SQL Neyha Amar CS 157A, Fall 2006

Inserting The insert statement is used to add a row of data into a table Strings should be enclosed in single quotes, and numbers should not. Consider the following table: Employee EmpIDnameDeptID 1JohnHats 2DavidShoes

insert into Employee values(3, ‘Steve’, ‘Glasses’) OR insert into Employee(EmpID, name, DeptID) values(3, ‘Steve’, ‘Glasses’) OR insert into Employee(name, DeptID, EmpID) values(‘Steve’, ‘Glasses’, 3) EmpIDnameDeptID 1JohnHats 2DavidShoes 3SteveGlasses  Row inserted Example of Insertion

Updating The update statement is used to change a value in a tuple that matches a specified criteria. General Form: update tablename set column = new value, nextcolumn = new value2, … where somecolumn [and | or othercolumn] OPERATOR value Example1 (from textbook): update account set balance = balance * 1.05 where balance >= 1000 Without the WHERE all values under the specified column will be updated

Example 2: Updating update Employee set DeptID = ‘Shoes’ where name = ‘Steve’ EmpIDnameDeptID 1JohnHats 2DavidShoes 3SteveGlasses Employee EmpIDnameDeptID 1JohnHats 2DavidShoes 3SteveShoes updated row 

Deletion The delete statement is used to delete tuples from the table We can only delete whole tuples, not values in only particular attributes General Form: delete from tablename where somecolumn [and | or othercolumn] OPERATOR value Example 1: delete from account where branch_name = ‘Perryridge’ w/o the WHERE all records will be deleted !!!

Example 2: Deletion EmpIDnameDeptID 1JohnHats 2DavidShoes 3SteveGlasses delete from Employee where EmpID = 3 EmpIDnameDeptID 1JohnHats 2DavidShoes Employee

Set Operations SQL operations Union, Intersect, and Except operate on relations and correspond to relational algebra operations union, intersection and set difference Relations participating in operations must be compatible, that is they must have the same attributes

Union Operation Example: Find all the bank customers having a loan, an account, or both at the bank. (select customer_name from depositor) union (select customer_name from borrower) o Union operation automatically eliminates duplicates If customer has several accounts or loans (or both) at the bank then he/she will appear only ONCE in the result.

Continue… Union Operation o If you want to retain all duplicates, you must write UNION ALL in place of UNION (select customer_name from depositor) union all (select customer_name from borrower)

Intersect Operation Example: Find all the bank customers having a loan AND an account at the bank. (select distinct customer_name from depositor) intersect (select distinct customer_name from borrower) o automatically eliminates duplicates o to retain them use INTERSECT ALL in place of INTERSECT

Except Operation Example: Find all the bank customers having an account but NO loan at the bank. (select distinct customer_name from depositor) except (select distinct customer_name from borrower) o automatically eliminates duplicates, o To retain use EXCEPT ALL in place of EXCEPT

Aggregate Functions Functions that take a collection (a set or multiset) of values as input and return a single value. SQL has five built-in aggregate functions: - avg ( [distinct | all] n) – returns average value of n - min ( [distinct | all] expr) – returns minimum value of expr - max ( [distinct | all] expr) – returns maximum value of expr - sum ( [distinct | all] n) – returns total sum of values in expr - count (* | [distinct | all] expr) - returns # of rows * - return # of rows including NULL values from relation distinct - return # of rows eliminating duplicates and NULL values from expr all - return # of rows including duplicates but no NULL values from expr The input to sum and avg must be a collection of numbers Others can operate on collections of nonnumeric data types, such as strings, as well.

Example: Aggregate Function Example: Find the average account balance at the Perryridge branch. select avg (balance) from account [as average] where branch_name = ‘Perryridge’ o Result will be a relation with a single attribute, containing a single tuple that equals the average account balance at Perryridge o Can give a name to the attribute by using the AS clause

Group By Clause GROUP BY is used when we are selecting multiple columns from a table (or tables) and at least one arithmetic operator appears in the SELECT statement. When that happens, we need to GROUP BY all the columns except the one(s) operated on by the arithmetic operator

Example: GROUP BY Consider the following relation schema: Store_Information Store_nameSalesDate Los Angeles$ San Diego$ Boston$

Example GROUP BY… Query: Find the total sales for each store. Answer: select Store_name, sum(Sales) from Store_Information group by store_name Result: Store_namesum(Sales) Los Angeles$1800 San Diego$250 Boston$700

HAVING clause HAVING serves as the WHERE clause for grouped data This condition does not apply to a single tuple; it applies to each group constructed by the group by clause.

Example: HAVING Query: Find the total sales for each store that has a total sale greater than $1500. Answer: select Store_name, sum(Sales) from Store_Information group by store_name having sum(Sales) > 1500 Result: Store_namesum(Sales) Los Angeles$1800

Null Values SQL allows the use of null values to indicate absence of information about the value of an attribute. We can use the special keyword null in a predicate to test for a null value

Examples: Null Values Query: Find all loan numbers that appear in the loan relation with null values for amount. Answer: select loan_number from loan where amount is null Query: Find all loan numbers that appear in the loan relation that do not have null values for amount. Answer: select loan_number from loan where amount is not null

References Database System Concepts, 5 th edition, Silberschatz, Korth, Sudarshan