COMP 3715 Spring 05

Working with data in a DBMS Any database system must allow user to  Define data Relations Attributes Constraints  Manipulate data Insert Delete Retrieval

Working with data in DBMS Thus a DBMS must provide  Data Definition Language (DDL) Define tables structures Define attributes and domains Define constraints  Data Manipulation Language (DML) To insert, delete and update data To query and retrieve data

Structured Query Language -- SQL The “standard” query language for DBMS Contains both DDL & DML Supported by every DBMS on the market Some system may have GUI  But SQL is still the standard way for computer programs interact with the DBMS

Data Definition – Create table Need to specify  Table name  Attribute names  Attribute domains  Primary keys  Constraints

Data Definition – Create table CREATE TABLE Customer (ssn CHAR(9); name CHAR(30); address CHAR(50); age INTEGER; income INTEGER; PRIMARY KEY (ssn))

Data Definition – Create table CREATE TABLE Book (title CHAR(30); isbn CHAR(20); author CHAR(30); quantity INTEGER; price REAL; PRIMARY KEY (isbn))

Data Definition – Create table CREATE TABLE Buy (isbn CHAR(20); customer CHAR(9); quantity INTEGER NOT NULL; date DATE; PRIMARY KEY (isbn, customer, date); FOREIGN KEY (isbn) REFERENCES Books FOREIGN KEY (customer) REFERENCES Customer(ssn))

Data Manipulation -- Query The standard query for SQL SELECT FROM WHERE ORDER BY Other parameters (to be discuss in senior class…)

Data Manipulation – Query: Selection (single table) “Find the ssn of the customer name John Doe” SELECT ssn FROM Customer WHERE Name = “John Doe”

Data Manipulation – Query: Selection (single table) “Find the quantity of books in the bookstore for “Tax break 2005” written by “Mike Rich” SELECT quantity FROM Book WHERE title = “Tax break 2005” AND author = “Mike Rich”

Data Manipulation – Query: Selection (single table) “Find the isbn of all books that is sold on 2/14/2005 and also return how many of each book is sold” SELECT isbn, quantity FROM Buy WHERE date = “2/14/2005”

Data Manipulation – Query: Selection (single table) “Find the ssn of the customers who bought the book with isbn = ‘A ’” SELECT ssn FROM Buy WHERE isbn = “A ”

Data Manipulation – Query: Join (Multiple tables) “Find the name of the customers who bought the book with isbn = ‘A ’” SELECT name FROM Buy, Customer WHERE Buy.isbn = “A ” and Buy.customer = Customer.ssn

Data Manipulation – Query: Join (Multiple tables) Join  Combining information of multiple tables  Consider the Cartesian Product of the tuples from the table  i.e. consider ALL pairs of tuples from the two tables  For each possible pair, the condition is checked

Data Manipulation – Query: Join (Multiple tables) “Find the name of the customers who bought the book with isbn = ‘A ’”, also return the name of the book SELECT Customer.name, Book.title FROM Buy, Customer, Book WHERE Buy.isbn = “A ” and Buy.customer = Customer.ssn and Book.isbn = Buy.isbn

Data Manipulation – Query: negative information “Find the customer who buy a book on a date other then “2/14/2005” SELECT customer FROM Buy WHERE Date ≠ “2/14/2005”

Data Manipulation – Query: negative information “Find the customer who does not buy a book on “2/14/2005” SELECT customer FROM Buy WHERE Date ≠ “2/14/2005” Does NOT work! Why?  Each tuple is examined separately  Whenever the condition is satisfied, the tuple is returned

Data Manipulation – Query: Aggregate queries Functions available for counting, averaging etc. AVG(), COUNT(), SUM (), MIN (), MAX() Example, find the most expensive book SELECT MAX(price) FROM Book

Data Manipulation – Query: Aggregate queries Example, find the most expensive book written by “J. K. Roaming” SELECT MAX(price) FROM Book WHERE author = “J. K. Roaming”

Data Manipulation – Query: Aggregate queries Example, find the most expensive book written by “J. K. Roaming” and return its title SELECT title, MAX(price) FROM Book WHERE author = “J. K. Roaming” This does NOT work. Convention in SQL, you cannot max aggregate and non-aggregate values (except using Group by)

Data Manipulation – Query: Aggregate queries Example, find the number of authors that has book in the bookstore SELECT COUNT(author) FROM Book This does NOT work. It will count duplicates as separate

Data Manipulation – Query: Aggregate queries Example, find the number of authors that has book in the bookstore SELECT COUNT(DISTINCT author) FROM Book

Data Manipulation – Query: Group By For reporting, we may want to separate data into groups For example, I may want to list the number of customers based on age Using “Group By” enable it

Data Manipulation – Query: Group By List the number of customer for each age SELECT age, COUNT(*) FROM Customer GROUP BY age

Data Manipulation – Query: Group By List the number of rich customers (with income > 100,000) for each age SELECT age, COUNT(*) FROM Customer WHERE income > 100,000 GROUP BY age

Data Manipulation – Query: Group By List the number of rich customers (with income > 100,000) for each age, also list the maximum salary for each group SELECT age, COUNT(*), MAX(salary) FROM Customer WHERE income > 100,000 GROUP BY age

Data Manipulation – Insertion, Deletion, Inserting a tuple INSERT INTO Books VALUES (“How to cut taxes”, “A ”, “R. Rich”, 3, 19.95) Deleting a tuple DELETE FROM Books WHERE author = “R. Rich” Delete all books from “R. Rich”

Data Manipulation – Update Update tuple Update Books SET price = price * 1.1 WHERE author = “R. Rich” Increase the price of all books from R. Rich for 10%