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ORDERS Table ORDER_NUM ORDER_DATE 112961 113012 112989 2 17-DEC-89 11-JAN-90 03-JAN-90
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1 QUERY Results ORDER_NUM 112961 AMOUNT $31,500.00
2 COMPANY J.P. Sinclair CREDIT_LIMIT $35,000.00
FIGURE 7-2
Manually processing a multitable query
Of course this isn t the only way to generate the query results, but regardless of how you do it, two things will be true: Each row of query results draws its data from a specific pair of rows, one from the ORDERS table and one from the CUSTOMERS table. The pairs of rows are found by matching the data values in corresponding columns from the tables.
Simple Joins (Equi-Joins)
The process of forming pairs of rows by matching the contents of related columns is called joining the tables. The resulting table (containing data from both of the original tables) is called a join between the two tables. (A join based on an exact match between two columns is more precisely called an equi-join. Joins can also be based on other kinds of column comparisons, as described later in this chapter.)
Part II:
Retrieving Data
Joins are the foundation of multitable query processing in SQL. All of the data in a relational database is stored in its columns as explicit data values, so all possible relationships between tables can be formed by matching the contents of related columns. Joins thus provide a powerful facility for exercising the data relationships in a database. In fact, because relational databases do not contain pointers or other mechanisms for relating rows to one another, joins are the only mechanism for exercising cross-table data relationships. Because SQL handles multitable queries by matching columns, it should come as no surprise that the SELECT statement for a multitable query contains a search condition that specifies the column match. Here is a SELECT statement for the query that was performed manually in Figure 7-2: List all orders showing order number, amount, customer name ( company ), and the customer s credit limit.
SELECT ORDER_NUM, AMOUNT, COMPANY, CREDIT_LIMIT FROM ORDERS, CUSTOMERS WHERE CUST = CUST_NUM; ORDER_NUM AMOUNT COMPANY CREDIT_LIMIT ---------- ----------- ------------------ ------------112989 $1,458.00 Jones Mfg. $65,000.00 112968 $3,978.00 First Corp. $65,000.00 112963 $3,276.00 Acme Mfg. $50,000.00 112987 $27,500.00 Acme Mfg. $50,000.00 112983 $702.00 Acme Mfg. $50,000.00 113027 $4,104.00 Acme Mfg. $50,000.00 112993 $1,896.00 Fred Lewis Corp. $65,000.00 113065 $2,130.00 Fred Lewis Corp. $65,000.00 113036 $22,500.00 Ace International $35,000.00 113034 $632.00 Ace International $35,000.00 113058 $1,480.00 Holm & Landis $55,000.00 113055 $150.00 Holm & Landis $55,000.00 113003 $5,625.00 Holm & Landis $55,000.00 . . .
Recall that different SQL tools format results in different ways, so your results may vary, particularly for the dollar amounts shown in the example. This query looks just like the queries from the previous chapter, with two new features. First, the FROM clause lists two tables instead of just one. Second, the search condition
CUST = CUST_NUM
compares columns from two different tables. We call these two columns the matching columns for the two tables. Like all search conditions, this one restricts the rows that appear in the query results. Because this is a two-table query, the search condition restricts the pairs of rows that generate the query results. In fact, the search condition specifies the same matching columns you used in the paper-and-pencil query processing. It actually captures the spirit of the manual column matching very well, saying:
7:
Multitable Queries (Joins)
Generate query results only for pairs of rows where the customer number (CUST) in the ORDERS table matches the customer number (CUST_NUM) in the CUSTOMERS table. Notice that the SELECT statement doesn t say anything about how the DBMS should execute the query. There is no mention of starting with orders or starting with customers. Instead, the query tells the DBMS what the query results should look like and leaves it up to the DBMS to decide how to generate them.
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