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CHAPTER 9 QUERY TRANSFORMATION
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every worker earned more than the average wage). Since we have 20,000 rows in our emp table, the optimizer s estimate of 1,000 rows in 8i and 9i is out by a factor of 10, and the estimate in 10g is out by a factor of 60. That s a problem with generic code, of course; the optimizer only sees a subquery, it doesn t understand the particularly special effect of the average function that happens to be embedded in that subquery.
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So we find from our experiments with filtering that 8i hasn t got a clue about costs, 9i uses a circular argument for costing, and 10g knows what s going on in terms of cost but does something about the cardinality that is most inappropriate for the specific example. We will return to subqueries later in this chapter. The critical point I wanted to make with this particular example was that the area of query transformation shows a great deal of evolutionary change as you work through different versions of the optimizer.
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With the evolution of the optimizer, Oracle is increasingly likely to eliminate subqueries by using various transformations, perhaps making the filter operation we saw earlier nearly obsolete. Even so, there are cases where subqueries cannot, or perhaps should not, be transformed away, so it is worth discussing their treatment.
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THE MEANING OF FILTER
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The name filter is applied to many different types of operation: for example, table elimination in partitioned views, evaluation of the having clause in aggregate queries, and the evaluation of correlated subqueries shown in this chapter. Until the filter_predicates column appeared in the 9i plan_table, it was sometimes difficult to decide exactly what a filter line in an execution plan was supposed to mean. Make sure you stay up to date when it comes to knowing what s in the plan_table. Sometimes a new version gives you extra information in new columns.
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Consider the following SQL, taken from the script push_subq.sql in the online code suite: select /*+ push_subq */ par.small_vc1, chi.small_vc1 from parent child where and and par.id1 between 100 and 200 chi.id1 = par.id1 exists ( par, chi
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CHAPTER 9 QUERY TRANSFORMATION
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select /*+ no_unnest */ null subtest sub sub.small_vc1 = par.small_vc1 sub.id1 = par.id1 sub.small_vc2 >= '2'
from where and and ) ;
This joins two tables of a parent/child relationship the data has been set up so that each parent row has eight related child rows. The query uses a subquery based on values found in the parent to eliminate some of the data. (The no_unnest hint in the extract is there to make 9i and 10g reproduce the default behavior of 8i.) When the optimizer can t fold a subquery into the main body of a query, the test performed by the subquery occurs at a very late stage of execution. In this example, the subquery would normally take place after the join to the child table and (because of the way I have designed the example) there will be lots of unnecessary joins to child rows from parent rows that should have been discarded before the join took place, and the workload will be higher than necessary. (It is also possible that in more general cases, the subquery could execute much too often, but this may not occur because of the special filter optimization described later on in this chapter.) In cases like this, you may want to override the default behavior. To do this, you can use the push_subq hint, as I have done in the example, to force the optimizer to generate an execution plan that applies the subquery at the earliest possibly moment. With autotrace enabled to produce both the execution plan and the execution statistics, we can see how the plan and statistics change as we introduce the hint (I ve only reported the one statistic that actually changed): Execution Plan (8.1.7.4 WITHOUT push_subq hint - subquery postponed) --------------------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=22 Card=7 Bytes=126) 1 0 FILTER 2 1 NESTED LOOPS (Cost=22 Card=7 Bytes=126) 3 2 TABLE ACCESS (BY INDEX ROWID) OF 'PARENT' (Cost=4 Card=6 Bytes=54) 4 3 INDEX (RANGE SCAN) OF 'PAR_PK' (UNIQUE) (Cost=2 Card=6) 5 2 TABLE ACCESS (BY INDEX ROWID) OF 'CHILD' (Cost=3 Card=817 Bytes=7353) 6 5 INDEX (RANGE SCAN) OF 'CHI_PK' (UNIQUE) (Cost=2 Card=817) 7 1 TABLE ACCESS (BY INDEX ROWID) OF 'SUBTEST' (Cost=2 Card=1 Bytes=14) 8 7 INDEX (UNIQUE SCAN) OF 'SUB_PK' (UNIQUE) (Cost=1 Card=1)
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