Tables as Sets in SQL
4.3.4 Tables as Sets in SQL
As we mentioned earlier, SQL usually treats a table not as a set but rather as a multiset ; duplicate tuples can appear more than once in a table, and in the result of a query. SQL does not automatically eliminate duplicate tuples in the results of queries, for the following reasons:
Duplicate elimination is an expensive operation. One way to implement it is to sort the tuples first and then eliminate duplicates.
The user may want to see duplicate tuples in the result of a query.
When an aggregate function (see Section 5.1.7) is applied to tuples, in most cases we do not want to eliminate duplicates.
An SQL table with a key is restricted to being a set, since the key value must be dis-
tinct in each tuple. 10 If we do want to eliminate duplicate tuples from the result of
an SQL query, we use the keyword DISTINCT in the SELECT clause, meaning that only distinct tuples should remain in the result. In general, a query with SELECT DISTINCT eliminates duplicates, whereas a query with SELECT ALL does not. Specifying SELECT with neither ALL nor DISTINCT —as in our previous examples— is equivalent to SELECT ALL . For example, Q11 retrieves the salary of every employee; if several employees have the same salary, that salary value will appear as many times in the result of the query, as shown in Figure 4.4(a). If we are interested only in distinct salary values, we want each value to appear only once, regardless of how many employees earn that salary. By using the keyword DISTINCT as in Q11A ,
we accomplish this, as shown in Figure 4.4(b). Query 11. Retrieve the salary of every employee ( Q11 ) and all distinct salary
values ( Q11A ). Q11:
SELECT
ALL Salary
FROM
EMPLOYEE ;
Q11A: SELECT
DISTINCT Salary
FROM
EMPLOYEE ;
104 Chapter 4 Basic SQL
Fname Lname
Results of additional SQL queries when
applied to the COM-
Fname Lname PANY database state
James Borg shown in Figure 3.6.
(a) Q11. (b) Q11A. (c) Q16. (d) Q18.
SQL has directly incorporated some of the set operations from mathematical set theory, which are also part of relational algebra (see Chapter 6). There are set union
( UNION ), set difference ( EXCEPT ), 11 and set intersection ( INTERSECT ) operations. The relations resulting from these set operations are sets of tuples; that is, duplicate tuples are eliminated from the result. These set operations apply only to union-com- patible relations, so we must make sure that the two relations on which we apply the operation have the same attributes and that the attributes appear in the same order in both relations. The next example illustrates the use of UNION .
Query 4. Make a list of all project numbers for projects that involve an employee whose last name is ‘Smith’, either as a worker or as a manager of the department that controls the project.
Q4A: ( SELECT
DISTINCT Pnumber
FROM
PROJECT , DEPARTMENT , EMPLOYEE
WHERE
Dnum = Dnumber AND Mgr_ssn = Ssn AND Lname =‘Smith’ )
UNION ( SELECT
DISTINCT Pnumber
FROM
PROJECT , WORKS_ON , EMPLOYEE
WHERE
Pnumber = Pno AND Essn = Ssn AND Lname =‘Smith’ );
The first SELECT query retrieves the projects that involve a ‘Smith’ as manager of the department that controls the project, and the second retrieves the projects that involve a ‘Smith’ as a worker on the project. Notice that if several employees have the last name ‘Smith’, the project names involving any of them will be retrieved. Applying the UNION operation to the two SELECT queries gives the desired result.
SQL also has corresponding multiset operations, which are followed by the keyword ALL ( UNION ALL , EXCEPT ALL , INTERSECT ALL ). Their results are multisets (dupli- cates are not eliminated). The behavior of these operations is illustrated by the examples in Figure 4.5. Basically, each tuple—whether it is a duplicate or not—is considered as a different tuple when applying these operations.
4.3 Basic Retrieval Queries in SQL 105
a2 a4 a2 a3 Figure 4.5 a5 a2
The results of SQL multiset
a2 (d)
operations. (a) Two tables, a3 A R(A) and S(A). (b) R(A) a4 a1 UNION ALL S(A). (c) R(A) EXCEPT ALL S(A). (d) R(A)
a5 a2 INTERSECT ALL S(A).
Parts
» Fundamentals_of_Database_Systems,_6th_Edition
» Characteristics of the Database Approach
» Advantages of Using the DBMS Approach
» A Brief History of Database Applications
» Schemas, Instances, and Database State
» The Three-Schema Architecture
» The Database System Environment
» Centralized and Client/Server Architectures for DBMSs
» Classification of Database Management Systems
» Domains, Attributes, Tuples, and Relations
» Key Constraints and Constraints on NULL Values
» Relational Databases and Relational Database Schemas
» Integrity, Referential Integrity, and Foreign Keys
» Update Operations, Transactions, and Dealing with Constraint Violations
» SQL Data Definition and Data Types
» Specifying Constraints in SQL
» The SELECT-FROM-WHERE Structure of Basic SQL Queries
» Ambiguous Attribute Names, Aliasing, Renaming, and Tuple Variables
» Substring Pattern Matching and Arithmetic Operators
» INSERT, DELETE, and UPDATE Statements in SQL
» Comparisons Involving NULL and Three-Valued Logic
» Nested Queries, Tuples, and Set/Multiset Comparisons
» The EXISTS and UNIQUE Functions in SQL
» Joined Tables in SQL and Outer Joins
» Grouping: The GROUP BY and HAVING Clauses
» Discussion and Summary of SQL Queries
» Specifying General Constraints as Assertions in SQL
» Introduction to Triggers in SQL
» Specification of Views in SQL
» View Implementation, View Update, and Inline Views
» Schema Change Statements in SQL
» Sequences of Operations and the RENAME Operation
» The UNION, INTERSECTION, and MINUS Operations
» The CARTESIAN PRODUCT (CROSS PRODUCT) Operation
» Variations of JOIN: The EQUIJOIN and NATURAL JOIN
» Additional Relational Operations
» Examples of Queries in Relational Algebra
» The Tuple Relational Calculus
» The Domain Relational Calculus
» Using High-Level Conceptual Data Models
» Entity Types, Entity Sets, Keys, and Value Sets
» Relationship Types, Relationship Sets, Roles, and Structural Constraints
» ER Diagrams, Naming Conventions, and Design Issues
» Example of Other Notation: UML Class Diagrams
» Relationship Types of Degree Higher than Two
» Subclasses, Superclasses, and Inheritance
» Constraints on Specialization and Generalization
» Specialization and Generalization Hierarchies
» Modeling of UNION Types Using Categories
» A Sample UNIVERSITY EER Schema, Design Choices, and Formal Definitions
» Data Abstraction, Knowledge Representation, and Ontology Concepts
» ER-to-Relational Mapping Algorithm
» Discussion and Summary of Mapping for ER Model Constructs
» Mapping EER Model Constructs
» The Role of Information Systems
» The Database Design and Implementation Process
» Use of UML Diagrams as an Aid to Database Design Specification 6
» Rational Rose: A UML-Based Design Tool
» Automated Database Design Tools
» Introduction to Object-Oriented Concepts and Features
» Object Identity, and Objects versus Literals
» Complex Type Structures for Objects and Literals
» Encapsulation of Operations and Persistence of Objects
» Type Hierarchies and Inheritance
» Other Object-Oriented Concepts
» Object-Relational Features: Object Database Extensions to SQL
» Overview of the Object Model of ODMG
» Built-in Interfaces and Classes in the Object Model
» Atomic (User-Defined) Objects
» Extents, Keys, and Factory Objects
» The Object Definition Language ODL
» Differences between Conceptual Design of ODB and RDB
» Mapping an EER Schema to an ODB Schema
» Query Results and Path Expressions
» Overview of the C++ Language Binding in the ODMG Standard
» Structured, Semistructured, and Unstructured Data
» XML Hierarchical (Tree) Data Model
» Well-Formed and Valid XML Documents and XML DTD
» XPath: Specifying Path Expressions in XML
» XQuery: Specifying Queries in XML
» Extracting XML Documents from
» Database Programming: Techniques
» Retrieving Single Tuples with Embedded SQL
» Retrieving Multiple Tuples with Embedded SQL Using Cursors
» Specifying Queries at Runtime Using Dynamic SQL
» SQLJ: Embedding SQL Commands in Java
» Retrieving Multiple Tuples in SQLJ Using Iterators
» Database Programming with SQL/CLI Using C
» JDBC: SQL Function Calls for Java Programming
» Database Stored Procedures and SQL/PSM
» PHP Variables, Data Types, and Programming Constructs
» Overview of PHP Database Programming
» Imparting Clear Semantics to Attributes in Relations
» Redundant Information in Tuples and Update Anomalies
» Normal Forms Based on Primary Keys
» General Definitions of Second and Third Normal Forms
» Multivalued Dependency and Fourth Normal Form
» Join Dependencies and Fifth Normal Form
» Inference Rules for Functional Dependencies
» Minimal Sets of Functional Dependencies
» Properties of Relational Decompositions
» Dependency-Preserving Decomposition
» Dependency-Preserving and Nonadditive (Lossless) Join Decomposition into 3NF Schemas
» Problems with NULL Values and Dangling Tuples
» Discussion of Normalization Algorithms and Alternative Relational Designs
» Further Discussion of Multivalued Dependencies and 4NF
» Other Dependencies and Normal Forms
» Memory Hierarchies and Storage Devices
» Hardware Description of Disk Devices
» Magnetic Tape Storage Devices
» Placing File Records on Disk
» Files of Unordered Records (Heap Files)
» Files of Ordered Records (Sorted Files)
» External Hashing for Disk Files
» Hashing Techniques That Allow Dynamic File Expansion
» Other Primary File Organizations
» Parallelizing Disk Access Using RAID Technology
» Types of Single-Level Ordered Indexes
» Some General Issues Concerning Indexing
» Algorithms for External Sorting
» Implementing the SELECT Operation
» Implementing the JOIN Operation
» Algorithms for PROJECT and Set
» Notation for Query Trees and Query Graphs
» Heuristic Optimization of Query Trees
» Catalog Information Used in Cost Functions
» Examples of Cost Functions for SELECT
» Examples of Cost Functions for JOIN
» Example to Illustrate Cost-Based Query Optimization
» Factors That Influence Physical Database Design
» Physical Database Design Decisions
» An Overview of Database Tuning in Relational Systems
» Transactions, Database Items, Read and Write Operations, and DBMS Buffers
» Why Concurrency Control Is Needed
» Transaction and System Concepts
» Desirable Properties of Transactions
» Serial, Nonserial, and Conflict-Serializable Schedules
» Testing for Conflict Serializability of a Schedule
» How Serializability Is Used for Concurrency Control
» View Equivalence and View Serializability
» Types of Locks and System Lock Tables
» Guaranteeing Serializability by Two-Phase Locking
» Dealing with Deadlock and Starvation
» Concurrency Control Based on Timestamp Ordering
» Multiversion Concurrency Control Techniques
» Validation (Optimistic) Concurrency
» Granularity of Data Items and Multiple Granularity Locking
» Using Locks for Concurrency Control in Indexes
» Other Concurrency Control Issues
» Recovery Outline and Categorization of Recovery Algorithms
» Caching (Buffering) of Disk Blocks
» Write-Ahead Logging, Steal/No-Steal, and Force/No-Force
» Transaction Rollback and Cascading Rollback
» NO-UNDO/REDO Recovery Based on Deferred Update
» Recovery Techniques Based on Immediate Update
» The ARIES Recovery Algorithm
» Recovery in Multidatabase Systems
» Introduction to Database Security Issues 1
» Discretionary Access Control Based on Granting and Revoking Privileges
» Mandatory Access Control and Role-Based Access Control for Multilevel Security
» Introduction to Statistical Database Security
» Introduction to Flow Control
» Encryption and Public Key Infrastructures
» Challenges of Database Security
» Distributed Database Concepts 1
» Types of Distributed Database Systems
» Distributed Database Architectures
» Data Replication and Allocation
» Example of Fragmentation, Allocation, and Replication
» Query Processing and Optimization in Distributed Databases
» Overview of Transaction Management in Distributed Databases
» Overview of Concurrency Control and Recovery in Distributed Databases
» Current Trends in Distributed Databases
» Distributed Databases in Oracle 13
» Generalized Model for Active Databases and Oracle Triggers
» Design and Implementation Issues for Active Databases
» Examples of Statement-Level Active Rules
» Time Representation, Calendars, and Time Dimensions
» Incorporating Time in Relational Databases Using Tuple Versioning
» Incorporating Time in Object-Oriented Databases Using Attribute Versioning
» Temporal Querying Constructs and the TSQL2 Language
» Spatial Database Concepts 24
» Multimedia Database Concepts
» Clausal Form and Horn Clauses
» Datalog Programs and Their Safety
» Evaluation of Nonrecursive Datalog Queries
» Introduction to Information Retrieval
» Types of Queries in IR Systems
» Evaluation Measures of Search Relevance
» Web Analysis and Its Relationship to Information Retrieval
» Analyzing the Link Structure of Web Pages
» Approaches to Web Content Analysis
» Trends in Information Retrieval
» Data Mining as a Part of the Knowledge
» Goals of Data Mining and Knowledge Discovery
» Types of Knowledge Discovered during Data Mining
» Market-Basket Model, Support, and Confidence
» Frequent-Pattern (FP) Tree and FP-Growth Algorithm
» Other Types of Association Rules
» Approaches to Other Data Mining Problems
» Commercial Data Mining Tools
» Data Modeling for Data Warehouses
» Difficulties of Implementing Data Warehouses
» Grouping, Aggregation, and Database Modification in QBE
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