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SQL

SQL. Sangeeta Devadiga CS157A, Fall 2006. Outline. Background Data Definition Basic Structure Set Operation. Background. IBM developed the original version named sequel in early 1970’s Sequel language has evolved into SQL SQL (Structured Query Language) Versions of SQL

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SQL

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  1. SQL Sangeeta Devadiga CS157A, Fall 2006

  2. Outline • Background • Data Definition • Basic Structure • Set Operation

  3. Background • IBM developed the original version named sequel in early 1970’s • Sequel language has evolved into SQL • SQL (Structured Query Language) Versions of SQL SQL 92 SQL 99 SQL 2003 (latest version)

  4. Parts of SQL • Data Definition Language (DDL) • Data manipulation Language (DML) • Integrity • View Definition • Transaction control • Embedded SQL and Dynamic SQL • Authorization

  5. Basic Domain Types • char(n): A fixed length character string with user specifed length n. character can be used instead. • varchar(n): A variable length character string with user specified max length n. charactervarying is equivalent. • int: An Integer, the full form integer is equivalent. • smallint: A small integer, a subset of integer domain type • numeric(p,d): A fixed point number with user specified precision. E.g: numeric(3,1) allows 31.5 to be defined precisely • real, double precision: Floating-point and double precision floating-point numbers with machine-dependent precision. • float(n): A floating point number, with precision of at least n digits.

  6. Basic Schema Definition in SQL We create SQL relation using the create table command create table r(A1D1 , A2D2, ….. , AnDn , (integrity constraint1), ………. , (integrity constraintk)) r  is the name of relation A1….An  are the names of attributes D1…Dn  are the types of values in the domain

  7. Examples create table Example 1: create tablecustomer (customer_name char(20), customer_street char(30), customer_city char(30), primary key (customer_name))

  8. Example 2 create tableaccount (account_number char(10), branch_name char(15), balance numeric (12, 2), primary key (account_number))

  9. Basic SQL Query Structure • SQL is based on set and relational operations with some modification and enhancement. • SQL query has the form selectA1,A2, … ,An from r1, r2, …. ,rm whereP • A1 is a attribute • r1 represents a relation • P is a predicate • Equivalent Query:  A1, A2, … , An(P (r1X r2X …… X rm)) • The result of a SQL query is a relation

  10. Example select Clause • selectbranch_name fromloan • In relational Algebra, the query would be branch_name(loan) • SQL allows duplicates in query result • use distinct if no duplicates in result • use all if duplicates required in result • selectdistinctbranch_name from loan (result has distinct branch names) • selectallbranch_name from loan (result may have duplicates)

  11. Where Clause • Corresponds to the selection predicate of relational algebra • To find loan numbers for loans made at San Jose branch with loan amounts greater than $500 selectloan_number fromloan wherebranch_name = “San Jose” and amount > $500 • Comparison result can be combined with logical connectives and, or, and not • SQL includes between comparison operator • To find loan numbers between amt. 900 and 10,000 select loan_number from loan whereamount between 900 and 10000

  12. From Clause • Corresponds to Cartesian product operation of relational algebra • Example: To find name, loan number, amount of all customers having loan at San Jose branch. selectcustomer_name, loan_number, amount from borrower , loan where borrower.loan_number=loan.loan_number and branch_name = “San Jose”

  13. Rename • SQL allows renaming relations and attributes using as clause • Example: To find name, loan_number, amount of all customers and rename column loan_number as loan_id. selectcustomer_name, loan_number as loan_id, amount from borrower , loan where borrower.loan_number=loan. loan_number

  14. Set Operation • The set operations union, intersect and except corresponds to U,, - respectivelyof relational algebra. • Each of the above operation automatically eliminates duplicates • To retain all duplicates use union all, intersect all, except all

  15. Examples Set Operations • Find all customers who have a loan, a account or both: selectcustomer_namefromdepositor union selectcustomer_namefromborrower • Find all customers who have both loan and an account: selectcustomer_namefromdepositor intersect selectcustomer_namefromborrower • Find all customers who have an account, but no loan: selectcustomer_namefromdepositor except selectcustomer_namefromborrower

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