The SELECT-FROM-WHERE Structure of Basic SQL Queries
4.3.1 The SELECT-FROM-WHERE Structure of Basic SQL Queries
Queries in SQL can be very complex. We will start with simple queries, and then progress to more complex ones in a step-by-step manner. The basic form of the
98 Chapter 4 Basic SQL
formed of the three clauses SELECT , FROM , and WHERE and has the following form: 9
SELECT
<attribute list>
FROM
<table list>
<attribute list> is a list of attribute names whose values are to be retrieved by the query.
<table list> is a 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.
In SQL, the basic logical comparison operators for comparing attribute values with one another and with literal constants are =, <, <=, >, >=, and <>. These corre- spond to the relational algebra operators =, <, ≤, >, ≥, and ≠, respectively, and to the C/C++ programming language operators =, <, <=, >, >=, and !=. The main syntac- tic difference is the not equal operator. SQL has additional comparison operators that we will present gradually.
We illustrate the basic SELECT statement in SQL with some sample queries. The queries are labeled here with the same query numbers used in Chapter 6 for easy cross-reference.
Query 0. Retrieve the birth date and address of the employee(s) whose name is ‘John B. Smith’.
Q0:
SELECT
Bdate , Address
FROM
EMPLOYEE
Fname =‘John’ AND Minit =‘B’ AND Lname =‘Smith’; This query involves only the EMPLOYEE relation listed in the FROM clause. The
WHERE
query selects the individual EMPLOYEE tuples that satisfy the condition of the WHERE clause, then projects the result on the Bdate and Address attributes listed in the SELECT clause.
The SELECT clause of SQL specifies the attributes whose values are to be retrieved, which are called the projection attributes, and the WHERE clause specifies the Boolean condition that must be true for any retrieved tuple, which is known as the selection condition . Figure 4.3(a) shows the result of query Q0 on the database of Figure 3.6.
We can think of an implicit tuple variable or iterator in the SQL query ranging or looping over each individual tuple in the EMPLOYEE table and evaluating the condi- tion in the WHERE clause. Only those tuples that satisfy the condition—that is,
4.3 Basic Retrieval Queries in SQL 99
(a) Bdate 1965-01-09 731Fondren, Houston, TX
John Franklin Ramesh Joyce
Smith Wong Narayan English
731 Fondren, Houston, TX 638 Voss, Houston, TX 975 Fire Oak, Humble, TX 5631 Rice, Houston, TX
E.Fname John Franklin Alicia
Jennifer Wallace
Ahmad Jabbar
Smith Wong
Narayan English
Jennifer James
Jennifer
Franklin James
Franklin Franklin
Wallace Borg
Wallace
Wong Borg
Wong Wong
E.Lname
S.Fname
S.Lname
Fname John Franklin
K Ramesh
Smith Wong
975 Fire Oak, Humble, TX
731 Fondren, Houston, TX 638 Voss, Houston, TX
1962-09-15
1965-09-01 1955-12-08
666884444
123456789 333445555
Minit Lname
Salary Dno Super_ssn
(g)
(e)
E.Fname 123456789 333445555 999887777
(c) Pnumber 10 30
1941-06-20 1941-06-20
Wallace 291Berry, Bellaire, TX Wallace 291Berry, Bellaire, TX
Dnum
Lname
Address Bdate
Research Research Research Research Research Research Research Research Administration Administration Administration Administration
Administration Administration Administration Administration Headquarters Headquarters Headquarters
Headquarters Headquarters Headquarters Headquarters Headquarters
Dname
Figure 4.3
Results of SQL queries when applied to the COMPANY database state shown in Figure 3.6. (a) Q0. (b) Q1. (c) Q2. (d) Q8. (e) Q9. (f) Q10. (g) Q1C.
100 Chapter 4 Basic SQL
those tuples for which the condition evaluates to TRUE after substituting their cor- responding attribute values—are selected.
Query 1. Retrieve the name and address of all employees who work for the ‘Research’ department.
Q1:
SELECT
Fname , Lname , Address
FROM
EMPLOYEE , DEPARTMENT
Dname =‘Research’ AND Dnumber = Dno ; In the WHERE clause of Q1 , the condition Dname = ‘Research’ is a selection condi-
WHERE
tion that chooses the particular tuple of interest in the DEPARTMENT table, because Dname is an attribute of DEPARTMENT . The condition Dnumber = Dno is called a join condition , because it combines two tuples: one from DEPARTMENT and one from EMPLOYEE , whenever the value of Dnumber in DEPARTMENT is equal to the value of Dno in EMPLOYEE . The result of query Q1 is shown in Figure 4.3(b). In general, any number of selection and join conditions may be specified in a single SQL query.
A query that involves only selection and join conditions plus projection attributes is known as a select-project-join query. The next example is a select-project-join query with two join conditions.
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
Pnumber , Dnum , Lname , Address , Bdate
FROM
PROJECT , DEPARTMENT , EMPLOYEE
WHERE
Dnum = Dnumber AND Mgr_ssn = Ssn AND Plocation =‘Stafford’;
The join condition Dnum = Dnumber relates a project tuple to its controlling depart- ment tuple, whereas the join condition Mgr_ssn = Ssn relates the controlling depart- ment tuple to the employee tuple who manages that department. Each tuple in the result will be a combination of one project, one department, and one employee that satisfies the join conditions. The projection attributes are used to choose the attrib- utes to be displayed from each combined tuple. The result of query Q2 is shown in Figure 4.3(c).
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
Show more