Structured Query Language (SQL) - Lecture 5 - Introduction to Databases (WE-DINF-9552)

Introduction to DatabasesStructured Query LanguageProf. Beat SignerDepartment of Computer ScienceVrije Universiteit Brusselhttp://www.beatsigner.com 2 December 2005

Context of Todays Lecture Programmers Users DB Admins Application Database Queries Programs Schema DBMS DML Query DDL Preprocessor Compiler Compiler Program Authorisation Catalogue Object Code Control Manager Integrity Command Query Checker Processor Optimiser Transaction Scheduler Manager Data Manager Buffer Recovery Manager Manager Database Manager Access File Methods Manager System Database and Based on Components of a DBMS, Database Systems, Buffers System Catalogue T. Connolly and C. Begg, Addison-Wesley 2010March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 2

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)March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 3

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 SQLMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 4

History of SQL  SEQUEL (70s)  structured english query language  developed by Raymond F. Boyce and Donald D. Chamberlin  access data stored in IBMs System R relational database  SQL-86 Donald D. Chamberlin Raymond F. Boyce  first ANSI standard version  SQL-89 / SQL 1  SQL-92 / SQL 2  we will mainly discuss features of the SQL-92 standardMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 5

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 databasesMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 6

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  We will use the Extended Backus–Naur Form (EBNF) to describe different SQL concepts  http://en.wikipedia.org/wiki/Extended_Backus-Naur_FormMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 7

Extended Backus-Naur Form (EBNF)  Notation to describe computer program- ming languages (context-free grammars)  developed by Niklaus Wirth Notation Meaning = Definition , Sequence ; Termination Niklaus Wirth | Choice [...] Option {...} Repetition (...) Grouping "..." Terminal StringMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 8

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 relationsMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 9

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 environment - relations/tables catalogue catalogue - views schema schema - assertions - indexes schema  users and user groupsMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 10

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 createSchema = "CREATE SCHEMA" , name , "AUTHORIZATION" , creator , [ ddlStatements ]; dropSchema = "DROP SCHEMA" , name , [ "RESTRICT" | "CASCADE" ];  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 droppedMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 11

Relational Database Example customer 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 cd cdID name duration price year 1 Falling into Place 2007 17.90 2007 2 Carcassonne 3156 15.50 1993 3 Chromatic 3012 16.50 1993March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 12

Relational Database Example ... order orderID customerID cdID date amount status 1 53 2 13.02.2010 2 open 2 2 1 15.02.2010 1 delivered supplier supplierID name city 5 Max Frisch Zurich 2 Mario Botta Lugano 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 schemaMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 13

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, name VARCHAR(30) NOT NULL, duration TIME, price NUMERIC(6,2), year SMALLINT );March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 14

Table Definition Example ... CREATE TABLE Supplier ( supplierID INTEGER PRIMARY KEY, name VARCHAR(30) NOT NULL, 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 );March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 15

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)March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 16

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 , ")" );March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 17

Table Definition ... referentialAction = ( "ON UPDATE" | "ON DELETE" ) , ( "CASCADE" | "SET DEFAULT" | "SET NULL" | "NO ACTION" );March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 18

SQL Datatypes  Character data  fixed-length or variable-length sequence of characters  optional multibyte character sets (e.g. for Japanese etc.) char = fixedChar | varyingChar [charSet]; fixedChar = "CHAR" , [ "(" , length , ")" ]; varyingChar = "VARCHAR" , [ "(" , maxLength , ")" ]; charSet = "CHARACTER SET" charSetName;  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 lob = clob | blob; clob = "CLOB" , [ "(" , size , ")" ]; blob = "BLOB" , [ "(" , size , ")" ];March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 19

SQL Datatypes ...  Numeric data numeric = decimal | int | smallInt | float | real | double; decimal = "DECIMAL" , [ "(" , precision , [ "," , scale ] , ")" ]; int = "INTEGER"; smallInt = "SMALLINT"; float = "FLOAT" , [ "(" , precision , ")" ]; real = "REAL"; double = "DOUBLE PRECISION";  The DECIMAL datatype is sometimes used as a synonym for the NUMERIC datatypeMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 20

SQL Datatypes ...  Datetime data datetime = date | time | timestamp; date = "DATE"; time = "TIME" , [ "(" , precision , ")" ] , [ "WITH TIME ZONE" , timezone ]; timestamp = "TIMESTAMP" , [ "(" , precision , ")" ] , [ "WITH TIME ZONE" , timezone ];  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:mmMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 21

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 boolean = "BOOLEAN";  Bit data  fixed or varying sequence of binary digits (0 or 1) bit = fixedBit | varyingBit; fixedBit = "BIT" , [ "(" , length , ")" ]; varyingBit = "BIT VARYING" , [ "(" , maxLength , ")" ];March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 22

SQL Datatypes ...  For further details about the presented datatypes as well as information about vendor-specific datatypes one has to consult the specific database manuals datatype = char | lob | numeric | datetime | boolean | bit;March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 23

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 insert = "INSERT INTO" , table , [ "(" , column , { "," , column } , ")" ] , ( "VALUES (" , expr , { "," , expr } , ")" ) | ( "(" , query , ")" );  Example INSERT INTO Customer VALUES(8,Max Frisch,ETH Zentrum, 8001, Zurich);March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 24

Expressions expr = exprElement { ( "+" | "-" | "*" | "/" ) , exprElement }; exprElement = column | value | "COUNT" , "(" ( "*" | ( [ "ALL" | "DISTINCT" ] , column ) , ")" | ( "MIN" | "MAX" ) , "(" , expr , ")" | ( "SUM" | "AVG" ) , "(" , [ "DISTINCT" ] , expr , ")";March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 25

Data Manipulation ...  The DELETE statement can be used to delete tuples delete = "DELETE FROM" , table [ "WHERE" , searchCondition ];  Tuples can be updated via the UPDATE statement update = "UPDATE" , table , "SET" , column , "=" , ( "NULL" | expr | "(" , query , ")" ) , { "," , column , "=" , ("NULL" | expr | "(" , query , ")" ) } , [ "WHERE" , searchCondition ];  Example UPDATE Customer SET name = Walter Faber WHERE customerID = 8;March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 26

Data Manipulation ...  The DROP TABLE statement can be used to delete a relation from the database dropTable = "DROP TABLE" , table;  A relation schema can be modified via the ALTER TABLE command  existing tuples are assigned a NULL value for the new attribute alterTable = "ALTER TABLE" , table , "ADD" , ( columnElement | columnConstraint );  Example ALTER TABLE Customer ADD birthdate DATE;March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 27

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 tuplesMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 28

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 attributes (or results of expressions) specified in the SELECT clauseMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 29

Basic SQL Query Structure ...  The order of clauses in an SQL query cannot be changed  Note that the SELECT is equivalent to a relational algebra projection SELECT A1, A2,..., An pA1,A2,...,An(sP(r1  r2  ...  rm) FROM r1, r2,..., rm is equivalent to WHERE P  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 keywordMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 30

SELECT Clause SELECT * FROM Customer; 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  A * can be used in the SELECT clause as a shortcut to get all tuple attributesMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 31

SELECT Clause ... SELECT name FROM Customer; name Max Frisch Eddy Merckx Claude Debussy Albert Einstein Max Frisch  Duplicate tuples resulting from a projection to specific attributes are not eliminated by defaultMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 32

SELECT Clause ... SELECT DISTINCT name FROM Customer; name Max Frisch Eddy Merckx Claude Debussy Albert Einstein  The DISTINCT keyword can be used to eliminate duplicatesMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 33

Computed Attributes and Rename SELECT name, price * 1.5 AS newPrice FROM CD; name newPrice Falling into Place 26.85 Carcassonne 23.20 Chromatic 24.75  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 namesMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 34

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)March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 35

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 16.50March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 36

WHERE Clause ...  Check for set membership with the IN construct SELECT * FROM Customer WHERE city IN (Zurich, Brussels); customerID name street postcode city 1 Max Frisch Bahnhofstrasse 7 8001 Zurich 2 Eddy Merckx Pleinlaan 25 1050 Brussels 53 Albert Einstein Bergstrasse 18 8037 Zurich 8 Max Frisch ETH Zentrum 8092 ZurichMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 37

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% SELECT DISTINCT name FROM Customer WHERE name LIKE %Ein%; name Albert EinsteinMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 38

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 AND OR True False Unknown True False Unknown True True False Unknown True True True True False False False False False True False Unknown Unknown Unknown False Unknown Unknown True Unknown Unknown = NOT True False Unknown True False Unknown True True False Unknown False True Unknown False False True Unknown Unknown Unknown Unknown UnknownMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 39

Null Values ...  The NULL keyword can also be used in predicates to check for null values SELECT * FROM CD WHERE price IS NOT NULL; 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  Note that a check for NULL is not the same as a check for the empty String March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 40

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 18March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 41

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 SELECT * FROM R NATURAL JOIN S; SELECT * FROM R CROSS JOIN S; SELECT * FROM R JOIN S ON R.A > S.B;  Outer Joins 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;March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 42

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 ChromaticMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 43

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 ZurichMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 44

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 FrischMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 45

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" city number SELECT city, COUNT(customerID) AS number Zurich 3 FROM Customer GROUP BY city; Brussels 1 Paris 1  Aggregate functions (except COUNT(*)) ignore NULL values in the input set  input set might be empty in which case NULL is returnedMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 46

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" SELECT DISTINCT name FROM Supplier WHERE name NOT IN (SELECT name FROM Customer); name Mario BottaMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 47

Nested Subqueries ...  Example  "Find all CDs with a price smaller than average" SELECT * FROM CD WHERE price < (SELECT AVG(price) FROM CD; cdID name duration price year 2 Carcassonne 3156 15.50 1993 3 Chromatic 3012 16.50 1993March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 48

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 8092March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 49

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 FrischMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 50

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 3March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 51

Basic SQL Query Structure 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" ] };  The query statement can be used to retrieve information from one or multiple database tables  can perform the relational algebras selection, projection and join operation in a single SELECT FROM WHERE commandMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 52

Basic SQL Query Structure ... 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 , ")";March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 53

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 EinsteinMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 54

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;March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 55

Views  Example 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;  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 permittedMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 56

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 laterMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 57

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 SQLMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 58

Exercise 5  Structured Query Language (SQL)  PostgreSQLMarch 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 59

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 1974March 15, 2013 Beat Signer - Department of Computer Science - bsigner@vub.ac.be 60

Next LectureAdvanced SQL 2 December 2005

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Structured Query Language (SQL) - Lecture 5 - Introduction to Databases (WE-DINF-9552)

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Structured Query Language (SQL) - Lecture 5 - Introduction to Databases (WE-DINF-9552) Presentation Transcript