Analyzing Your Data with Analytic Functions
Carl Dudley
University of Wolverhampton, UK
UKOUG Council
Oracle ACE Director
carl.dudley@wlv.ac.uk
Introduction
Working with Oracle since 1986
Oracle DBA - OCP Oracle7, 8, 9, 10
Oracle DBA of the Year – 2002
Oracle ACE Director
Regular Presenter at Oracle Conferences
Consultant and Trainer
Technical Editor for a number of Oracle texts
UK Oracle User Group Council
Member of IOUC
Day job – University of Wolverhampton, UK
Carl Dudley University of Wolverhampton, UK
2
Analyzing Your Data with Analytic Functions
Overview of Analytic Functions
Ranking Functions
Partitioning
Aggregate Functions
Sliding Windows
Row Comparison Functions
Analytic Function Performance
Carl Dudley University of Wolverhampton, UK
3
Analytic Functions
 New set of functions introduced in Oracle 8.1.6
— Analytic functions or Window functions
 Intended for OLAP (OnLine Analytic Processing) or data warehouse purposes
 Provide functionality that would require complex conventional SQL
programming or other tools
 Advantages
— Improved performance
• The optimizer “understands” the purpose of the query
— Reduced dependency on report generators and client tools
— Simpler coding
Carl Dudley University of Wolverhampton, UK
4
Analytic Function Categories
 The analytic functions fall into four categories
Ranking functions
Aggregate functions
Row comparison functions
Statistical functions
 The Oracle documentation describes all of the functions
 Processed as the last step before ORDER BY
— Work on the result set of the query
— Can operate on an intermediate ordering of the rows
— Actions can be based on :
• Partitions of the result set
• A sliding window of rows in the result set
Carl Dudley University of Wolverhampton, UK
5
Processing Sequence
 There may be several intermediate sort steps if required
Analytic process
Rows
WHERE
evaluation
GROUPING
HAVING
evaluation
Intermediate
ordering
Analytic
function
Final
ORDER BY
Carl Dudley University of Wolverhampton, UK
Output
6
The Analytic Clause
 Syntax :
<function>(<arguments>) OVER(<analytic clause>)
 The enclosing parentheses are required even if there are no arguments
RANK() OVER (ORDER BY sal DESC)
Carl Dudley University of Wolverhampton, UK
7
Sequence of Processing
 Being processed just before the final ORDER BY means :
— Analytic functions are not allowed in WHERE and HAVING conditions
• Allowed only in the final ORDER BY clause
 Ordering the final result set
— OVER clause specifies sort order of result set before analytic function is
computed
— Can have multiple analytic functions with different OVER clauses, requiring
multiple intermediate sorts
— Final ordering does not have to match ordering in OVER clause
Carl Dudley University of Wolverhampton, UK
8
The emp and dept Tables
Analytic Functions
emp
dept
DEPTNO
-----10
20
30
40
EMPNO
----7934
7782
7839
7369
7876
7566
7902
7788
7900
7521
7654
7844
7499
7698
DNAME
LOC
-------------- -------ACCOUNTING
NEW YORK
RESEARCH
DALLAS
SALES Overview of
CHICAGO
Analytic Functions
OPERATIONS
BOSTON
Ranking Functions
ENAME
JOB
MGR HIREDATE
SAL COMM DEPTNO
------- --------- ----- ----------- ----- ----- -----MILLER Partitioning
CLERK
7782 23-JAN-1982 1300
10
CLARK
MANAGER
7839 09-JUN-1981 2450
10
KING
PRESIDENTFunctions
17-NOV-1981 5000
10
Aggregate
SMITH
CLERK
7902 17-DEC-1980
800
20
ADAMS
CLERK
7788 12-JAN-1983 1100
20
Windows
JONES Sliding
MANAGER
7839 02-APR-1981 2975
20
FORD
ANALYST
7566 03-DEC-1981 3000
20
SCOTT
ANALYST
7566 09-DEC-1982 3000
20
Row
Comparison
Functions
JAMES
CLERK
7698 03-DEC-1981
950
30
WARD
SALESMAN
7698 22-FEB-1981 1250
500
30
MARTIN Analytic
SALESMAN
7698Performance
28-SEP-1981 1250 1400
30
Function
TURNER SALESMAN
7698 08-SEP-1981 1500
0
30
ALLEN
SALESMAN
7698 20-FEB-1981 1600
300
30
BLAKE
MANAGER
7839 01-MAY-1981 2850
30
Carl Dudley University of Wolverhampton, UK
9
Example of Ranking
 Ranking with ROW_NUMBER
— No handling of ties
• Rows retrieved by the query are intermediately sorted on descending
salary for the analysis
SELECT ROW_NUMBER() OVER(
ORDER BY sal DESC) rownumber
,sal
,ename
FROM
emp
ORDER BY sal DESC;
— If the final ORDER BY specifies the same sort
order as the OVER clause only one sort is required
— ROW_NUMBER is different from ROWNUM
ROWNUMBER
--------1
2
3
4
5
6
7
8
9
10
11
12
13
14
Carl Dudley University of Wolverhampton, UK
SAL
---5000
3000
3000
2975
2850
2450
1600
1500
1300
1250
1250
1100
950
800
ENAME
----KING
SCOTT
FORD
JONES
BLAKE
CLARK
ALLEN
TURNER
MILLER
WARD
MARTIN
ADAMS
JAMES
SMITH
10
Different Sort Order in Final ORDER BY
 If the OVER clause sort is different from the final ORDER BY
— An extra sort step is required
SELECT ROW_NUMBER() OVER(
ORDER BY sal DESC) rownumber
,sal
,ename
FROM emp
ORDER BY ename;
ROWNUMBER
--------12
7
5
6
3
13
4
1
11
9
2
14
8
10
Carl Dudley University of Wolverhampton, UK
SAL
---1100
1600
2850
2450
3000
950
2975
5000
1250
1300
3000
800
1500
1250
ENAME
-----ADAMS
ALLEN
BLAKE
CLARK
FORD
JAMES
JONES
KING
MARTIN
MILLER
SCOTT
SMITH
TURNER
WARD
11
Multiple Functions With Different Sort Order
 Multiple OVER clauses can be used
SELECT ROW_NUMBER() OVER(ORDER BY sal DESC) sal_n
,sal
,ROW_NUMBER() OVER(ORDER BY comm DESC NULLS LAST) comm_n
,comm
,ename
FROM emp
ORDER BY ename;
Carl Dudley University of Wolverhampton, UK
12
RANK and DENSE_RANK
 ROW_NUMBER increases even if several rows have identical values
— Does not handle ties
 RANK and DENSE_RANK handle ties
— Rows with the same value are given the same rank
— After the tie value, RANK skips numbers, DENSE_RANK does not
 Ranking using analytic functions has better performance, because the
table is not read repeatedly
Carl Dudley University of Wolverhampton, UK
13
RANK and DENSE_RANK (continued)
SELECT ROW_NUMBER() OVER(ORDER BY sal DESC) rownumber
,RANK()
OVER(ORDER BY sal DESC) rank
,DENSE_RANK() OVER(ORDER BY sal DESC) denserank
,sal
,ename
FROM emp
ORDER BY sal DESC,ename;
ROWNUMBER RANK DENSERANK
SAL ENAME
--------- ---- ---------- ----- -----1
1
1 5000 KING
2
2
2 3000 FORD
3
2
2 3000 SCOTT
4
4
3 2975 JONES
5
5
4 2850 BLAKE
6
6
5 2450 CLARK
7
7
6 1600 ALLEN
8
8
7 1500 TURNER
9
9
8 1300 MILLER
10
10
9 1250 MARTIN
11
10
9 1250 WARD
12
12
10 1100 ADAMS
13
13
11
950 JAMES
14
14
12
800 SMITH
Multiple OVER clauses may be
used specifying different orderings
Carl Dudley University of Wolverhampton, UK
14
Analytic Function in ORDER BY
 Analytic functions are computed before the final ordering
— Can be referenced in the final ORDER BY clause
— An alias is used in this case
SELECT RANK() OVER(
ORDER BY sal DESC) sal_rank
,sal
,ename
FROM emp
ORDER BY sal_rank
,ename;
SAL_RANK
-------1
2
2
4
5
6
7
8
9
10
10
12
13
14
Carl Dudley University of Wolverhampton, UK
SAL
---5000
3000
3000
2975
2850
2450
1600
1500
1300
1250
1250
1100
950
800
ENAME
-----KING
FORD
SCOTT
JONES
BLAKE
CLARK
ALLEN
TURNER
MILLER
MARTIN
WARD
ADAMS
JAMES
SMITH
15
WHERE Conditions
 Analytic (window) functions are computed after the WHERE condition and
hence not available in the WHERE clause
SELECT RANK() OVER(ORDER BY sal DESC) rank
,sal
,ename
FROM emp
WHERE RANK() OVER(ORDER BY sal DESC) <= 5
ORDER BY rank
WHERE
RANK() OVER(ORDER BY sal DESC) <= 5
*
ERROR at line 5:
ORA-30483: window functions are not allowed here
Carl Dudley University of Wolverhampton, UK
16
WHERE Conditions
(continued)
 Use an inline view to force the early processing of the analytic
SELECT *
FROM (SELECT RANK() OVER(ORDER BY sal DESC) rank
,sal
,ename
FROM emp)
WHERE rank <= 5
ORDER BY rank
,ename;
RANK
SAL ENAME
---------- ---------- ---------1
5000 KING
2
3000 FORD
2
3000 SCOTT
4
2975 JONES
5
2850 BLAKE
— Inline view is processed before the WHERE clause
Carl Dudley University of Wolverhampton, UK
17
Grouping, Aggregate Functions and Analytics
 Rank the departments by number of employees
SELECT deptno
,COUNT(*) employees
,RANK() OVER(ORDER BY COUNT(*) DESC) rank
FROM emp
GROUP BY deptno
ORDER BY employees
,deptno;
DEPTNO EMPLOYEES
RANK
------ ---------- --------10
3
3
20
5
2
30
6
1
 Analytic functions are illegal in the HAVING clause
— The workaround is the same; use an inline view
— Ordering subclause may not reference a column alias
Carl Dudley University of Wolverhampton, UK
18
Analytic Functions
Overview of Analytic Functions
Ranking Functions
Partitioning
Aggregate Functions
Sliding Windows
Row Comparison Functions
Analytic Function Performance
Carl Dudley University of Wolverhampton, UK
19
Partitioning
 Analytic functions can be applied to logical groups within the result set
rather than the full result set
— Partitions
... OVER(PARTITION BY mgr ORDER BY sal DESC)
— PARTITION BY specifies the grouping
— ORDER BY specifies the ordering within each group
— Not connected with database table partitioning
 If partitioning is not specified, the full result set behaves as one partition
 NULL values are grouped together in one partition, as in GROUP BY
 Can have multiple analytic functions with different partitioning subclauses
Carl Dudley University of Wolverhampton, UK
20
Partitioning Example
 Rank employees by salary within their manager
SELECT ename
,mgr
,sal
,RANK() OVER(PARTITION BY mgr ORDER BY sal DESC) m_rank
FROM emp
ORDER BY mgr
,m_rank;
ENAME
MGR
SAL
M_RANK
---------- ---------- ---------- ---------SCOTT
7566
3000
1
FORD
7566
3000
1
ALLEN
TURNER
WARD
MARTIN
JAMES
7698
7698
7698
7698
7698
1600
1500
1250
1250
950
1
2
3
3
5
MILLER
7782
1300
1
ADAMS
7788
1100
1
JONES
BLAKE
CLARK
7839
7839
7839
2975
2850
2450
1
2
3
SMITH
7902
800
1
5000
1
KING
Carl Dudley University of Wolverhampton, UK
21
Result Sets With Different Partitioning
 Rank the employees by salary within their manager, within the year they
were hired, as well as overall
SELECT ename
,sal
,manager
,RANK()
OVER(PARTITION BY mgr
ORDER BY sal DESC) m_rank
,TRUNC(TO_NUMBER(TO_CHAR(date_hired,'YYYY'))) year_hired
,RANK()
OVER(PARTITION BY TRUNC(TO_NUMBER(TO_CHAR(date_hired,'YYYY'))
ORDER BY sal DESC) d_rank
,RANK() OVER(ORDER BY sal DESC) rank
FROM emp
ORDER BY rank
,ename;
Carl Dudley University of Wolverhampton, UK
22
Result Sets With Different Partitioning (continued)
ENAME
------KING
FORD
SCOTT
JONES
BLAKE
CLARK
ALLEN
TURNER
MILLER
MARTIN
WARD
ADAMS
JAMES
SMITH
SAL
---5000
3000
3000
2975
2850
2450
1600
1500
1300
1250
1250
1100
950
800
MGR
M_RANK YEAR_HIRED
D_RANK
RANK
---- ---------- ---------- ---------- ---------1
1981
1
1
7566
1
1981
2
2
7566
1
1987
1
2
7839
1
1981
3
4
7839
2
1981
4
5
7839
3
1981
5
6
7698
1
1981
6
7
7698
2
1981
7
8
7782
1
1982
1
9
7698
3
1981
8
10
7698
3
1981
8
10
7788
1
1987
2
12
7698
5
1981
10
13
7902
1
1980
1
14
Carl Dudley University of Wolverhampton, UK
23
Hypothetical Rank
 Rank a specified hypothetical value (2999) in a group ('what-if' query)
SELECT RANK(2999) WITHIN GROUP (ORDER BY sal DESC) H_S_rank
,PERCENT_RANK(2999) WITHIN GROUP (ORDER BY sal DESC) PR
,CUME_DIST(2999) WITHIN GROUP (ORDER BY sal DESC) CD
FROM emp;
H_S_RANK
PR
CD
-------- ---------- ---------4 .214285714 .266666667
3/14
4/15
SELECT deptno
,RANK(20,'CLERK') WITHIN GROUP
(ORDER BY deptno DESC,job ASC) H_D_J_rank
FROM emp
GROUP BY deptno;
A clerk in 20 would be higher than anyone in 10
DEPTNO H_D_J_RANK
A clerk would be third in ascending job
------ ---------order in department 20 (below analysts)
10
1
20
3
A clerk in 20 would be lower than anyone in 30 (6 employees)
30
7
Carl Dudley University of Wolverhampton, UK
24
Frequent Itemsets (dbms_frequent_itemset)
 Typical question
— When a customer buys product x, how likely are they to also buy product y?
SELECT CAST(itemset AS fi_char) itemset
,support
,length
,total_tranx
FROM TABLE(DBMS_FREQUENT_ITEMSET.FI_TRANSACTIONAL(
CURSOR(SELECT TO_CHAR(sales.cust_id)
,TO_CHAR(sales.prod_id)
Minimum fraction of different
FROM sh.sales
'Documentation' customers
,sh.products
having this combination
WHERE products.prod_id = sales.prod_id
AND products.prod_subcategory = 'Documentation'),
0.5,
mimimum items in set
2,
include items
3,
Number of
maximum items in set
NULL,
Different customers
exclude items
NULL));
ITEMSET
SUPPORT
LENGTH TOTAL_TRANX
-------------------------------------- --------- ---------- ----------FI_CHAR('40', '41')
3692
2
6077
2 or 3 items per set
FI_CHAR('40', '42')
3900
2
6077
FI_CHAR('40', '45')
3482
2
6077
FI_CHAR('41', '42')
2
6077
Number of instances 3163
FI_CHAR('40', '41', '42')
3141
3
6077
Carl Dudley University of Wolverhampton, UK
25
Frequent Itemsets (continued)
 Need to create type to accommodate the set
— Ranking
functions
can be
theVARCHAR2(100);
itemset
CREATE
TYPE
fi_char
AS applied
TABLE toOF
 The total transactions (TOTAL_TRANX) is the number of different customers
involved with any product within the set of products under examination
SELECT COUNT(DISTINCT cust_id)
FROM sales
WHERE prod_id BETWEEN 40 AND 45;
COUNT(DISTINCTCUST_ID)
---------------------6077
prod_ids for
'Documentation'
— Ranking functions can be applied to the itemset
 Itemsets containing certain items can be included/excluded
,CURSOR(SELECT * FROM table(fi_char(40,45)))
,CURSOR(SELECT * FROM table(fi_char(42)))
Include any sets
involving 40 or 45
Exclude any sets
involving 42
Carl Dudley University of Wolverhampton, UK
26
Plan of Itemset Query
 Only one full table scan of sales
-------------------------------------------------------------------------------|Id | Operation
| Name
|Rows |
-------------------------------------------------------------------------------| 0| SELECT STATEMENT
|
|
8|
| 1| FIC RECURSIVE ITERATION
|
|
|
| 2|
FIC LOAD ITEMSETS
|
|
|
| 3|
FREQUENT ITEMSET COUNTING
|
|
8|
| 4|
SORT GROUP BY NOSORT
|
|
|
| 5|
BITMAP CONVERSION COUNT
|
|
|
| 6|
FIC LOAD BITMAPS
|
|
|
| 7|
SORT CREATE INDEX
|
| 500|
| 8|
BITMAP CONSTRUCTION
|
|
|
| 9|
FIC ENUMERATE FEED
|
|
|
| 10|
SORT ORDER BY
|
|43755|
|*11|
HASH JOIN
|
|43755|
| 12|
TABLE ACCESS BY INDEX ROWID| PRODUCTS
|
3 |
|*13|
INDEX RANGE SCAN
| PRODUCTS_PROD_SUBCAT_IX
|
3 |
| 14|
PARTITION RANGE ALL
|
| 918K|
| 15|
TABLE ACCESS FULL
| SALES
| 918K|
| 16|
TABLE ACCESS FULL
| SYS_TEMP_0FD9D6605_153B1EE|
|
--------------------------------------------------------------------------------
Carl Dudley University of Wolverhampton, UK
27
Applying Analytics to Frequent Itemsets
SELECT itemset, support, length, total_tranx, rnk
FROM (SELECT itemset, support, length, total_tranx
,RANK() OVER (PARTITION BY length ORDER BY support DESC) rnk
FROM (SELECT CAST(ITEMSET AS fi_char) itemset
,support
,length
,total_tranx
FROM TABLE(dbms_frequent_itemset.fi_transactional
(CURSOR(SELECT TO_CHAR(sales.cust_id)
,TO_CHAR(sales.prod_id)
FROM sh.sales
,sh.products
WHERE products.prod_id = sales.prod_id
AND products.prod_subcategory = 'Documentation')
,0.5
,2
,3
,NULL
,NULL))))
WHERE rnk < 4;
ITEMSET
SUPPORT
LENGTH TOTAL_TRANX
RNK
-------------------------------- ---------- ---------- ----------- ---------FI_CHAR('40', '42')
3900
2
6077
1
FI_CHAR('40', '41')
3692
2
6077
2
FI_CHAR('40', '45')
3482
2
6077
3
FI_CHAR('40', '41', '42')
3141
3
6077
1
Carl Dudley University of Wolverhampton, UK
28
Analytic Functions
Overview of Analytic Functions
Ranking Functions
Partitioning
Aggregate Functions
Sliding Windows
Row Comparison Functions
Analytic Function Performance
Carl Dudley University of Wolverhampton, UK
29
Expanding Windows
Partition (first) or entire result set
Window
OVER (ORDER BY col_name)
ROWS BETWEEN UNBOUNDED
PRECEDING AND CURRENT ROW
Default value for window setting produces an expanding window
Partition (second)
Sliding Windows
Partition (first) or entire result set
3 ROWS
5 ROWS
Window
OVER (ORDER BY col_name)
ROWS BETWEEN 2 PRECEDING
AND 2 FOLLOWING
Produces a sliding window
Partition (second)
Aggregate Functions
 Aggregate functions can be used as analytic functions
— Must be embedded in the OVER clause
 Analytic aggregate values can be easily included within row-level reports
— Analytic functions are applied after computation of result set
— Optimizer often produces a better execution plan
 Aggregate level is determined by the partitioning subclause
— Similar effect to GROUP BY clause
— If no partitioning subclause, aggregate is across the complete result set
Carl Dudley University of Wolverhampton, UK
32
Aggregate Functions – the OVER Clause
SELECT deptno
,AVG(sal)
FROM emp
GROUP BY deptno;
SELECT deptno
,AVG(sal) OVER (PARTITION BY deptno) avg_dept
,AVG(sal) OVER () avg_all
FROM emp;
DEPTNO
AVG(SAL)
---------- ---------30 1566.66667
20
2175
10 2916.66667
DEPTNO
---------10
10
10
20
20
20
20
20
30
30
30
30
30
30
AVG_DEPT
---------2916.66667
2916.66667
2916.66667
2175
2175
2175
2175
2175
1566.66667
1566.66667
1566.66667
1566.66667
1566.66667
1566.66667
AVG_ALL
---------2073.21429
2073.21429
2073.21429
2073.21429
2073.21429
2073.21429
2073.21429
2073.21429
2073.21429
2073.21429
2073.21429
2073.21429
2073.21429
2073.21429
No subclause
Analytic aggregates
cause no reduction
in rows
 Could easily include row-level data
— e.g. ename and sal
Carl Dudley University of Wolverhampton, UK
33
Analytic versus Conventional SQL Performance
 The requirement
— Data at different levels of grouping
ENAME
-----CLARK
KING
MILLER
JONES
FORD
ADAMS
SMITH
SCOTT
WARD
TURNER
ALLEN
JAMES
BLAKE
MARTIN
Average sal
per department
SAL DEPTNO
AVG_DEPT
AVG_ALL
---- ------ ---------- ---------2450
10 2916.66667 2073.21429
5000
10 2916.66667 2073.21429
1300
10 2916.66667 2073.21429
2975
20
2175 2073.21429
3000
20
2175 2073.21429
1100
20
2175 2073.21429
800
20
2175 2073.21429
3000
20
2175 2073.21429
1250
30 1566.66667 2073.21429
1500
30 1566.66667 2073.21429
1600
30 1566.66667 2073.21429
950
30 1566.66667 2073.21429
2850
30 1566.66667 2073.21429
1250
30 1566.66667 2073.21429
Carl Dudley University of Wolverhampton, UK
Overall
average sal
34
Conventional SQL Performance
SELECT r.ename,r.sal,g.deptno,g.ave_dept,a.ave_all
FROM emp r
,(SELECT deptno,AVG(sal) ave_dept
FROM emp GROUP BY deptno) g
,(SELECT AVG(sal) ave_all
FROM emp) a
WHERE g.deptno = r.deptno
ORDER BY r.deptno;
----------------------------------------------| Id | Operation
| Name | Rows |
----------------------------------------------|
0 | SELECT STATEMENT
|
|
15 |
|
1 | MERGE JOIN
|
|
15 |
|
2 |
SORT JOIN
|
|
3 |
|
3 |
NESTED LOOPS
|
|
3 |
|
4 |
VIEW
|
|
1 |
|
5 |
SORT AGGREGATE
|
|
1 |
|
6 |
TABLE ACCESS FULL| EMP |
14 |
|
7 |
VIEW
|
|
3 |
|
8 |
SORT GROUP BY
|
|
3 |
|
9 |
TABLE ACCESS FULL| EMP |
14 |
|* 10 |
SORT JOIN
|
|
14 |
| 11 |
TABLE ACCESS FULL
| EMP |
14 |
-----------------------------------------------
1M row emp table :
48.35 seconds
230790 consistent gets
Carl Dudley University of Wolverhampton, UK
35
Analytic Function Performance
SELECT ename,sal,deptno
,AVG(sal) OVER (PARTITION BY deptno) ave_dept
,AVG(sal) OVER () ave_all
FROM emp;
------------------------------------------| Id | Operation
| Name | Rows |
------------------------------------------|
0 | SELECT STATEMENT
|
|
14 |
|
1 | WINDOW SORT
|
|
14 |
|
2 |
TABLE ACCESS FULL| EMP |
14 |
-------------------------------------------
1M row emp table :
21.20 seconds
76930 consistent gets
Carl Dudley University of Wolverhampton, UK
36
Aggregating Over an Ordered Set of Rows –
Running Totals
 The ORDER BY clause creates an expanding window (running total) of rows
SELECT empno
,ename
,sal
,SUM(sal) OVER(ORDER BY empno) run_total
FROM emp5
ORDER BY empno;
EMPNO
----7369
7499
7521
7566
7654
7698
7782
7788
7839
7844
7876
7900
7902
7934
:
ENAME
-----SMITH
ALLEN
WARD
JONES
MARTIN
BLAKE
CLARK
SCOTT
KING
TURNER
ADAMS
JAMES
FORD
MILLER
:
SAL RUN_TOTAL
---- --------800
800
1600
2400
1250
3650
2975
6625
1250
7875
2850
10725
2450
13175
3000
16175
5000
21175
1500
22675
1100
23775
950
24725
3000
27725
1300
29025
:
:
------------------------------|Id| Operation
| Name|
------------------------------| 0| SELECT STATEMENT
|
|
| 1| WINDOW SORT
|
|
| 2|
TABLE ACCESS FULL| EMP5|
-------------------------------
emp table of 5000 rows
0.07 seconds
33 consistent gets
No index necessary
Carl Dudley University of Wolverhampton, UK
37
Running Total With Conventional SQL (1)
 Self-join solution
SELECT e1.empno
,e1.sal
,SUM(e2.sal)
FROM emp5 e1, emp5 e2
WHERE e2.empno <= e1.empno
GROUP BY e1.empno, e1.sal
ORDER BY e1.empno;
13.37 seconds
66 consistent gets
------------------------------------------------| Id | Operation
| Name
|
------------------------------------------------|
0 | SELECT STATEMENT
|
|
|
1 | SORT GROUP BY
|
|
|
2 |
MERGE JOIN
|
|
|
3 |
SORT JOIN
|
|
|
4 |
TABLE ACCESS BY INDEX ROWID| EMP5
|
|
5 |
INDEX FULL SCAN
| PK_EMP5|
|* 6 |
SORT JOIN
|
|
|
7 |
TABLE ACCESS FULL
| EMP5
|
------------------------------------------------Carl Dudley University of Wolverhampton, UK
38
Running Total With Conventional SQL (2)
 Subquery in SELECT list solution – column expression
SELECT empno
,ename
,sal
,(SELECT SUM(sal) sumsal
FROM emp5
WHERE empno <= b.empno) a
FROM emp5 b
ORDER BY empno;
4.62 seconds
97948 consistent gets
----------------------------------------------| Id | Operation
| Name
|
----------------------------------------------|
0 | SELECT STATEMENT
|
|
|
1 | SORT AGGREGATE
|
|
|
2 |
TABLE ACCESS BY INDEX ROWID| EMP5
|
|* 3 |
INDEX RANGE SCAN
| PK_EMP5|
|
4 | TABLE ACCESS BY INDEX ROWID | EMP5
|
|
5 |
INDEX FULL SCAN
| PK_EMP5|
----------------------------------------------Carl Dudley University of Wolverhampton, UK
39
Aggregate Functions With Partitioning
 Find average salary of employees within each manager
— Use PARTITION BY to specify the grouping
SELECT ename, mgr, sal
,ROUND(AVG(sal) OVER(PARTITION BY mgr)) avgsal
,sal - ROUND(AVG(sal) OVER(PARTITION BY mgr)) diff
FROM emp;
ENAME
MGR
SAL
AVGSAL
DIFF
---------- ------- ---------- ---------- ---------SCOTT
7566
3000
3000
0
FORD
7566
3000
3000
0
ALLEN
WARD
JAMES
TURNER
MARTIN
7698
7698
7698
7698
7698
1600
1250
950
1500
1250
1310
1310
1310
1310
1310
290
-60
-360
190
-60
MILLER
7782
1300
1300
0
ADAMS
7788
1100
1100
0
JONES
CLARK
BLAKE
7839
7839
7839
2975
2450
2850
2758
2758
2758
217
-308
92
SMITH
7902
800
800
0
5000
5000
0
KING
Carl Dudley University of Wolverhampton, UK
40
Analytics on Aggregates
 Analytics are processed last
SELECT deptno
,SUM(sal)
,SUM(SUM(sal)) OVER () Totsal
,SUM(SUM(sal)) OVER (ORDER BY deptno) Runtot_deptno
,SUM(SUM(sal)) OVER (ORDER BY SUM(sal)) Runtot_sumsal
FROM emp
GROUP BY deptno
ORDER BY deptno;
DEPTNO SUM(SAL) TOTSAL RUNTOT_DEPTNO RUNTOT_SUMSAL
------ -------- ------ ------------- ------------10
8750 29025
8750
8750
+ sum(20)
20
10875 29025
19625
29025
30
9400
29025
29025
+ sum(30)
18150
Carl Dudley University of Wolverhampton, UK
+ sum(30)
+ sum(20)
41
Aggregate Functions and the WHERE clause
 Analytic functions are applied after production of the complete result set
— Rows excluded by the WHERE clause are not included in the aggregate value
 Include only employees whose name starts with a ‘S’ or ‘M’
— The average is now only for those rows starting with 'S' or 'M'
SELECT ename
,sal
,ROUND(AVG(sal) OVER()) avgsal
,sal - ROUND(AVG(sal) OVER()) diff
FROM emp
WHERE ename LIKE 'S%'
OR ename LIKE 'M%';
ENAME
-----SMITH
MARTIN
SCOTT
MILLER
SAL AGSAL DIFF
---- ----- ----800 1588 -788
1250 1588
338
3000 1588 1412
1300 1588 -288
Carl Dudley University of Wolverhampton, UK
42
RATIO_TO_REPORT
 Each row’s fraction of total salary can easily be found when the total salary
value is available
— Example: sal/SUM(sal) OVER()
— The function RATIO_TO_REPORT performs this calculation
SELECT ename
,sal
,SUM(sal) OVER() sumsal
,sal/SUM(sal) OVER() ratio
,RATIO_TO_REPORT(sal) OVER() ratio_rep
FROM emp;
Carl Dudley University of Wolverhampton, UK
43
RATIO_TO_REPORT (continued)
 The query on the previous slide gives this result
ENAME
SAL
SUMSAL
RATIO RATIO_REP
---------- ------- ---------- ---------- ---------SMITH
800
29025 .027562446 .027562446
ALLEN
1600
29025 .055124892 .055124892
WARD
1250
29025 .043066322 .043066322
JONES
2975
29025 .102497847 .102497847
MARTIN
1250
29025 .043066322 .043066322
BLAKE
2850
29025 .098191214 .098191214
CLARK
2450
29025 .084409991 .084409991
SCOTT
3000
29025 .103359173 .103359173
KING
5000
29025 .172265289 .172265289
TURNER
1500
29025 .051679587 .051679587
ADAMS
1100
29025 .037898363 .037898363
JAMES
950
29025 .032730405 .032730405
FORD
3000
29025 .103359173 .103359173
MILLER
1300
29025 .044788975 .044788975
Carl Dudley University of Wolverhampton, UK
44
Analytic Functions
Overview of Analytic Functions
Ranking Functions
Partitioning
Aggregate Functions
Sliding Windows
Row Comparison Functions
Analytic Function Performance
Carl Dudley University of Wolverhampton, UK
45
Sliding Windows
 The OVER clause can have a sliding window subclause
— Not permitted without ORDER BY subclause
— Specifies size of window (set of rows) to be processed by the analytic
function
— Window defined relative to current row
• Slides through result set as different rows become current
 Size of window is governed by ROWS or RANGE
— ROWS
• physical offset, a number of rows relative to the current row
— RANGE
• logical offset, a value interval relative to value in current row
 Syntax for sliding window :
— BETWEEN <starting point> AND <ending point>
Carl Dudley University of Wolverhampton, UK
46
Sliding Windows Example
 For each employee, show the sum of the salaries of the preceding, current,
and following employee (row)
— Window includes current row as well as the preceding and following ones
— Must have order subclause for “preceding” and “following” to be meaningful
— First row has no preceding row and last row has no following row
SELECT ename
,sal
,SUM(sal) OVER(ORDER BY sal DESC
ROWS BETWEEN 1 PRECEDING
AND 1 FOLLOWING) sal_window
FROM
emp
ORDER BY sal DESC
,ename;
Carl Dudley University of Wolverhampton, UK
47
Sliding Windows Example (continued)
ENAME
SAL SAL_WINDOW
---------- ---------- ---------KING
5000
8000
FORD
3000
11000
SCOTT
3000
8975
JONES
2975
8825
BLAKE
2850
8275
CLARK
2450
6900
ALLEN
1600
5550
TURNER
1500
4400
MILLER
1300
4050
MARTIN
1250
3800
WARD
1250
3600
ADAMS
1100
3300
JAMES
950
2850
SMITH
800
1750
Calculation:
=5000+3000
=5000+3000+3000
=3000+3000+2975
=3000+2975+2850
=2975+2850+2450
=2850+2450+1600
=2450+1600+1500
=1600+1500+1300
=1500+1300+1250
=1300+1250+1250
=1250+1250+1100
=1250+1100+950
=1100+950+800
=950+800
Carl Dudley University of Wolverhampton, UK
48
Partitioned Sliding Windows
 Partitioning can be used with sliding windows
— A sliding window does not span partitions
SELECT ename
,job
,sal
,SUM(sal) OVER(PARTITION BY job
ORDER BY sal DESC
ROWS BETWEEN 1 PRECEDING
AND 1 FOLLOWING) sal_window
FROM emp
ORDER BY job
,sal DESC
,ename;
Carl Dudley University of Wolverhampton, UK
49
Partitioned Sliding Windows (continued)
ENAME
---------FORD
SCOTT
JOB
SAL SAL_WINDOW
--------- ---------- ---------ANALYST
3000
6000
ANALYST
3000
6000
MILLER
ADAMS
JAMES
SMITH
CLERK
CLERK
CLERK
CLERK
1300
1100
950
800
2400
3350
2850
1750
=1300+1100
=1300+1100+950
=1100+950+800
=950+800
JONES
BLAKE
CLARK
MANAGER
MANAGER
MANAGER
2975
2850
2450
5825
8275
5300
=2975+2850
=2975+2850+2450
=2850+2450
KING
PRESIDENT
5000
5000
=5000
ALLEN
TURNER
MARTIN
WARD
SALESMAN
SALESMAN
SALESMAN
SALESMAN
1600
1500
1250
1250
3100
4350
4000
2500
=1600+1500
=1600+1500+1250
=1500+1250+1250
=1250+1250
Calculation
=3000+3000
=3000+3000
Carl Dudley University of Wolverhampton, UK
50
Sliding Window With Logical (RANGE) Offset
 Physical offset
— Specified number of rows
 Logical offset
— A RANGE of values
• Numeric or date
— Values in the ordering column indirectly determine number of rows in
window
SELECT ename
,sal
,SUM(sal) OVER(ORDER BY sal DESC
RANGE BETWEEN 150 PRECEDING
AND 75 FOLLOWING) sal_window
FROM emp
ORDER BY sal DESC
,ename;
Carl Dudley University of Wolverhampton, UK
51
Sliding Window With Logical (RANGE) Offset
(continued)
ENAME
SAL SAL_WINDOW
---------- ---------- ---------KING
5000
5000
FORD
3000
8975
SCOTT
3000
8975
JONES
2975
8975
BLAKE
2850
11825
CLARK
2450
2450
ALLEN
1600
1600
TURNER
1500
3100
MILLER
1300
3800
MARTIN
1250
3800
WARD
1250
3800
ADAMS
1100
3600
JAMES
950
2050
SMITH
800
1750
Range for this row is
3000 to 2775
Carl Dudley University of Wolverhampton, UK
52
UNBOUNDED and CURRENT ROW
 Sliding windows have starting and ending points
— BETWEEN <starting point> AND <ending point>
 Ways for specifying starting and ending points
— UNBOUNDED PRECEDING specifies the first row as starting point
— UNBOUNDED FOLLOWING specifies the last row as ending point
— CURRENT ROW specifies the current row
 Create a window that grows with each row in ename order
— The RANGE clause is not necessary if a running total is required (default)
SELECT ename
,sal
,SUM(sal) OVER(ORDER BY ename
RANGE BETWEEN UNBOUNDED PRECEDING
AND CURRENT ROW) run_total
FROM emp
ORDER BY ename;
Carl Dudley University of Wolverhampton, UK
53
Keywords UNBOUNDED and CURRENT ROW
(continued)
 Running Total
— Produced by default 'expanding' window when window not specified
ENAME
SAL RUN_TOTAL
---------- ---------- ---------ADAMS
1100
1100
ALLEN
1600
2700
BLAKE
2850
5550
CLARK
2450
8000
FORD
3000
11000
JAMES
950
11950
JONES
2975
14925
KING
5000
19925
MARTIN
1250
21175
MILLER
1300
22475
SCOTT
3000
25475
SMITH
800
26275
TURNER
1500
27775
WARD
1250
29025
Explanation:
=1100
=1600+1100
=2700+2850
=5550+2450
=8000+3000
=11000+950
=11950+2975
=14925+5000
=19925+1250
=21175+1300
=22475+3000
=25475+800
=26275+1500
=27775+1250
Carl Dudley University of Wolverhampton, UK
54
Keywords UNBOUNDED and CURRENT ROW
(continued)
 Be aware of the subtle difference between RANGE and ROWS in this context
— Apparent only when adjacent rows have equal values
SELECT ename
,sal
,SUM(sal) OVER(ORDER BY sal DESC
ROWS BETWEEN UNBOUNDED PRECEDING
AND CURRENT ROW) row_tot
,SUM(sal) OVER(ORDER BY sal DESC
RANGE BETWEEN UNBOUNDED PRECEDING
AND CURRENT ROW) range_tot
,SUM(sal) OVER(ORDER BY sal DESC) default_tot
FROM EMP
ORDER BY sal DESC
,ename;
Carl Dudley University of Wolverhampton, UK
55
Difference between ROWS and RANGE
 Ford and Scott fall within the same range - also applies to Martin and Ward
— For example Scott is included in range when the value for Ford is calculated
ENAME
SAL
ROW_TOT RANGE_TOT DEFAULT_TOT
---------- ---------- ---------- --------- ----------KING
5000
5000
5000
5000
FORD
3000
8000
11000
11000
SCOTT
3000
11000
11000
11000
JONES
2975
13975
13975
13975
BLAKE
2850
16825
16825
16825
CLARK
2450
19275
19275
19275
ALLEN
1600
20875
20875
20875
TURNER
1500
22375
22375
22375
MILLER
1300
23675
23675
23675
MARTIN
1250
24925
26175
26175
WARD
1250
26175
26175
26175
ADAMS
1100
27275
27275
27275
JAMES
950
28225
28225
28225
SMITH
800
29025
29025
29025
Carl Dudley University of Wolverhampton, UK
56
Time Intervals
 Sliding windows are often based on time intervals
 Example: Compare the salary of each employee to the maximum and
minimum salaries of hirings made within three months of their own hiring
date
SELECT ename
,hiredate
,sal
,MIN(sal) OVER(ORDER BY hiredate
RANGE BETWEEN INTERVAL
AND INTERVAL '3'
,MAX(sal) OVER(ORDER BY hiredate
RANGE BETWEEN INTERVAL
AND INTERVAL '3'
FROM emp;
'3' MONTH PRECEDING
MONTH FOLLOWING) min
'3' MONTH PRECEDING
MONTH FOLLOWING) max
Carl Dudley University of Wolverhampton, UK
57
Time Intervals
(continued)
 Sliding time window
ENAME
---------SMITH
ALLEN
WARD
JONES
BLAKE
CLARK
TURNER
MARTIN
KING
JAMES
FORD
MILLER
SCOTT
ADAMS
HIREDATE
SAL
MIN
MAX
--------- ---------- ---------- ---------17-DEC-80
800
800
1600
20-FEB-81
1600
800
2975
22-FEB-81
1250
800
2975
02-APR-81
2975
1250
2975
01-MAY-81
2850
1250
2975
09-JUN-81
2450
1500
2975
08-SEP-81
1500
950
5000
28-SEP-81
1250
950
5000
17-NOV-81
5000
950
5000
03-DEC-81
950
950
5000
03-DEC-81
3000
950
5000
23-JAN-82
1300
950
5000
09-DEC-82
3000
1100
3000
12-JAN-83
1100
1100
3000
Carl Dudley University of Wolverhampton, UK
58
Analytic Functions
Overview of Analytic Functions
Ranking Functions
Partitioning
Aggregate Functions
Sliding Windows
Row Comparison Functions
Analytic Function Performance
Carl Dudley University of Wolverhampton, UK
59
LAG and LEAD Functions
 Useful for comparing values across rows
— Need to specify count of rows which separate target row from current row
• No need for self-join
— LAG provides access to a row at a given offset prior to the current position
— LEAD provides access to a row at a given offset after the current position
{LAG | LEAD} ( value_expr [, offset] [, default] )
OVER ( [query_partition_clause] order_by_clause )
— offset is an optional parameter and defaults to 1
— default is an optional parameter and is the value returned if offset falls
outside the bounds of the table or partition
• In this case, NULL will be returned if no default is specified
Carl Dudley University of Wolverhampton, UK
60
LAG/LEAD Simple Example
SELECT hiredate
,sal AS salary
,LAG(sal,1) OVER (ORDER BY hiredate) AS LAG1
,LEAD(sal,1) OVER (ORDER BY hiredate) AS LEAD1
FROM emp;
HIREDATE
SALARY
LAG1
LEAD1
--------- ---------- ---------- ---------17-DEC-80
800
1600
20-FEB-81
1600
800
1250
22-FEB-81
1250
1600
2975
Comparison of salaries
02-APR-81
2975
1250
2850
with those for nearest
01-MAY-81
2850
2975
2450
recruits in terms of
09-JUN-81
2450
2850
1500
proximity of hiredates
08-SEP-81
1500
2450
1250
28-SEP-81
1250
1500
5000
17-NOV-81
5000
1250
950
03-DEC-81
950
5000
3000
03-DEC-81
3000
950
1300
23-JAN-82
1300
3000
3000
09-DEC-82
3000
1300
1100
12-JAN-83
1100
3000
Carl Dudley University of Wolverhampton, UK
61
FIRST_VALUE and LAST_VALUE
 Hold first or last value in a partition (based on ordering) as a start point
SELECT empno, deptno, hiredate
,FIRST_VALUE(hiredate)
OVER (PARTITION BY deptno ORDER BY hiredate) firstdate
,hiredate - FIRST_VALUE(hiredate)
OVER (PARTITION BY deptno ORDER BY hiredate) Day_Gap
FROM emp
ORDER BY deptno, Day_Gap;
EMPNO DEPTNO HIREDATE FIRSTDATE DAY_GAP
----- ------ --------- --------- ------7782
10 09-JUN-81 09-JUN-81
0
7839
10 17-NOV-81 09-JUN-81
161
7934
10 23-JAN-82 09-JUN-81
228
7369
7566
7902
7788
7876
20
20
20
20
20
17-DEC-80
02-APR-81
03-DEC-81
09-DEC-82
12-JAN-83
17-DEC-80
17-DEC-80
17-DEC-80
17-DEC-80
17-DEC-80
0
106
351
722
756
7499
7521
7698
7844
7654
7900
30
30
30
30
30
30
20-FEB-81
22-FEB-81
01-MAY-81
08-SEP-81
28-SEP-81
03-DEC-81
20-FEB-81
20-FEB-81
20-FEB-81
20-FEB-81
20-FEB-81
20-FEB-81
0
2
70
200
220
286
Days after hiring of first
employee in this department
Works with partitioning and
windowing subclauses
Carl Dudley University of Wolverhampton, UK
62
Influence of Window on LAST_VALUE
SELECT deptno,ename,sal
,LAST_VALUE(ename) OVER (PARTITION BY deptno
ORDER BY sal
ROWS BETWEEN UNBOUNDED PRECEDING
AND UNBOUNDED FOLLOWING) AS hsal1
,LAST_VALUE(ename) OVER (PARTITION BY deptno
ORDER BY sal) AS hsal2
FROM emp
ORDER BY deptno,sal;
DEPTNO
-----10
10
10
20
20
20
20
20
30
30
30
30
30
30
ENAME
-----MILLER
CLARK
KING
SMITH
ADAMS
JONES
FORD
SCOTT
JAMES
MARTIN
WARD
TURNER
ALLEN
BLAKE
SAL
---1300
2450
5000
800
1100
2975
3000
3000
950
1250
1250
1500
1600
2850
HSAL1
---------KING
KING
KING
SCOTT
SCOTT
SCOTT
SCOTT
SCOTT
BLAKE
BLAKE
BLAKE
BLAKE
BLAKE
BLAKE
HSAL2
---------MILLER
CLARK
KING
SMITH
ADAMS
JONES
SCOTT
SCOTT
JAMES
WARD
WARD
TURNER
ALLEN
BLAKE
Last value in
expanding window
(based on range)
Carl Dudley University of Wolverhampton, UK
63
Ignoring Nulls in First and Last Values
SELECT ename
,FIRST_VALUE (ename) OVER (PARTITION BY deptno
ORDER BY ename) fv
,LAST_VALUE (ename) OVER (PARTITION BY deptno
Highest value (1400) is
ORDER BY ename) lv
'kept' for null values
,comm
,FIRST_VALUE (comm) OVER (PARTITION BY deptno
ORDER BY comm) fv_comm
,LAST_VALUE (comm) OVER (PARTITION BY deptno
ORDER BY comm) lv_comm
,LAST_VALUE (comm IGNORE NULLS) OVER (PARTITION BY deptno
ORDER BY comm) lv_ignore
FROM emp
WHERE deptno = 30;
ENAME
---------ALLEN
BLAKE
JAMES
MARTIN
TURNER
WARD
FV
---------ALLEN
ALLEN
ALLEN
ALLEN
ALLEN
ALLEN
LV
COMM
FV_COMM
LV_COMM LV_IGNORE
---------- ---------- ---------- ---------- ---------ALLEN
300
0
300
300
BLAKE
0
1400
JAMES
0
1400
MARTIN
1400
0
1400
1400
TURNER
0
0
0
0
WARD
500
0
500
500
Carl Dudley University of Wolverhampton, UK
64
NTH_VALUE
SELECT deptno
,ename
,sal
,FIRST_VALUE(sal) OVER (PARTITION BY deptno
ORDER BY sal DESC)
- NTH_VALUE(sal,3)
NTH_VALUE(sal,2) FROM FIRST OVER (PARTITION BY deptno
ORDER BY sal DESC) t2_diff
FROM emp;
DEPTNO
ENAME
SAL
T2_DIFF
DEPTNO
ENAME
SAL
T2_DIFF
---------------------- --------------------------------------10 KING
5000
10 KING
5000
10 CLARK
2450
10 CLARK
2450 2550
10 MILLER
1300
10 MILLER
1300 2550 3700
20 SCOTT
3000
0
0??
20 SCOTT
3000
20 FORD
3000
0
20 FORD
3000
20 JONES
2975
0
20 JONES
2975
25
20 ADAMS
1100
0
20 ADAMS
1100
25
20 SMITH
800
0
20 SMITH
800
25
30
BLAKE
2850
30 BLAKE
2850
30 ALLEN
1600
30 ALLEN
1600 1250
30 TURNER
1500 1250 1350
30 TURNER
1500
30 MARTIN
1250 1250 1350
30 MARTIN
1250
30 WARD
1250 1250 1350
30 WARD
1250
30 JAMES
950 1250 1350
30 JAMES
1250
Could use
FROM LAST
Reports difference between first and
second member of each partition
Carl Dudley University of Wolverhampton, UK
65
LISTAGG Function
 Example - show columns in indexes in an ordered list
SELECT table_name
,index_name
,LISTAGG(column_name,’;’) WITHIN GROUP (
ORDER BY column_position) “Column List”
FROM user_ind_columns
GROUP BY table_name
,index_name;
TABLE_NAME
-----------EMP
PROJ_ASST
DEPT
INDEX_NAME
-----------------EMP_PK
SYS_C0011223
DEPT$DIVNO_DEPTNO
Column List
----------------------------EMPNO
PROJNO;EMPNO;START_DATE
DIVNO;DEPTNO
Carl Dudley University of Wolverhampton, UK
66
FIRST and LAST
 Compare each employee's
salary with the average
salary of the first year of
hirings of their department
— Must use KEEP
— Must use DENSE_RANK
SELECT empno
,deptno
,TO_CHAR(hiredate,'YYYY') Hire_Yr
,sal
,TRUNC(AVG(sal) KEEP (DENSE_RANK FIRST
ORDER BY TO_CHAR(hiredate,'YYYY') )
OVER (PARTITION BY deptno)) Avg_Sal_Yr1_Hire
FROM emp
ORDER BY deptno, empno, Hire_Yr;
EMPNO
DEPTNO HIRE_YR
SAL AVG_SAL_YR1_HIRE
----- ---------- ------- ------- ---------------7782
10 1981
2450
3725
7839
10 1981
5000
3725
7934
10 1982
1300
3725
7369
7566
7788
7876
7902
20
20
20
20
20
1980
1981
1982
1983
1981
800
2975
3000
1100
3000
800
800
800
800
800
7499
7521
7654
7698
7844
7900
30
30
30
30
30
30
1981
1981
1981
1981
1981
1981
1600
1250
1250
2850
1500
950
1566
1566
1566
1566
1566
1566
Carl Dudley University of Wolverhampton, UK
67
FIRST and LAST (Continued)
 Compare salaries to the average
of the 'LAST' department
— Note no ORDER BY inside the
OVER clause
— No support for any
<window> clause
SELECT empno
,deptno
,TO_CHAR(hiredate,'YYYY') Hire_Yr
,sal
,TRUNC(AVG(sal) KEEP (DENSE_RANK LAST
ORDER BY deptno )
OVER () ) AVG_SAL_LAST_DEPT
FROM emp
ORDER BY deptno, empno, Hire_Yr;
EMPNO DEPTNO Hire_Yr SAL AVG_SAL_LAST_DEPT
----- ------ ------- ---- ----------------7782
10 1981
2450
1566
7839
10 1981
5000
1566
7934
10 1982
1300
1566
7369
20 1980
800
1566
7566
20 1981
2975
1566
7788
20 1982
3000
1566
7876
20 1983
1100
1566
7902
20 1981
3000
1566
7499
30 1981
1600
1566
7521
30 1981
1250
1566
7654
30 1981
1250
1566
7698
30 1981
2850
1566
7844
30 1981
1500
1566
7900
30 1981
950
1566
Carl Dudley University of Wolverhampton, UK
68
Bus Times
SELECT route,stop,bus,TO_CHAR(bustime,'DD-MON-YYYY HH24.MI.SS') bustime
FROM bustimes ORDER BY route,stop,bustime;
ROUTE
---------1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
STOP
-------1
1
1
1
1
2
2
2
2
2
3
3
3
3
3
4
4
4
4
4
5
5
5
5
5
BUS
-------10
30
20
40
50
10
30
40
50
20
10
40
20
50
30
10
40
30
20
50
40
50
20
10
30
BUSTIME
-------------------01-MAR-2011 12.17.33
01-MAR-2011 12.58.10
01-MAR-2011 13.58.41
01-MAR-2011 14.06.13
01-MAR-2011 14.11.45
01-MAR-2011 12.56.19
01-MAR-2011 13.00.09
01-MAR-2011 14.20.45
01-MAR-2011 14.24.01
01-MAR-2011 14.31.04
01-MAR-2011 13.58.53
01-MAR-2011 14.35.58
01-MAR-2011 14.58.41
01-MAR-2011 15.18.09
01-MAR-2011 15.28.33
01-MAR-2011 14.17.33
01-MAR-2011 15.11.26
01-MAR-2011 15.30.30
01-MAR-2011 15.42.25
01-MAR-2011 15.55.54
01-MAR-2011 15.51.14
01-MAR-2011 16.02.19
01-MAR-2011 16.18.09
01-MAR-2011 16.30.21
01-MAR-2011 16.47.58
Times for 5 buses stopping
at 5 stops on route 1
Carl Dudley University of Wolverhampton, UK
69
Journey Times of Buses Between Stops
SELECT route
,stop
,bus
,TO_CHAR(bustime,'dd/mm/yy hh24:mi:ss') bus_stop_time
,TO_CHAR(LAG(bustime,1)
OVER (PARTITION BY bus
ORDER BY route,stop,bustime)
,'dd/mm/yy hh24:mi:ss') prev_bus_stop_time
,SUBSTR(NUMTODSINTERVAL(bustime - LAG(bustime,1)
OVER (PARTITION BY bus
ORDER BY route,stop,bustime),'DAY'),12,8) time_between_stops
,SUBSTR(NUMTODSINTERVAL(bustime - FIRST_VALUE(bustime)
OVER (PARTITION BY bus
ORDER BY route,stop,bustime),'DAY'),12,8) jrny_time
FROM bustimes;
Carl Dudley University of Wolverhampton, UK
70
Journey Times of Buses Between Stops (cont'd)
ROUTE STOP BUS BUS_STOP_TIME
PREV_BUS_STOP_TIM
----- ---- --- ----------------- ----------------1
1 10 01/03/11 12:17:33
1
2 10 01/03/11 12:56:19 01/03/11 12:17:33
1
3 10 01/03/11 13:58:53 01/03/11 12:56:19
1
4 10 01/03/11 14:17:33 01/03/11 13:58:53
1
5 10 01/03/11 16:30:21 01/03/11 14:17:33
1
1 20 01/03/11 13:58:41
1
2 20 01/03/11 14:31:04 01/03/11 13:58:41
1
3 20 01/03/11 14:58:41 01/03/11 14:31:04
1
4 20 01/03/11 15:42:25 01/03/11 14:58:41
1
5 20 01/03/11 16:18:09 01/03/11 15:42:25
1
1 30 01/03/11 12:58:10
1
2 30 01/03/11 13:00:09 01/03/11 12:58:10
1
3 30 01/03/11 15:28:33 01/03/11 13:00:09
1
4 30 01/03/11 15:30:30 01/03/11 15:28:33
1
5 30 01/03/11 16:47:58 01/03/11 15:30:30
1
1 40 01/03/11 14:06:13
1
2 40 01/03/11 14:20:45 01/03/11 14:06:13
1
3 40 01/03/11 14:35:58 01/03/11 14:20:45
1
4 40 01/03/11 15:11:26 01/03/11 14:35:58
1
5 40 01/03/11 15:51:14 01/03/11 15:11:26
1
1 50 01/03/11 14:11:45
1
2 50 01/03/11 14:24:01 01/03/11 14:11:45
1
3 50 01/03/11 15:18:09 01/03/11 14:24:01
1
4 50 01/03/11 15:55:54 01/03/11 15:18:09
1
5 50 01/03/11 16:02:19 01/03/11 15:55:54
TIME_BET
-------00:38:46
01:02:34
00:18:40
02:12:48
00:32:23
00:27:37
00:43:44
00:35:44
00:01:59
02:28:24
00:01:57
01:17:28
00:14:32
00:15:13
00:35:28
00:39:48
00:12:16
00:54:08
00:37:45
00:06:25
Carl Dudley University of Wolverhampton, UK
JRNY_TIM
-------00:00:00
00:38:46
01:41:20
02:00:00
04:12:48
00:00:00
00:32:23
01:00:00
01:43:44
02:19:28
00:00:00
00:01:59
02:30:23
02:32:20
03:49:48
00:00:00
00:14:32
00:29:45
01:05:13
01:45:01
00:00:00
00:12:16
01:06:24
01:44:09
01:50:34
71
Average Wait Times for a Bus
SELECT v.route
,v.stop
,v.bus
,v.bustime
,v.prev_bus_time
,SUBSTR(NUMTODSINTERVAL(v.numgap,'DAY'),12,8) wait_for_next_bus
,CASE WHEN bustime = FIRST_VALUE(bustime)
OVER (PARTITION BY stop
ORDER BY route,stop,bustime)
THEN SUBSTR(NUMTODSINTERVAL(AVG(v.numgap)
OVER (PARTITION BY stop),'DAY'),12,8)
ELSE NULL END ave_wait
FROM (SELECT route
,stop
,bus
,TO_CHAR(bustime,'dd/mm/yy hh24:mi:ss') bustime
,TO_CHAR(LAG(bustime,1)
OVER (PARTITION BY stop
ORDER BY route,stop,bustime)
,'dd/mm/yy hh24:mi:ss') prev_bus_time
,bustime - LAG(bustime,1)
OVER (PARTITION BY stop
ORDER BY route,stop,bustime) numgap
FROM bustimes) v;
Carl Dudley University of Wolverhampton, UK
72
Average Waiting Times for a Bus (continued)
ROUTE STOP BUS BUSTIME
----- ---- --- -----------------1
1 10 01/03/11 12:17:33
1
1 30 01/03/11 12:58:10
1
1 20 01/03/11 13:58:41
1
1 40 01/03/11 14:06:13
1
1 50 01/03/11 14:11:45
1
2 10 01/03/11 12:56:19
1
2 30 01/03/11 13:00:09
1
2 40 01/03/11 14:20:45
1
2 50 01/03/11 14:24:01
1
2 20 01/03/11 14:31:04
1
3 10 01/03/11 13:58:53
1
3 40 01/03/11 14:35:58
1
3 20 01/03/11 14:58:41
1
3 50 01/03/11 15:18:09
1
3 30 01/03/11 15:28:33
1
4 10 01/03/11 14:17:33
1
4 40 01/03/11 15:11:26
1
4 30 01/03/11 15:30:30
1
4 20 01/03/11 15:42:25
1
4 50 01/03/11 15:55:54
1
5 40 01/03/11 15:51:14
1
5 50 01/03/11 16:02:19
1
5 20 01/03/11 16:18:09
1
5 10 01/03/11 16:30:21
1
5 30 01/03/11 16:47:58
PREV_BUS_TIME
-----------------
WAIT_FOR
--------
01/03/11
01/03/11
01/03/11
01/03/11
12:17:33
12:58:10
13:58:41
14:06:13
00:40:37
01:00:31
00:07:32
00:05:32
01/03/11
01/03/11
01/03/11
01/03/11
12:56:19
13:00:09
14:20:45
14:24:01
00:03:50
01:20:36
00:03:16
00:07:03
01/03/11
01/03/11
01/03/11
01/03/11
13:58:53
14:35:58
14:58:41
15:18:09
00:37:05
00:22:43
00:19:28
00:10:24
01/03/11
01/03/11
01/03/11
01/03/11
14:17:33
15:11:26
15:30:30
15:42:25
00:53:53
00:19:04
00:11:55
00:13:29
01/03/11
01/03/11
01/03/11
01/03/11
15:51:14
16:02:19
16:18:09
16:30:21
00:11:05
00:15:50
00:12:12
00:17:37
Carl Dudley University of Wolverhampton, UK
AVE_WAIT
-------00:28:33
00:23:41
00:22:25
00:24:35
00:14:11
73
Analytic Functions
Overview of Analytic Functions
Ranking Functions
Partitioning
Aggregate Functions
Sliding Windows
Row Comparison Functions
Analytic Function Performance
Carl Dudley University of Wolverhampton, UK
74
Finding Holes in 'Sequences'
SELECT DISTINCT prod_id
FROM sales
ORDER BY prod_id;
PROD_ID
------:
46
47
48
113
114
115
:
 Sales table has 918843 rows
— Gap in prod_ids from 48 to 113
SELECT prod_id
,next_prod_id
FROM ( SELECT prod_id
,LEAD(prod_id) OVER(ORDER BY prod_id) next_prod_id
FROM sales)
WHERE next_prod_id - prod_id > 1;
PROD_ID NEXT_PROD_ID
---------- -----------48
113
Elapsed time : 3.17 secs
Carl Dudley University of Wolverhampton, UK
75
Eliminating Duplicate rows
 dup_emp table has 3670016 rows with unique empno values and no primary key
INSERT INTO dup_emp SELECT * FROM dup_emp WHERE empno = 1;
— dup_emp now has one extra duplicate row
 Use conventional SQL to eliminate the duplicate row
DELETE FROM dup_emp y WHERE ROWID <>(SELECT MAX(ROWID)
FROM dup_emp WHERE y.empno = empno);
1 row deleted.
Elapsed: 00:01:38.76











------------------------------------------------| Id | Operation
| Name
| Rows |
------------------------------------------------|
0 | DELETE STATEMENT
|
| 3670K|
|
1 | DELETE
| DUP_EMP |
|
|* 2 |
HASH JOIN
|
| 3670K|
|
3 |
VIEW
| VW_SQ_1 | 3670K|
|
4 |
SORT GROUP BY
|
| 3670K|
|
5 |
TABLE ACCESS FULL| DUP_EMP | 3670K|
|
6 |
TABLE ACCESS FULL | DUP_EMP | 3670K|
------------------------------------------------Carl Dudley University of Wolverhampton, UK
76
Eliminating Duplicate rows (continued)
 Use the ranking function to efficiently eliminate the same duplicate row
— ORDER BY clause is necessary so NULL is used as a dummy
DELETE FROM dup_emp WHERE ROWID IN
(SELECT rid
FROM (SELECT ROWID rid
,ROW_NUMBER() OVER (PARTITION BY empno
ORDER BY NULL) rnk
FROM dup_emp)
WHERE rnk > 1);
1 row deleted.
Elapsed: 00:00:19.61
--------------------------------------------------------| Id | Operation
| Name
| Rows |
--------------------------------------------------------|
0 | DELETE STATEMENT
|
|
1 |
|
1 | DELETE
| DUP_EMP |
|
|
2 |
NESTED LOOPS
|
|
1 |
|
3 |
VIEW
| VW_NSO_1 | 3670K|
|
4 |
SORT UNIQUE
|
|
1 |
|* 5 |
VIEW
|
| 3670K|
|
6 |
WINDOW SORT
|
| 3670K|
|
7 |
TABLE ACCESS FULL
| DUP_EMP | 3670K|
|
8 |
TABLE ACCESS BY USER ROWID| DUP_EMP |
1 |
Carl Dudley University of Wolverhampton, UK
Similar story with
index on empno
77
Analytic Function Performance
 Example based on sales table in sh schema
— 918843 rows, 72 different prod_ids
PROD_ID
CUST_ID TIME_ID
CHANNEL_ID
PROMO_ID QUANTITY_SOLD AMOUNT_SOLD
------- ---------- --------- ---------- ---------- ------------- ----------46
11702 15-FEB-98
3
999
1
24.92
125
942 27-MAR-98
3
999
1
16.86
46
6406 17-JUL-98
2
999
1
24.83
127
4080 11-SEP-98
3
999
1
38.14
14
19810 20-JUL-98
3
999
1
1257.35
123
3076 24-OCT-98
3
999
1
64.38
48
11403 28-OCT-98
2
999
1
12.95
148
6453 27-MAR-99
2
999
1
20.25
119
609 27-NOV-99
4
999
1
6.54
30
4836 13-DEC-99
2
999
1
10.15
31
1698 17-FEB-00
3
999
1
9.47
119
22354 09-FEB-00
2
999
1
7.75
114
6609 01-JUN-00
3
999
1
21.06
21
8539 28-AUG-00
3
999
1
1097.9
143
11073 14-JAN-01
3
999
1
21.59
119
2234 18-FEB-01
3
999
1
7.51
43
488 25-JUN-01
3
999
1
47.63
27
1577 17-SEP-01
4
999
1
46.16
:
:
:
:
:
:
:
Carl Dudley University of Wolverhampton, UK
78
Analytic Function Performance - Scenario
 Number of times products are on order
SELECT prod_id
,COUNT(*)
FROM sh.sales
GROUP BY prod_id;
PROD_ID
COUNT(*)
------- ---------22
3441
25
19557
30
29282
34
13043
42
12116
43
8340
123
139
129
7557
138
5541
13
6002
28
16796
116
17389
120
19403
:
:
Carl Dudley University of Wolverhampton, UK
79
nth Best Product – "Conventional" SQL Solution
 Find nth ranked product in terms of numbers of orders for each product
SELECT prod_id
,ycnt
FROM (SELECT prod_id
,COUNT(*) ycnt
FROM sh.sales y
GROUP BY prod_id) v
WHERE &position - 1 = (SELECT COUNT(*)
FROM (SELECT COUNT(*) zcnt
FROM sh.sales z
GROUP BY prod_id) w
5
WHERE w.zcnt > v.ycnt);
PROD_ID
YCNT
------- ---------33
22768
Elapsed: 00:00:24.09
Carl Dudley University of Wolverhampton, UK
80
"Conventional" SQL Solution - Trace
---------------------------------------------------------------------------| Id | Operation
| Name
| Rows | Cost |
---------------------------------------------------------------------------|
0 | SELECT STATEMENT
|
|
72 |
134|
|* 1 | FILTER
|
|
|
|
|
2 |
VIEW
|
|
72 |
67|
|
3 |
HASH GROUP BY
|
|
72 |
67|
|
4 |
PARTITION RANGE ALL
|
|
918K|
29|
|
5 |
BITMAP CONVERSION COUNT
|
|
918K|
29|
|
6 |
BITMAP INDEX FAST FULL SCAN | SALES_PROD_BIX |
|
|
|
7 |
SORT AGGREGATE
|
|
1 |
|
|
8 |
VIEW
|
|
4 |
67|
|* 9 |
FILTER
|
|
|
|
| 10 |
SORT GROUP BY
|
|
4 |
67|
| 11 |
PARTITION RANGE ALL
|
|
918K|
29|
| 12 |
BITMAP CONVERSION TO ROWIDS |
|
918K|
29|
| 13 |
BITMAP INDEX FAST FULL SCAN| SALES_PROD_BIX |
|
|
---------------------------------------------------------------------------Predicate Information (identified by operation id):
--------------------------------------------------1 - filter( (SELECT COUNT(*) FROM (SELECT COUNT(*) "ZCNT" FROM
"SH"."SALES" "Z" GROUP BY "PROD_ID" HAVING COUNT(*)>:B1) "W")=4)
9 - filter(COUNT(*)>:B1)
Statistics
---------------------------------------------------------8468 consistent gets
72 sorts (memory)
Carl Dudley University of Wolverhampton, UK
81
nth Best Product – "Failed" SQL Solution
 Find nth ranked product in terms of numbers of orders for each product
SELECT prod_id
,ycnt
FROM (SELECT prod_id
,COUNT(*) ycnt
FROM sh.sales y
GROUP BY prod_id) v
WHERE &position - 1 = (SELECT COUNT(*)
FROM (SELECT ycnt FROM v) w
WHERE w.ycnt > v.ycnt);
*
ERROR at line 8:
ORA-04044: procedure, function, package,
or type is not allowed here
Carl Dudley University of Wolverhampton, UK
82
nth Best Product – Factored Subquery Solution
 Find nth ranked product in terms of numbers of orders for each product
WITH v AS
(SELECT prod_id
,COUNT(*) ycnt
FROM sh.sales y
GROUP BY prod_id)
5
SELECT prod_id
,ycnt
FROM v
WHERE &position - 1 = (SELECT COUNT(*)
FROM (SELECT ycnt
FROM v) w
WHERE w.ycnt > v.ycnt);
PROD_ID
YCNT
------- ---------33
22768
Elapsed: 00:00:00.07
Carl Dudley University of Wolverhampton, UK
83
Factored Subquery Solution - Trace
--------------------------------------------------------------------------------------| Id | Operation
| Name
| Rows | Cost |
--------------------------------------------------------------------------------------|
0 | SELECT STATEMENT
|
|
1 |
71 |
|
1 | TEMP TABLE TRANSFORMATION
|
|
|
|
|
2 |
LOAD AS SELECT
|
|
|
|
|
3 |
HASH GROUP BY
|
|
72 |
67 |
|
4 |
PARTITION RANGE ALL
|
|
918K|
29 |
|
5 |
BITMAP CONVERSION COUNT
|
|
918K|
29 |
|
6 |
BITMAP INDEX FAST FULL SCAN| SALES_PROD_BIX
|
|
|
|* 7 |
FILTER
|
|
|
|
|
8 |
VIEW
|
|
72 |
2 |
|
9 |
TABLE ACCESS FULL
| SYS_TEMP_0FD9D661A_14D8441 |
72 |
2 |
| 10 |
SORT AGGREGATE
|
|
1 |
|
|* 11 |
VIEW
|
|
72 |
2 |
| 12 |
TABLE ACCESS FULL
| SYS_TEMP_0FD9D661A_14D8441 |
72 |
2 |
--------------------------------------------------------------------------------------Predicate Information (identified by operation id):
--------------------------------------------------7 - filter( (SELECT COUNT(*) FROM (SELECT /*+ CACHE_TEMP_TABLE ("T1") */ "C0"
"PROD_ID","C1" "YCNT"
"SYS"."SYS_TEMP_0FD9D661A_14D8441" "T1") "V" WHERE "YCNT">:B1)=4)
11 - filter("YCNT">:B1)
Statistics
---------------------------------------------------------355 consistent gets
0 sorts (memory)
Carl Dudley University of Wolverhampton, UK
84
nth Best Product – Analytic Function Solution
 Find nth ranked product in terms of numbers of orders for each product
SELECT prod_id
,vcnt
FROM (SELECT prod_id
,vcnt
,RANK() OVER (ORDER BY vcnt DESC) rnk
FROM (SELECT prod_id
,COUNT(*) vcnt
FROM sh.sales z
GROUP BY z.prod_id)) qry
5
WHERE qry.rnk = &position;
PROD_ID
YCNT
------- ---------33
22768
Elapsed: 00:00:00.01
Carl Dudley University of Wolverhampton, UK
85
Analytic Function Solution - Trace
-------------------------------------------------------------------------| Id | Operation
| Name
| Rows | Cost |
-------------------------------------------------------------------------|
0 | SELECT STATEMENT
|
|
72 |
105|
|* 1 | VIEW
|
|
72 |
105|
|* 2 |
WINDOW SORT PUSHED RANK
|
|
72 |
105|
|
3 |
HASH GROUP BY
|
|
72 |
105|
|
4 |
PARTITION RANGE ALL
|
|
918K|
29|
|
5 |
BITMAP CONVERSION COUNT
|
|
918K|
29|
|
6 |
BITMAP INDEX FAST FULL SCAN| SALES_PROD_BIX |
|
|
-------------------------------------------------------------------------Predicate Information (identified by operation id):
--------------------------------------------------1 - filter("QRY"."RNK"=5)
2 - filter(RANK() OVER ( ORDER BY COUNT(*) DESC )<=5)
Statistics
---------------------------------------------------------116 consistent gets
1 sorts (memory)
Carl Dudley University of Wolverhampton, UK
86
Analytic Function Performance
 Defining the PARTITION BY and ORDER BY clauses on indexed columns
will provide optimum performance
— For example, a composite index on (deptno, hiredate) columns will
prove effective
 Analytic functions still provide acceptable performance in absence of
indexes but need to do sorting for computing based on partition and order
by columns
— If the query contains multiple analytic functions, sorting and partitioning on
two different columns should be avoided if they are both not indexed
Carl Dudley University of Wolverhampton, UK
87
Performance
 Hiding analytics in views can prevent the use of indexes
— SUM(sal) has to be computed across all rows before the analysis
CREATE OR REPLACE VIEW vv AS
SELECT *, SUM(sal) OVER (PARTITION BY deptno) Deptno_Sum_Sal
FROM emp;
SELECT * FROM vv WHERE empno = 7900;
EMPNO ENAME JOB
MGR HIREDATE
SAL COMM DEPTNO DEPTNO_SUM_SAL
----- ----- ----- ---- --------- ---- ---- ------ -------------7900 JAMES CLERK 7698 03-DEC-81 950
30
9400
-------------------------------------------| Id | Operation
| Name | Rows |
-------------------------------------------|
0 | SELECT STATEMENT
|
|
14 |
|* 1 | VIEW
| VV
|
14 |
|
2 |
WINDOW SORT
|
|
14 |
|
3 |
TABLE ACCESS FULL| EMP |
14 |
-------------------------------------------SELECT * FROM emp WHERE empno = 7900;
-----------------------------------------------------------| Id | Operation
| Name
| Rows |
-----------------------------------------------------------|
0 | SELECT STATEMENT
|
|
1 |
|
1 | TABLE ACCESS BY INDEX ROWID| EMP
|
1 |
|* 2 |
INDEX UNIQUE SCAN
| SYS_C0017750 |
1 |
-----------------------------------------------------------Carl Dudley University of Wolverhampton, UK
88
Steamy Windows
SELECT empno, ename, sal, deptno
,SUM(sal) OVER (PARTITION BY deptno ORDER BY sal) sumsal
FROM emp ORDER BY deptno, sal;
EMPNO
---------7934
7782
7839
7369
7876
7566
7788
7902
7900
7654
7521
7844
7499
7698
ENAME
SAL
DEPTNO
SUMSAL
---------- ---------- ---------- ---------MILLER
1300
10
1300
CLARK
2450
10
3750
KING
5000
10
8750
SMITH
800
20
800
ADAMS
1100
20
1900
JONES
2975
20
4875
SCOTT
3000
20
10875
FORD
3000
20
10875
JAMES
950
30
950
MARTIN
1250
30
3450
WARD
1250
30
3450
TURNER
1500
30
4950
ALLEN
1600
30
6550
BLAKE
2850
30
9400
Default window is RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
Carl Dudley University of Wolverhampton, UK
89
Steamy Windows (continued)
SELECT empno, ename, sal, deptno
,SUM(sal) OVER (PARTITION BY deptno ORDER BY sal) sumsal
FROM emp
WHERE ename LIKE '%M%'
ORDER BY deptno ,sal
EMPNO
---------7934
7369
7876
7900
7654
ENAME
SAL
DEPTNO
SUMSAL
---------- ---------- ---------- ---------MILLER
1300
10
1300
SMITH
800
20
800
ADAMS
1100
20
1900
JAMES
950
30
950
MARTIN
1250
30
2200
SELECT *
FROM (SELECT empno, ename, sal, deptno
,SUM(sal) OVER (PARTITION BY deptno ORDER BY sal) sumsal
FROM emp )
Includes WARD who is in department 30
WHERE ename LIKE '%M%'
and has a salary of 1250. which is within
ORDER BY deptno ,sal;
the RANGE with MARTIN
EMPNO ENAME
SAL
DEPTNO
SUMSAL
---------- ---------- ---------- ---------- ---------7934 MILLER
1300
10
1300
7369 SMITH
800
20
800
7876 ADAMS
1100
20
1900
7900 JAMES
950
30
950
7654 MARTIN
1250
30
3450
Carl Dudley University of Wolverhampton, UK
90
In the Final Analysis
So we have discussed
 The ranking of data using analytic functions
 Partitioning datasets from queries
 Using aggregate functions in analytic scenarios
 How to apply sliding windows to query results
 Comparing values across rows
 Performance characteristics
Carl Dudley University of Wolverhampton, UK
91
Analytic Functions
Carl Dudley
University of Wolverhampton, UK
UKOUG Council
Oracle ACE Director
carl.dudley@wlv.ac.uk
Carl Dudley University of Wolverhampton, UK
92