Lecture05sql 110406195130-phpapp02

2 December 2005
Introduction to Databases
Structured Query Language
Prof. Beat Signer
Department of Computer Science
Vrije Universiteit Brussel
http://www.beatsigner.com

Beat Signer - Department of Computer Science - bsigner@vub.ac.be 2March 13, 2015
Context of Today's Lecture
Access
Methods
System
Buffers
Authorisation
Control
Integrity
Checker
Command
Processor
Program
Object Code
DDL
Compiler
File
Manager
Buffer
Manager
Recovery
Manager
Scheduler
Query
Optimiser
Transaction
Manager
Query
Compiler
Queries
Catalogue
Manager
DML
Preprocessor
Database
Schema
Application
Programs
Database and
System Catalogue
Database
Manager
Data
Manager
DBMS
Programmers Users DB Admins
Based on 'Components of a DBMS', Database Systems,
T. Connolly and C. Begg, Addison-Wesley 2010

Structured Query Language (SQL)
 Declarative query language to create database schemas,
insert, update, delete and query information based on a
data definition and data manipulation language
 Data definition language (DDL)
 definition of database structure (relation schemas)
 data access control
 Data manipulation language (DML)
 query language to create, read, update and delete tuples
(CRUD operations)

Structured Query Language (SQL) ...
 The SQL language further deals with the following issues
 transaction control
 integrity constraints (DDL)
 auhorisation (DDL)
 views (DDL)
 embedded SQL and dynamic SQL

 SEQUEL (70's)
 structured english query language
 developed by Raymond F. Boyce
and Donald D. Chamberlin
 access data stored in IBM's
System R relational database
 SQL-86
 first ANSI standard version
 SQL-89 / SQL 1
 SQL-92 / SQL 2
 we will mainly discuss features of the SQL-92 standard
History of SQL
Donald D. Chamberlin Raymond F. Boyce

History of SQL ...
 SQL:1999 / SQL 3
 recursive queries, triggers, object-oriented features, ...
 SQL:2003
 window functions, XML-related features, ...
 SQL:2006
 XML Query Language (XQuery) support, ...
 SQL:2008
 SQL:2011
 improved support for temporal databases

SQL "Standard"
 Each specific SQL implementation by a database vendor
is called a dialect
 The vendors implement parts of the SQL standard
(e.g. most implement SQL-92) but add their vendor-
specific extensions
 Most relational database vendors conform to a set of
Core SQL features but portability might still be limited
due to missing or additional features

Data Definition Language (DDL)
 The data definition language (DDL) is used to specify the
relation schemas as well as other information about the
relations
 relation schemas
 attribute domain types
 integrity constraints
 relation indexes
 access information
 physical storage structure of relations

Database Creation
 The concrete process of creating a new database might
differ for different relational database products
 According to the SQL standard, an SQL environment
contains one or more catalogues
 Each catalogue manages various metadata
 set of schemas consisting of
- relations/tables
- views
- assertions
- indexes
 users and user groups
environment
catalogue catalogue
schema
schema schema

Database Creation ...
 The creation of catalogues is not covered by the SQL
standard and therefore implementation specific
 Schemas can be created and deleted via the CREATE and
DROP statements
 The default parameter of the DROP SCHEMA statement is
RESTRICT
 only empty schema can be deleted
 If CASCADE is specified, all objects associated with the
schema will be dropped
createSchema = "CREATE SCHEMA" , name , "AUTHORIZATION" , creator ,
[ ddlStatements ];
dropSchema = "DROP SCHEMA" , name , [ "RESTRICT" | "CASCADE" ];

Extended Backus-Naur Form (EBNF)
 Notation to describe computer program-
ming languages (context-free grammars)
 developed by Niklaus Wirth
Notation Meaning
= Definition
, Sequence
; Termination
| Choice
[...] Option
{...} Repetition
(...) Grouping
"..." Terminal String
Niklaus Wirth
 We use the EBNF
to describe different
SQL concepts
http://en.wikipedia.org/wiki/Extended_Backus-Naur_Form

Relational Database Example
customerID name street postcode city
1 Max Frisch Bahnhofstrasse 7 8001 Zurich
2 Eddy Merckx Pleinlaan 25 1050 Brussels
5 Claude Debussy 12 Rue Louise 75008 Paris
53 Albert Einstein Bergstrasse 18 8037 Zurich
8 Max Frisch ETH Zentrum 8092 Zurich
cdID name duration price year
1 Falling into Place 2007 17.90 2007
2 Carcassonne 3156 15.50 1993
3 Chromatic 3012 16.50 1993
customer
cd

Relational Database Example ...
orderID customerID cdID date amount status
1 53 2 13.02.2010 2 open
2 2 1 15.02.2010 1 delivered
order
supplierID name city
5 Max Frisch Zurich
2 Mario Botta Lugano
supplier
Customer (customerID, name, street, postcode, city)
CD (cdID, name, duration, price, year)
Order (orderId, customerID, cdID, date, amount, status)
Supplier (supplierID, name, city)
relational database schema

Table Definition Example
CREATE TABLE Customer (
customerID INTEGER CHECK (customerID > 0) PRIMARY KEY,
name VARCHAR(30) NOT NULL,
street VARCHAR(30) NOT NULL,
postcode SMALLINT CHECK (postcode > 0),
city VARCHAR(20)
);
CREATE TABLE CD (
cdID INTEGER PRIMARY KEY,
duration TIME,
price NUMERIC(6,2),
year SMALLINT
);

Table Definition Example ...
CREATE TABLE Supplier (
supplierID INTEGER PRIMARY KEY,
postcode SMALLINT CHECK (postcode > 0),
city VARCHAR(20)
);
CREATE TABLE Order (
orderID INTEGER CHECK (orderID > 0) PRIMARY KEY,
customerID INTEGER,
cdID INTEGER ,
date DATE,
amount INTEGER,
Status VARCHAR(20) NOT NULL DEFAULT 'open',
UNIQUE (customerID, cdID, date),
FOREIGN KEY (customerID) REFERENCES Customer(customerID)
ON UPDATE CASCADE ON DELETE SET NULL,
FOREIGN KEY (cdID) REFERENCES CD(cdID)
ON UPDATE CASCADE
);

Table Constraints
 We can have only one PRIMARY KEY constraint but
multiple UNIQUE constraints
 if no primary key is defined, duplicates are allowed (bag)
 Referential integrity
 a foreign key always has to have a matching value in the
referenced table (or it can be null)
 different referential actions can be defined for update (ON UPDATE)
and delete (ON DELETE) operations on the referenced candidate
key
- CASCADE: propagate operations to the foreign keys which might lead to further
cascaded operations
- SET DEFAULT: set the foreign keys to their default value
- SET NULL: set the foreign keys to NULL
- NO ACTION: the operation on the candidate key will be rejected (default)

Table Definition
createTable = "CREATE TABLE" , table , "(" ,
( columnElement | tableConstraint ) ,
{ "," , ( columnElement | tableConstraint ) } , ")";
columnElement = column , datatype ,
[ "DEFAULT" , ( value | "NULL" ) ] , { columnConstraint };
columnConstraint = "NOT NULL" | "UNIQUE" | "PRIMARY KEY" |
( "REFERENCES" , table , [ "(" , column , ")" ] ,
{ referentialAction } ) |
( "CHECK (" , searchCondition , ")" );
tableConstraint = ( ( "UNIQUE" | "PRIMARY KEY ) , "(" , column ,
{ "," , column } , ")" ) |
( "FOREIGN KEY (" , column , { "," , column } , ")" ,
"REFERENCES" , table , [ "(" , column , { "," , column } , ")" ] ,
{ referentialAction } ) |
( "CHECK (" , searchCondition , ")" );

Table Definition ...
referentialAction = ( "ON UPDATE" | "ON DELETE" ) ,
( "CASCADE" | "SET DEFAULT" | "SET NULL" | "NO ACTION" );

SQL Datatypes
 Character data
 fixed-length or variable-length sequence of characters
 optional multibyte character sets (e.g. for Japanese etc.)
 Large character data or binary data
 often a so-called locator is returned to access a large object in
pieces instead of loading the entire object into memory
char = fixedChar | varyingChar [charSet];
fixedChar = "CHAR" , [ "(" , length , ")" ];
varyingChar = "VARCHAR" , [ "(" , maxLength , ")" ];
charSet = "CHARACTER SET" charSetName;
lob = clob | blob;
clob = "CLOB" , [ "(" , size , ")" ];
blob = "BLOB" , [ "(" , size , ")" ];

SQL Datatypes ...
 Numeric data
 The DECIMAL datatype is sometimes used as a synonym
for the NUMERIC datatype
numeric = decimal | int | smallInt | float | real | double;
decimal = "DECIMAL" , [ "(" , precision , [ "," , scale ] , ")" ];
int = "INTEGER";
smallInt = "SMALLINT";
float = "FLOAT" , [ "(" , precision , ")" ];
real = "REAL";
double = "DOUBLE PRECISION";

SQL Datatypes ...
 Datetime data
 Format of the datetime values
 date: YYYY-MM-DD
 time: hh:mm:ss.p ± hh:mm
 timestamp: YYYY-MM-DD hh:mm:ss.p ± hh:mm
datetime = date | time | timestamp;
date = "DATE";
time = "TIME" , [ "(" , precision , ")" ] ,
[ "WITH TIME ZONE" , timezone ];
timestamp = "TIMESTAMP" , [ "(" , precision , ")" ] ,
[ "WITH TIME ZONE" , timezone ];

SQL Datatypes ...
 Boolean
 the domain of boolean values consist of the two truth values TRUE
and FALSE
 a thrid UNKNOWN truth value is used to represent NULL values
 introduced in SQL:1999
 Bit data
 fixed or varying sequence of binary digits (0 or 1)
boolean = "BOOLEAN";
bit = fixedBit | varyingBit;
fixedBit = "BIT" , [ "(" , length , ")" ];
varyingBit = "BIT VARYING" , [ "(" , maxLength , ")" ];

Data Manipulation
 After a table has been created, we can use the INSERT
command to add tuples
 unspecified attribute values are set to the default value or NULL
 attribute order can be changed via optional column names
 "bulk loader" utilities to insert large amounts of tuples
 Example
INSERT INTO Customer VALUES(8,'Max Frisch','ETH Zentrum', 8001, 'Zurich');
insert = "INSERT INTO" , table ,
[ "(" , column , { "," , column } , ")" ] ,
( "VALUES (" , expr , { "," , expr } , ")" ) | ( "(" , query , ")" );

Expressions
expr = exprElement { ( "+" | "-" | "*" | "/" ) , exprElement };
exprElement = column | value |
"COUNT" , "(" ( "*" | ( [ "ALL" | "DISTINCT" ] , column ) , ")" |
( "MIN" | "MAX" ) , "(" , expr , ")" |
( "SUM" | "AVG" ) , "(" , [ "DISTINCT" ] , expr , ")";

Data Manipulation ...
 The DELETE statement can be used to delete tuples
 Tuples can be updated via the UPDATE statement
 Example
UPDATE Customer SET name = 'Walter Faber' WHERE customerID = 8;
update = "UPDATE" , table , "SET" ,
column , "=" , ( "NULL" | expr | "(" , query , ")" ) ,
{ "," , column , "=" , ("NULL" | expr | "(" , query , ")" ) } ,
[ "WHERE" , searchCondition ];
delete = "DELETE FROM" , table [ "WHERE" , searchCondition ];

Data Manipulation ...
 The DROP TABLE statement can be used to delete a
relation from the database
 A relation schema can be modified via the ALTER TABLE
command
 existing tuples are assigned a NULL value for the new attribute
 Example
alterTable = "ALTER TABLE" , table , "ADD" ,
( columnElement | columnConstraint );
ALTER TABLE Customer ADD birthdate DATE;
dropTable = "DROP TABLE" , table;

Basic SQL Query Structure
 A basic SQL query consists of a SELECT, a FROM and a
WHERE clause
 SELECT
- specifies the columns to appear in the result (projection in relational algebra)
 FROM
- specifies the relations to be used (cartesian product in relational algebra)
 WHERE
- filters the tuples (selection in relational algebra)
- join conditions are explicitly specified in the WHERE clause
 GROUP BY
- groups rows with the same column values
- the HAVING construct can be used to further filter the groups
 ORDER BY
- defines the order of the resulting tuples

Basic SQL Query Structure ...
 In general, the SELECT FROM WHERE parts are evaluated as
follows
1. generate a cartesian product of the relations listed in the FROM
clause
2. apply the predicates specified in the WHERE clause on the result
of the first step
3. for each tuple in the result of the second step output the attri-
butes (or results of expressions) specified in the SELECT clause
 The evaluation is normally optimised by a query optimiser

 The order of clauses in an SQL query cannot be
changed
 Note that the SELECT is equivalent to a relational algebra
projection
 In contrast to the relational algebra, SQL does not
eliminate duplicates automatically
 the automatic elimination of duplicates would be time consuming
 user has to eliminate duplicates explicitly via DISTINCT keyword
SELECT A1, A2,..., An
FROM r1, r2,..., rm
WHERE P
pA1,A2,...,An
(sP(r1  r2  ...  rm)
is equivalent to

SELECT Clause
 A '*' can be used in the SELECT clause as a shortcut to
get all tuple attributes
SELECT *
FROM Customer;
5 Claude Debussy 12 Rue Louise 75008 Paris

SELECT Clause ...
 Duplicate tuples resulting from a projection to specific
attributes are not eliminated by default
SELECT name
FROM Customer;
name
Max Frisch
Eddy Merckx
Claude Debussy
Albert Einstein
Max Frisch

SELECT Clause ...
 The DISTINCT keyword can be used to eliminate
duplicates
SELECT DISTINCT name
FROM Customer;
name
Max Frisch
Eddy Merckx
Claude Debussy
Albert Einstein

Computed Attributes and Rename
 Computations can be performed in the SELECT clause
 multiple numeric attributes can be used in a computation
 The rename operation (AS) is used to rename relations
as well as attributes
 computed columns have no name by default
 also used when multiple relations have the same attribute names
SELECT name, price * 1.5 AS newPrice
FROM CD;
name newPrice
Falling into Place 26.85
Carcassonne 23.20
Chromatic 24.75

WHERE Clause
 In the WHERE clause we can use five basic predicates
(search conditions)
 comparison
- compare two expressions
 range
- check whether the value is within a specified range of values (BETWEEN)
 set membership
- check whether the value is equal to a value of a given set (IN)
 pattern matching
- test whether the expression matches a specifies string pattern (LIKE)
 check for NULL values
- check whether the expression is a NULL value (IS NULL)

WHERE Clause ...
SELECT name, postcode
FROM Customer
WHERE city = 'Zurich' AND postcode >= 8040;
name postcode
Max Frisch 8092
SELECT name, price
FROM CD
WHERE price BETWEEN 15.0 AND 17.0;
name price
Carcassonne 15.50
Chromatic 16.50

WHERE Clause ...
 Check for set membership with the IN construct
SELECT *
FROM Customer
WHERE city IN ('Zurich', 'Brussels');

Pattern Matching
 Strings are enclosed in single quotes
 use a double single quote for escaping
 The LIKE operator is used for pattern matching
 the underscore (_) is a placeholder for a single character
 the percent sign (%) is a placeholder for any substring
 e.g. LIKE '_e%'
name
Albert Einstein
FROM Customer
WHERE name LIKE '%Ein%';

Null Values
 Missing (unknown) info is represented by NULL values
 result of any comparison involving a NULL value is Unknown
 three-valued logic (3VL) based on True, False and Unknown
True False Unknown
True True False Unknown
False False False False
Unknown Unknown False Unknown
AND
True False Unknown
True True True True
False True False Unknown
Unknown True Unknown Unknown
OR
=
True False Unknown
True True False Unknown
False False True Unknown
Unknown Unknown Unknown Unknown
NOT
True False Unknown
False True Unknown

Null Values ...
 The NULL keyword can also be used in predicates to
check for null values
 Note that a check for NULL is not the same as a check for
the empty String ''
SELECT *
FROM CD
WHERE price IS NOT NULL;
1 Falling into Place 2007 17.90 2007
2 Carcassonne 3156 15.50 1993
3 Chromatic 3012 16.50 1993

FROM Clause
 The FROM clause creates a cartesian product of multiple
relations and can be used to specify join operations
 In a previous lecture we have seen the following
relational algebra expression
- "list the name and street of customers whose order is still open"
- pname, street(sstatus="open"(order ⋈ customer))
- the same can be achieved in SQL by explicitly specifying the matching attributes
SELECT name, street
FROM Customer, Order
WHERE Order.customerID = Customer.customerID AND status = 'open';
name street
Albert Einstein Bergstrasse 18

Inner and Outer Joins
 Note that there exist SQL extensions to perform join
operations between two relations R and S in the FROM
clause
 Inner Joins
 Outer Joins
SELECT * FROM R NATURAL JOIN S;
SELECT * FROM R CROSS JOIN S;
SELECT * FROM R JOIN S ON R.A > S.B;
SELECT * FROM R LEFT OUTER JOIN S ON R.A = S.B;
SELECT * FROM R RIGHT OUTER JOIN S ON R.A = S.B;
SELECT * FROM R FULL OUTER JOIN S ON R.A = S.B;

Correlation Variable
 A correlation variable can be used as an alias for a table
 Example
 "Find all pairs of CDs that were produced in the same year"
SELECT c1.name AS name1, c2.name AS name2
FROM CD c1, CD c2
WHERE c1.year = c2.year AND c1.cdID < c2.cdID;
name1 name2
Carcassonne Chromatic

Sorting
 The ORDER BY clause can be used to arrange the result
tuples in acending (ASC) or descending (DESC) order
 multiple sort keys can be specified; highest priority first
 tuples with NULL values are either before or after non-NULL tuples
SELECT name, street, city
FROM Customer
ORDER BY city ASC, name DESC;
name street city
Eddy Merckx Pleinlaan 25 Brussels
Claude Debussy 12 Rue Louise Paris
Max Frisch ETH Zentrum Zurich
Max Frisch Bahnhofstrasse 7 Zurich
Albert Einstein Bergstrasse 18 Zurich

Set Operations
 The UNION, INTERSECT and EXCEPT operations correspond
to the , and - relational algebra operations
 the relations have to be compatible (same attributes)
 these operations remove duplicates by default
- the ALL keyword has to be used to retain duplicates
(SELECT name
FROM Customer)
INTERSECT
(SELECT name
FROM Supplier);
name
Max Frisch

Aggregate Functions and Grouping
 In SQL there are five aggregate functions (MIN, MAX, AVG,
SUM and COUNT) that take a set or multiset of values as
input and return a single value
 Example
 "Find the number of customers in each city"
 Aggregate functions (except COUNT(*)) ignore NULL
values in the input set
 input set might be empty in which case NULL is returned
SELECT city, COUNT(customerID) AS number
FROM Customer
GROUP BY city;
city number
Zurich 3
Brussels 1
Paris 1

Subqueries
 A subquery is a SELECT FROM WHERE expression that is
nested within another query
 e.g. via check for set membership (IN or NOT IN)
 Example
 "Find all the suppliers who are no customers"
FROM Supplier
WHERE name NOT IN (SELECT name
FROM Customer);
name
Mario Botta

Nested Subqueries ...
 Example
 "Find all CDs with a price smaller than average"
SELECT *
FROM CD
WHERE price < (SELECT AVG(price)
FROM CD;
2 Carcassonne 3156 15.50 1993
3 Chromatic 3012 16.50 1993

Set Comparison
 For nested queries with conditions like "greater than at
least one" we can use these set comparison operators
 > SOME, >= SOME, < SOME, <= SOME, = SOME, <> SOME as well as the
same combination with ALL
 Example
 "Find the customers with a postcode greater than all supplier postcodes"
SELECT name ,postcode
FROM Customer
WHERE postcode > ALL (SELECT postcode
FROM Supplier);
name postcode
Claude Debussy 75008
Max Frisch 8092

Existence Test
 The EXISTS operator can be used to check if a tuple
exists in a subquery
 Example
SELECT name
FROM Customer
WHERE EXISTS (SELECT *
FROM Supplier
WHERE Supplier.name = Customer.name);
name
Max Frisch

Derived Relations
 A subquery expression can also be used in the FROM
clause
 in this case, a name has to be given to the relation
 Example
 "Find the number of customers in the city with the most
customers"
SELECT MAX(noCustomers) AS max
FROM (SELECT city, COUNT(customerID)
FROM Customer
GROUP BY city) AS CityTotal(city, noCustomers);
max
3

Basic SQL Query Structure
 The query statement can be used to retrieve information
from one or multiple database tables
 can perform the relational algebra's selection, projection and join
operation in a single SELECT FROM WHERE command
query = select { ("UNION" | "INTERSECT" | "EXCEPT") , [ "ALL" ] , select};
select = "SELECT" [ "ALL" | "DISTINCT" ] ,
("*" | ( expr , [ "AS" , newName ] ,
{ "," , expr , [ "AS" , newName ] } ) ,
"FROM" , table , [ correlationVar ] ,
{ "," , table , [ correlationVar ] } ,
[ "WHERE" , searchCondition ] ,
[ "GROUP BY" , column , { "," , column } ,
[ "HAVING" , searchCondition ] ];
orderedQuery = query , "ORDER BY" , column , [ "ASC" | "DESC" ] ,
{ "," , column , [ "ASC" | "DESC" ] };

searchCondition = [ "NOT" ] , search ,
{ ( "AND" | "OR" ) , [ "NOT" ] , search };
search = ( expr , [ "NOT" ] , "BETWEEN" , expr , "AND" , expr ) |
( expr , [ "NOT" ] , "LIKE" , "'" , ( string | "_" | "%" ) ,
{ string | "_" | "%" } , "'" ) |
( column | ( "(" , expr , ")" ) , "IS" , [ "NOT" ] , "NULL" ) |
( expr , ( "=" | "<>" | ">" | ">=" | "<" | "<=" ) , ( expr |
( [ "SOME" | "ALL" ] , "(" , query , ")" ) ) ) |
( expr , [ "NOT" ] , "IN (" ,
( ( value , { "," , value } ) | query ) , ")" |
( "EXISTS (" , query , ")";

WITH Clause
 The WITH clause can be used to improve the readability
by introducing temporary new relations
 introduced only in SQL:1999 and not supported by all databases
 Example
 "Find all customers who bought one of the most expensive CDs"
WITH Expensive(price) AS
SELECT MAX(price)
FROM CD
SELECT Customer.name
FROM Customer, CD, Order
WHERE CD.price = Expensive.price AND CD.cdID = Order.cdID AND
Order.customerID = Customer.customerID;
name
Albert Einstein

Views
 New virtual relations (views) can be defined on top of an
existing logical model
 simplify queries
 provide access to only parts of the logical model (security)
 computed by executing the query whenever the view is used
 Some DBMS allow views to be stored (materialised
views)
 materialised views have to be updated when its relations change
(view maintenance)
createView = "CREATE VIEW" , table ,
[ "(" , column , { "," , column } , ")" ] ,
"AS" , query;

Views
 Example
 Note that a view can be used like any other relation
 Views are useful for queries but they present a serious
problem for UPDATE, INSERT and DELETE operations
 modifications are difficult to be propagated to the actual relations
 modifications on views are therefore generally not permitted
CREATE VIEW CustomerCD AS
SELECT Customer.customerID, Customer.name, CD.cdID, CD.name AS cdName
FROM Customer, Order, CD
WHERE Customer.customerID = Order.customerID AND
Order.cdID = CD.cdID;

Transactions
 A transaction consists of a sequence of query and/or
update statements
 atomic set of statements
 A transaction explicitly starts when an SQL statement is
executed and is ended by
 a COMMIT statement
 a ROLLBACK statement
 In many SQL implementations each SQL statement is a
transaction on its own (automatic commit)
 this default behaviour can be disabled
 SQL:1999 introduced BEGIN ATOMIC ... END blocks
 Transactions will be discussed in detail later

Homework
 Study the following chapters of the
Database System Concepts book
 chapter 3
- sections 3.1-3.10
- Introduction to SQL
 chapter 4
- sections 4.1-4.5 and section 4.7
- Intermediate SQL

Exercise 5
 Structured Query Language (SQL)


References
 A. Silberschatz, H. Korth and S. Sudarshan,
Database System Concepts (Sixth Edition),
McGraw-Hill, 2010
 Donald D. Chamberlin and Raymond F. Boyce,
SEQUEL: A Structured English Query Language,
Proceedings of the 1974 ACM SIGFIDET Workshop on
Data Description, Access and Control (SIGFIDET '74),
Michigan, USA, May 1974

2 December 2005
Next Lecture
Advanced SQL

Lecture05sql 110406195130-phpapp02

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