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Implementations can vary with regard to how they support the ROLLUP and CUBE operators. For example, in SQL Server, you must add WITH ROLLUP or WITH CUBE to the end of the GROUP BY clause, rather than defining the clause in the way the SQL:2006 standard specifies. Be sure to check your product documentation to determine how these operators are supported.
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Now when you execute the SELECT statement, the query results include an additional row for each value in the CATEGORY column, plus a grand total row at the end:
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CATEGORY -----------Instrumental Instrumental Instrumental Instrumental Vocal Vocal Vocal Vocal NULL PRICE ----14.99 15.99 16.99 NULL 14.99 15.99 16.99 NULL NULL TOTAL_ON_HAND ------------5 23 50 78 99 73 45 217 295
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The two additional CATEGORY rows provide totals for each value in the CATEGORY column. In the preceding example, the Instrumental group includes a total of 78 CDs, and the Vocal group includes a total of 217 CDs. Notice that the PRICE column includes a null
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value for these particular rows. A value cannot be calculated for this column because all three subgroups (from the PRICE column) are represented here. The last row (the one with NULL for both the CATEGORY and PRICE columns) contains a grand total of all CDs counted by the query (both category groups and all three price subgroups). The CUBE operator returns the same data as the ROLLUP operator, and then some. Notice that, in the following SELECT statement, I ve merely replaced the CUBE keyword for ROLLUP:
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SELECT CATEGORY, PRICE, SUM(ON_HAND) AS TOTAL_ON_HAND FROM COMPACT_DISC_STOCK GROUP BY CUBE (CATEGORY, PRICE);
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This statement returns the following query results:
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CATEGORY -----------Instrumental Instrumental Instrumental Instrumental Vocal Vocal Vocal Vocal NULL NULL NULL NULL PRICE ----14.99 15.99 16.99 NULL 14.99 15.99 16.99 NULL NULL 14.99 15.99 16.99 TOTAL_ON_HAND ------------5 23 50 78 99 73 45 217 295 104 96 95
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You can see that three additional rows have been added to the query results, one row for each different value in the PRICE column. Unlike the ROLLUP operator, the CUBE operator summarizes the values for each subgroup. Also notice that a null value is shown for the CATEGORY column for these rows. This is because both Vocal and Instrumental values are included in each subgroup summary. As you can see, the GROUP BY clause can be a valuable tool when trying to summarize data, particularly when you make use of the many functions available in SQL, such as SUM and AVG. In 10, I discuss these and many other functions that you can use to make your SELECT statement more robust and applicable to your needs.
Use the HAVING Clause to Specify Group Search Conditions
The HAVING clause is similar to the WHERE clause in that it defines a search condition. However, unlike the WHERE clause, the HAVING clause is concerned with groups, not individual rows:
If a GROUP BY clause is specified, the HAVING clause is applied to the groups created by the GROUP BY clause.
7:
Querying SQL Data
If a WHERE clause is specified and no GROUP BY clause is specified, the HAVING clause is applied to the output of the WHERE clause and that output is treated as one group. If no WHERE clause and no GROUP BY clause are specified, the HAVING clause is applied to the output of the FROM clause and that output is treated as one group.
The best way to understand the HAVING clause is to remember that the clauses in a SELECT statement are processed in a definite order. A WHERE clause can receive input only from a FROM clause, but a HAVING clause can receive input from a GROUP BY, WHERE, or FROM clause. This is a subtle, yet important, distinction, and the best way to illustrate it is to look at a couple of examples. In the first example, which is based on the COMPACT_DISC_STOCK table in Figure 7-4, I use a WHERE clause to specify that the query results should include only rows whose ON_HAND value is less than 20, as shown in the following SELECT statement:
SELECT FROM WHERE GROUP CATEGORY, AVG(PRICE) AS AVG_PRICE COMPACT_DISC_STOCK ON_HAND < 20 BY CATEGORY;
The statement returns two columns: CATEGORY and AVG_PRICE, which is the average of all prices for each category. The averages include only those rows where ON_HAND values are less than 20. If you executed this statement, the results would look similar to the following:
CATEGORY -----------Instrumental Vocal AVG_PRICE --------15.656666 15.990000
As you would expect, the query result returns two rows one for the Instrumental group and one for the Vocal group. If you were to use the HAVING clause, rather than the WHERE clause, to limit values to less than 20, you might use the following SELECT statement:
SELECT FROM GROUP HAVING CATEGORY, AVG(PRICE) AS AVG_PRICE COMPACT_DISC_STOCK BY CATEGORY ON_HAND < 20;
However, if you were to try to execute this statement, you would receive an error because you cannot apply individual ON_HAND values to the groups. For a column to be included in the HAVING clause, it must be a grouped column or it must be summarized in some way. Now let s take a look at another example that uses the HAVING clause. In this case, the clause includes a summarized column:
SELECT FROM GROUP HAVING PRICE, CATEGORY, SUM(ON_HAND) AS TOTAL_ON_HAND COMPACT_DISC_STOCK BY PRICE, CATEGORY SUM(ON_HAND) > 10;
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