary string. The expression
can be of any JSON type, any character string type, or bytea in UTF8 encoding. The
returned type used in RETURNING can be any character string type or bytea. The default is text.
json_serialize('{ "a" : 1 } ' RETURNING bytea) →
\x7b20226122203a2031207d20
a
For example, the hstore extension has a cast from hstore to json, so that hstore values converted via the JSON creation
functions will be represented as JSON objects, not as primitive string values.
Table 9.48 details SQL/JSON facilities for testing JSON.
Table 9.48. SQL/JSON Testing Functions
Function signature
Description
Example(s)
expression IS [ NOT ] JSON [ { VALUE | SCALAR | ARRAY | OBJECT } ] [ { WITH |
WITHOUT } UNIQUE [ KEYS ] ]
This predicate tests whether expression can be parsed as JSON, possibly of a specified type. If SCALAR or ARRAY or OBJECT is specified, the test is whether or not the
JSON is of that particular type. If WITH UNIQUE KEYS is specified, then any object in
the expression is also tested to see if it has duplicate keys.
SELECT js,
js IS JSON "json?",
js IS JSON SCALAR "scalar?",
js IS JSON OBJECT "object?",
js IS JSON ARRAY "array?"
FROM (VALUES
('123'), ('"abc"'), ('{"a": "b"}'), ('[1,2]'),
('abc')) foo(js);
js
| json? | scalar? | object? | array?
------------+-------+---------+---------+-------123
| t
| t
| f
| f
"abc"
| t
| t
| f
| f
{"a": "b"} | t
| f
| t
| f
[1,2]
| t
| f
| f
| t
abc
| f
| f
| f
| f
SELECT js,
js IS JSON OBJECT "object?",
js IS JSON ARRAY "array?",
js IS JSON ARRAY WITH UNIQUE KEYS "array w. UK?",
js IS JSON ARRAY WITHOUT UNIQUE KEYS "array w/o UK?"
FROM (VALUES ('[{"a":"1"},
{"b":"2","b":"3"}]')) foo(js);
-[ RECORD 1 ]-+-------------------js
| [{"a":"1"},
+
332
Functions and Operators
Function signature
Description
Example(s)
| {"b":"2","b":"3"}]
object?
| f
array?
| t
array w. UK? | f
array w/o UK? | t
Table 9.49 shows the functions that are available for processing json and jsonb values.
Table 9.49. JSON Processing Functions
Function
Description
Example(s)
json_array_elements ( json ) → setof json
jsonb_array_elements ( jsonb ) → setof jsonb
Expands the top-level JSON array into a set of JSON values.
select * from json_array_elements('[1,true, [2,false]]') →
value
----------1
true
[2,false]
json_array_elements_text ( json ) → setof text
jsonb_array_elements_text ( jsonb ) → setof text
Expands the top-level JSON array into a set of text values.
select * from json_array_elements_text('["foo", "bar"]') →
value
----------foo
bar
json_array_length ( json ) → integer
jsonb_array_length ( jsonb ) → integer
Returns the number of elements in the top-level JSON array.
json_array_length('[1,2,3,{"f1":1,"f2":[5,6]},4]') → 5
jsonb_array_length('[]') → 0
json_each ( json ) → setof record ( key text, value json )
jsonb_each ( jsonb ) → setof record ( key text, value jsonb )
Expands the top-level JSON object into a set of key/value pairs.
select * from json_each('{"a":"foo", "b":"bar"}') →
key | value
-----+------a
| "foo"
b
| "bar"
333
Functions and Operators
Function
Description
Example(s)
json_each_text ( json ) → setof record ( key text, value text )
jsonb_each_text ( jsonb ) → setof record ( key text, value text )
Expands the top-level JSON object into a set of key/value pairs. The returned values
will be of type text.
select * from json_each_text('{"a":"foo", "b":"bar"}') →
key | value
-----+------a
| foo
b
| bar
json_extract_path ( from_json json, VARIADIC path_elems text[] ) → json
jsonb_extract_path ( from_json jsonb, VARIADIC path_elems text[] ) →
jsonb
Extracts JSON sub-object at the specified path. (This is functionally equivalent to the #>
operator, but writing the path out as a variadic list can be more convenient in some cases.)
json_extract_path('{"f2":{"f3":1},"f4":
{"f5":99,"f6":"foo"}}', 'f4', 'f6') → "foo"
json_extract_path_text ( from_json json, VARIADIC path_elems text[] )
→ text
jsonb_extract_path_text ( from_json jsonb, VARIADIC path_elems text[]
) → text
Extracts JSON sub-object at the specified path as text. (This is functionally equivalent
to the #>> operator.)
json_extract_path_text('{"f2":{"f3":1},"f4":
{"f5":99,"f6":"foo"}}', 'f4', 'f6') → foo
json_object_keys ( json ) → setof text
jsonb_object_keys ( jsonb ) → setof text
Returns the set of keys in the top-level JSON object.
select * from json_object_keys('{"f1":"abc","f2":{"f3":"a",
"f4":"b"}}') →
json_object_keys
-----------------f1
f2
json_populate_record ( base anyelement, from_json json ) → anyelement
jsonb_populate_record ( base anyelement, from_json jsonb ) → anyelement
Expands the top-level JSON object to a row having the composite type of the base argument. The JSON object is scanned for fields whose names match column names of the
output row type, and their values are inserted into those columns of the output. (Fields
that do not correspond to any output column name are ignored.) In typical use, the value
of base is just NULL, which means that any output columns that do not match any object field will be filled with nulls. However, if base isn't NULL then the values it contains will be used for unmatched columns.
334
Functions and Operators
Function
Description
Example(s)
To convert a JSON value to the SQL type of an output column, the following rules are
applied in sequence:
• A JSON null value is converted to an SQL null in all cases.
• If the output column is of type json or jsonb, the JSON value is just reproduced exactly.
• If the output column is a composite (row) type, and the JSON value is a JSON object,
the fields of the object are converted to columns of the output row type by recursive
application of these rules.
• Likewise, if the output column is an array type and the JSON value is a JSON array,
the elements of the JSON array are converted to elements of the output array by recursive application of these rules.
• Otherwise, if the JSON value is a string, the contents of the string are fed to the input
conversion function for the column's data type.
• Otherwise, the ordinary text representation of the JSON value is fed to the input conversion function for the column's data type.
While the example below uses a constant JSON value, typical use would be to reference
a json or jsonb column laterally from another table in the query's FROM clause. Writing json_populate_record in the FROM clause is good practice, since all of the
extracted columns are available for use without duplicate function calls.
create type subrowtype as (d int, e text); create type myrowtype as (a int, b text[], c subrowtype);
select * from json_populate_record(null::myrowtype, '{"a":
1, "b": ["2", "a b"], "c": {"d": 4, "e": "a b c"}, "x":
"foo"}') →
a |
b
|
c
---+-----------+------------1 | {2,"a b"} | (4,"a b c")
jsonb_populate_record_valid ( base anyelement, from_json json ) →
boolean
Function for testing jsonb_populate_record. Returns true if the input jsonb_populate_record would finish without an error for the given input JSON object;
that is, it's valid input, false otherwise.
create type jsb_char2 as (a char(2));
select jsonb_populate_record_valid(NULL::jsb_char2, '{"a":
"aaa"}'); →
jsonb_populate_record_valid
----------------------------f
(1 row)
select * from jsonb_populate_record(NULL::jsb_char2, '{"a":
"aaa"}') q; →
ERROR:
value too long for type character(2)
335
Functions and Operators
Function
Description
Example(s)
select jsonb_populate_record_valid(NULL::jsb_char2, '{"a":
"aa"}'); →
jsonb_populate_record_valid
----------------------------t
(1 row)
select * from jsonb_populate_record(NULL::jsb_char2, '{"a":
"aa"}') q; →
a
---aa
(1 row)
json_populate_recordset ( base anyelement, from_json json ) → setof
anyelement
jsonb_populate_recordset ( base anyelement, from_json jsonb ) → setof
anyelement
Expands the top-level JSON array of objects to a set of rows having the composite type
of the base argument. Each element of the JSON array is processed as described above
for json[b]_populate_record.
create type twoints as (a int, b int);
select * from json_populate_recordset(null::twoints,
'[{"a":1,"b":2}, {"a":3,"b":4}]') →
a | b
---+--1 | 2
3 | 4
json_to_record ( json ) → record
jsonb_to_record ( jsonb ) → record
Expands the top-level JSON object to a row having the composite type defined by an
AS clause. (As with all functions returning record, the calling query must explicitly
define the structure of the record with an AS clause.) The output record is filled from
fields of the JSON object, in the same way as described above for json[b]_populate_record. Since there is no input record value, unmatched columns are always
filled with nulls.
create type myrowtype as (a int, b text);
select * from json_to_record('{"a":1,"b":[1,2,3],"c":
[1,2,3],"e":"bar","r": {"a": 123, "b": "a b c"}}') as x(a
int, b text, c int[], d text, r myrowtype) →
a |
b
|
c
| d |
r
---+---------+---------+---+--------------1 | [1,2,3] | {1,2,3} |
| (123,"a b c")
json_to_recordset ( json ) → setof record
336
Functions and Operators
Function
Description
Example(s)
jsonb_to_recordset ( jsonb ) → setof record
Expands the top-level JSON array of objects to a set of rows having the composite type
defined by an AS clause. (As with all functions returning record, the calling query
must explicitly define the structure of the record with an AS clause.) Each element of the
JSON array is processed as described above for json[b]_populate_record.
select * from json_to_recordset('[{"a":1,"b":"foo"},
{"a":"2","c":"bar"}]') as x(a int, b text) →
a | b
---+----1 | foo
2 |
jsonb_set ( target jsonb, path text[], new_value jsonb [, create_if_missing boolean ] ) → jsonb
Returns target with the item designated by path replaced by new_value, or with
new_value added if create_if_missing is true (which is the default) and the
item designated by path does not exist. All earlier steps in the path must exist, or the
target is returned unchanged. As with the path oriented operators, negative integers
that appear in the path count from the end of JSON arrays. If the last path step is an
array index that is out of range, and create_if_missing is true, the new value is
added at the beginning of the array if the index is negative, or at the end of the array if it
is positive.
jsonb_set('[{"f1":1,"f2":null},2,null,3]', '{0,f1}',
'[2,3,4]', false) → [{"f1": [2, 3, 4], "f2": null}, 2, null,
3]
jsonb_set('[{"f1":1,"f2":null},2]', '{0,f3}', '[2,3,4]') →
[{"f1": 1, "f2": null, "f3": [2, 3, 4]}, 2]
jsonb_set_lax ( target jsonb, path text[], new_value jsonb [, create_if_missing boolean [, null_value_treatment text ]] ) → jsonb
If new_value is not NULL, behaves identically to jsonb_set. Otherwise behaves according to the value of null_value_treatment which must be one
of 'raise_exception', 'use_json_null', 'delete_key', or 'return_target'. The default is 'use_json_null'.
jsonb_set_lax('[{"f1":1,"f2":null},2,null,3]', '{0,f1}',
null) → [{"f1": null, "f2": null}, 2, null, 3]
jsonb_set_lax('[{"f1":99,"f2":null},2]', '{0,f3}', null,
true, 'return_target') → [{"f1": 99, "f2": null}, 2]
jsonb_insert ( target jsonb, path text[], new_value jsonb [, insert_after
boolean ] ) → jsonb
Returns target with new_value inserted. If the item designated by the path is an
array element, new_value will be inserted before that item if insert_after is false
(which is the default), or after it if insert_after is true. If the item designated by the
path is an object field, new_value will be inserted only if the object does not already
contain that key. All earlier steps in the path must exist, or the target is returned unchanged. As with the path oriented operators, negative integers that appear in the path
count from the end of JSON arrays. If the last path step is an array index that is out of
range, the new value is added at the beginning of the array if the index is negative, or at
the end of the array if it is positive.
337
Functions and Operators
Function
Description
Example(s)
jsonb_insert('{"a": [0,1,2]}', '{a, 1}', '"new_value"') →
{"a": [0, "new_value", 1, 2]}
jsonb_insert('{"a": [0,1,2]}', '{a, 1}', '"new_value"',
true) → {"a": [0, 1, "new_value", 2]}
json_strip_nulls ( json ) → json
jsonb_strip_nulls ( jsonb ) → jsonb
Deletes all object fields that have null values from the given JSON value, recursively.
Null values that are not object fields are untouched.
json_strip_nulls('[{"f1":1, "f2":null}, 2, null, 3]') →
[{"f1":1},2,null,3]
jsonb_path_exists ( target jsonb, path jsonpath [, vars jsonb [, silent
boolean ]] ) → boolean
Checks whether the JSON path returns any item for the specified JSON value. (This is
useful only with SQL-standard JSON path expressions, not predicate check expressions,
since those always return a value.) If the vars argument is specified, it must be a JSON
object, and its fields provide named values to be substituted into the jsonpath expression. If the silent argument is specified and is true, the function suppresses the same
errors as the @? and @@ operators do.
jsonb_path_exists('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min
&& @ <= $max)', '{"min":2, "max":4}') → t
jsonb_path_match ( target jsonb, path jsonpath [, vars jsonb [, silent
boolean ]] ) → boolean
Returns the SQL boolean result of a JSON path predicate check for the specified JSON
value. (This is useful only with predicate check expressions, not SQL-standard JSON
path expressions, since it will either fail or return NULL if the path result is not a single
boolean value.) The optional vars and silent arguments act the same as for jsonb_path_exists.
jsonb_path_match('{"a":[1,2,3,4,5]}', 'exists($.a[*] ? (@
>= $min && @ <= $max))', '{"min":2, "max":4}') → t
jsonb_path_query ( target jsonb, path jsonpath [, vars jsonb [, silent
boolean ]] ) → setof jsonb
Returns all JSON items returned by the JSON path for the specified JSON value. For
SQL-standard JSON path expressions it returns the JSON values selected from target. For predicate check expressions it returns the result of the predicate check: true,
false, or null. The optional vars and silent arguments act the same as for
jsonb_path_exists.
select * from jsonb_path_query('{"a":[1,2,3,4,5]}',
'$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2, "max":4}')
→
jsonb_path_query
-----------------2
3
4
jsonb_path_query_array ( target jsonb, path jsonpath [, vars jsonb [,
silent boolean ]] ) → jsonb
338
Functions and Operators
Function
Description
Example(s)
Returns all JSON items returned by the JSON path for the specified JSON value, as a
JSON array. The parameters are the same as for jsonb_path_query.
jsonb_path_query_array('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >=
$min && @ <= $max)', '{"min":2, "max":4}') → [2, 3, 4]
jsonb_path_query_first ( target jsonb, path jsonpath [, vars jsonb [,
silent boolean ]] ) → jsonb
Returns the first JSON item returned by the JSON path for the specified JSON value, or
NULL if there are no results. The parameters are the same as for jsonb_path_query.
jsonb_path_query_first('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >=
$min && @ <= $max)', '{"min":2, "max":4}') → 2
jsonb_path_exists_tz ( target jsonb, path jsonpath [, vars jsonb [, silent
boolean ]] ) → boolean
jsonb_path_match_tz ( target jsonb, path jsonpath [, vars jsonb [, silent
boolean ]] ) → boolean
jsonb_path_query_tz ( target jsonb, path jsonpath [, vars jsonb [, silent
boolean ]] ) → setof jsonb
jsonb_path_query_array_tz ( target jsonb, path jsonpath [, vars jsonb [,
silent boolean ]] ) → jsonb
jsonb_path_query_first_tz ( target jsonb, path jsonpath [, vars jsonb [,
silent boolean ]] ) → jsonb
These functions act like their counterparts described above without the _tz suffix, except that these functions support comparisons of date/time values that require timezone-aware conversions. The example below requires interpretation of the date-only value 2015-08-02 as a timestamp with time zone, so the result depends on the current
TimeZone setting. Due to this dependency, these functions are marked as stable, which
means these functions cannot be used in indexes. Their counterparts are immutable, and
so can be used in indexes; but they will throw errors if asked to make such comparisons.
jsonb_path_exists_tz('["2015-08-01 12:00:00-05"]', '$[*] ?
(@.datetime() < "2015-08-02".datetime())') → t
jsonb_pretty ( jsonb ) → text
Converts the given JSON value to pretty-printed, indented text.
jsonb_pretty('[{"f1":1,"f2":null}, 2]') →
[
{
"f1": 1,
"f2": null
},
2
]
json_typeof ( json ) → text
jsonb_typeof ( jsonb ) → text
Returns the type of the top-level JSON value as a text string. Possible types are object,
array, string, number, boolean, and null. (The null result should not be confused with an SQL NULL; see the examples.)
json_typeof('-123.4') → number
json_typeof('null'::json) → null
339
Functions and Operators
Function
Description
Example(s)
json_typeof(NULL::json) IS NULL → t
9.16.2. The SQL/JSON Path Language
SQL/JSON path expressions specify item(s) to be retrieved from a JSON value, similarly to XPath
expressions used for access to XML content. In PostgreSQL, path expressions are implemented as the
jsonpath data type and can use any elements described in Section 8.14.7.
JSON query functions and operators pass the provided path expression to the path engine for evaluation. If the expression matches the queried JSON data, the corresponding JSON item, or set of items, is
returned. If there is no match, the result will be NULL, false, or an error, depending on the function.
Path expressions are written in the SQL/JSON path language and can include arithmetic expressions
and functions.
A path expression consists of a sequence of elements allowed by the jsonpath data type. The path
expression is normally evaluated from left to right, but you can use parentheses to change the order of
operations. If the evaluation is successful, a sequence of JSON items is produced, and the evaluation
result is returned to the JSON query function that completes the specified computation.
To refer to the JSON value being queried (the context item), use the $ variable in the path expression.
The first element of a path must always be $. It can be followed by one or more accessor operators,
which go down the JSON structure level by level to retrieve sub-items of the context item. Each
accessor operator acts on the result(s) of the previous evaluation step, producing zero, one, or more
output items from each input item.
For example, suppose you have some JSON data from a GPS tracker that you would like to parse,
such as:
SELECT '{
"track": {
"segments": [
{
"location":
[ 47.763, 13.4034 ],
"start time": "2018-10-14 10:05:14",
"HR": 73
},
{
"location":
[ 47.706, 13.2635 ],
"start time": "2018-10-14 10:39:21",
"HR": 135
}
]
}
}' AS json \gset
(The above example can be copied-and-pasted into psql to set things up for the following examples.
Then psql will expand :'json' into a suitably-quoted string constant containing the JSON value.)
To retrieve the available track segments, you need to use the .key accessor operator to descend
through surrounding JSON objects, for example:
=> select jsonb_path_query(:'json', '$.track.segments');
jsonb_path_query
340
Functions and Operators
----------------------------------------------------------------------------------------------------------------------------------------------------------------[{"HR": 73, "location": [47.763, 13.4034], "start time":
"2018-10-14 10:05:14"}, {"HR": 135, "location": [47.706, 13.2635],
"start time": "2018-10-14 10:39:21"}]
To retrieve the contents of an array, you typically use the [*] operator. The following example will
return the location coordinates for all the available track segments:
=> select jsonb_path_query(:'json',
'$.track.segments[*].location');
jsonb_path_query
------------------[47.763, 13.4034]
[47.706, 13.2635]
Here we started with the whole JSON input value ($), then the .track accessor selected the JSON
object associated with the "track" object key, then the .segments accessor selected the JSON
array associated with the "segments" key within that object, then the [*] accessor selected each
element of that array (producing a series of items), then the .location accessor selected the JSON
array associated with the "location" key within each of those objects. In this example, each of
those objects had a "location" key; but if any of them did not, the .location accessor would
have simply produced no output for that input item.
To return the coordinates of the first segment only, you can specify the corresponding subscript in the
[] accessor operator. Recall that JSON array indexes are 0-relative:
=> select jsonb_path_query(:'json',
'$.track.segments[0].location');
jsonb_path_query
------------------[47.763, 13.4034]
The result of each path evaluation step can be processed by one or more of the jsonpath operators
and methods listed in Section 9.16.2.3. Each method name must be preceded by a dot. For example,
you can get the size of an array:
=> select jsonb_path_query(:'json', '$.track.segments.size()');
jsonb_path_query
-----------------2
More examples of using jsonpath operators and methods within path expressions appear below in
Section 9.16.2.3.
A path can also contain filter expressions that work similarly to the WHERE clause in SQL. A filter
expression begins with a question mark and provides a condition in parentheses:
? (condition)
Filter expressions must be written just after the path evaluation step to which they should apply. The
result of that step is filtered to include only those items that satisfy the provided condition. SQL/JSON
defines three-valued logic, so the condition can produce true, false, or unknown. The unknown
value plays the same role as SQL NULL and can be tested for with the is unknown predicate. Further
path evaluation steps use only those items for which the filter expression returned true.
341
Functions and Operators
The functions and operators that can be used in filter expressions are listed in Table 9.51. Within a
filter expression, the @ variable denotes the value being considered (i.e., one result of the preceding
path step). You can write accessor operators after @ to retrieve component items.
For example, suppose you would like to retrieve all heart rate values higher than 130. You can achieve
this as follows:
=> select jsonb_path_query(:'json', '$.track.segments[*].HR ? (@ >
130)');
jsonb_path_query
-----------------135
To get the start times of segments with such values, you have to filter out irrelevant segments before
selecting the start times, so the filter expression is applied to the previous step, and the path used in
the condition is different:
=> select jsonb_path_query(:'json', '$.track.segments[*] ? (@.HR >
130)."start time"');
jsonb_path_query
----------------------"2018-10-14 10:39:21"
You can use several filter expressions in sequence, if required. The following example selects start
times of all segments that contain locations with relevant coordinates and high heart rate values:
=> select jsonb_path_query(:'json', '$.track.segments[*] ?
(@.location[1] < 13.4) ? (@.HR > 130)."start time"');
jsonb_path_query
----------------------"2018-10-14 10:39:21"
Using filter expressions at different nesting levels is also allowed. The following example first filters
all segments by location, and then returns high heart rate values for these segments, if available:
=> select jsonb_path_query(:'json', '$.track.segments[*] ?
(@.location[1] < 13.4).HR ? (@ > 130)');
jsonb_path_query
-----------------135
You can also nest filter expressions within each other. This example returns the size of the track if it
contains any segments with high heart rate values, or an empty sequence otherwise:
=> select jsonb_path_query(:'json', '$.track ?
(exists(@.segments[*] ? (@.HR > 130))).segments.size()');
jsonb_path_query
-----------------2
9.16.2.1. Deviations from the SQL Standard
PostgreSQL's implementation of the SQL/JSON path language has the following deviations from the
SQL/JSON standard.
342
Functions and Operators
9.16.2.1.1. Boolean Predicate Check Expressions
As an extension to the SQL standard, a PostgreSQL path expression can be a Boolean predicate,
whereas the SQL standard allows predicates only within filters. While SQL-standard path expressions
return the relevant element(s) of the queried JSON value, predicate check expressions return the single
three-valued jsonb result of the predicate: true, false, or null. For example, we could write
this SQL-standard filter expression:
=> select jsonb_path_query(:'json', '$.track.segments ?(@[*].HR >
130)');
jsonb_path_query
-------------------------------------------------------------------------------{"HR": 135, "location": [47.706, 13.2635], "start time":
"2018-10-14 10:39:21"}
The similar predicate check expression simply returns true, indicating that a match exists:
=> select jsonb_path_query(:'json', '$.track.segments[*].HR >
130');
jsonb_path_query
-----------------true
Note
Predicate check expressions are required in the @@ operator (and the jsonb_path_match
function), and should not be used with the @? operator (or the jsonb_path_exists function).
9.16.2.1.2. Regular Expression Interpretation
There are minor differences in the interpretation of regular expression patterns used in like_regex
filters, as described in Section 9.16.2.4.
9.16.2.2. Strict and Lax Modes
When you query JSON data, the path expression may not match the actual JSON data structure. An
attempt to access a non-existent member of an object or element of an array is defined as a structural
error. SQL/JSON path expressions have two modes of handling structural errors:
• lax (default) — the path engine implicitly adapts the queried data to the specified path. Any structural errors that cannot be fixed as described below are suppressed, producing no match.
• strict — if a structural error occurs, an error is raised.
Lax mode facilitates matching of a JSON document and path expression when the JSON data does
not conform to the expected schema. If an operand does not match the requirements of a particular
operation, it can be automatically wrapped as an SQL/JSON array, or unwrapped by converting its
elements into an SQL/JSON sequence before performing the operation. Also, comparison operators
automatically unwrap their operands in lax mode, so you can compare SQL/JSON arrays out-of-thebox. An array of size 1 is considered equal to its sole element. Automatic unwrapping is not performed
when:
• The path expression contains type() or size() methods that return the type and the number of
elements in the array, respectively.
343
Functions and Operators
• The queried JSON data contain nested arrays. In this case, only the outermost array is unwrapped,
while all the inner arrays remain unchanged. Thus, implicit unwrapping can only go one level down
within each path evaluation step.
For example, when querying the GPS data listed above, you can abstract from the fact that it stores
an array of segments when using lax mode:
=> select jsonb_path_query(:'json', 'lax
$.track.segments.location');
jsonb_path_query
------------------[47.763, 13.4034]
[47.706, 13.2635]
In strict mode, the specified path must exactly match the structure of the queried JSON document, so
using this path expression will cause an error:
=> select jsonb_path_query(:'json', 'strict
$.track.segments.location');
ERROR: jsonpath member accessor can only be applied to an object
To get the same result as in lax mode, you have to explicitly unwrap the segments array:
=> select jsonb_path_query(:'json', 'strict
$.track.segments[*].location');
jsonb_path_query
------------------[47.763, 13.4034]
[47.706, 13.2635]
The unwrapping behavior of lax mode can lead to surprising results. For instance, the following query
using the .** accessor selects every HR value twice:
=> select jsonb_path_query(:'json', 'lax $.**.HR');
jsonb_path_query
-----------------73
135
73
135
This happens because the .** accessor selects both the segments array and each of its elements,
while the .HR accessor automatically unwraps arrays when using lax mode. To avoid surprising results, we recommend using the .** accessor only in strict mode. The following query selects each
HR value just once:
=> select jsonb_path_query(:'json', 'strict $.**.HR');
jsonb_path_query
-----------------73
135
The unwrapping of arrays can also lead to unexpected results. Consider this example, which selects
all the location arrays:
344
Functions and Operators
=> select jsonb_path_query(:'json', 'lax
$.track.segments[*].location');
jsonb_path_query
------------------[47.763, 13.4034]
[47.706, 13.2635]
(2 rows)
As expected it returns the full arrays. But applying a filter expression causes the arrays to be unwrapped
to evaluate each item, returning only the items that match the expression:
=> select jsonb_path_query(:'json', 'lax
$.track.segments[*].location ?(@[*] > 15)');
jsonb_path_query
-----------------47.763
47.706
(2 rows)
This despite the fact that the full arrays are selected by the path expression. Use strict mode to restore
selecting the arrays:
=> select jsonb_path_query(:'json', 'strict
$.track.segments[*].location ?(@[*] > 15)');
jsonb_path_query
------------------[47.763, 13.4034]
[47.706, 13.2635]
(2 rows)
9.16.2.3. SQL/JSON Path Operators and Methods
Table 9.50 shows the operators and methods available in jsonpath. Note that while the unary operators and methods can be applied to multiple values resulting from a preceding path step, the binary
operators (addition etc.) can only be applied to single values. In lax mode, methods applied to an array
will be executed for each value in the array. The exceptions are .type() and .size(), which
apply to the array itself.
Table 9.50. jsonpath Operators and Methods
Operator/Method
Description
Example(s)
number + number → number
Addition
jsonb_path_query('[2]', '$[0] + 3') → 5
+ number → number
Unary plus (no operation); unlike addition, this can iterate over multiple values
jsonb_path_query_array('{"x": [2,3,4]}', '+ $.x') → [2, 3,
4]
number - number → number
Subtraction
jsonb_path_query('[2]', '7 - $[0]') → 5
- number → number
345
Functions and Operators
Operator/Method
Description
Example(s)
Negation; unlike subtraction, this can iterate over multiple values
jsonb_path_query_array('{"x": [2,3,4]}', '- $.x') → [-2, -3,
-4]
number * number → number
Multiplication
jsonb_path_query('[4]', '2 * $[0]') → 8
number / number → number
Division
jsonb_path_query('[8.5]', '$[0] / 2') → 4.2500000000000000
number % number → number
Modulo (remainder)
jsonb_path_query('[32]', '$[0] % 10') → 2
value . type() → string
Type of the JSON item (see json_typeof)
jsonb_path_query_array('[1, "2", {}]', '$[*].type()') →
["number", "string", "object"]
value . size() → number
Size of the JSON item (number of array elements, or 1 if not an array)
jsonb_path_query('{"m": [11, 15]}', '$.m.size()') → 2
value . boolean() → boolean
Boolean value converted from a JSON boolean, number, or string
jsonb_path_query_array('[1, "yes", false]',
'$[*].boolean()') → [true, true, false]
value . string() → string
String value converted from a JSON boolean, number, string, or datetime
jsonb_path_query_array('[1.23, "xyz", false]',
'$[*].string()') → ["1.23", "xyz", "false"]
jsonb_path_query('"2023-08-15 12:34:56"', '$.timestamp().string()') → "2023-08-15T12:34:56"
value . double() → number
Approximate floating-point number converted from a JSON number or string
jsonb_path_query('{"len": "1.9"}', '$.len.double() * 2') →
3.8
number . ceiling() → number
Nearest integer greater than or equal to the given number
jsonb_path_query('{"h": 1.3}', '$.h.ceiling()') → 2
number . floor() → number
Nearest integer less than or equal to the given number
jsonb_path_query('{"h": 1.7}', '$.h.floor()') → 1
number . abs() → number
Absolute value of the given number
jsonb_path_query('{"z": -0.3}', '$.z.abs()') → 0.3
346
Functions and Operators
Operator/Method
Description
Example(s)
value . bigint() → bigint
Big integer value converted from a JSON number or string
jsonb_path_query('{"len": "9876543219"}', '$.len.bigint()')
→ 9876543219
value . decimal( [ precision [ , scale ] ] ) → decimal
Rounded decimal value converted from a JSON number or string (precision and
scale must be integer values)
jsonb_path_query('1234.5678', '$.decimal(6, 2)') → 1234.57
value . integer() → integer
Integer value converted from a JSON number or string
jsonb_path_query('{"len": "12345"}', '$.len.integer()') →
12345
value . number() → numeric
Numeric value converted from a JSON number or string
jsonb_path_query('{"len": "123.45"}', '$.len.number()') →
123.45
string . datetime() → datetime_type (see note)
Date/time value converted from a string
jsonb_path_query('["2015-8-1", "2015-08-12"]', '$[*] ?
(@.datetime() < "2015-08-2".datetime())') → "2015-8-1"
string . datetime(template) → datetime_type (see note)
Date/time value converted from a string using the specified to_timestamp template
jsonb_path_query_array('["12:30", "18:40"]', '$[*].datetime("HH24:MI")') → ["12:30:00", "18:40:00"]
string . date() → date
Date value converted from a string
jsonb_path_query('"2023-08-15"', '$.date()') → "2023-08-15"
string . time() → time without time zone
Time without time zone value converted from a string
jsonb_path_query('"12:34:56"', '$.time()') → "12:34:56"
string . time(precision) → time without time zone
Time without time zone value converted from a string, with fractional seconds adjusted
to the given precision
jsonb_path_query('"12:34:56.789"', '$.time(2)') →
"12:34:56.79"
string . time_tz() → time with time zone
Time with time zone value converted from a string
jsonb_path_query('"12:34:56 +05:30"', '$.time_tz()') →
"12:34:56+05:30"
string . time_tz(precision) → time with time zone
Time with time zone value converted from a string, with fractional seconds adjusted to
the given precision
347
Functions and Operators
Operator/Method
Description
Example(s)
jsonb_path_query('"12:34:56.789 +05:30"', '$.time_tz(2)') →
"12:34:56.79+05:30"
string . timestamp() → timestamp without time zone
Timestamp without time zone value converted from a string
jsonb_path_query('"2023-08-15 12:34:56"', '$.timestamp()')
→ "2023-08-15T12:34:56"
string . timestamp(precision) → timestamp without time zone
Timestamp without time zone value converted from a string, with fractional seconds adjusted to the given precision
jsonb_path_query('"2023-08-15 12:34:56.789"', '$.timestamp(2)') → "2023-08-15T12:34:56.79"
string . timestamp_tz() → timestamp with time zone
Timestamp with time zone value converted from a string
jsonb_path_query('"2023-08-15 12:34:56 +05:30"', '$.timestamp_tz()') → "2023-08-15T12:34:56+05:30"
string . timestamp_tz(precision) → timestamp with time zone
Timestamp with time zone value converted from a string, with fractional seconds adjusted to the given precision
jsonb_path_query('"2023-08-15 12:34:56.789 +05:30"',
'$.timestamp_tz(2)') → "2023-08-15T12:34:56.79+05:30"
object . keyvalue() → array
The object's key-value pairs, represented as an array of objects containing three fields:
"key", "value", and "id"; "id" is a unique identifier of the object the key-value
pair belongs to
jsonb_path_query_array('{"x": "20", "y": 32}', '$.keyvalue()') → [{"id": 0, "key": "x", "value": "20"}, {"id": 0,
"key": "y", "value": 32}]
Note
The result type of the datetime() and datetime(template) methods can be date,
timetz, time, timestamptz, or timestamp. Both methods determine their result type
dynamically.
The datetime() method sequentially tries to match its input string to the ISO formats for
date, timetz, time, timestamptz, and timestamp. It stops on the first matching
format and emits the corresponding data type.
The datetime(template) method determines the result type according to the fields used
in the provided template string.
The datetime() and datetime(template) methods use the same parsing rules as
the to_timestamp SQL function does (see Section 9.8), with three exceptions. First, these
methods don't allow unmatched template patterns. Second, only the following separators are
allowed in the template string: minus sign, period, solidus (slash), comma, apostrophe, semicolon, colon and space. Third, separators in the template string must exactly match the input
string.
If different date/time types need to be compared, an implicit cast is applied. A date value
can be cast to timestamp or timestamptz, timestamp can be cast to timestamptz,
348
Functions and Operators
and time to timetz. However, all but the first of these conversions depend on the current
TimeZone setting, and thus can only be performed within timezone-aware jsonpath functions. Similarly, other date/time-related methods that convert strings to date/time types also do
this casting, which may involve the current TimeZone setting. Therefore, these conversions
can also only be performed within timezone-aware jsonpath functions.
Table 9.51 shows the available filter expression elements.
Table 9.51. jsonpath Filter Expression Elements
Predicate/Value
Description
Example(s)
value == value → boolean
Equality comparison (this, and the other comparison operators, work on all JSON scalar
values)
jsonb_path_query_array('[1, "a", 1, 3]', '$[*] ? (@ == 1)')
→ [1, 1]
jsonb_path_query_array('[1, "a", 1, 3]', '$[*] ? (@ ==
"a")') → ["a"]
value != value → boolean
value <> value → boolean
Non-equality comparison
jsonb_path_query_array('[1, 2, 1, 3]', '$[*] ? (@ != 1)') →
[2, 3]
jsonb_path_query_array('["a", "b", "c"]', '$[*] ? (@ <>
"b")') → ["a", "c"]
value < value → boolean
Less-than comparison
jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ < 2)') → [1]
value <= value → boolean
Less-than-or-equal-to comparison
jsonb_path_query_array('["a", "b", "c"]', '$[*] ? (@ <=
"b")') → ["a", "b"]
value > value → boolean
Greater-than comparison
jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ > 2)') → [3]
value >= value → boolean
Greater-than-or-equal-to comparison
jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ >= 2)') → [2,
3]
true → boolean
JSON constant true
jsonb_path_query('[{"name": "John", "parent": false},
{"name": "Chris", "parent": true}]', '$[*] ? (@.parent ==
true)') → {"name": "Chris", "parent": true}
false → boolean
JSON constant false
349
Functions and Operators
Predicate/Value
Description
Example(s)
jsonb_path_query('[{"name": "John", "parent": false},
{"name": "Chris", "parent": true}]', '$[*] ? (@.parent ==
false)') → {"name": "John", "parent": false}
null → value
JSON constant null (note that, unlike in SQL, comparison to null works normally)
jsonb_path_query('[{"name": "Mary", "job": null}, {"name":
"Michael", "job": "driver"}]', '$[*] ? (@.job == null) .name') → "Mary"
boolean && boolean → boolean
Boolean AND
jsonb_path_query('[1, 3, 7]', '$[*] ? (@ > 1 && @ < 5)') → 3
boolean || boolean → boolean
Boolean OR
jsonb_path_query('[1, 3, 7]', '$[*] ? (@ < 1 || @ > 5)') → 7
! boolean → boolean
Boolean NOT
jsonb_path_query('[1, 3, 7]', '$[*] ? (!(@ < 5))') → 7
boolean is unknown → boolean
Tests whether a Boolean condition is unknown.
jsonb_path_query('[-1, 2, 7, "foo"]', '$[*] ? ((@ > 0) is
unknown)') → "foo"
string like_regex string [ flag string ] → boolean
Tests whether the first operand matches the regular expression given by the second
operand, optionally with modifications described by a string of flag characters (see
Section 9.16.2.4).
jsonb_path_query_array('["abc", "abd", "aBdC", "abdacb",
"babc"]', '$[*] ? (@ like_regex "^ab.*c")') → ["abc", "abdacb"]
jsonb_path_query_array('["abc", "abd", "aBdC", "abdacb",
"babc"]', '$[*] ? (@ like_regex "^ab.*c" flag "i")') →
["abc", "aBdC", "abdacb"]
string starts with string → boolean
Tests whether the second operand is an initial substring of the first operand.
jsonb_path_query('["John Smith", "Mary Stone", "Bob Johnson"]', '$[*] ? (@ starts with "John")') → "John Smith"
exists ( path_expression ) → boolean
Tests whether a path expression matches at least one SQL/JSON item. Returns unknown if the path expression would result in an error; the second example uses this to
avoid a no-such-key error in strict mode.
jsonb_path_query('{"x": [1, 2], "y": [2, 4]}', 'strict
$.* ? (exists (@ ? (@[*] > 2)))') → [2, 4]
jsonb_path_query_array('{"value": 41}', 'strict $ ? (exists
(@.name)) .name') → []
9.16.2.4. SQL/JSON Regular Expressions
350
Functions and Operators
SQL/JSON path expressions allow matching text to a regular expression with the like_regex filter.
For example, the following SQL/JSON path query would case-insensitively match all strings in an
array that start with an English vowel:
$[*] ? (@ like_regex "^[aeiou]" flag "i")
The optional flag string may include one or more of the characters i for case-insensitive match, m
to allow ^ and $ to match at newlines, s to allow . to match a newline, and q to quote the whole
pattern (reducing the behavior to a simple substring match).
The SQL/JSON standard borrows its definition for regular expressions from the LIKE_REGEX operator, which in turn uses the XQuery standard. PostgreSQL does not currently support the LIKE_REGEX
operator. Therefore, the like_regex filter is implemented using the POSIX reg