1. INTRODUCTION TO DATABASE MANAGEMENT SYSTEMS Dr. Adam Anthony Fall 2012

2. Lecture 4 Overview  Chapter 3, sections 1-4  SQL language introduction  Data Definition Language  Data Query Language  Basic Query Practice

3. History of SQL  IBM Sequel language developed as part of System R project at the IBM San Jose Research Laboratory  Renamed Structured Query Language (SQL)  ANSI and ISO standard SQL:  SQL-86, SQL-89, SQL-92  SQL:1999, SQL:2003, SQL:2008  Commercial systems offer most, if not all, SQL-92 features, plus varying feature sets from later standards and special proprietary features.  Not all examples here may work on your particular system.

4. Data Definition Language  Converts a schema into a real thing!  New information: Domain Types  Managing data integrity  Other Security, efficiency features (take Database 2 class for more info)  Indexing (pre-sorting, basically)  Security and access privilege  How to store on disk

5. Common Data Types  Most systems have these types exactly, or something very close in name and usage:  char(n). Fixed length character string, with user-specified length n.  varchar(n). Variable length character strings, with user-specified maximum length n.  int. Integer (a finite subset of the integers that is machine-dependent).  smallint. Small integer (a machine-dependent subset of the integer domain type).  numeric(p,d). Fixed point number, with user-specified precision of p digits, with n digits to the right of decimal point.  real, double precision. Floating point and double-precision floating point numbers, with machine-dependent precision.  float(n). Floating point number, with user-specified precision of at least n digits.  More are covered in Chapter 4.

6. Example Schema Performer Performer_ID name Song Song_ID Title Genre Album Performer-Song Performer_ID Song_ID

7. Create Table Statement  create table NAME ( A 1 D 1, A 2 D 2,..., A n D n, (integrity-constraint 1 ),..., (integrity-constraint k ) );  create table PERFORMER ( performer_id int, name varchar(30), primary key (performer_id) );

8. Integrity Constraints  Not Null  Primary Key  Foreign Key  create table PERFORMER-SONG ( Performer_ID int, Song_ID int, primary key (Performer_ID,Song_ID) foreign key (Performer_ID) references PERFORMER foreign key (Song_ID) references SONG ); **A primary key specification requires values to be not null!

9. Not Null Example  Create table SONG( Song_ID int, Title varchar(30) not null, Genre varchar (10), Album varchar(50) not null, primary key (Song_ID) );

10. Multi-Valued Keys  create table takes ( ID varchar(5), course_id varchar(8), sec_id varchar(8), semester varchar(6), year numeric(4,0), grade varchar(2), primary key (ID, course_id, sec_id, semester, year), foreign key (ID) references student, foreign key (course_id, sec_id, semester, year) references section );  Note: sec_id can be dropped from primary key above, to ensure a student cannot be registered for two sections of the same course in the same semester

11. Drop and Alter Table  drop table student  Deletes the table and its contents  delete from student  Deletes all contents of table, but retains table  alter table  alter table r add A D where A is the name of the attribute to be added to relation r and D is the domain of A. All tuples in the relation are assigned null as the value for the new attribute.  alter table r drop A where A is the name of an attribute of relation r Dropping of attributes not supported by many databases

12. Data Manipulation Language  SQL features that allow us to query data in novel ways, and to update and delete specific values  Basic query construct: the Select clause: SELECT Name FROM PERFORMER WHERE Performer_ID < 5000;  All SQL clauses are case insensitive—performer_id = PERFORMER_ID = Performer_ID, etc.  Use capitalization, line breaks to enhance readability  Connect each portion above to the relational algebra equivalent

13. Projection, Plus!  If you want everything: SELECT * FROM Student;  When we get to multi-table queries: SELECT Student.* FROM Student, Takes WHERE …  Expressions for columns: SELECT ID, name, salary*1.10 FROM instructor

14. INTERACTIVE LECTURE TIME!  Queries are best learned through doing!  Download and extract:  http://www.bw.edu/~apanthon/courses/CSC280/Dat aFiles/InClassLec4-5.zip http://www.bw.edu/~apanthon/courses/CSC280/Dat aFiles/InClassLec4-5.zip

15. DISTINCT and ALL  Output of an SQL statement is a relation, but it MAY have duplicates!  Differs from theoretical underpinnings  Find all the courses that have ever been taken by a student:  Try:Then Try: SELECT course_id FROM takes; SELECT DISTINCT course_id FROM takes;

16. Using WHERE  The WHERE clause does most of the interesting work  True/False tests based on attribute values  Use AND, OR, NOT to combine tests just like you would in an IF statement  SQL provides many useful operators for WHERE clauses

17. Comparing Strings  Strings represented with singe quote: ‘Comp. Sci.’  Comparisons are case-sensitive ‘Comp. Sci.’ != ‘comp. sci.’  Upper(s) and Lower(s) to get all-upper or all-lower  Trim(s) to get rid of trailing white space  LIKE Wildcards:  % represents any number or characters  _ represents exactly one character  Ex: WHERE name LIKE ‘%Thompson’

18. Practice  Find all students with  A name more than 5 characters long  A name that starts with P or ends in S (case insensitive)  A name that is exactly five letters and starts with B

19. Range Parameters  Cool trick to save time  Find all students with total credits more than 3 and less than 10  Inclusive range: SELECT * FROM Student WHERE tot_cred BETWEEN 4 and 9  This construct is completely optional  How to do this with >=, <= ?

20. Using more than one table  The FROM clause automatically produces a cartesian (cross) product:  Command.headers ON  Try: SELECT * FROM Student, Takes  Is this useful, ever? What should we add?

21. Manual Joins  Just filter out the junk! SELECT * FROM Student, Takes WHERE Student.ID = Takes.ID  Called a ‘Join’ because it results in a combined table that is true to the intent of relational database design  Depending on the design, a Join can still be HUGE and COSTLY to compute!

22. Natural Joins  IF the names of the columns you wish to join on match PERFECTLY you can let SQL join automatically: SELECT * FROM STUDENT NATURAL JOIN TAKES  If the names don’t match, or you need to do something clever, stick with a manual join  Just a time-saver

23. Natural Join Problems  Danger in natural join: beware of unrelated attributes with same name which get equated incorrectly  List the names of instructors along with the the titles of courses that they teach  Incorrect version (makes course.dept_name = instructor.dept_name) select name, title from instructor natural join teaches natural join course;  Correct version select name, title from instructor natural join teaches, course where teaches.course_id = course.course_id;