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CHAPTER 6 SELECTIVITY ISSUES
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Caution My experiments with the opt_estimate hint showed the effects changing across different
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versions of 10.1. Do not try using it as a hint on a production system until it is documented for public use.
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Transitive Closure
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One of the problems you can have with the optimizer is that the code is sometimes too clever. When an execution plan seems unreasonable and you are busy trying to work out where the selectivity went wrong, remember that there may be some predicates floating around that you didn t write and can t see (unless you make proper use of the latest version of explain plan). The optimizer may have used a mechanism known as transitive closure to generate a few predicates that will only show up if you use a proper tool to display the full execution plan. Transitive closure works by logical inference. Assume you have two predicates (see script trans_close_01.sql in the online code suite): and n1 = 100 n2 = n1
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then the optimizer will be able to create the predicate n2 = 100 and include that in its calculations. But there is a catch as it introduces the predicate with the constant, the optimizer may be allowed to eliminate the predicate without the constant, so that the final where clause looks like the following: and n1 = 100 n2 = 100
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This can be useful in some cases, but it can have some strange side effects. Consider this example (script trans_close_02.sql in the online code suite): create table t1 as select mod(rownum,10) mod(rownum,10) to_char(rownum) rpad('x',100) from all_objects where rownum <= 1000 ;
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n1, n2, small_vc, padding
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CHAPTER 6 SELECTIVITY ISSUES
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create table t2 as select * from t1; -select count(*) from t1, t2 where and ; t1.n1 = 5 t2.n1 = t1.n1 Collect statistics using dbms_stats here
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The definitions of t1 and t2 are identical. They both have 1,000 rows, and the column n1 is defined to hold 100 copies each of the values 1 to 10. When we run the query, we should join 100 rows in t1 to 100 rows in t2 for an output count of 10,000 rows. This is the execution plan we get from 9.2.0.6 using dbms_xplan.display(): --------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost | --------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | 6 | 404 | | 1 | SORT AGGREGATE | | 1 | 6 | | | 2 | MERGE JOIN CARTESIAN| | 10000 | 60000 | 404 | |* 3 | TABLE ACCESS FULL | T1 | 100 | 300 | 4 | | 4 | BUFFER SORT | | 100 | 300 | 400 | |* 5 | TABLE ACCESS FULL | T2 | 100 | 300 | 4 | --------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - filter("T1"."N1"=5) 5 - filter("T2"."N1"=5) The cardinality is correct, but look at the cost. Notice the merge join Cartesian that appears in line 2, and the way that the buffer sort in line 4 has acquired a cost of 400 (which looks remarkably like the cost of the t2 tablescan 100 times once for each row in t1). Checking the predicate information, you find no predicate joining the two tables the join predicate disappears as the second constant predicate is created. But if you can interfere with the closure algorithm in a suitable way, the plan changes to the following (note how the cost seems reasonable, but the cardinality [with heading Rows ] is now much too low):
CHAPTER 6 SELECTIVITY ISSUES
-------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost | -------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | 6 | 9 | | 1 | SORT AGGREGATE | | 1 | 6 | | |* 2 | HASH JOIN | | 1000 | 6000 | 9 | |* 3 | TABLE ACCESS FULL | T1 | 100 | 300 | 4 | |* 4 | TABLE ACCESS FULL | T2 | 100 | 300 | 4 | -------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - access("T2"."N1"="T1"."N1") 3 - filter("T1"."N1"=5) 4 - filter("T2"."N1"=5) There are two ways to achieve this effect: Add the predicate t2.n1 = 5 explicitly. Add a duplicate of the predicate t2.n1 = t1.n1. A third (undesirable) option is the good old rule-based trick changing the join predicate to t2.n1 = t1.n1 + 0. Unfortunately, when you adopt this worst-practice approach, the optimizer gets the right join cardinality (10,000) and a sensible cost (9). So be a little careful if you find joins that are producing strange plans and cardinalities; you may have to doctor them (and document the hacks in anticipation of the next upgrade) to con the optimizer into treating them sensibly. Of course, it is easiest to consider transitive closure with equality signs all around, but the optimizer can be cleverer than that. In the more general case, the optimizer is able to infer the following: if n1 operator constant and n2 = n1, then n2 operator constant. For example: if n1 < 10 and n2 = n1, then n2 < 10. Similarly, if col1 > col2 and col2 > {constant K}, then Oracle can infer the predicate col1 > {constant K}. You can even have cases like this one: if n1 > 10 and n1 < 0, then 0 > 10, which is always false, and therefore can short-circuit an entire branch of an execution plan. The predicates involved can even be ones that have been generated from constraint definitions. (See script trans_close_03.sql in the online code suite for some examples.) The investigation of transitive closure involving the more generalized predicates (such as n1 < 10) for the nonjoin highlights a nasty little anomaly. The mechanism is not self-consistent. Consider Table 6-8.
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