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Of course, if the salesperson's employee number is not needed in the query results, you can eliminate it entirely from the select list, giving: Calculate the total orders for each salesperson. SELECT NAME, SUM(AMOUNT) FROM ORDERS, SALESREPS WHERE REP = EMPL_NUM GROUP BY NAME NAME -------------Bill Adams Dan Roberts Larry Fitch Mary Jones Nancy Angelli Paul Cruz Sam Clark Sue Smith Tom Snyder SUM(AMOUNT) ----------$39,327.00 $26,628.00 $58,633.00 $7,105.00 $34,432.00 $2,700.00 $32,958.00 $22,776.00 $23,132.00
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A NULL value poses a special problem when it occurs in a grouping column. If the value of the column is unknown, which group should the row be placed into In the WHERE clause, when two different NULL values are compared, the result is NULL (not TRUE), that is, the two NULL values are not considered to be equal. Applying the same convention to the GROUP BY clause would force SQL to place each row with a NULL grouping column into a separate group by itself. In practice this rule proves too unwieldy. Instead, the ANSI/ISO SQL standard considers two NULL values to be equal for purposes of the GROUP BY clause. If two rows have NULLs in the same grouping columns and identical values in all of their non-NULL grouping columns, they are grouped together into the same row group. The small sample table in Figure 8-4 illustrates the ANSI/ISO handling of NULL values by the GROUP BY clause, as shown in this query:
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Figure 8-4: The PEOPLE table
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SELECT HAIR, EYES, COUNT(*) FROM PEOPLE GROUP BY HAIR, EYES HAIR ----Brown NULL NULL Brown Brown Brown EYES -----Blue Blue NULL NULL Brown Brown COUNT(*) -------1 2 2 3 2 2
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Although this behavior of NULLs in grouping columns is clearly specified in the ANSI/ISO standard, it is not implemented in all SQL dialects. It's a good idea to build a small test table and check the behavior of your DBMS brand before counting on a specific behavior.
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Group Search Conditions (HAVING Clause)
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Just as the WHERE clause can be used to select and reject the individual rows that participate in a query, the HAVING clause can be used to select and reject row groups. The format of the HAVING clause parallels that of the WHERE clause, consisting of the keyword HAVING followed by a search condition. The HAVING clause thus specifies a search condition for groups. An example provides the best way to understand the role of the HAVING clause. Consider this query: What is the average order size for each salesperson whose orders total more than $30,000 SELECT REP, AVG(AMOUNT) FROM ORDERS GROUP BY REP HAVING SUM(AMOUNT) > 30000.00
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REP AVG(AMOUNT) ---- ----------105 $7,865.40 106 $16,479.00 107 $11,477.33 108 $8,376.14 Figure 8-5 shows graphically how SQL carries out the query. The GROUP BY clause first arranges the orders into groups by salesperson. The HAVING clause then eliminates any group where the total of the orders in the group does not exceed $30,000. Finally, the SELECT clause calculates the average order size for each of the remaining groups and generates the query results.
Figure 8-5: A grouped search condition in operation
The search conditions you can specify in the HAVING clause are the same ones used in the WHERE clause, as described in s 6 and 9. Here is another example of the use of a group search condition: For each office with two or more people, compute the total quota and total sales for all salespeople who work in the office. SELECT CITY, SUM(QUOTA), SUM(SALESREPS.SALES) FROM OFFICES, SALESREPS WHERE OFFICE = REP_OFFICE GROUP BY CITY HAVING COUNT(*) >= 2 CITY -----------Chicago Los Angeles New York SUM(QUOTA) ----------$775,000.00 $700,000.00 $575,000.00 SUM(SALESREPS.SALES) -------------------$735,042.00 $835,915.00 $692,637.00
The following steps show the rules for SQL query processing, expanded once again to include group search conditions. To generate the query results for a SELECT statement: 1. If the statement is a UNION of SELECT statements, apply Steps 2 through 7 to each
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of the statements to generate their individual query results. 2. Form the product of the tables named in the FROM clause. If the FROM clause names a single table, the product is that table. 3. If there is a WHERE clause, apply its search condition to each row of the product table, retaining those rows for which the search condition is TRUE (and discarding those for which it is FALSE or NULL). 4. If there is a GROUP BY clause, arrange the remaining rows of the product table into row groups, so that the rows in each group have identical values in all of the grouping columns. 5. If there is a HAVING clause, apply its search condition to each row group, retaining those groups for which the search condition is TRUE (and discarding those for which it is FALSE or NULL). 6. For each remaining row (or row group), calculate the value of each item in the select list to produce a single row of query results. For a simple column reference, use the value of the column in the current row (or row group). For a column function, use the current row group as its argument if GROUP BY is specified; otherwise, use the entire set of rows. 7. If SELECT DISTINCT is specified, eliminate any duplicate rows of query results that were produced. 8. If the statement is a UNION of SELECT statements, merge the query results for the individual statements into a single table of query results. Eliminate duplicate rows unless UNION ALL is specified. 9. If there is an ORDER BY clause, sort the query results as specified. The rows generated by this procedure comprise the query results. Following the preceding steps, SQL handles this query as follows: 1. Joins the OFFICES and SALESREPS tables to find the city where each salesperson works. 2. Groups the resulting rows by office. 3. Eliminates groups with two or fewer rows these represent offices that don't meet the HAVING clause criterion. 4. Calculates the total quota and total sales for each group. Here is one more example, which uses all of the SELECT statement clauses: Show the price, quantity on hand, and total quantity on order for each product where the total quantity on order is more than 75 percent of the quantity on hand. SELECT DESCRIPTION, PRICE, QTY_ON_HAND, SUM(QTY) FROM PRODUCTS, ORDERS WHERE MFR = MFR_ID AND PRODUCT = PRODUCT_ID GROUP BY MFR_ID, PRODUCT_ID, DESCRIPTION, PRICE, QTY_ON_HAND
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HAVING SUM(QTY) > (.75 * QTY_ON_HAND) ORDER BY QTY_ON_HAND DESC DESCRIPTION PRICE ------------------ ------Reducer $355.00 Widget Adjuster $25.00 Motor Mount $243.00 Right Hinge $4,500.00 500-lb Brace $1,425.00 QTY_ON_HAND ----------38 37 15 12 5 SUM(QTY) -------32 30 16 15 22
To process this query, SQL conceptually performs the following steps: 1. Joins the ORDERS and PRODUCTS tables to find the description, price, and quantity on hand for each product ordered. 2. Groups the resulting rows by manufacturer and product I.D. 3. Eliminates groups where the quantity ordered (the total of the QTY column for all orders in the group) is less than 75 percent of the quantity on hand. 4. Calculates the total quantity ordered for each group. 5. Generates one summary row of query results for each group. 6. Sorts the query results so that products with the largest quantity on hand appear first. As described previously, DESCRIPTION, PRICE, and QTY_ON_HAND must be specified as grouping columns in this query solely because they appear in the select list. They actually contribute nothing to the grouping process, because the MFR_ID and PRODUCT_ID completely specify a single row of the PRODUCTS table, automatically making the other three columns single-valued per group.
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