SQL Select Statement

Prof. Yitzchak Rosenthal

Syntax for SELECT statement

• Clauses must be written in the following order– SELECT– FROM– WHERE– GROUP BY– HAVING– ORDER BY

Order that the DBMS processes a SELECT statement

• Order that the DBMS processes a SELECT statement– Step 1: FROM

– Step 2: WHERE

– Step 3: GROUP BY

– Step 4: HAVING

– Step 5: SELECT (this must be "writtten" first)

– Step 6: ORDER BY

• At each step the DBMS keeps track of the "interim result set" which is then further refined by the next step

• Keep reading for more info ...

Step 1: FROM clause• Step 1: FROM clause

– This step is processed slightly differently depending on whether the SQL 92 syntax is used or the pre-SQL 92 syntax is used

– SQL 92 Syntax (i.e. JOIN/ON in FROM clause)• generate cartesian product (ie "cross join") of (1) the first table and (2) the

table in the first JOIN clause• filter out records from cartesian product that don't match the first ON

clause• generate cartesian product of (1) the results so far and (2) the table in the

next JOIN clause• filter out records from cartesian product that don't match the associated

"ON" clause• keep proceeding in this way until all tables are joined

– Older Syntax (i.e. no JOIN/ON in FROM clause)• generate cartesian product (ie "cross join") of all the tables• (the filtering will hapen later when the "WHERE" clause is processed)

Step 2: WHERE clause

• Step 2: WHERE clause– Filter out records from the "interim result set" generated

in Step 1 by filtering out (i.e. "throwing out") records that don't match the conditions in the WHERE clause

Each record in the interim result set is looked at separately and the results of the WHERE clause is calculated.

If the result of the WHERE clause for that row is TRUE then the row is kept. If the result of the WHERE clause for that row is FALSE then the row is "thrown away".

Step 3: GROUP BY

• Step 3: GROUP BY– Create separate groups of rows that match in all of the

values listed in the GROUP BY list.

There may be a single group for all records in the interim result set or there may be many groups.

There is ALWAYS at least one group.

Step 4: HAVING

• Step 4: HAVING– Filter out all groups that don't match the conditions in

the HAVING clause

Step 5: SELECT

• Step 5: SELECT– Figure out what values will actually be included in the

final result set by processing the SELECT clause

Step 6: ORDER BY

• Step 6: ORDER BY– Sort the result set in the order specified in the ORDER

BY clause

WHERE vs HAVING

WHERE vs. HAVING• Similarities:

– The WHERE and HAVING clauses are both used to exclude records from the result set.

• Differences– WHERE clause

• The WHERE clause is processed before the groups are created• Therefore, the WHERE clause can refer to any value in the original tables

– HAVING clause• The HAVING clause is processed after the groups are created • Therefore, the HAVING clause can only refer to aggregate information for

the group (including fields that are part of the GROUP BY clause).• The HAVING clause CANNOT refer to individual columns from a table

that are not also part of the group.

Example

Example: Table

• The example on the following slides will use the following table:

Table: Invoices

VendorId PaymentTotal InvoiceTotal

001 5 20

001 10 20

001 15 1500

002 10 10

002 20 4000

002 30 4000

Example of HAVING clause without a WHERE clause

HAVING clause but NO WHERE clause

• In the following SELECT statement:– There is a HAVING clause but no WHERE clause

– The GROUP BY clause works to group several rows from the original table together to get aggregate information about the group.

– The HAVING clause eliminates some of the resulting rows of aggregate information.

• SELECT vendorId, avg(PaymentTotal) as avgPaymentTotalFROM invoicesGROUP BY vendorIdHAVING avg(PaymentTotal) <=10ORDER BY avgPaymentTotal

Processing the select without WHERE

Table: Invoices    

VendorId PaymentTotal InvoiceTotal

001 5 20

001 10 20

001 15 1500

002 10 10

002 20 4000

002 30 4000

avgPaymentTotal

group2

Step 1: Create the groups based on the GROUP BYStep 2: Generate the aggregate information (e.g. avg) for each group.

group1

Interim result set

Processing the select without WHERE

Table: Invoices    

VendorId PaymentTotal InvoiceTotal

001 5 20

001 10 20

001 15 1500

002 10 10

002 20 4000

002 30 4000

avgPaymentTotal

group1

group2

Step 3: Remove records from the result set based on the HAVING clause

Final Results

Adding a WHERE clause to the example

Same select statement with WHERE• We will now examine what happens when we add a WHERE clause

to the same SELECT statement we used above.

• SELECT vendorId, avg(PaymentTotal) as avgPaymentTotalFROM invoicesWHERE invoiceTotal < 1000GROUP BY vendorIdHAVING avg(PaymentTotal) <=10ORDER BY avgPaymentTotal

Processing the select with where

TablenName: Invoices    

VendorId PaymentTotal InvoiceTotal

001 5 20

001 10 20

001 15 1500

002 10 10

002 20 4000

002 30 4000

Step 1: Process WHERE clause to eliminate some rows from consideration

Processing the select with where

TablenName: Invoices    

VendorId PaymentTotal InvoiceTotal

001 5 20

001 10 20

001 15 1500

002 10 10

002 20 4000

002 30 4000

Step 2: Process the GROUP BY to create groups from the remaining rows.

avgPaymentTotal

group1

group2

Interim result set

Processing the select with where

TablenName: Invoices    

VendorId PaymentTotal InvoiceTotal

001 5 20

001 10 20

001 15 1500

002 10 10

002 20 4000

002 30 4000

Step 3: Process the HAVING clause to possibly remove some rows from the result set (in this example no rows need to be removed)

avgPaymentTotal

group1

group2

Final results