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You can now issue the following SQL statement that makes use of the bind variable you just created: SQL> SELECT ename FROM scottemp WHERE empid = :bindvar; ENAME JAMES You can execute this statement multiple times with different values for the bind variable The statement is parsed only once and executes many times Unlike when you use a literal value for the emp_id column (7499, for example), Oracle reuses the execution plan it created the first time, instead of creating a separate execution plan for each such statement This cuts hard parsing (and high latch activity) and the attendant CPU usage drastically, and dramatically reduces the time taken to retrieve data For example, all the following statements can use the parsed version of the query that uses the bind variable: SELECT ename FROM scottemp WHERE empid = 7499; SELECT ename FROM scott.

emp WHERE empid = 7788; SELECT ename FROM scottemp WHERE empid = 7902; Unfortunately, in too many applications, literal values rather than bind values are used You can alleviate this problem to some extent by setting up the following initialization parameter: CURSOR_SHARING=FORCE Or you could use the following parameter: CURSOR_SHARING=SIMILAR By default, the CURSOR_SHARING initialization parameter is set to EXACT, meaning that only statements that are identical in all respects will be shared among different executions of the statement Either of the alternative values for the CURSOR_SHARING parameter, FORCE or SIMILAR, ensures Oracle will reuse statements even if they aren t identical in all respects For example, if two statements are identical in all respects and differ only in literal values for some variables, using CURSOR SHARING=FORCE will enable Oracle to reuse the parsed SQL statements in its library cache.

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Package body created. I must point out that the preceding performance benchmark suffers from the drawback that, of necessity, I had to choose specific scenarios for updates, deletes, and selects, among other things. In particular, for comparing updates, I ran my tests on the case where I update all records of rows in the child table, which may not represent reality in general. Also, the nested tables based approach can be improved by storing the nested tables as an index-organized table as mentioned in an earlier note. Having said that, the conclusions drawn from the benchmark should more or less hold true. I used the runstats utility to measure the differences between the elapsed time for inserting 50 parents, each having 500 child records, for each of the three cases compared. To conserve space, I do not show the actual program runs, but that should not be an issue since I give the details of the package I used to run the various comparisons. Table 8-1 summarizes the elapsed times and latches for various scenarios that I observed in my runs. For each case I show in bold and within parentheses, the values normalized to the lowest value of the elapsed time or the latches for a given DML. For example, inserts in the nested tables based approach took 2.86 times the elapsed time and consumed 16.20 times the latches as compared to the relational approach.

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