Database Theory
Jason Fan
Outline
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Basic Concepts
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Database Design
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The Relational Data Model (Chapter 7)
Relational Algebra (Chapter 7)
SQL – A Relational Database Language (Chapter 8)
Relational Calculus (Chapter 9)
Database Implementation
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Database Design Process (Chapter 16)
Entity-Relationship (ER) Modeling (Chapter3)
Functional Dependencies and Normalization for Relational Database (Chapter 14)
Relational Design Algorithms (Chapter 15)
Relational Data Model Mapping (Chapter 9)
Relational Database
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Database and Database Users (Chapter 1)
Database System Concepts and Architecture (Chapter 2)
Transaction Processing (Chapter 19)
Concurrency Control (Chapter 20)
Database Recovery (Chapter 21)
Advanced Topics
Database and Database Users
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Basic Concepts
Main Characteristics of Database Technology
Classes of Database Users
Additional Database Characteristics
When not to use a DBMS
Chapter 1 Database and Database Users
Basic Concepts
• Database
A collection of related data.
• Data
Known facts that can be recorded and have implicit meaning.
• Mini-world
Some part of the real world about which data is stored in database.
• Database Management System (DBMS)
A software package to facilitate the creation and maintenance of a computerized
database.
• Database System
The DBMS software together with the data itself.
Chapter 1 Database and Database Users
Main Characteristics of Database Technology
• Self-contained nature of a database system
A DBMS catalog stores the description (meta-data) of the database. This
allows the DBMS software to work with different databases.
• Insulations between program and data
–Data abstractions
A data model is used to hide storage details and present the user with a conceptual
view of the database.
–Program-data independence
Allows changing data storage structures without having to change the DBMS access
programs.
–Program-operation independence
Allows changing operation implementation without having to change the DBMS
access programs.
• Support of multiple views of data
Chapter 1 Database and Database Users
Additional Characteristics of Database Technology
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Controlling data redundancy
Restricting unauthorized access to data.
Providing persistent storage for program objects and data structure.
Providing multiple interfaces to different classes of users.
Representing complex relationships among data.
Enforcing integrity constraints on the database.
Providing backup and recovery services.
Potential for enforcing standards.
Flexibility to change data structures.
Reduced application development time.
Availability of up-to-date information.
Economies of scale.
Chapter 1 Database and Database Users
Classes of Database Users
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Workers on the scene : Persons whose job involves daily use of a large database.
– Database administrators (DBAs): Responsible for managing the database system.
– Database designers : Responsible for designing the database.
– End users : Access the database for querying , updating , generating reports, etc.
• Casual end users : Occasional users.
• Parametric (or naive) end users : They use pre-programmed canned transactions to interact
continuously with the database. For example, bank tellers or reservation clerks.
• Sophisticated end users : Use full DBMS capabilities for implementing complex applications.
• System Analysts/Application programmers : Design and implement canned transactions for
Parametric users.
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Workers behind the scene: Persons whose job involves design , development ,
operation, and maintenance of the DBMS software and system environment.
– DBMS designers and implementers : Design and implement the DBMS software package
itself.
– Tool developers : Design and implement tools that facilitate the use of DBMS software.
Tools include design tools , performance tools , special interfaces , etc.
– Operators and maintenance personnel : Work on running and maintaining the hardware
and software environment for the database system.
Chapter 1 Database and Database Users
When not to Use a DBMS
• Main costs of using a DBMS
– High initial investment and possible need for additional hardware.
– Overhead for providing generality, security, recovery, integrity, and
concurrency control.
• When DBMS may be unnecessary:
– If the database and applications are simple, well defined and not expected to
change.
– If there are stringent real-time requirements that may not be met because of the
DBMS overhead
– If access to data by multiple users is not required.
Chapter 1 Database and Database Users
Database System Concepts and Architecture
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Data Models
Three-Schema Architecture
Data Independence
DBMS Languages
DBMS Interfaces
DBMS Architecture
Database System Utilities
Classification of DBMS
Chapter 2 Database Concepts and Atchitecture
Data Models
• Data Model
A set of concepts to describe the structure of a database, and certain constraints
that the database should obey.
• Data Model Operations
Operations for specifying database retrievals and updates by referring to the
concepts of data model.
• Categories of data models
– Conceptual (high-level, semantic) data models: Provide concepts that are
close to the way many users perceive data. (Also called entity-based or
object-based data models)
– Physical (low-level, internal) data models: Provide concepts that describe the
details of how data is stored in the computer.
– Implementation (record-oriented) data models: Provide concepts that fall
between above two, balancing user views with some computer storage
details.
Chapter 2 Database Concepts and Atchitecture
Data Models
• Database Schema
The description of database. Includes description of database structure and the
constraints that should hold on the database.
• Database catalog
Stores database schema.
• Schema Diagram
A diagrammatic display of ( some aspects of) a database schema.
• Database Instance
The actual data stored in a database at a particular moment in time. Also called
database state (or occurrence)
• The database schema changes very infrequently. The database state changes
every time the database is updated. Schema is also called intension,
whereas the state is called extension.
Chapter 2 Database Concepts and Atchitecture
Three Schema Architecture
• Internal schema at the internal level to describe data storage structures and
access paths. Typically uses a physical data model.
• Conceptual schema at the conceptual level to describe the structure and
constraints for the whole database. Uses a conceptual or an implementation
data model .
• External schemas at the external level to describe the various user views.
Usually uses the same data model as the conceptual level.
• Mappings transform requests and results between levels.
Chapter 2 Database Concepts and Atchitecture
Database System Architecture
External Level
External View
External View
external/conceptual mapping
Conceptual Level
Conceptual Schema
conceptual/internal Mapping
Internal Level
Internal Schema
Stored Databases
Chapter 2 Database Concepts and Atchitecture
Data Independence
• Logical Data Independence
The capacity to change the conceptual schema without having to change the
external schemas and their application programs.
• Physical Data Independence
The capacity to change the internal schema without having to change the
conceptual schema.
• When a schema at a lower level is changed, only the mappings between
this schema and higher level schemas need to be changed in a DBMS that
fully supports data independence.
• Mappings create overhead
Chapter 2 Database Concepts and Atchitecture
Database System Languages
• Data Definition Language (DDL)
Used by the DBA and database designers to specify the conceptual schema of a
database. In many DBMSs, the DDL is also used to define internal and external
schemas(views). In some DBMSs, separate storage definition language (SDL)
and view definition language (VDL) are used to define internal and external
schemas.
• Data Manipulation Language (DML)
Used to specify database retrievals and updates.
– High-level (nonprocedural) DML can be used on its own to specify database
operations.
– Low-level (procedural) DML retrieves a record at a time and must be
embedded in a general-purpose programming language.
– When DML is embedded in a general-purpose programming language (host
language), it is called data sublanguage.
– When DML is used in a stand-alone interactive manner, it is called query
language
Chapter 2 Database Concepts and Atchitecture
DBMS Interfaces
• Stand-alone query language interfaces.
• Programmer interfaces for embedding DML in programming languages:
– Pre-compiler Approach
– Procedure (Subroutine) Call Approach
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Menu-based
Graphic-based
Forms-based
Natural language
Combination of above
Parametric interfaces using function keys
Report generation languages
Interfaces for DBA:
– Creating accounts, granting authorizations
– Setting system parameters
– Changing schemas or access path
Chapter 2 Database Concepts and Atchitecture
Database System Utilities
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Loading data stored in files into a database.
Backing up the database periodically on tape.
Reorganizing database file structures.
Generating Report.
Performance monitoring.
Sorting files.
User monitoring.
Data compression.
Chapter 2 Database Concepts and Atchitecture
Classification of DBMSs
• Based on the data model used:
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Relational
Multidimensional
Network
Hierarchical.
Object-oriented
Semantic
Entity-Relationship
• Other Classifications:
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Single-user vs. multi-user
Centralized vs. distributed
Homogeneous vs. Heterogeneous
OLTP vs. OLAP
Chapter 2 Database Concepts and Atchitecture
Database Design
• Goals of Database Design
– Satisfy the information content requirements of the specified users and
applications
– Provide natural and easy-to-understand structuring of information
– Support processing requirements and any performance objectives
• Database Design Process
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Requirement collection and analysis
Conceptual database design
Choice of DBMS
Data model mapping (Logical database design)
Physical database design
Database system implementation and tuning
Chapter 16 Practical Database Design and Tuning
Requirement Collection and Analysis
• The major application areas and user groups that will use the database or
whose work will be affected by it are identified. Key individuals and
committees within each group are chosen to carry out subsequent steps of
requirement analysis
• Existing documentations concerning the applications is studied and
analyzed.
• The current operating environment and planned use of the information is
studied.
• Written responses to sets of questions are sometimes collected from
potential users or user groups. Key individuals may be interviewed to help
in assessing the worth of information and in setting up of priorities.
Chapter 16 Practical Database Design and Tuning
Conceptual Database Design
• Conceptual Schema Design
– Choice of high-level conceptual data model such as ER model and dimensional
model
– Approaches to conceptual schema design
• centralized schema design approach
• view integration approach
– Strategies for conceptual schema design
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top-down strategy
bottom-up strategy
inside-out strategy
mixed strategy
• Transaction Design
Chapter 16 Practical Database Design and Tuning
Physical Database Design
• Criteria for guiding the physical database design
– Response time
– Space utilization
– Transaction throughput
• Physical database design in relational database
– Factors that influent the physical database design
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Analyzing the database queries and transactions
Analyzing the expected frequencies of invocation of queries and transactions
Analyzing the time constraints for queries and transactions
Analyzing the expected frequencies of update operations
Analyzing the uniqueness constraints on attributes
• Physical database design decisions
– Indexing
– De-normalization
– Storage design
Chapter 16 Practical Database Design and Tuning
Database Tuning in Relational Database
• Goals
– Make application run fast
– lower the response time of queries and transactions
– improve the overall throughput of transactions
• Tuning indexes
– Some queries may take too long for lack of an index
– Some indexes may not get utilized
– Some indexes may causing excessive overhead
• Tuning database design
– De-normalization
– Table partition
– Duplicate attributes
• Tuning queries
Chapter 16 Practical Database Design and Tuning
Automated Design Tools
• Database Design Tools
– Erwin
– Rational Rose
– Power Designer
• Schema Diagram Notation
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UML (Unified Modeling Language)
IDEF1X (Integration Definition for Information Modeling)
IE (Information Engineering)
CHEN's ERD Notation
Chapter 16 Practical Database Design and Tuning
Entity-Relationship (ER) Modeling
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Example Database Application (COMPANY)
ER Model Concepts
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Entities and Attributes
Entity Types, Value Sets, and Key Attributes
Relationships and Relationship Types
Structural Constraints and Roles
Weak Entity Types
ER Diagrams Notation
Relationships of Higher Degree
Enhanced ER Modeling
Chapter 3 Data Modeling Using the EntityRelationship Model
Example of COMPANY Database
• Requirements for the COMPANY Database:
– The company is organized into departments. Each department has a name,
number, and a employee who manages the department. We keep track of the
start date of the department manager. A department may have several locations.
– Each department controls a number of projects. Each project has a name,
number, and is located at a single location.
– We store each employee's social security number, address, salary, sex and birth
date. Each employee works for one department but may work on several
projects. We keep track of the number of hours per week that an employee
currently works on each project. We also keep track of the direct supervisor of
each employee.
– each employee may have a number of dependents. For each dependent, we
keep their name, sex, birth date, and relationship to the employee.
Chapter 3 Data Modeling Using the EntityRelationship Model
ER Model Concepts: Entities and Attributes
• Entities
Entities are specific objects or things in the mini-world that are represented in the database;
for example, the EMPLOYEE John Smith, the Research DEPARTMENT, the ProductX
PROJECT.
• Attributes
Attributes are properties used to describe an entity; for example, an EMPLOYEE entity may
have a Name, SSN, Address, Sex, BirthDate. A specific entity will have a value for each
of its attributes; for example a specific employee entity may have Name = 'John Smith',
SSN = '123456789', Address = '731 Fondren , Houston, TX', Sex = 'M', BirthDate = '09JAN-55'.
• Attribute Types
– Simple: each entity has a single atomic value for the attribute; for example SSN or Sex.
– Composite: Attribute may be composed of several components; for example
Name(FirstName, MiddleName, LastName). Composition may form a hierarchy where
some components are themselves composite.
– Multi-Valued: An entity may have multiple values for that attribute; for example Color
of a CAR or PreviousDegrees of a STUDENT. Denoted as {Color} or {
PreviousDegrees}.
Chapter 3 Data Modeling Using the EntityRelationship Model
ER Model Concept: Entity Types and Key Attributes
• Entity Type
Entity type defines a set of entities that have the same attributes. For example, the
EMPLOYEE entity type or the PROJECT entity type.
• Key Attribute
An attribute of an entity type for which each entity must have a unique value is
called a key attribute of the entity type. For example, SSN of EMPLOYEE.
– A key attribute may be composite. For example, VehicleRegistrationNumber is
a key of the CAR entity type with components(Number, State).
– An entity type may have more than one key. For example, the CAR entity type
may have two keys: VehicleIdentificationNumber and
VehicleRegistrationNumber(Number, State).
• Domains (Value Sets) of Attributes
Each simple attribute of an entity type is associates with a domain, which specifies
the set of values the may be assigned to that attribute for each individual entity.
Chapter 3 Data Modeling Using the EntityRelationship Model
ER Model Concepts: Relationships and Relationship Types
• Relationship
A relationship relates two or more distinct entities with a specific meaning; for
example, EMPLOYEE John Smith works on the ProductX PROJECT or
EMPLOYEE Franklin Wong manages the Research DEPARTMENT.
• Relationship Type
Relationship of the same type are grouped or typed into a relationship type. For
example, the WORKS_ON relationship type in which EMPLOYEEs and
PROJECTs participate, or the MANAGEs relationship type in which
EMPLOYEEs and DEPARTMENTs participate.
More than one relationship type can exist with the same participating entity
types; for example, MANAGES and WORKS_FOR are distinct relationships
between EMPLOYEE and DEPARTMENT participate.
• The degree of a relationship type
The degree of a relationship type is the number of participating entity types.
binary relationships, ternary relationship, n-ary relationship
Chapter 3 Data Modeling Using the EntityRelationship Model
ER Model Concepts: Structural Constraints and roles
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A relationship can relate two entities of the same entity type; for example,
a SUPERVISION relationship type relates one EMPLOYEE ( in the role
of supervisee) to another EMPLOYEE ( in the role of supervisor). This is
called a recursive relationship type.
A relationship type can have attributes; for example, HoursPerWeek of
WORKS_ON; its value for each relationship instance describes the
number of hours per week that an EMPLOYEE works on a PROJECT.
Structural constraints on relationships
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Cardinality ratio ( of a binary relationship): 1:1, 1:N, N:1, or M:N.
Participation constraint (on each participating entity type): total (called
existence dependency) or partial.
Chapter 3 Data Modeling Using the EntityRelationship Model
ER Model Concepts: Weak Entity Types
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An entity type that does not have a key attribute
A weak entity type must participate in an identifying relationship type
with an owner or identifying entity type
Entities are identified by the combination of a partial key of the weak
entity type and the key of the identifying entity type.
Example
Suppose that a DEPENDENT entity is identified by the dependent’s first name
and birth date, and the specific EMPLOYEE that the dependent is related to.
DEPENDENT is a weak entity type with EMPLOYEE as its identifying
entity type via the identifying relationship type DEPENDENT_OF.
Chapter 3 Data Modeling Using the EntityRelationship Model
Conceptual Design of COMPANY Database
• Entity types
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DEPARTMENT
PROJECT
EMPLOYEE
DEPENDENT
• Relationship types
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Manage (1:1)
Work_for (1:n)
Supervision (1:n)
Controls (1:n)
Works_on (m:n)
Has_dependent (1:n)
Chapter 3 Data Modeling Using the EntityRelationship Model
ER Diagram of COMPANY Database
Fname
Minit
Lname
Name
SSN
Number
Address
Name
N
Sex
Name
1
Location
Works_for
DEPARTMENT
Bdate
Manages
EMPLOYEE
1
1
StartDate
1
1
1
Supervision
N
Works_on
Has_dependents
M
N
DEPENDENT
Name
Sex
Bdate Relationship
Controls
N
N
Hours
PROJECT
Name
Number
Location
Chapter 3 Data Modeling Using the EntityRelationship Model
Alternative Notation for Relationship Structural Constraints
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Specified on each participation of an entity type E in a relationship type
R.
Specifies that each entity e in E participates in at least min and at most
max relationship instances in R.
Default(no constraint): min = 0, max = n.
Must have min  max, min  0, max  1.
Examples
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A department has exactly one manager and an employee can manage at most
one department.
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Specify (1,1) for participation of DEPARTMENT in MANAGES
Specify (0,1) for participation of EMPLOYEE in MANAGES
An employee can work for exactly one department but a department can
have any number of employees.
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Specify (1,1) for participation of EMPLOYEE in WORKS_FOR
Specify (0,n) for participation of DEPARTMENT in WORKS_FOR
Chapter 3 Data Modeling Using the EntityRelationship Model
ER Diagram of COMPANY Database
Fname
Minit
Lname
Name
SSN
Number
Address
Name
Name
Sex
0:N
Works_for
DEPARTMENT
1:1
Bdate
Location
Manages
EMPLOYEE
0:1
1:1
StartDate
1
1
1
Supervision
N
Works_on
Has_dependents
M
N
DEPENDENT
Name
Sex
Bdate Relationship
Controls
N
N
Hours
PROJECT
Name
Number
Location
Chapter 3 Data Modeling Using the EntityRelationship Model
Enhanced Entity-Relationship and Object Modeling
• Subclass, Superclass and Inheritance
• Specialization and Generalization
– Disjoin/Overlapping
– Total/Partial
• Union/Categories
Chapter 4 Enhanced Entity-Relationship and
Object Modeling
The Relational Data Model
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Relational Model Concepts
Characteristics of Relations
Relational Integrity Constraints
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Domain Constraints
Key Constraints
Entity Integrity Constraints
Referential Integrity Constraints
Update Operations on Relations
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Relational Model Concepts
Attributes
Relation name
PRODUCT
Tuples
ORDER_ITEM
ProductID
1
3
11
OrderID
1
1
2
2
3
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
ProductName
Chai
Aniseed Syrup
Queso Cabrales
ProductID
1
3
1
11
11
UnitPrice
18.00
22.00
21.00
Quantity
20
15
30
10
35
UnitInStock
39
53
22
Discount
0.1
0.1
0.2
0.2
0.15
UnitPrice
20.00
25.00
18.00
22.00
21.00
Relational Model Concepts
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Relation ( informally).
A table of values. Each column in the table has a column header called an attribute. Each
row is called a tuple.
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Formal relational concepts.
– Domain: A set of atomic (indivisible) values.
– Attribute: A name to suggest the meaning that a domain plays in a particular relation.
Each attribute Ai has a domain Dom(Ai).
– Relation schema: A relation name R and a set of attributes Ai that define the relation.
Denoted by R(A1, A2, ... ,an). For example: student(name, SSN, BirthDate, Addr).
– Relational Database Schema: A set S of relation schemas that belong to the same
database. S is the name of the database. S = {R1, R2, ...,Rn}.
– Degree of a relation: its number of attributes n.
– Tuple t of R(A1, A2,....,An): a (ordered) set of values t = < v1, v2, ..., vn> where each value
vi is an element of Dom(Ai). Also called a n-tuple.
– Relation instance r(r): A set of tuples r(r) = {t1, t2,...,Tm}, or alternatively r(r) dom(a1) 
dom(a2)  ...  dom(an).
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Characteristics of Relations
• The tuples are not considered to be ordered, even though they appear to be
in the tabular form.
• We will consider the attributes in R(A1, A2, ...,An) and the values in t = <
v1, v2, .., vn> to be orderd.( However, a more general alternative definition
of relation does not require this ordering).
• All values are considered atomic (indivisible). A special null value is used
to represent values that are unknown or inapplicable to certain tuples.
• Notation
– We refer to component values of a tuple t by t[Ai] = vi (the value of attribute Ai
for tuple t)
– Similarly, t[Au, Av, ..., Aw] refer to the sub-tuple of t containing the values of
attributes Au, Av, ..., Aw, respectively.
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Relational Constraints
Relation name Primary key
PRODUCT
Tuples
ProductID
1
3
11
Attributes
ProductName
Chai
Aniseed Syrup
Queso Cabrales
Primary key
ORDER_ITEM
OrderID
1
1
2
2
3
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
UnitPrice
18.00
22.00
21.00
UnitInStock
39
53
22
Foreign key
ProductID
1
3
1
11
11
Quantity
20
15
30
10
30
Discount
0.1
0.1
0.2
0.2
0.15
UnitPrice
20.00
25.00
18.00
22.00
20.00
Relational Constraints
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Constraints are conditions that must hold on all valid relation instances. There are
three main types of constraints:
Domain Constraints
Values of each attribute must be atomic.
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Key Constraints
– Superkey of R: A set of attributes SK of R such that no two tuples in any valid relation
instance r(R) will have the same value for SK. That is, for any distinct tuples t1 and t2 in
r(R), t1[SK]  t2[SK]
– Key (candidate key) of R: A “minimal” superkey; that is, a superkey K such that removal
of any attribute form K results in a set of attributes that is not a superkey.
– Example: The CAR relation schema:
CAR(State, Reg#, SerialNo, make, Model, Year) has two keys: Key1 = {State, Reg#}, Key2
{SerialNo}; which are also superkeys. {SerialNo, Make} is a superkey but not a key.
– If a relation has several candidate keys, one is chosen arbitrarily to be the primary key.
The primary key attributes are underlined.
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Relational Constraints
• Entity Integrity
The primary key attributes PK of each relation schema R in S can not have null
values in any tuple of r(R). This is because primary key values are used to
identify the individual tuples. t[PK]  null for any tuple t in r(R).
• Referential Integrity
Referential integrity constraint is used to specify a relationship among tuples in
two relations: the referencing relation and referenced relation. It involves two
relations. Tuples in the referencing relation R1 have attributes FK (called
foreign key attributes) that reference the primary key attributes PK of the
referenced relation R2. A tuple t1 in R1 is said to reference a tuple t2 in R2 if
t1[FK] = t2 [PK]. A referential integrity constraint can be displayed in a
relational database schema as a directed arc from R1.FK to R2.
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Operations
Attributes
Relation name
PRODUCT
Tuples
ORDER_ITEM
ProductID
1
3
11
13
13
OrderID
1
1
2
2
3
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
ProductName
Chai
Aniseed Syrup
Queso Cabrales
Syrup
xyz
ProductID
1
3
1
11
11
UnitPrice
18.00
22.00
21.00
23.00
22.00, 21.00
Quantity
20
15
30
10
35
UnitInStock
39
53
22
20
25, 35
Discount
0.1
0.1
0.2
0.2
0.15
UnitPrice
20.00
25.00
18.00
22.00
21.00
Update Operations on Relations
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Update Operations
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Integrity constraints should not be violated by the update operations.
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INSERT a tuple
DELETE a tuple
MODIFY a tuple
Insert operation could violate any constraint.
Delete operation could violate referential constraints.
Modify a primary key or foreign key attribute is equivalent to delete one tuple and
insert another. Modify other attributes cause no problems.
Several update operations may have to be grouped together.
Updates may propagate to cause other updates automatically. This may be
necessary to maintain integrity constraints.
In case of integrity violation, several actions can be taken:
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cancel the operation that causes the violation
perform the operation but inform the user of violation
trigger additional updates so the violation is corrected
execute a user-specified error-correction routine
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Data Model Mapping
• ER-to-Relational Mapping
• EER-to-Relational Mapping
Capter 9 ER- and EER-to-Relational Mapping,
and Other Relational Languages
Relational Model of COMPANY Database
EMPLOYEE
FNAME MINIT LNAME SSN BDATE ADDRESS SEX SALARY SUPERSSN DNO
DEPARTMENT
DNAME DNUMBER
MGRSSN MGRSTARTDATE
DEPT_LOCATION
DNUMBER
DLOCATION
PROJECT
PNAME PNUMBER
PLOCATION
DNUM
WORKS_ON
ESSN
PNO
HOURS
DEPENDENT
ESSN
DEPENDENT_NAME
Capter 9 ER- and EER-to-Relational Mapping,
and Other Relational Languages
SEX
BDATE
RELATIONSHIP
ER-to-Relational Mapping
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STEP 1: For each regular (strong) entity type E in the ER schema, create a relation R that
includes all the simple attributes of E. Include only the simple component attributes of a
composite attribute. Choose one of the key attributes of E as primary key for R. If the chosen
key of E is composite, the set of simple attributes that form it will together form the primary
key of R.
STEP 2: For each weak entity type W in the ER schema with owner entity type E, create a
relation R, and include all simple attributes (or simple components of composite attributes) of
W as attributes of R. In addition, include as foreign key attributes of R the primary key
attribute(s) of the relation(s) that correspond to the owner entity type(s); this takes care of the
identifying relationship type of W. The primary key of R is the combination of the primary
key(s) of the owner(s) and the partial key of the weak entity type W, if any.
STEP 3: For each binary 1:1 relationship type R in the ER schema, identify the relations S
and T that correspond to the entity types participating in R. Choose one of the relations—S,
say—and include as foreign key in S the primary key of T. It is better to choose an entity type
with total participation in R in the role of S. Include all the simple attributes (or simple
components of composite attributes) of the 1:1 relationship type R as attributes of S.
STEP 4: For each regular binary 1:N relationship type R, identify the relation S that
represents the participating entity type at the N-side of the relationship type. Include as
foreign key in S the primary key of the relation T that represents the other entity type
participating in R. Include any simple attributes (or simple components of composite
attributes) of the 1:N relationship type as attributes of S.
Capter 9 ER- and EER-to-Relational Mapping,
and Other Relational Languages
ER-to-Relational Mapping
•
•
•
STEP 5: For each binary M:N relationship type R, create a new relation S to represent R.
Include as foreign key attributes in S the primary keys of the relations that represent the
participating entity types; their combination will form the primary key of S. Also include any
simple attributes of the M:N relationship type (or simple components of composite attributes)
as attributes of S. Notice that we cannot represent an M:N relationship type by a single
foreign key attribute in one of the participating relations—as we did for 1:1 or 1:N
relationship types—because of the M:N cardinality ratio.
STEP 6: For each multivalued attribute A, create a new relation R. This relation R will
include an attribute corresponding to A, plus the primary key attribute K—as a foreign key in
R—of the relation that represents the entity type or relationship type that has A as an attribute.
The primary key of R is the combination of A and K. If the multivalued attribute is composite,
we include its simple components.
STEP 7: For each n-ary relationship type R, where n > 2, create a new relation S to represent
R. Include as foreign key attributes in S the primary keys of the relations that represent the
participating entity types. Also include any simple attributes of the n-ary relationship type (or
simple components of composite attributes) as attributes of S. The primary key of S is usually
a combination of all the foreign keys that reference the relations representing the participating
entity types. However, if the cardinality constraints on any of the entity types E participating
in R is 1, then the primary key of S should not include the foreign key attribute that references
the relation E’ corresponding to E. This concludes the mapping procedure.
Capter 9 ER- and EER-to-Relational Mapping,
and Other Relational Languages
EER-to-Relational Mapping
•
STEP 8: Convert each specialization with m subclasses {S1, S2, . . ., Sm} and
(generalized) superclass C, where the attributes of C are {k, a1, . . ., an} and k is the
(primary) key, into relation schemas using one of the four following options:
– Option 8A: Create a relation L for C with attributes Attrs(L) = {k, a1, . . ., an} and PK(L)
= k. Create a relation Li for each subclass Si, 1 1 i 1 m, with the attributes Attrs(Li) =
{k}D {attributes of Si} and PK(Li) = k.
– Option 8B: Create a relation Li for each subclass Si, 1 1 i 1 m, with the attributes
Attrs(Li) = {attributes of Si}D {k, a1, . . ., an} and PK(Li) = k.
– Option 8C: Create a single relation L with attributes Attrs(L) = {k, a1, . . ., an} D
{attributes of S1} D . . . D {attributes of Sm} D {t} and PK(L) = k. This option is for a
specialization whose subclasses are disjoint, and t is a type (or discriminating) attribute
that indicates the subclass to which each tuple belongs, if any. This option has the
potential for generating a large number of null values.
– Option 8D: Create a single relation schema L with attributes Attrs(L) = {k, a1, . . ., an} D
{attributes of S1} D . . . D {attributes of Sm} D {t1, t2, . . ., tm} and PK(L) = k. This
option is for a specialization whose subclasses are overlapping (but will also work for a
disjoint specialization), and each ti, 1 1 i 1 m, is a Boolean attribute indicating whether a
tuple belongs to subclass Si.
Capter 9 ER- and EER-to-Relational Mapping,
and Other Relational Languages
ER-to-Relational Mapping
ER Model
Relational Model
Entity
Entity Relation
1:1 and 1:N relationship type
Foreign key
M:N relationship type
Relationship relation and two foreign keys
N-ary relationship type
Relationship relation and n foreign keys
Simple attribute
Attribute
Composite attribute
Set of component attributes
Multi-valued attributes
Relation and foreign keys
Value set (Domain)
Domain
Key attribute
Primary (or candidate) key
Capter 9 ER- and EER-to-Relational Mapping,
and Other Relational Languages
The Relational Algebra
•
•
•
Relational algebra is a collection of operations to manipulate relations
Query result is in the form of a relation
Relational Operations
–
–
–
–
SELECT 
PROJECT  operations
Sequences of operations and renaming of attributes
Set operations
•
•
•
•
UNION 
INTERSECTION 
DIFFERENCE 
CARTESIAN PRODUCT 
– JOIN operations
– Other relational operations
•
•
•
DIVISION
OUTER JOIN
AGGREGATE FUNCTIONS.
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Relational Operations
• SELECT operation (denoted by )
– Selects the tuples (rows) from a relation R that satisfy a certain selection
condition c
– Form of the operation: c(R)
– The condition c is an arbitrary Boolean expression on the attributes of R
– Resulting relation has the same attributes as R
– Resulting relation includes each tuple in r(R) whose attribute values
satisfy condition c
– Examples:
 DNO = 4 (EMPLOYEE)
 SALARY > 30333(EMPLOYEE)
 (( DNO = 4 AND SALARY > 25000 ) OR DNO = 5) (EMPLOYEE)
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Relational Operations
– Examples:

DNO = 4
(EMPLOYEE)
Jennifer S Wallace 987654321 1941-06-20 Berry, Bellaire, TX F 43000.00 888665555 4
Ahmad V Jabbar 987987987 1969-03-29 Dallas, Huston, TX M 25000.00 987654321 4
Alicia J Zelaya
999887777 1968-07-19 Castle, Spring, TX F 25000.00 987654321 4

SALARY > 30333(EMPLOYEE)
Franklin T Wong 333445555 1955-12-08
Ramesh K Narayan 666884444 1962-09-15
James E Borg
888665555 1937-11-10
Jennifer S Wallace 987654321 1941-06-20

638 Voss, Huston, TX
975 Fire Oak, Humble, TX
450 Stone, Huston, TX
291 Berry, Bellaire, TX
M 40000.00 888665555
M 38000.00 333445555
M 55000.00 null
F 43000.00 888665555
5
5
1
4
(( DNO = 4 AND SALARY > 25000 ) OR DNO = 5) (EMPLOYEE)
Franklin T Wong 333445555 1955-12-08 638 Voss, Huston, TX
M 40000.00
Ramesh K Narayan 666884444 1962-09-15 975 Fire Oak, Humble, TX M 38000.00
Jennifer S Wallace 987654321 1941-06-20 291 Berry, Bellaire, TX
F 43000.00
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
888665555 5
333445555 5
888665555 4
Relational Operations
• PROJECT operation(denoted by )
– Keeps only certain attributes (columns) from a relation R specified in
an attribute list L
– Form of operation:  L(R)
– Resulting relation has only those attributes of R specified in L
– The PROJECT operation eliminates duplicate tuples in the resulting
relation so that it remains a mathematical set ( no duplicate elements)
– Example
 FNAME,LNAME, SALARY(EMPLOYEE)
 SEX, SALARY(EMPLOYEE)
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Relational Operations
– Example

FNAME,LNAME, SALARY(EMPLOYEE)
John
Franklin
Joice
Ramesh
James
Jennifer
Ahmad
Alicia
Smith
Wong
English
Narayan
Borg
Wallace
Jabbar
Zelaya
30000.00
40000.00
25000.00
38000.00
55000.00
43000.00
25000.00
25000.00

SEX, SALARY(EMPLOYEE)
F
F
M
M
M
M
M
25000.00
43000.00
25000.00
30000.00
38000.00
40000.00
55000.00
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Relational Operations
• Sequence of operations:
Several operations can be combined to form a relational algebra expression (query)
•
Example
– Retrieve the names and salaries of employees who work in department 4.
 FNAME, LNAME, SALARY( DNO=4 (EMPLOYEE))
Jennifer
Wallace
43000.00
Ahmad
Jabbar
25000.00
Alicia
Zelaya
25000.00
– Alternatively we specify explicit intermediate relations for each step:
DEPT4_EMPS  DNO= 4 (EMPLOYEE)
R   FNAME, LNAME, SALLRY(DEPT4_EMPS)
•
Attributes can optionally be renamed in the resulting left-hand side relation(this
may be required for some operations that will be presented later):
DEPT4_MPS   DNO =4 (EMPLOYEE)
R(FIRSTNAME,LASTNAME,SALARY) 
 FNAME,LNAME,SALARY(DEPT4_EMPS)
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Relational Operations
• Set Operations
UNION: R1  R2,
INTERSECTION: R1  R2
SET DIFFERENCE: R1  R2
CARTESIAN PRODUCT: R1  R2
For , , , the operand relations R1(A1, A2, ...,An) and R2(B1, B2, ...,Bn) must
have the same number of attributes, and the domains of corresponding attributes
must be compatible; that is dom(Ai) = dom(Bi) for i = 1,2,..,n. This condition is
called union compatibility.
– The resulting relation for ,  or  has the same attribute names as the first
operand relation R1(by convention).
–
–
–
–
–
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Relational Operations
• Cartesian product
– R(A1, A2,....,Am,B1,B2,...,Bn)  R1(A1, A2,....,Am)  R2(B1,B2,...,Bn)
– A tuple t exists in R for each combination of tuples t1 from R1 and t2 from R2
such that t[A1, A2,....,Am ] = t1 and t[B1,B2,...,Bn] = t2
– If R1 has n1 tuples and R2 has n2 tuples, then R will have n1*n2 tuples
– CARTESIAN PRODUCT is a meaningless operation on its own. It can
combine related tuples from two relations if followed y the appropriate
SELECT operation.
– Example:
Combine each DEPARTMENT tuple with the EMPLOYEE tuple of the manager.
DEP_EMP  DEPARTMENT  EMPLOYEE
DEPT_MANAGER  MGRSSN = SSN(DEP_EMP)
James
Jennifer
Franklin
E
S
T
Borg
Wallace
Wong
888665555 … Headquarters
987654321 … Administration
333445555 … Research
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
1 888665555 1981-06-19
4 987654321 1995-01-01
5 333445555 1988-05-22
Relational Operations
• JOIN operation
– THETA JOIN
Similar to CARTESIAN PRODUCT followed by a SELECT. The condition c is called the
join condition.
R(A1, A2,....,Am,B1,B2,...,Bn)  R1(A1, A2,....,Am) c R2(B1,B2,...,Bn)
c is in the form of <condition> AND <condition> AND . . . AND <condition> , where
each condition is of the form Ai θ Bj, Ai is an attribute of R, Bj is an attribute of S, Ai
and Bj have the same domain, and θ (theta) is one of the comparison operators {=, <,
, >, , }.
– EQUIJOIN
• The condition c uses only operator '='.
• The attributes appear in condition c are called join attributes
Examples: Retrieve each DEPARTMENT’s name and its manager’s name:
T  DEPARTMENT
MGRSSN = SSN
EMPLOYEE
RESULT   DNAME,FNAME,LNAME (T)
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Relational Operations
• JOIN operations
– NATURAL JOIN(*):
In an EQUIJOIN R  R1 cR2, the join attributes of R2 appear redundantly in the
result relation R. In a NATURAL JOIN, the redundant join attributes of R2 are
eliminated from R. The equality condition is implied and need not be specified.
R  R1 * ( join attributes of R1), (join attributes of R2) R2
– If the join attributes have the same names in both relations, they need not be
specified and we can write R R1*R2.
– Examples:
• Retrieve each EMPLOYEE’s name and the name of the DEPARTMENT he/she
works for:
T  EMPLOYEE* (DNO),(DNUMBER)DEPARTMENT
RESULT   FNAME, LNAME, DNAME (T)
• retrieve each EMPLOYEE’s name and the name of his/ser SUPERVISOR:
SUPERVISOR (SUPERSSN,SFN,SLN)   SSN,FNAME,LNAME (EMPLOYEE)
T  EMPLOYEE*SUPERVISOR
RESULT   FNAME,LNAME,SFN,SLN(T)
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Relational Operations
• Complete Set of Relational Algebra Operations:
– All the operations discussed so far can be described as a sequence of only the
operations SELECT, PROJECT, UNION, SET DIFFERENCE, and
CARTESIAN PRODUCT.
– Hence, the set {, , , -,  } is called a complete set of relational algebra
operations. Any query language equivalent to these operations is called
relationally complete.
– For database applications, additional operations are needed that were not part
of the original relational algebra. These include:
• Aggregate functions and grouping
• OUTER JOIN and OUTER UNION.
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Relational Operations
• Aggregate Functions
– Functions such as SUM, COUNT, AVERAGE, MIN, MAX are often applied to
sets of values or sets of tuples in database applications
– < grouping attributes>  <function list> (R)
– The grouping attributes are optional
– Example 1: retrieve the average salary of all employees ( no grouping needed):
R(AVGSAL)   AVERAGE SALARY (EMPLOYEE)
35125.000000
– Example 2: For each department, retrieve the department number, the number
of employees , and the average salary ( in the department):
R(DNO,NUMEMPS,AVGSAL) 
DNO  COUNT SSN,AVERAGE SALARY (EMPLOYEE)
1 1
4 3
5 4
55000.000000
31000.000000
33250.000000
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
Relational Operations
• OUTER JOIN:
– In a regular EQUIJOIN or NATURAL JOIN operation, tuples in R1 or R2 that
do not have matching tuples in other relation do not appear in the result. Some
queries require all tuples in R1 (or R2 or both) to appear in the result. When no
matching tuples are found, nulls are placed for the missing attributes
– LEFT OUTER JOIN: R1 R2 lets every tuple in R1 appear in the result
– RIGHT OUTER JOIN: R1 R2 lets every tuple in R2 appear in the result
– FULL OUTER JOIN: R1
R2 lets every tuple in R1 or R2 appear in the result
Chapter 7 The Relational Data Model, Relational
Constraints, and the Relational Algebra
SQL - A Relational Database Language
•
•
•
•
•
•
•
•
Basic Concepts
Data Definition in SQL
Retrieval Queries in SQL
Specifying Updates in SQL
Relational Views in SQL
Creating Indexes in SQL
Embedding SQL in a Programming Language
Recent Advances in SQL
Chapter 8 SQL - The Relational Database
Standard
Basic Concept
•
•
•
•
•
•
Catalog: A collection of schemas
Schema: A collections of tables and other constructs such as constraints.
Table: Represents a relation. It includes base tables and views.
Column: Represents an attribute.
Name Space Hierarchy: catalog -> schema -> table -> column
Qualified Name
catalog_name[.schema_name[.table_name[.column_name]]]
Chapter 8 SQL - The Relational Database
Standard
Data Definition in SQL
• CREATE TABLE :
Specifies a new base relation by giving it a name and specifying each of its
attributes and their data types (INTEGER, FLOAT , DECIMAL (i,j),
CHAR(n), VARCHAR(n)). A constraint NOT NULL may be specified on an
attribute .
• Example :
CREATE TABLE DEPARTMENT
( DNAME VARCHAR(15) NOT NULL ,
DNUMBER INT NOT NULL ENIQUE,
MGRSSN CHAR(9) NOT NULL,
MGRSTARTDATE DATETIME,
PRIMARY KEY(DNUMBER),
FOREIGN KEY (MGRSSN) REFERENCES EMPLOYEE
);
Chapter 8 SQL - The Relational Database
Standard
Data Definition in SQL
• DROP TABLE
Used to remove a relation (base table) and its definition. The relation can no
longer be used in queries , updates or any other commands since its description
no longer exists.
Example :
DROP TABLE DEPENDENT ;
• ALTER TABLE
Used to add an attribute to one of the base relations. The new attribute will have
NULLs in all the tuples of the relation right after the command is executed ;
hence, the NOT NULL constraint is not allowed for such an attribute.
Example :
ALTER TABLE EMPLOYEE ADD JOB VARCHAR(12) ;
– The database users must still enter a value for the new attribute JOB for each
EMPLOYEE tuple. This can be done using the UPDATE command.
Chapter 8 SQL - The Relational Database
Standard
DDL for COMPANY Database
CREATE TABLE EMPLOYEE
( FNAME VARCHAR(15) NOT NULL,
MINIT
CHAR,
LNAME VARCHAR(15) NOT NULL,
SSN
CHAR(9)
NOT NULL,
BDATE
DATETIME,
ADDRESS VARCHAR(30),
SEX
CHAR,
SALARY DECIMAL(19,2),
SUPERSSN CHAR(9),
DNO
INT
NOT NULL,
PRIMARY KEY(SSN),
FOREIGN KEY (SUPERSSN) REFERENCES EMPLOYEE(SSN),
);
Chapter 8 SQL - The Relational Database
Standard
DDL for COMPANY Database
CREATE TABLE DEPARTMENT
( DNAME
VARCHAR(15) NOT NULL ,
DNUMBER
INT
NOT NULL UNIQUE,
MGRSSN
CHAR(9)
NOT NULL,
MGRSTARTDATE DATETIME,
PRIMARY KEY(DNUMBER),
FOREIGN KEY (MGRSSN) REFERENCES EMPLOYEE
);
ALTER TABLE EMPLOYEE
ADD FOREIGN KEY (DNO)
REFERENCES DEPARTMENT(DNUMBER);
Chapter 8 SQL - The Relational Database
Standard
DDL for COMPANY Database
CREATE TABLE DEPT_LOCATIONS
(DNUMBER INT
NOT NULL,
DLOCATION VARCHAR(15) NOT NULL,
PRIMARY KEY (DNUMBER, DLOCATION),
FOREIGN KEY (DNUMBER) REFERENCES DEPARTMENT(DNUMBER)
);
CREATE TABLE PROJECT
(PNAME
VARCHAR(15) NOT NULL,
PNUMBER INT
NOT NULL,
PLOCATION VARCHAR(15),
DNUM
INT
NOT NULL,
PRIMARY KEY (PNUMBER),
FOREIGN KEY (DNUM) REFERENCES DEPARTMENT(DNUMBER)
);
Chapter 8 SQL - The Relational Database
Standard
DDL for COMPANY Database
CREATE TABLE WORKS_ON
(ESSN CHAR(9)
NOT NULL,
PNO
INT
NOT NULL,
HOURS DECIMAL(3,1) NOT NULL,
PRIMARY KEY (ESSN, PNO),
FOREIGN KEY (ESSN) REFERENCES EMPLOYEE(SSN),
FOREIGN KEY (PNO) REFERENCES PROJECT(PNUMBER)
);
CREATE TABLE DEPENDENT
(ESSN
CHAR(9)
NOT NULL,
DEPENDENT_NAME VARCHAR(15) NOT NULL,
SEX
CHAR,
BDATE
DATETIME,
RELATIONSHIP
VARCHAR(8),
PRIMARY KEY (ESSN,DEPENDENT_NAME),
FOREIGN KEY (ESSN) REFERENCES EMPLOYEE(SSN)
);
Chapter 8 SQL - The Relational Database
Standard
Basic Queries in SQL
•
•
•
•
SQL has one basic statement for retrieving information from a database;
the SELECT statement
This is not the same as the SELECT operation of the relational algebra
Important distinction between SQL and the formal relational model:
SQL allows a table (relation) to have two or more tuples that are
identical in all their attribute values.
SQL relations can be constrained to be sets by a key constraint, or by
using the DISTINCT option in the SELECT statement.
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement
• Basic form of the SQL SELECT statement is called a mapping or a SELECTFROM-WHERE block
SELECT
<attribute-list>
FROM
<table list>
WHERE
<condition>
where <attribute list> is a list of attribute names whose values are to be retrieved
by the query. < table list> is al list of the relation names required to process the
query. < condition> is a conditional (Boolean) expression that identifies the
tuples to be retrieved by the query
• Basic SQL queries correspond to using the SELECT, PROJECT, and JOIN
operations of the relational algebra.
Chapter 8 SQL - The Relational Database
Standard
Sample Basic Queries
• Query 0: Retrieve the birth date and address of the employee whose name is 'John B. Smith'.
Q0:
SELECT
FROM
WHERE
BDATE, ADDRESS
EMPLOYEE
FNAME = 'John' AND MINIT = 'B' AND LNAME = 'Smith'
• Query 1: Retrieve the name and address of all employees who work for the 'Research'
department.
Q1:
SELECT
FROM
WHERE
FNAME, LNAME, ADDRESS
EMPLOYEE, DEPARTMENT
DNAME='Research' AND DNUMBER = DNO
• Query 2: For every project located in 'Stafford' , list the project number, the controlling
department number, and the department manager's last name, address and birth date.
Q2:
SELECT
FROM
WHERE
Q2x: SELECT
FROM
WHERE
PNUMBER, DNUM,LNAME, BDATE, ADDRESS
PROJECT, DEPARTMENT, EMPLOYEE
DNUM=DNUMBER AND MGRSSN = SSN AND
PLOCATION = 'Stafford'
PNUMBER, DNUM,LNAME, BDATE, ADDRESS
DEPARTMENT JOIN PROJECT ON (DNUM=DNUMBER)
JOIN EMPLOYEE ON (MGRSSN = SSN)
PLOCATION = 'Stafford'
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: Aliases
• ALIASES:
Some queries need to refer to the same relation twice. In this case, aliases are given to the relation name
Query 8: For each employee, retrieve the employee’s name, and the name of his/her immediate supervisor.
Q8:
SELECT E.FNAME, E.LNAME, S.FNAME, S.LNAME
FROM
EMPLOYEE E, EMPLOYEE S
WHERE
E.SUPERSSN = S.SSN
• Renaming attributes
Q8A: SELECT
E.FNAME as "Employee First Name", E.LNAME as "Employee Last Name",
S.FNAME as "Supervisor First Name" , S.LNAME as "Supervisor Last Name"
FROM
EMPLOYEE E, EMPLOYEE S
WHERE
E.SUPERSSN = S.SSN
Q8B (SQL Server): SELECT E.LNAME + ', ' + E.FNAME as "Employee Name",
S.LNAME + ', ' + S.FNAME as "Supervisor Name"
FROM EMPLOYEE E, EMPLOYEE S
WHERE E.SUPERSSN = S.SSN
• In Q8, the alternate relation names E and S are called aliases for the EMPLOYEE relation
• We can think of E and S as two different copies of the EMPLOYEE relation; E represents
employee in the role of supervisees and S represents employees in the role of supervisors
• Aliasing can also be used in any SQL query for convenience
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: Unspecified WHERE-clause
• Unspecified WHERE-clause:
– A missing WHERE-clause indicates no condition; hence, all tuples of the
relations in the FROM-clause are selected. This is equivalent to the condition
WHERE TRUE
Query 9: Retrieve the ssn values of all employees.
Q9:
SELECT SSN
FROM
EMPLOYEE
– If more than one relation is specified in the FROM-clause and there is no join
condition, then the CARTESIAN PRODUCT of tuples is selected
Q10:
SELECT SSN, DNAME
FROM
EMPLOYEE, DEPARTNEMT
– It is extremely important not to overlook specifying any selection and join
conditions in the WHERE-clause; otherwise, incorrect and very large relations
may result
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: DISTINCT and *
•
Use of *:
To retrieve all the attribute values of the selected tuples, a * is used, which stands for all the
attributes.
Q1C: SELECT *
FROM EMPLOYEE
WHERE DNO = 5
Q1D: SELECT *
FROM EMPLOYEE , DEPARTMENT
WHERE DNAME = 'Research' AND DNO = DNUMBER
•
Tables as Set
SQL does not treat a relation as a set; duplicate tuples can appear. To eliminate duplicate
tuples, the keyword DISTINCT is used.
Q11: SELECT SALARY
FROM EMPLOYEE
Q11A: SELECT DISTINCT SALARY
FROM EMPLOYEE
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: Set Operations
• Set Operations:
– SQL has directly incorporated some set operations. There is a union operation
(UNION), and in some versions of SQL there are set difference (MINUS) and
intersection (INTERSECT) operations
– The resulting relations of these set operations are sets of tuples; duplicate tuples
are eliminated from tuples
– The set operations apply only to union compatible relations; the two relations
must have the same attributes and the attributes must appear in the same order
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: Set Operations
• Set Operations:
– Example
Query 4: Make a list of all project numbers for projects that involve an employee
whose last name is 'Smith' as a worker or as a manager of the department that controls
the project
Q4: (SELECT DISTINCT PNAME
FROM PROJECT, DEPARTMENT, EMPLOYEE
WHERE DNUM=DNUMBER AND MGRSSN = SSN AND LNAME = 'Smith')
UNION
(SELECT PNAME
FROM PROJECT, WORKS_ON, EMPLOYEE
WHERE PNUMBER = PNO AND ESSN = SSN AND
LNAME = 'Smith')
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: Substring Comparision
• Substring Comparison:
– The LIKE comparison operator is used to compare partial strings
– Two reserved characters are used : '%' ( or * in some implementations) replaces
an arbitrary number of characters, and '_' replaces a single arbitrary character
– Query 12: Retrieve all employees whose address is in 'Houston, Texas'. Here, the
value of the ADDRESS attribute must contain substring 'Houston, TX'
Q12:
SELECT FNAME, LNAME
FROM
EMPLOYEE
WHERE ADDRESS LIKE '% Houston,TX%'
– Query 12A: Retrieve all employees who were born during 1950s. Here, '5' must
be the 8th character of the string ( according to our format for date), so that the
BDATE value is '_______5_', with each underscore as a place holder for a single
arbitrary character
Q12A:
SELECT FNAME, LNAME
FROM
EMPLOYEE
WHERE BDATE LIKE '_______5_'
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: Arithmetic Operators
• Arithmetic Operators:
– The standard arithmetic operators '+', '-', '*', and '/' (for addition, subtraction,
multiplication, and division, respectively) can be applied to numeric values in an
SQL query result
Query 13: Show the effect of giving all employees who work on the 'ProductX' project a
10% raise.
Q13: SELECT FNAME, LNAME, 1.1*SALARY
FROM EMPLOYEE, WORKS_ON, PROJECT
WHERE SSN = ESSN AND PNO = PNUMBER AND PNAME = 'ProductX'
Query 14: Retrieve all employees in department 5 whose salary is between $30,000 and
$40,000.
Q14: SELECT *
FROM EMPLOYEE
WHERE (SALARY BETWEEN 30000 AND 40000) AND DNO = 5;
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: ORDER BY
• ORDER BY:
– The ORDER BY clause is used to sort the tuples in a query result based on the
values of some attributes.
– The default order is in ascending order of values
– We can specify the keyword DESC if we want a descending order; the keyword
ASC can be used to explicitly specify ascending order, even though it is default
– Example
Query 15: Retrieve a list of employees and the project each works on , ordered by
employee's department and within each department ordered alphabetically by
employee last name
Q15: SELECT DNAME, LNAME, FNAME, PNAME
FROM DEPARTMENT, EMPLOYEE, WORKS_ON, PROJECT
WHERE DNUMBER = DNO AND SSN = ESSN AND PNO = PNUMBER
ORDER BY DNAME, LNAME
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: Nesting of Queries
• Nesting of Queries
– A complete SELECT query, called a nested query , can be specified within the
WHERE-clause of another query, called the outer query
Query 1: Retrieve the name and the address of all employees who work for the
'Research' department.
Q1a:
SELECT FNAME, LNAME, ADDRESS
FROM
EMPLOYEE
WHERE DNO IN ( SELECT DNUMBER
FROM DEPARTMENT
WHERE DNAME='Research')
– In general, we can have several levels of nested queries
– A reference to an unqualified attribute refers to the relation declared in the
innermost nested query
– Only the first level select statement can have order by clause
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: Correlated Nested Queries
• Correlated nested queries:
– If a condition in the WHERE-clause of a nested query references an attribute of a relation
declared in the outer query, the two queries are said to be correlated
– The result of a correlated nested query is different for each tuple ( or combination of tuples)
of the relation(s) the outer query
Query 16: Retrieve the name of each employee who has dependent with the same first name as the
employee.
Q16: SELECT DISTINCT E.FNAME, E.LNAME
FROM EMPLOYEE E
WHERE E.SSN IN ( SELECT ESSN
FROM DEENDENT
WHERE ESSN = E.SSN AND
E.FNAME = DEPENDENT_NAME)
– A query written with nested SELECT...FROM ...WHERE ... blocks and using the = or IN
comparison operators can always be expressed as a single block query. For example, Q4a
may be written as
Q16A: SELECT DISTINCT E.FNAME, E.LNAME
FORM EMPLOYEE E, DEPENDENT D
WHERE E.SSN = D.ESSN AND E.FNAME = D.DEPENDENT_NAME
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: The Exists Function
• The Exists Function:
– EXISTS used to check whether the result of a correlated nested query is empty (contains no
tuples )
Query 16: Retrieve the name of each employee who has a dependent with the same first name as the
employee
Q16B: SELECT FNAME, LNAME
FROM EMPLOYEE AS e
WHERE EXISTS ( SELECT *
FROM DEPENDENT
WHERE e.SSN = ESSN AND e.FNAME = DEPENDENT_NAME)
Query 6: Retrieve the names of employees who have no dependents
Q6: SELECT FNAME, LNAME
FROM EMPLOYEE
WHERE NOT EXISTS (SELECT *
FROM DEPENDENT
WHERE SSN = ESSN )
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: Explicit Sets
• Explicit Sets
– It is also possible to use an explicit set of values in the WHERE-clause rather then a nested
query.
Query 17: Retrieve the social security numbers of all employees who work on project number 1, 2 or 3
Q17:
SELECT DISTINCT ESSN
FROM
WORKS_ON
WHERE
PNO IN (1, 2, 3)
• NULLs in SQL Queries
– SQL allows queries that check if a value is NULL ( missing or undefined or not applicable)
– SQL uses IS or IS NOT to compare NULLs because it considers each NULL value distinct
form other NULL values, so equality comparison is not appropriate
Query 18: Retrieve the names of all employees who do not have supervisors
Q18:
SELECT FNAME, LNAME
FROM
EMPLOYEE
WHERE
SUPERSSN IS NULL
Note: If a join condition is specified, tuples with NULL values for the join attributes are not included
in the result
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: Aggregate Functions
• Aggregate Functions: COUNT, SUM, MAX, MIN and AVG
Query 19: Find the maximum salary, the minimum salary, and the average salaries among all employees.
Q19: SELECT MAX (SALARY), MIN(SALARY), AVG(SALARY)
FROM
EMPLOYEE
Query 20: Find the maximum salary, the minimum salary, and the average salaries among employees who
work for the 'Research' department.
Q20: SELECT MAX(SALARY). MIN(SALARY), AVG(SALARY)
FROM
EMPLOYEE, DEPARTMENT
WHERE
DNO = DNUMBER AND DNAME = 'Research'
Queries 21 and 22: Retrieve the total number of employees in the company (Q21), and the number of
employees in the 'Research' department(Q22)
Q21: SELECT COUNT (*)
FROM
EMPLOYEE
Q22: SELECT COUNT (*)
FROM
EMPLOYEE , DEPARTMNET
WHERE
DNO = DNUMBER AND DNAME = 'Research'
Query 5: Retrieve the names of all employees who have two or more dependents
Q5: SELECT LNAME, FNAME
FROM EMPLOYEE
WHERE (SELECT COUNT (*) FROM DEPENDENT WHERE SSN=ESSN) >= 2;
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: Grouping
• Grouping:
– In many cases, we want to apply the aggregate functions to subgroups of tuples in a relation
– Each subgroup of tuples consists of the set of tuples that have the same value for grouping
attribute(s)
– The function is applied to each subgroup independently
– SQL has a GROUP BY-clause for specifying the grouping attributes, which must also
appear in the SELECT-clause
Query 24: For each department, retrieve the department number, the number of employees in the
department and thier average salary
Q24:
SELECT DNO, COUNT(*), AVG(SALARY)
FROM
EMPLOYEE
GROUP BY
DNO
– A join condition can be used in conjunction with grouping
Query 25: For each project, retrieve the project number, project name, and the number of
employees who work on that project
Q25:
SELECT PNUMBER, PNAME, COUNT (*)
FROM
PROJECT, WORKS_ON
WHERE
PNUMBER = PNO
GROUP BY
PNUMBER, PNAME
Chapter 8 SQL - The Relational Database
Standard
The SELECT Statement: The Having Clause
• The Having-clause:
– Sometimes we want to retrieve the values of these functions for only those groups that
satisfy certain conditions
– The HAVING-clause is used for specifying a selection condition on groups (rather than on
individual tuples)
– Example
Query 26: For each project on which more than two employees work, retrieve the project number,
project name, and the number of employees who work on that project
Q26: SELECT PNUMBER, PNAME, COUNT(*)
FROM PROJECT, WORKS_ON
WHERE PNUMBER = PNO
GROUP BY
PNUMBER, PNAME
HAVING
COUNT (*) > 2
Query 28: For each department that has more than five employees, retrieve the department number and
the number of its employees who are making more than $40,000.
Q28: SELECT DNUMBER, COUNT (*)
FROM DEPARTMENT, EMPLOYEE
WHERE DNUMBER=DNO AND SALARY>40000 AND DNO IN
(SELECT DNO FROM EMPLOYEE GROUP BY DNO HAVING COUNT (*) > 5)
GROUP BY DNUMBER;
Chapter 8 SQL - The Relational Database
Standard
Summary of SQL Queries
• A query in SQL can consist of up to six clauses, but only the first two, SELECT and FROM ,
are mandatory. The clauses are specified in the following order:
SELECT
FROM
[WHERE
[GROUP BY
[ HAVING
[ORDER BY
< attribute list>
< table list>
<condition>]
< grouping attribute(s)>]
< group condition>]
< attribute list>]
• The SELECT-clause lists the attributes or functions to be retrieved
• The FROM-clause specifies all relations(or aliases) needed in the query but not those needed in
the nested queries
• The WHERE-clause specifies the conditions for selection and join of tuples from the relations
specified in the FROM-clause
• GROUP BY specifies grouping attributes
• HAVING specifies a condition for selection of groups
• ORDER BY specifies an order for displaying the result of a query
• A query is evaluated by first applying the WHERE-clause, then GROUP BY and HAVING, and
finally the SELECT-clause
Chapter 8 SQL - The Relational Database
Standard
Insert Statement
• In its simples form, it is used to add a single tuple to a relation. Attribute
values should be listed in the same order as the attributes were specified in
the CREATE TABLE command
U1: INSERT INTO EMPLOYEE
VALUES ('Richard ' , 'K', 'Marini', '653298653', '1962-12-30',
'98 Oak Forest, Katy, TX', 'M', 37000, '987654321', 4)
• An alternate form of INSERT specifies explicitly the attribute names that
correspond to the values in the new tuple. Attributes with NULL values can
be left out
Insert a tuple for a new EMPLOYEE for whom we only have values for FNAME,
LNAME, and the SSN attributes
U1A: INSERT INTO EMPLOYEE (FNAME, LNAME, SSN)
VALUES ('Richard' , 'Marini', '653298653')
Chapter 8 SQL - The Relational Database
Standard
Insert Statement
• Another variation of INSERT allows insertion of multiple tuples in a relation in a
single command
Example: Suppose we want to create a temporary table that has the name, number of
employees and total salaries for each department. A table DEPTS_INFO is created by U3A
, and is loaded with the summary information retrieved from the database by the query in
U3B
U3A: CREATE TABLE DEPTS_INFO
(DEPT_NAME VARCHAR(10),
NO_OF_EMPS INTEGER,
TOTAL_SAL INTEGER);
U3B: INSERT INTO DEPTS_INFO (DEPT_NAME, NO_OF_EMPS, TOTAL_SAL)
SELECT DNAME, COUNT (*), SUM (SALARY)
FROM
DEPARTMENT , EMPLOYEE
WHERE DNUMBER = DNO
GROUP BY DNAME;
Chapter 8 SQL - The Relational Database
Standard
DELETE Statement
• Removes tuples from a relation
• Includes a WHERE - clause to select the tuples to be deleted
Examples :
U4A: DELETE FROM EMPLOYEE
WHERE LNAME='Brown'
U4B: DELETE FROM EMPLOYEE
WHERE SSN='123456789'
U4C : DELETE FROM EMPLOYEE
WHERE DNO IN ( SELECT DNUMBER
FROM DEPARTMENT
WHERE DNAME='Research' )
U4D : DELETE FROM EMPLOYEE
Chapter 8 SQL - The Relational Database
Standard
UPDATE Statement
•
•
•
•
Used to modify attribute values of one or more selected tuples
A WHERE-clause selects the tuples to be modified.
An additional SET-clause specifies the attributes to be modified and their new
values
Example : Change the location and controlling department number of project
number 10 to 'Bellaire' and 5, respectively.
U5 : UPDATE PROJECT
SET
PLOCATION='Bellaire' , DNUM=5
WHERE PNUMBER=10
Example : Give all employees in the 'Research' department a 10% raise in salary.
U6 : UPDATE EMPLOYEE
SET
SALARY=SALARY*1.1
WHERE DNO IN (SELECT DNUMBER
FROM DEPARTMENT
WHERE DNAME='Research' )
Chapter 8 SQL - The Relational Database
Standard
Views in SQL
• A view is a single virtual table that is derived from other base tables or views.
• A view does not necessarily exist in physical form, which limits the possible update operations
that can be applied to views.
• The CREATE VIEW command is used to specify a view by specifying a (virtual) table and a
defining query.
• The view attribute names can be inherited from the tables in the defining query.
• A view is not realized at the time of view definition, but rather at the time we specify a query
on the view.
• Examples :
V1 : CREATE VIEW WORKS_ON1
AS SELECT FNAME,LNAME,PNAME,HOURS
FROM EMPLOYEE , PROJECT, WORKS_ON
WHERE SSN=ESSN AND PNO=PNUMBER ;
V2 : CREATE VIEW DEPT_INFO(DEPT_NAME,NO_OF_EMPS,TOTAL_SAL)
AS SELECT DNAME,COUNT(*),SUM(SALARY)
FROM DEPARTMENT, EMPLOYEE
WHERE DNUMBER=DNO
GROUP BY DNAME ;
V3: CREATE VIEW EMP_V
AS SELECT FNAME,MINIT,LNAME,SSN,BDATE,ADDRESS,SEX,SUPERSSN,DNO
FROM EMPLOYEE
Chapter 8 SQL - The Relational Database
Standard
Views in SQL
• A view is removed using the DROP VIEW command.
Example :
V1A : DROP VIEW WORKS_ON1 ;
V2A : DROP VIEW DEPT_INFO ;
Views can also be used as a security and authorization mechanism (See chapter 20)
• Updating The Views :
– A view update operation may be mapped in multiple ways to update operations on defining
base relations
– The topic of updating views is still an active research area.
– view update is unambiguous only if one update on the base relations can accomplish the
desired update effect on the view
– If a view update can be mapped to more than one update on the underlying base relations,
we must have a certain procedure to choose the desired update
– We can make the following general observations :
• A view with a single defining table is updatable if the view attributes contain the primary key
• Views defined on multiple tables using joins are generally not updatable
• Views defined aggregate functions are not updatable
Chapter 8 SQL - The Relational Database
Standard
The Relational Calculus
•
•
•
•
A formal language based on first-order predicate calculus
Many commercial relational languages based on some aspects of relational
calculus, including SQL.
QBE (Chapter 9) is closer to relational calculus than SQL.
Difference from Relational Algebra :
–
–
–
–
–
•
One declarative calculus expression specifies a retrieval query
A sequence of operation is used in relational algebra
Relational Algebra is more procedural
Relational calculus is more declarative (less procedural)
Expressive power of the two languages is identical.
Relational Completeness :
–
–
–
A relational query language L is relationally complete if we can express in L any
query that can be expressed in the relational calculus (or Algebra).
Most relational query languages are relationally complete
More expressive power is provided by operations such as aggregate functions,
grouping and ordering .
Capter 9 ER- and EER-to-Relational Mapping,
and Other Relational Languages
Functional Dependencies and Normalization
for Relational Database
•
Informal Design Guidelines for Relational Databases
–
–
–
–
•
Functional Dependencies(FDs)
–
–
–
–
•
Definition of FD
Inference Rules for FDs
Equivalence of Sets of FDs
Minimal Sets of FDs
Normal Forms Based on Primary Keys
–
–
–
–
•
•
Semantics of the Relational Attributes
Redundant Information in Tuples and Update Anomalies
Null Values in Tuples
Spurious Tuples
Introduction to Normalization
First Normal Form
Second Normal Form
Third Normal Form
General Normal Form Definitions(for Multiple Keys)
BCNF(Boyce-Codd Normal Form)
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
Informal Design Guidelines for Relational Databases
• Guideline 1 (Semantics of the Relation Attributes)
Design a relation schema so that it is easy to explain its meaning. Do not combine
attributes from multiple entity types and relationship types into a single
relation. Intuitively, if a relation schema corresponds to one entity type or one
relationship type, the meaning tends to be clear. Otherwise, the relation
corresponds to a mixture of multiple entities and relationships and hence
becomes semantically unclear.
• Bad design example
EMP_DEPT
FNAME MINIT LNAME SSN BDATE ADDRESS DNO DNAME MGRSSN
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
Informal Design Guidelines for Relational Databases
• Guideline 2 (Redundant information in Tuples And Update Anomalies)
Design the base relation schemas so that no insertion, deletion, or modification
anomalies are present in the relations. If any anomalies are present, note them
clearly and make sure that the programs that update the database will operate
correctly.
– Insertion anomalies
• To insert a new employee, we must provide values for attributes of department
correctly so they are consistent among all employees who work for the same
department.
• It is difficult to insert a new department that has no employee as yet.
– Deletion anomalies
• When the last employee of a department is deleted, the information of the
department is lost.
– Modification anomalies
• To change the value of an attribute of a department, we have to change all
employees who work for the department.
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
Informal Design Guidelines for Relational Databases
• Guideline 3 (Null Values in Tuples)
As far as possible, avoid placing attributes in a base relation whose values may
frequently be null. If nulls are unavoidable, make sure that they apply in
exceptional cases only and do not apply to a majority of tuples in the relation.
– Null has different interpretations
• The attribute does not apply to this tuple.
• The attribute value for this tuple is unknown.
• The value is known but absent; that is, it has not been recorded yet.
– Attributes that are NULL frequently could be placed in separate relations (with
the primary key)
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
Informal Design Guidelines for Relational Databases
• Guideline 4 (Spurious Tuples)
Design relation schemas so that they can be JOINed with equality conditions on
attributes that are either primary keys or foreign keys in a way that guarantees
that no spurious tuples are generated. Do not have relations that contain
matching attributes other than foreign key-primary key combinations. If such
relations are unavoidable, do not join them on such attributes, because the join
may produce spurious tuples.
– Bad designs for a relational database may result in erroneous results for certain
JOIN operations
– The "lossless join" property is used to guarantee meaningful results for join
operations
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
Informal Design Guidelines for Relational Databases
•
Guideline 4 (Spurious Tuples)
–
Example
EMP_DEPT
FNAME MINIT LNAME SSN BDATE ADDRESS DNO DNAME MGRSSN
John
Franklin
B
T
Smith
Wang
1111
2222
xxx
xxx
xxx
xxx
1
1
Research
Research
xxx
xxx
WORKS_ON
ESSN
PNO
1111
2222
1
2
PNAME DNO HOURS
xxx
xxx
FNAME MINIT LNAME
John
B
Smith
John
B
Smith
Franklin
T
Wang
Franklin
T
Wang
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
1
1
20
30
SSN
1111
1111
2222
2222
DNO
1
1
1
1
DNAME SSN
Research 1111
Research 2222
Research 1111
Research 2222
PNO
1
2
1
2
DNO
1
1
1
1
HOURS
20
30
20
30
Functional Dependencies
• Definition of FD
– A set of attributes X functionally determines a set of attributes Y if the value of
X determines a unique value for Y
• Written as X Y or can be displayed graphically on a relational schema
• For any two tuples t1 and t2 in any relation instance r(R):
if t1[X] = t2[X] , then t1[Y] = t2[Y]
– An FD is a property of the attributes in the schema R
– If K is a key of R , then K functionally determines all attributes in R(since we
never have two distinct tuples with t1[K] = t2[K] )
– Examples of FD constraints:
• social security number determines employee name: SSN  ENAME
• project number determines project name and location: PNUMBER{PNAME ,
PLOCATION}
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
Functional Dependencies
• Inference Rules for FDs
– Given a set of FDs, we can infer additional FDs that hold whenever the FDs in
F hold
– Armstrong's inference rules:
•
•
•
•
IR1. (Reflexive) If X  Y , then XY
IR2. (Augmentation) If X Y , then XZYZ
IR3. (Transitive) If XY and Y  Z , then X Z
IR1 , IR2 ,IR3 form a sound and complete set of inference rules.
– Some additional inference rules that are useful:
•
•
•
•
(Decomposition) If X YZ ,then XY and X Z
(Union)If X  Y and X  Z , then X  YZ
(Pseudotransitivity) If X  Y and WY  Z , then WX  Z
The last three inference rules, as well as any other inference rule can be deduced
from IR1, IR2 , and IR3 (completeness property)
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
Functional Dependencies
• Inference Rules for FDs
– Closure of a set F of FDs, denoted as F+, is the set of all FDs that can be
inferred from F
– Armstrong's inference rules are sound and complete
• Any FD that can infer from F using IR1, IR2, and IR3 holds in every relation.
• F+ can calculated by repeatedly applying IR1, IR2, and IR3 using the FDs in F
– Closure of a set of attributes X with respect to F, denoted as X+, is the set of all
attributes that are functionally determined by X
– Algorithm 14.1: calculates X+ with respect to F
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
Functional Dependencies
• Equivalence of sets of FDs
– F covers G if every FD in G can be inferred from F (i.e., if G+  F+)
– Two sets of FDs F and G are equivalent if :
• every FD in F can be inferred from G, and
• every FD in G can be inferred from F
– F and G are equivalent if F covers G and G covers F
– F and G are equivalent if F+ = G+
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
Functional Dependencies
• Minimal Sets of FDs
– A set of FDs is minimal if it satisfies the following conditions:
• Every dependency in F has a single attribute for its RHS
• We can not remove any dependency from F and have a set of dependencies that is
equivalent to F.
• We can not replace any dependency X  A in F with a dependency Y  A , where
YX and still have a set of dependencies that is equivalent to F.
– Every set of FDs has an equivalent minimal set (also called minimal cover)
– There can be several equivalent minimal sets
– Algorithm 14.2: Finding a minimal cover of a set of FDs F.
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
Normal Forms Based on Primary Keys
• Introduction to Normalization
– Normalization: Process of decomposing unsatisfactory "bad" relations by
breaking up their attributes into smaller relations
– Normal Form: Condition using keys and FDs of a relation to certify whether a
relation schema is in a particular normal form
– 2NF, 3NF, BCNF based on keys and FDs of a relation schema.
– 4NF based on keys, MVDs; 5NF based on keys, JDs
– Additional properties may be needed to ensure a good relational design
– Primary attribute: a member of some candidate key.
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
Normal Forms Based on Primary Keys
• First Normal Form
– Disallows composite attributes, multi-valued attributes, and nested relations;
attributes whose values for an individual tuple are non-atomic
– Considered to be part of the definition of relation
– Example:
DEPARTMENT
DNAME DNUMBER
MGRSSN MGRSTARTDATE DLOCATIONS
DEPARTMENT
DNAME DNUMBER
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
MGRSSN MGRSTARTDATE DLOCATIONS
Normal Forms Based on Primary Keys
• Second Normal Form
– Prime Attribute: attribute that is a member of primary key K
– Full Functional Dependency: A FD Y  Z , where removal of any attribute
from Y means the FD does not hold any more.
Examples:
{ SSN, PNUMBER}  HOURS is a full FD since neither SSN  HOURS nor
PNUMBER  HOURS hold
– A relation schema R is in second normal form (2NF) if every non-prime
attribute A in R is fully functionally dependent on the primary key
– R can be decomposed into 2NF relations via the process of 2NF normalization
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
Normal Forms Based on Primary Keys
• Second Normal Form
EMP_PROJ
ESSN
PNO
ENAME
PNO
HOURS
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
SSN
PNAME
PLOCATION
PROJECT
EMP
WORKS_ON
ESSN
HOURS
ENAME
PNO
PLOCATION
PNAME
Normal Forms Based on Primary Keys
• Third Normal Form
– Transitive Functional Dependency: a FD YZ that can be derived from two
FDs YX and XZ
– A relation schema R is in third normal form (3NF) if it is in 2NF and no nonprime attribute A in R is transitively dependent on the primary key
– R can be decomposed into 3NF relations via the process of 3NF normalization
EMP_DEPT
SSN
ENAME
BDATE
ADDRESS
DNUM
EMP
SSN
DNAME
DMGRSSN
DEPT
ENAME
BDATE
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
ADDRESS
DNUM
DNUM DNAME DMGRSSN
General Normal Form Definitions
• General Normal Form Definitions (for Multiple Keys)
– A relation schema R is in second normal form (2NF) if every non-prime
attribute A in R is fully functionally dependent on every key of R
– Superkey of relation schema R : a set of attributes S of R that contains a key of
R
– A relation schema R is in third normal form (3NF) if whenever a FD X  A
holds in R , then either:
• (a) X is a superkey of R, or
• (b) A is a prime attribute of R
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
BCNF (Boyce-Codd Normal Form)
•
•
•
•
•
•
•
•
•
•
•
•
•
A relation schema R is in ( Boyce-Codd Normal Form) BCNF if whenever a FD X
 A holds in R, then X is a superkey of R
Each normal form is strictly stronger than the previous one:
Every 2NF relation is in 1NF
Every 3NF relation is in 2NF
Every BCNF relation is in 3NF
There exist relations that are in 3NF but not in BCNF
The goal is to have each relation in BCNF (or 3NF)
Additional criteria may be needed to ensure that the set of relations in a relational
database are satisfactory ( see Chapter 14)
Lossless join property
Dependency preservation property
Additional normal forms are discussed in Chapter 14
4NF ( based on multi-valued dependencies)
5NF (based on join dependencies)
Chapter 14 Functional Dependencies and
Nomalization for Relational Databases
Relational Database Design Algorithms
• Normal Forms are not a sufficient criteria for a good design
– Example : Any relation with two attributes is always in BCNF - so we can
create 2-attribute relations arbitrarily and get BCNF
•
•
•
•
•
Additional conditions are needed to ensure a good design
Relational Decomposition
The Dependency Preservation Property
The lossless Join Property
Null Values and Dangling Tuples
Chapter 15 Relational Database Design
Algorithms and Further Dependencies
Relational Decomposition
• We start with a universal relation schema R containing all the database
attributes: R = { A1, A2, ....,An}
• The design goal is a decomposition D of R into m relation schemas: D =
{R1, R2, ...,Rm}, where
– Each relation Schema Ri contains a subset of the attributes of R.
– Every attribute in R should appear in at least one Ri.
Chapter 15 Relational Database Design
Algorithms and Further Dependencies
The Dependency Preservation Property
•
•
The database designers define a set F of functional dependencies that should hold on the
attributes of R
Decomposition should preserve the dependencies; informally, the collection of all
dependencies that hold on the individual relations Ri should be equivalent to F. Formally:
–
–
–
•
Relational synthesis algorithm: decompose R into a dependency preserving decomposition D
= { R1,R2,...,Rm} with respect to F such that each Ri is in 3NF
–
–
–
•
Define the projection of F on Ri, denoted by F(Ri),to be the set of FDs X  Y in F+ such that (X 
Y) in Ri
A decomposition D = {R1,R2,...,Rm} is dependency preserving if (F (R1) ...  F(Rm))+ = F+
This property makes it possible to ensure that the FDs in F hold simply by ensuring that the
dependencies on each relation Ri hold individually.
Find a minimal set of FDs G equivalent to F
For each X of an FD X A in G, create a relation schema Ri in D with the attributes {X  A1  A2
 ...  Ak} where the Aj's are all the attributes appearing in an FD in G with X as left hand side.
If any attributes in R are not placed in any Ri, create another relation in D for these attributes.
Problems:
–
–
–
must find a minimal cover G for F
no efficient algorithm for finding a minimal cover
several minimal covers can exist for F; the result of algorithm can be different depending on which is
chosen.
Chapter 15 Relational Database Design
Algorithms and Further Dependencies
The lossless (Non-Additive) Join Property
• Informally, this property ensures that no spurious tuples appear when the
relations in the decompositions are JOINed. Formally:
– A decomposition D = { R1,R2,...,Rm} of R has the lossless join property with
respect to a set f of FDs if, for every relation instance r(R) whose tuples satisfy all
the FDs in F, we have: (R1(r(R))* R2(r(R))*... * Rm(r(R))) = r(R)
– This condition ensures that whenever a relation instance r(R) satisfies F, no
spurious tuples are generated by joining the decomposed relations r(Ri)
– Since we actually store the decomposed relations as base relations, this condition
is necessary to generate meaningful results for queries involving JOINs
Chapter 15 Relational Database Design
Algorithms and Further Dependencies
The lossless (Non-Additive) Join Property
•
Algorithm 15.2 for testing whether a decomposition D satisfies the lossless join
property with respect to a set of FDs.
1. Create an initial matrix S with one row i for each relation Ri in D, and one column j for
each attribute Aj in R.
2. Set S(i,j)=bij for all matrix entries. (each bij is a distinct symbol associated with indices
(i,j))
3. For each row i representing relation schema Ri
For each column j representing attribute Aj
if relation Ri includes attribute Aj, then set S(i, j) = a j
4. Repeat the following loop until a complete loop execution results in no changes to S
For each functional dependency X Y in F
for all rows in S which have the same symbols in the columns corresponding to attributes in X,
make the symbols in each column that correspond to an attribute in Y be the same in all these
rows as follows: if any of the rows has an “a” symbol for column, set the other rows to that same
“a” symbol in the column. If no “a” symbol exists for the attribute in any of the rows, choose
one of the “b” symbols that appear in one of the rows for the attribute and set the other rows to
that same “b” symbol in the column
5. If a row is made up entirely of “a” symbols, then the decomposition has the loss-less join
property, otherwise it does not.
Chapter 15 Relational Database Design
Algorithms and Further Dependencies
The lossless (Non-Additive) Join Property
•
Algorithm 15.3: decomposing R into BCNF relations such that the decompositions
have the lossless join property with respect to a set of FDs F on R
1.Set D ¬ {R}
2.while there is a relation schema Q in D that is not in BCNF do
begin
choose one q in D that is not in BCNF;
find a FD X  Y in Q that violates BCNF;
replace Q in D by two relation Schemas (Q-Y) and (X  Y)
end;
•
This is based on two properties of lossless join decomposition:
–
The decomposition D = {R1, R2} of r has the lossless join property w.r.t. F if and only if
either:
the FD (R1  R2)  (R1 -R2) is in F+, or
the FD (R1  R2)  (R2 -R1) is in F+
–
If D = {R1, R2,...,Rm} of R has the lossless join property w.r.t. F, and D1 = { Q1, Q2,
..,Qk} of Ri has the lossless join property w.r.t. Ri(F), then D = {R1, R2,..., Ri-1,Q1, Q2,
..,Qk , Rm} has the lossless join property w.r.t. F
Chapter 15 Relational Database Design
Algorithms and Further Dependencies
The lossless (Non-Additive) Join Property
• There is no algorithm for decomposition into BCNF relations that is
dependency preserving.
• A modification of the synthesis algorithm guarantees both the lossless join
and dependency preserving properties but into 3NF relations, not BCNF
• Fortunately, many 3NF relations are also in BCNF
• Lossless Join and Dependency Preserving Decomposition into 3NF Relations
(Algorithm 15.4):
– Find a minimal set of FDs G equivalent to F
– For each left hand side X of an FD X  Y in G
• create a relation schema Ri in D with the attributes {X  A1  A2  ...  Ak} where
Aj's are all the attributes appearing in all FDs in G with X as left hand side.
– If none of the relations in D contain a key of R, create a relation that contains a
key of R and add it to D
Chapter 15 Relational Database Design
Algorithms and Further Dependencies
Null Values and Dangling Tuples
• No fully satisfactory relational design theory that include null values
• Pay attention to nulls in foreign keys
• Dangling tuples
If a relation schema is decomposed into multiple relations, an inner join between
these relations will not reproduce all turples in the original relation. Some tuples
are lost and are called dangling turples
Chapter 15 Relational Database Design
Algorithms and Further Dependencies
Transaction Processing
•
•
•
•
•
•
•
•
•
•
Basic Concepts
Why Concurrent Control
Why Recovery is Needed
Transaction States
The System Log
Desirable Properties of Transaction
Schedules and Recoverability
Serializable Schedules
Use of Serializability
View Equivalence and View Serializability
Chapter 19 Transaction Processing Concepts
Basic Concepts
• Single user/Multi-user DBMS
A DBMS is single user if at most one user at a time can use the system ; it is multiuser if many users can use the system concurrently.
• If only a single CPU exists, concurrent execution of the programs is
interleaved; If the system has multiple CPU, execution is simultaneous.
• Transaction
A transaction is a logical unit of database processing that includes one or more
database operations (insertion, deletion, modification, retrieval).
• A simplified database model is used for explaining transaction processing
concepts. The basic database access operations include :
– read_item(x): reads a database item named X into the program variable X.
– write_item(x): write the value of program variable X into the database item
named x.
Chapter 19 Transaction Processing Concepts
Basic Concepts
•
Executing a read_item(x) includes the following steps:
– Find the address of the disk block that contains item x;
– Copy that disk block into a buffer in main memory;
– Copy item x from the buffer to the program variable named X.
•
Executing a write_item(x) includes the following steps:
–
–
–
–
•
Find the address of the disk block that contains item x;
Copy that disk block into a buffer in main memory;
Copy item x from the program variable named X into its correct location in the buffer;
Store the updated block from the buffer back to disk.
Example of concurrent transaction.
T1
read_item(X)
X=X-N
write_item(X)
read_item(Y)
Y=Y+N
write(item(Y)
Chapter 19 Transaction Processing Concepts
T2
read_item(X)
X=X+M
write_item(X)
Why Concurrent Control is Needed
•
Several problems can occur when concurrent transactions execute in an
uncontrolled manner.
– The lost update problem : This occurs when two transactions that access the same
database items have their operations interleaved in a way that makes the value of same
database item incorrect.
– The temporary update (or dirty read) problem : This occurs when one transaction updates
a database item and then the transaction fails for some reason. The updated item is
accessed by another transaction before it is changed back to its original value.
– The incorrect summary problem : If one transaction is calculating an aggregate function
on a number of records while other transaction is updating some of these records, the
aggregate function may calculate some values before they are updated and others after
they are updated.
•
Whenever a transaction is submitted to a DBMS for execution, the system must
make sure that :
– All the operations in the transaction are completed successfully and their effect is
recorded permanently in the database; or
– the transaction has no effect whatever on the database or on the other transactions in the
case of that a transaction fails after executing some of operations but before executing all
of them.
Chapter 19 Transaction Processing Concepts
Why Concurrent Control is Needed
T1
read_item(X)
X=X-N
T2
read_item(X)
X=X+M
write_item(X)
read_item(Y)
write_item(X)
T1
read_item(X)
X=X-N
write_item(X)
read_item(X)
sum=sum+X
read_item(Y)
sum=sum+Y
Y=Y+N
write(item(Y)
T1
read_item(X)
X=X-N
write_item(X)
T2
read_item(X)
X=X+M
write_item(X)
read_item(Y)
Chapter 19 Transaction Processing Concepts
T3
rum=0
read_item(A)
sum=sum+A
read_item(Y)
Y=Y+N
write(item(Y)
Why Recovery is Needed
• Possible reasons for a transaction to fail in the middle of execution
– System Crash : If the hardware crashes, the contents of main
memory may be lost.
– Transaction or system Error.
– Local error or exception conditions detected by the transaction
– Concurrency control enforcement.
– Disk failure during read or write operation of the transaction.
– Physical Problems and catastrophes.
• Whenever a failure occurs, the system must keep sufficient information to
recover from the failure.
Chapter 19 Transaction Processing Concepts
Transaction Concepts
•
A transaction is an atomic unit of work that either completed in its entirety or not
done at all. For recovery purposes, the recovery manager needs to keep track of the
following operations:
–
–
–
–
–
•
BEGIN_TRANSACTION
READ or WRITE
END_TRANSACTION
COMMIT_TRANSACTION
ROLLBACK (or ABORT)
States of transaction execution :
–
–
–
–
Active state : transaction starts execution, and issue READ and WRITE operations.
Partially committed state : checked by concurrency control and recovery protocol.
Committed state : concludes the transaction execution successfully.
Fail state : If it is aborted during active state or one of checks fails during partially
committed state.
– Terminated state: indicates the transaction left the system.
Chapter 19 Transaction Processing Concepts
The System Log
• To recover form transaction failures, the system maintains a log, which
keeps track of all transaction operations that affect the values of database
items. It is kept on disk, and periodically backed up to tape.
–
–
–
–
–
[ start_transaction , T ]
[ write_item, T, X, old_value, new_value ]
[ read_item, T,X]
[ commit, T]
[ abort, T]
Chapter 19 Transaction Processing Concepts
Commit Point of Transaction
• A transaction T reaches its commit point when all its operations that access
the database have been executed successfully and the effect of all
operations on the database has been recorded in the log. The transaction
writes an entry [ commit , T] into log.
• Force-writing the log file before committing a transaction.
• A [checkpoint] record is written into the log periodically at that point when
the system writes out to the database on disk the effect of all WRITE
operations of committed transactions.
Chapter 19 Transaction Processing Concepts
Desirable Properties of Transaction
• The desirable properties of atomic transactions (ACID properties)
– Atomicity : A transaction is an atomic unit of processing. the recovery may
need to undo the effects of the transaction if a transaction fails.
– Consistency preservation : A correct execution of the transaction must keep
consistent state (database program or DBMS module)
– Isolation : A transaction should not make its updates visible to other
transactions until it is committed. (Concurrency control).
– Durability or permanency : Once a transaction is committed, its changes to the
database must never be lost because of subsequent failure. (recovery)
Chapter 19 Transaction Processing Concepts
Schedules of Recoverability
•
Schedules of Transactions
– A schedule S of n transactions T1,T2,...,Tn is an ordering of the operations of the
transactions subject to the constraint that, for each transaction Ti, the operations of Ti in
S must appear in the same order in which they occur in Ti (Total ordering)
– The schedule of Figure 19.3(a)
Sa : r1(X); r2(X); w1(X);r1(Y);w2(X);c2; w1(Y);c1;
–
The schedule of Figure 19.3(b) :
Sb : r1(X); w1(X); r2(X); w2(X); c2; r1(Y); a1 ;
– Two operations in a schedule are said to conflict if they belong to different transactions,
and if they access the same item X, and if one of the two operations is a write_item(X).
– A schedule S of n transaction T1, T2, ..., Tn is said to be a complete schedule if the
following conditions hold :
• The operations in S are exactly those operations in T1, T2, ..., Tn, including a commit or abort
operations as the last operation for each transaction in the schedule.
• For any pair of operations from the same transaction Ti, their order of appearance in S is the
same as their order of appearance in Ti.
• For any two conflicting operations, one of the two must occur before the other in the schedule
– Committed projection C(S) of the schedule S includes only the operations in S that
belong to committed transactions.
Chapter 19 Transaction Processing Concepts
Schedules and Recoverability
• Characterizing Schedules Based on recoverability :
– A schedule S is recoverable if no transaction T in S commits until all
transaction T' that have written an item that T reads have committed.
– A transaction T is said to read from transaction T' in a schedule S if some item
X is first written by T' and later read by T.
Sc : r1(X); w1(X);r2(X), r1(Y); w2(X); c2 ; a1; is not recoverable.
Sd : r1(X); w1(X); r2(X),r1(Y);w2(X); w1(Y) ; c2; c1; is recoverable.
– Cascading rollback: an uncommitted transaction has to be rolled back because
it read an item from a transaction that failed.
Se : r1(X); w1(X); r2(X), r1(Y); w2(X); w1(Y); a1, a2;
– A schedule is said to be cascadeless (avoid cascading rollback) if every
transaction in the schedule reads only items that were written by committed
transactions.
– Strict schedule: transactions can neither read nor write an item X until the last
transaction that wrote X has committed (or aborted).
Sf : w1(X,5) ; w2(X,8) ; a1;
Chapter 19 Transaction Processing Concepts
Serialiable Schedules
•
Serializable Schedules
A schedule is serial if, for every transaction T participating the schedule, all the operations of
T are executed consecutively in the schedule. Otherwise it is called non-serial schedule.
•
•
•
Every serial schedule is considered correct; some nonserial schedules give
erroneous results.
A schedule S of n transactions is serializable if it is equivalent to some serial
schedule of the same n transactions; a nonserial schedule which is not equivalent to
any serial schedule is not serializable.
The definition of two schedules considered “equivalent”:
– result equivalent: producing same final state of the database (is not used)
– conflict equivalent: If the order of any two conflicting operations is the same in both
schedules.
– view equivalent: If each read operation of a transaction reads the result of the same write
operation in both schedules and the write operations of each transaction must produce the
same results.
•
Conflict serializable: if a schedule S is conflict equivalent to some serial schedule.
we can reorder the non-conflicting operations in S until we form the equivalent
serial schedule, and S is a serializable schedule.
Chapter 19 Transaction Processing Concepts
Testing for Conflict Serializability
• Precedence graph (Serialization graph)
A precedence graph is a directed graph G = (N,E) that consists of a set of nodes N
= { T1, T2, ...,Tn} and a set of directed edges E = {e1, e2,..,em}. There is one
node in the graph for each transaction Ti in the schedule. Each edge ei in the
graph is of the form (Tj  Tk) , if one of the operations in Tj appears in the
schedule before some conflicting operation in Tk. The edges can optionally be
labeled by the names of data item that lead to creating the edge.
• Algorithm
– If there is a cycle in the precedence graph, schedule S is not serializable.
– If there is no cycle in the precedence graph, we can create an equivalent serial
schedule S' that is equivalent to S by ordering the transactions that participate
in S using topological sorting.
Chapter 19 Transaction Processing Concepts
Uses of Serializability
•
•
•
•
•
•
A serial schedule represents inefficient processing because no interleaving of
operations from different transactions is permitted.
A serializable schedule gives us the benefits of concurrent execution without
loosing any correctness.
It is particularly impossible to determine how the operations of a schedule will be
interleaved beforehand to ensure serializability.
It is impractical that testing for serializability after transactions are executed, and
then cancel the effect of non-serializable schedule.
The most practical approach is to ensure serializability without having to test the
schedules to themselves for serializability after they are executed.
Using the theory of serializability to determine protocols or sets of rules which are
followed by every individual transaction or enforced by a DBMS concurrency
control system. (Chapter 20)
– Two-phase locking
– Timestamp ordering
Chapter 19 Transaction Processing Concepts
View Equivalence and View Serializability
•
•
Less restrictive definition.
Two schedules are said to be view equivalent if the following three conditions
hold:
– The same set of transactions participate in S and S'; and S and S' include the same
operations of those transactions.
– For any operation ri(X) of Ti in S, the value of X read by the operation has been written
by an operation Wj(X) of Tj ( or if it is the original value of X before the schedule
started), the same condition must hold for the value of X read by operation ri(X) of Ti in
S'.
– If the operation wk(Y) of Tk is the last operation to write item Y in S, then wk(Y) of Tk
must also be the last operation to write item Y in S'.
•
•
A schedule S is said to be view serializable if it is view equivalent to a serial
schedule.
Constrained Write assumption: any write operation wi(X) in Ti is preceded by a
ri(X) in Ti, and that the value written by Wi(X) in Ti depends only on the value of
X read by ri(X).(under this assumption, conflict serializability and view
serializability are similar)
Chapter 19 Transaction Processing Concepts
Transaction Support in SQL
•
Every transaction has certain characteristics attributed to it
– Access mode: Read only / Read write
– Diagnose area size
to indicate the number of conditions that can be held simultaneously in the diagnose area.
– Isolation level
•
•
•
•
•
Read uncommitted
Read committed
Repeatable read
Serializable
Violations
– Dirty read
– Non-repeatable read
– Phantoms
Chapter 19 Transaction Processing Concepts
Dirty read
Nonrepeatable
read
phantems
Read uncommitted
y
y
y
Read committed
n
y
y
Repeatable read
n
n
y
serializable
n
n
n
Concurrency Control
•
•
•
•
•
•
Locking Techniques
Concurrency Control Based on Timestamp Ordering
Multi-version Concurrency Control Techniques
Validation Concurrency Control Techniques
Granularity of Data Items
Some Other Concurrency Control Issues
Chapter 20 Concurrency Control Techniques
Locking Techniques
• Lock
A lock is a variable associated with a data item in the database and describes the
status of that item with respect to possible operations that can be applied to the
item. Generally, there is one lock for each data item.
•
•
•
•
•
Binary lock
Shared and exclusive lock
Two Phase Locking
Dead locks
Dead Lock Detection and Timeout
Chapter 20 Concurrency Control Techniques
Binary Lock
•
•
•
•
A binary lock has two states: locked(=1) , and unlocked (=0). the value of lock associated
with data item X is denoted as Lock(X).
Two operations, lock_item and unlock_item. They are implemented as critical sections, that
is, no interleaving is allowed until the operation terminates.
A binary lock enforces mutual exclusion on the data item.
A transaction requests access to an item X by issuing a lock_item(X). When the transaction is
through using the item, it issues an unlock_item (X). The DBMS has a lock manager
subsystem to keep track of and control access to locks. Every transaction must obey the
following rules:
–
–
–
–
•
•
A transaction T must issue the operation lock_item(X) before any read_item(X) or write_item(X)
operations are performed in T
A transaction T must issue the operation unlock_item(X) after all read_item(X) and write_item(X)
operations are completed in T
A transaction T will not issue a lock_item(X) operation if it already holds the lock on item X
A transaction T will not issue an unlock_item(X) operation unless it already holds the lock on item X
Between the lock_item(X) and unlock_item(X) in the transaction T, T is said to hold the lock
on item X. At most, one transaction can hold the lock on a particular item. No two
transactions can access the same item concurrently.
The system maintains a lock table, which includes a record with two fields: < data item
name, LOCK> f
Chapter 20 Concurrency Control Techniques
Shared and Exclusive Lock
• Multi-mode Lock
A multiple-mode lock has three possible states: “read-locked”, “write-locked”, or “unlocked”. A readlocked item is called share-locked, whereas a write-locked item is called exclusive-locked. There are
three operations:
• The system keeps track of the number of transactions the hold a shared lock on an item. Each
lock can be a record with three fields: <date item name, LOCK, no_of_reads>
• The system must enforce the following rules:
– A transaction T must issue the operation read_lock(X) or write_lock(X) before any read_item(X) is
performed in T
– A transaction T must issue the operation write_lock(X) before any write)item(X) is performed in T.
– A transaction T must issue the operation unlock(X) after all read_item(X) and write_item(X) operations
are completed in T
– A transaction T will not issue a read_lock(X) operation if it already holds a read(shared) lock or
write(exclusive) lock on item X
– A transaction T will not issue a write_lock(X) operation if it already holds a read(shared) lock or
write_exclusive) lock on item X
– A transaction T will not issue an unlock(X) operation unless it already holds a read(shared) lock or
write(exclusive) lock on item X.
• It is possible to relax conditions 4 and 5 to downgrade and upgrade the lock. (Conversion of
locks)
• Using binary locks or multiple-mode locks does not guarantee serializability of schedules.
Chapter 20 Concurrency Control Techniques
Two-Phase Locking
•
Guaranteeing Serializability by two-phase Locking
A transaction is called to follow two-phase locking protocol if all locking operations ( read_lock,
write_lock) precede the first unlock operation in the transaction.
•
Transaction can be divided into two phases:
–
–
•
•
It can be proved that , if every transaction in a schedule follows the two-phase locking
protocol, the schedule is guaranteed to be serializable.
Two-phase locking may limit the amount of concurrency that can occur in a schedule. This is
the price for guaranteeing serializabilty of all schedules without having to check the schedules
themselves.
–
–
•
•
Expending Phase, during which new locks on items can be acquired but no locks can be released;
Shrinking Phase, during which existing locks can be released but no new locks can b acquired.
A transaction T may not be able to release an item X after it is through using it if T needs lock other
items later. X must remain locked by T until all other items that T needs have been locked.
A transaction T must lock an item Y before it needs it so that it can release previous locked item X.
Meanwhile, another transaction seeking to access Y has to wait even though T is not using yet.
Conservative 2PL: a deadlock-free lock. A transaction locks all the items it accesses before
the transaction begins execution, by pre-declaring its read set and write set.
Strict 2PL: guarantees strict schedules. A transaction does not release any of its locks until
after it commits or aborts.
Chapter 20 Concurrency Control Techniques
Dead locks
• Dead lock
Deadlock occurs when each of two transactions is waiting for the other to release the lock on
the item. Meanwhile, neither can proceed to unlock the item that the other is waiting for.
• Deadlock prevention protocols:
– Conservative 2PL: Every transaction locks all the items it needs in advance; if any of the
items can not be obtained, none of the items are locked. This solution limits concurrency.
– Timestamp (TS(T)): a unique identifier assigned to each transaction based on the order of in
which they are started.
– wait-die: if TS(Ti)< TS(Tj)(Ti is older than Tj), then Ti is allowed to wait, otherwise abort
Ti(Ti dies) and restart it later with the same timestamp
– wound-wait: if TS(Ti)< TS(Tj)(Ti is older than Tj), then abort Tj(Ti wounds Tj) and restart
it later with the same timestamp , otherwise Ti is allowed to wait.
– no waiting algorithm: If transaction is unable to obtain a lock, it is immediately aborted and
then restarted after a certain time delay without checking whether a deadlock will actually
occur or not. This may cause transactions to abort and restart needlessly.
– cautious waiting algorithms: if Tj is not blocked (not waiting for some other locked item),
then Ti is blocked and allowed to wait, otherwise abort Ti . The cautious waiting rules
reduce the number of needless aborts/restarts. Cautious waiting is deadlock free since the
blocking times form a total ordering on all blocked transactions.
Chapter 20 Concurrency Control Techniques
Dead Lock Detection and Timeout
•
Using deadlock detection:
periodically check to see if the system is in a state of deadlock. This solution is attractive if
there will be little interference among the transactions. Otherwise, it is advantageous to
use a deadlock prevention protocol.
•
•
•
•
Construct a wait-for graph to detect the state of deadlock. Each node is created for
each transaction , and there is a directed edge(Ti  Tj) , if Ti is waiting to lock the
item X that is currently locked by Tj.
We have a state of deadlock if and only if the wait-for graph has a cycle.
When deadlock occurs, some of transactions causing the deadlock must be aborted.
Choosing which transaction to abort is known as victim selection. The algorithm
should avoid selecting transactions that have been running for a long time and
performed many updates.
Starvation occurs if the algorithm for dealing with deadlock selects the same
transaction as victim repeatedly, thus causing it to abort and never finish execution.
The wit-die and wound-wait can avoid this problem. The standard solution is to
have a fair waiting scheme, such as, first-come-first-serve queue.
Chapter 20 Concurrency Control Techniques
Concurrency Control Based on Timestamp Ordering
• Timestamps
A timestamp is a unique identifier by the DBMS to identify a transaction. TS(T)
are assigned in the order in which the transactions are submitted to the system.
Timestamp can be created using a counter or a system clock.
• The Timestamp Ordering Algorithm.
We order the transaction based on their timestamps so that a schedule in which the
transactions participate is serializable, and equivalent serial schedule has the
transactions in order of their timestamp values. The TO algorithm associates
with each database item X two timestamp values:
• 1. read_TS(X): the read timestamp of item X; this is the largest timestamp among
all the timestamps of transactions that have successfully read item X.
• 2. write_TS(X): the write timestamp of item X; this is the largest of all the
timestamps of transactions that have successfully written item X.
Chapter 20 Concurrency Control Techniques
Concurrency Control Based on Timestamp Ordering
• Basic TO algorithm: Guarantees serializability
– Transaction T issues a write_item(X) operation: If read_TS(X) > TS(T) or if write_TS(X) >
TS(T) then abort and roll back T and reject the operation. Otherwise, execute the
write_item(X) operation of T and set write_TS(X) to TS(T).
– Transaction T issues a read_item(X) operation: If write_TS(X) > TS(T), then abort and roll
back T and reject the operation. If write_TS(X)  TS(T), then execute the read_item(X)
operation of T and set read_TS(X) to the larger of TS(T) and the current read_TS(X).
– Cascading rollback: If T is aborted and rolled back, any transaction T1 that may have used a
value written by T must also be rolled back. Similarly, any transaction T2 that may have
used a value written by T1 must also be rolled back, and so on.
• Thomas’s write rule: does not force conflict serializability.
– If read_TS(X) > TS(T), then abort and rollback T and reject the operation.
– If write_TS(X) > TS(T) , then do not execute the write operation but continue processing.
– If neither the condition in part a nor the condition in part b occurs, then execute the
write_item(X) operation of T and set write_TS(X) to TS(T)
• Strict TO: Ensures that schedules are both strict and conflict serializable:
A transaction T issues a read_item(X) or write_item(X) such that TS(T) > write_TS(X) has its
read or write operation delayed until the transaction T' that wrote the value of X has
committed or aborted.
Chapter 20 Concurrency Control Techniques
Multiversion Concurrency Control Techniques
•
Multiversion Cocurrency Control
– Protocols that keep the several versions of a data item. The algorithms use the concept of
view serializability. More storage is needed to maintain multiple versions.
•
Multiversion Technique Based on Timestamp Ordering
– The system keeps several versions X1, X2, ...,Xk of each data item X. The following two
timestamps are kept for each version Xi: read_TS(Xi): The read timestamp of Xi and
write_TS(Xi): The write_timestamp of Xi.
– To ensure serializability, the two rules are used to control the reading and writing of data
items:
•
If transaction T issues a write_item(X) operation, and version i of x has the highest
write_TS(Xi) of all versions of X that is also less than or equal to TS(T), and
read_TS(Xi)>TS(T), then abort and roll back transaction T; otherwise , create a new version Xj
of X with read_TS(Xj) = write_TS(Xj) = TS(T).
• If transaction T issues a read_item(X) operation, find the version i of X that has the highest
write_TS(Xi) of all versions of X that is also less than or equal to TS(T); then return the value of
Xi to transaction T and set the value of read_TS(Xi) to the larger of TS(T) and the current
read_TS(Xi).
Chapter 20 Concurrency Control Techniques
Multiversion Concurrency Control Techniques
•
Multiversion Two-phase Locking
– Reads can proceed concurrently with a write operation.
– There are three locking modes for a data item: read, write, and certify. The state of an
item X can be one of “read locked”, “write locked”, “certify locked” and “unlocked”.
– Lock Compatibility Table
read
write
read
y
n
write
n
n
read
write
certify
read write certify
y
y
n
y
n
n
n
n
n
– Two versions, X old and X new, are kept for each item X. X old has been written by
some committed transaction. X new is created when a transaction T acquires a write
lock on the item X. Other transactions can continue to read X old while T holds the write
lock.
– T must obtain a certify lock, on all items that it currently holds write locks on before it
can commit. The cost is that T may have to delay its commit until it obtains exclusive
certify locks on all the items it has updated.
– X old is set to X new, and X new is discarded and the certify locks are then released.
Chapter 20 Concurrency Control Techniques
Validation Concurrency Control Techniques
•
Validation Concurrency Control
During transaction execution, all updates are applied to local copies of the data items ( they are not
applied directly to the database items). At the end of transaction execution, a validation phase checks
whether any of the updates violate serializability. If not, the transaction is committed and the database
is updated from the local copies, otherwise the transaction is aborted.
•
Three phases for this protocol:
–
–
–
•
•
The read phase: A transaction can read values of data items from the database However, updates are
applied only to local copies of the data items kept in the transaction workspace.
The validation phase: Checking is performed to ensure that serializability will not be violated if the
transaction updates are applied to the database.
The write phase: If the validation phase is successful, the transaction updates are applied to the
database; otherwise, the updates are discarded and the transaction is restarted.
Optimistic techniques work well under circumstances of little interference among the
transactions.
In the validation phase for transaction Ti, if any one of the following conditions holds, Ti does
not interfere with any committed transactions, Tj(or with any other transactions currently in
their validation phase).
–
–
–
Transaction Tj completes its write phase before Ti starts its read phase.
Ti starts its write phase after Tj completes its write phase, and the read_set of Ti has no items in
common with the write_set of Tj.
Both the read_set and the write_set of Ti have no items in common with the write_set of Tj , and Tj
completes its read phase before Ti completes its read phase.
Chapter 20 Concurrency Control Techniques
Granularity of Data Items
• The size of the data items is called the data item granularity. A database
item could be one of the following:
–
–
–
–
–
A database record.
A field value of database record.
A disk block
A whole file.
A whole database.
• Several trade-offs must be considered in choosing the data item size.
– The larger the data item size is, the lower is the degree of concurrency
permitted.
– The smaller the data item size is, the more items will exist in the database It
causes a higher overhead.
• What is the best size? It depends on the types of transaction involved
Chapter 20 Concurrency Control Techniques
Some Other Concurrent Control Issues
• Insertion, Deletion, And Phantom records:
– Phantom problem can occur when a new record that is being inserted by some
transaction T satisfies a condition that a set of records accessed by another
transaction T' must satisfy. The record that causes the conflict is a phantom
record, which may not b recognized by the concurrency control protocol.
– One solution is index locking, which can be used with a two-phase locking
protocol. If the index entry is locked before the record itself can be accessed,
then the conflict on the phantom record can be detected since the index locks
conflict.
• Interactive Transactions
– Problem: A user can input a value of a data item to a transaction T that is based
on some value written to the screen by transaction T that is based on some
value written to the screen by transaction T', which may not have committed.
– An approach is to postpone output of transactions to the screen until they have
committed.
Chapter 20 Concurrency Control Techniques
Database Recovery
•
•
•
•
Recovery Concepts
Recovery Techniques Based on Deferred Updates
Recovery Techniques Based on Immediate Updates
Shadow Paging
Chapter 21 Database Recovery Techniques
Recovery Concepts
• Recovery Outline
– Recovery from transaction failures :
The database is restored to some past state so that a correct state (which is close to the
time of failure) can be reconstructed from that past state. The system keeps
information about the changes to data items in the system log.
– There are different strategies for the catastrophic and non-catastrophic failures:
• Catastrophic failure
The recovery method restores a past copy of the database that was dumped to archival
storage (typically tape) and reconstructs a more current state by reapplying or redoing
committed transaction operations from the log up to the time of failure.
• Non-catastrophic failure of types 1 through 4 (system crash, transaction error, local
error, concurrency control enforcement)
The strategy is to reverse the changes that caused the inconsistency by undoing some
operations. It may also be necessary to redo some operations in order to restore a
consistent state of the database, as we shall see. In this case, we do not need a complete
archival copy of the database. Rather , the entries kept in the system log are consulted
during recovery.
Chapter 21 Database Recovery Techniques
Recovery Concepts
• Recovery Outline
– There are two main techniques for recovery from non-catastrophic transaction
failures:
• Deferred Update ( NO-UNDO/REDO algorithm):
Updates are not recorded in the database until after a transaction reaches its commit point;
before commit, all updates are recorded in the local workspace; during commit, the
updates are first recorded in the log and then written to the database.
• Immediate Update ( UNDO/REDO algorithm):
The database may be updated by some operations before a transaction reaches its commit
point; these operations are recorded in the log on the disk by force-writing before they are
applied to the database.
Chapter 21 Database Recovery Techniques
Recovery Concepts
• System concepts for recovery
– Disk pages: Contains one data item
– DBMS cache: a collection of in-memory buffer.
– A cache directory
• (  item name, buffer location ) is used to keep track of which database items are in
buffers.
• Associated with each item in the cache is a dirty bit, which can be included in the
directory , to indicate whether or not the item has been modified.
– Before image: the old value of data item before updating.
– After image: the new value after updating.
Chapter 21 Database Recovery Techniques
Recovery Concepts
• System concepts for recovery
– In-place updating: overwriting the old value of data item on the disk.
Write-ahead logging protocol:
– The before image of an item can not be overwritten by its after image on the disk until all
UNDO-type log records for the updating transaction up to this point in time have been forcewritten to the disk.
– The commit operation of a transaction can not be completed until all the REDO-type and
UNDO-type log records for that transaction have been force written to disk.
– shadowing: write a new item at a different disk location, so multiple copies of a
data item can be maintained.
• DBMS recovery subsystem maintains a number of lists to make the recovery process
more efficient.
• active transaction list;
• committed transaction list;
• aborted transaction list.
Chapter 21 Database Recovery Techniques
Recovery Concepts
• Transaction Rollback
– The log entries are used to recover the old values of the data items that must be
rolled back. Most recovery mechanisms are designed to avoid cascading
rollback.
– read_item operation entries in the log are needed only for determining
cascading rollback.
Chapter 21 Database Recovery Techniques
Recovery Techniques Based on Deferred Update
•
A typical deferred update protocol:
– A transaction can not change the database until it reaches the commit point.
– A transaction does not reach its commit point until all its update operations are recorded
in the log and the log is force written to the disk.
– The REDO is needed in case the system fails after the transaction commits but before all
its changes are recorded in the database.
•
Recovery Using Deferred Update in a Single User Environment
– The algorithm of redoing certain write_item operations:
PROCEDURE RDU_S Use two lists of transactions: the committed transactions since the last
check-point , and the active transactions(at most one transaction will fall in this category,
because the system is single user). Apply the REDO operation to all the write_item operations of
the committed transactions from the log in the order in which they were written to the log.
Restart the active transactions.
– The REDO procedure is defined as follows:
REDO(WRITE_OP) Redoing a write_item operation WRITE_OP consists of examining its log
entry [write_item, T, X, new_value] and setting the value of item X in the database to
new_value, which is the after image(AFIM).
– The REDO operation is required to be indempotent so that the result of recovery from a
crash during recovery should be the same as the result of recovering when there is no
crash during recovery.
– The transaction in the active list are ignored completely by the recovery process.
Chapter 21 Database Recovery Techniques
Recovery Techniques Based on Deferred Update
•
Deferred Update with Concurrent Execution in a Multi-user Environment
– The recovery algorithm is depend on the protocol for concurrency control.
– PROCEDURE (for strict two phase locking) RDU_M Use two lists of transactions
maintained by the (WITH CHECKPOINTS) system: the committed transaction T since
the last checkpoint, and the active transaction T'. REDO all the WRITE operations of the
committed transactions from the log, in the order in which they were written to the log.
The transactions that are active and did not commit are effectively canceled and must be
resubmitted.
– We can start from the end of the log to make the algorithm more efficient based on the
observation: if a data item X has been updated more than once by the committed
transactions, it is only necessary top REDO the last update of X from the log during
recovery.
– Draw back: it limits the concurrent execution of transactions because all items remain
locked until the transaction reaches its commit point.
– Benefit:
• A transaction does not record its change in the database until it reaches its commit point-that is,
until it completes its execution successfully. Hence , a transaction is never rolled back because
of failure during transaction execution.
• A transaction will never read the value of an item that is written by an uncommitted transaction ,
because items remain locked until a transaction reaches its commit point. Hence, no cascading
rollback will occur.
Chapter 21 Database Recovery Techniques
Recovery Techniques Based on Deferred Update
• Transaction Actions That Do Not Affect the Database.
– A common method is to issue the commands that generate the reports but keep
them as batch jobs, which are executed only after the transaction reaches its
commit point.
Chapter 21 Database Recovery Techniques
Recovery Techniques Based on Immediate Update
•
•
•
An update operation must be recorded in the log(disk) before it is applied to the
database so that we can recover in case of failure (write-ahead logging protocol).
Recovery technique includes the capability to rollback a transaction by undoing the
effect of its write_item operations.
UNDO/REDO Recovery Based on Immediate Update in a Single User environment
– PROCEDURE RIU_S
• Use two lists of transactions maintained by the system: the committed transactions since the last
checkpoint, and the active transactions(at most one transaction will fall in this category, because
the system is single-user).
• Undo all the write_item operations of the active transaction from the log, using the UNDO
procedure described hereafter.
• Redo all the write_item operations of the committed transaction from the log , in the order in
which they were written in the log, using the REDO procedure
– UNDO (WRITE_OP) Undoing a write_item operation WRITE_OP consists of
examining its log entry [ write_item, T, X, old_value, new_value] and setting the value
of item X in the database to old_value which is the before image(BFIM). Undoing a
number of write_item operations from one or more transactions from the log must
proceed in the reverse order from the order in which the operations were written in the
log.
Chapter 21 Database Recovery Techniques
Recovery Techniques Based on Immediate Update
• UNDO/REDO Immediate Update With Concurrent Execution
– PROCEDURE RUI_M
• Use two lists of transactions maintained by the system: the committed transactions
since the last check point, and the active transactions.
• Undo all the write_item operations of the active (uncommitted) transactions, using
the UNDO procedure. The operations should be undone in the reverse of the order
in which they were written in the log.
• Redo all the write_item operations of the committed transactions from the log, in
the order in which they were written into the log.
Chapter 21 Database Recovery Techniques
Shadow Paging
•
•
•
A page table is kept in the main memory. When a transaction begins executing, the current
page table is copied into a shadow page table, which is then saved on the disk. During
transaction execution, the shadow page table is never modified.
When a write_item operation is performed, a new copy of the modified database page is
created, which is written on a new disk block, and then the current page table is modified to
point to the new disk block. The two versions are kept: the old version is referenced by the
shadow page table, and the new version by the current page table.
To recover from a failure during transaction execution:
–
–
–
•
Committing a transaction:
–
–
•
•
•
free modified pages;
the shadow page table is reinstated so that it becomes the current page table.
discard the current page table;
discard the previous shadow page table;
free the old pages.
Advantage: no need to undo and redo. This technique can be categorized as a NO-UNDO/NOREDO recovery technique.
Disadvantage: overhead to maintain the page tables in the memory and database pages on the
disk.
This recovery scheme does not require the log in a single-user environment. In multi-user
environment, the log is needed for the concurrency control method.
Chapter 21 Database Recovery Techniques
Object Database
• Object Oriented Database (OODB)
– ODMG (Object Data Management Group) Standard
– Players: Ontos, ObjectStore
• Object-Relational Database Systems
– ADTs
– SQL3
Dimensional Modeling
Introduction
• Dimensional Modeling Approach
– Seeks user understandability, query performance, and resilience to change
• Data Warehouse
– Bill Inmon -- Data warehouse is a subject-oriented, nonvolatile, time variant collection
of data in support of management decisions.
– Ralph Kimball
• Typical Functionalities of Data Warehouse
– Roll-up: Data is summarized with increasing generalization (e.g., weekly to quarterly to
annually).
– Drill-down: Increasing levels of detail are revealed (the complement of roll-up).
– Pivot: Cross tabulation (also referred as rotation) is performed.
– Slice and dice: Performing projection operations on the dimensions.
– Sorting: Data is sorted by ordinal value.
– Selection: Data is available by value or range.
– Derived (computed) attributes: Attributes are computed by operations on stored and
derived values.
Database Requirements
•
OLTP (or Operational Database)
–
Data Capture
•
•
•
–
Short time frame, rapid change data
Entered/updated by DB users
High data input performance
Data Retrieval
•
•
Record-level access
Predictable usage pattern
•
OLAP (or Data Warehouse)
–
Data Capture
•
•
–
Long time frame, static data
Entered by IT
Data Retrieval
•
•
•
Summarizing huge blocks of data
Less predictable usage pattern
High query performance
Data Modeling
•
Entity-Relationship Modeling
– Model the structure of data
– Use normalization to facilitate data capture and storage
– Used in operational database design
•
Dimensional Modeling
– Model the semantics of data
– Use de-normalization to facilitate data presentation
– Used in data warehouse design
Multidimensional Database
•
•
•
•
ROLAP/MOLAP
Hypercube
Advantage – high query performance
Disadvantage
– Large dataset size
– Low load performance
• Suitable for Data Mart
SUV
Car
Truck
Bus
Q1
Q2
Q3
Q4
N
E
S
W
Dimensional Modeling Overview
• Present Data in a Standard Framework
• Distinguish Roles of Data
– Attributes – Descriptive data
– Facts – Measured values
• Dimensional Framework
– Categorize Attributes into Dimensions
– Categorize Facts into Fact Tables
Star Schema
•
Fact Table
– Foreign keys
– Facts
•
Dimension Tables
– Primary key
– Attributes
•
Use Surrogate Keys
– Simple integers
– Improve understandability by avoiding meaningful keys
– Improve performance
Star Schema and Report
Time Dimension
Sales Fact
Product dimension
time_key
product_key
SQL_date
day_of_week
week_number
month
more attributes
time_key
store_key
product_key
promotion_key
dollars
units
cost
Store dimension
SKU
description
brand
category
package_type
size
flavor
store_key
Promotion dimension
store_id
store_name
address
district
region
District
Atherton
Atherton
Atherton
Belmont
Belmont
Belmont
promotion_key
Brand
Clean Fast
More Power
Zippy
Clean Fast
More Power
Zippy
Total Dollars
$ 1,233
$ 2,239
$ 848
$ 2,097
$ 2,428
$ 633
Total Cost
$ 1,058
$ 2,200
$ 650
$ 1,848
$ 2,350
$ 580
Gross Profit
$ 175
$ 39
$ 198
$ 249
$ 78
$ 53
prmotion_name
promotion_type
price_treament
ad_treament
display_treatment
coupon_type
Dimensional Modeling Process
• Design Conformed Dimensions
• Establish Conformed Fact Definitions
• For Each Subject Area
– Decide the granularity of the fact table
– Choosing dimensions
– Choosing facts
Snowflake Schema
• Snow Flaking – Move low cardinality attributes in a dimension to separate
table (outrigger)
• Save Space
• Complicate Data Presentation
Snowflake Schema Example
Sales Fact
Product dimension
Brand Outrigger
brand_key
product_key
time_key
store_key
product_key
promotion_key
dollars
units
cost
brand_key
package_type_key
SKU
description
size
flavor
category_key
brand
brand_description
Package Type Outrigger
package_type_key
package_type
Category Outrigger
category_key
category
category_desc
Approaches to Common Modeling Situations
•
•
•
•
•
•
•
•
•
Slowly Changing Dimensions
Role-Playing Dimensions
Many-to-Many Dimensions
Rapidly Changing Monster Dimensions
Hierarchical Dimensions
Degenerate Dimensions
Junk Dimensions
Fact-less Fact Tables
Facts of Different Granularity
Slowly Changing Dimension
• Modify the dimension record
• Save old value of an attribute in a separate column
• Create new record
Role-Playing Dimension
• Definition
A single dimension appears multiple times in the same star
• Create multiple logical dimensions (Roles)
– Based on the same dimension table
– Each role has different column names
Many-to-Many Dimension
• Definition
Each fact table record corresponds to multiple dimension records
• Create role-playing dimensions
• Create bridge table
Aggregates
• Contain summarized data from fact tables
• Dramatically improve query performance by redirecting user queries to
aggregates
• Used in dimensional modeling
• Cost
– More disk space
– Maintaining aggregate metadata
Aggregate Example
Brand Rollup
Time Dimension
time_key
SQL_date
day_of_week
more attributes
District Rollup
district_key
Sales Aggregate
brand
category
time_key
district_key
brand_key
dollars
units
cost
district
region
product_key
time_key
SQL_date
day_of_week
more attributes
Store dimension
store_id
store_name
address
district
region
Sales Fact
time_key
store_key
product_key
promotion_key
dollars
units
cost
Redirected query:
SELECT district as District, brand as Brand,
SUM(dollars) as ‘Total Dollars’,
SUM(cost) ‘Total Cost’,
SUM(dollars)-SUM(cost) as ‘Gross Profit’
FROM Sales_Aggregate f,
District_Rollup s, Brand_Rollup p
Product dimension
Time Dimension
store_key
brand_key
SKU
description
brand
category
package_type
size
flavor
Promotion dimension
promotion_key
prmotion_name
promotion_type
price_treament
ad_treament
display_treatment
coupon_type
WHERE f.brand_key=p.brand_key and
f.district_key=s.district_key
GROUP BY district, brand
Original Query:
SELECT district as District, brand as Brand,
SUM(dollars) as ‘Total Dollars’,
SUM(cost) ‘Total Cost’,
SUM(dollars)-SUM(cost) as ‘Gross Profit’
FROM Sales_Fact f, Store_dimension s,
Product_dimension p
WHERE f.product_key=p.product_key and
f.store_key=s.store_key
GROUP BY district, brand
Aggregate Overview
•
•
•
Decide Aggregation Level
Choose Dimensions
Roll Dimensions Up
Create new dimension (rollup) by removing high cardinality attributes from a dimension
•
•
Summarize Base Fact Table
Aggregate Navigation
– Maintain aggregate Metadata
– Rewrite user query to access aggregate table