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CHAPTER 10 JOIN CARDINALITY
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histogram, I am not changing the data itself. The last column in the table shows the effect that the choice of bucket count had on our test query when the overlap between the two tables was fixed at 50%. The results are actually taken from the 8i tests, but similar (slightly less dramatic) results also came out of the 9i and 10g tests.
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Table 10-4. Picking the Right Bucket Count Is a Matter of Luck
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Computed Cardinality at 50% Overlap
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367,218 1,000,000 348,960 355,752 1,000,000 346,040 1,000,000 336,838
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The problem with depending on histograms to get correct join cardinalities is that you dare not regenerate the histograms in case the data has just changed enough to hide all popular values for the number of columns you have selected. On the other hand, you dare not leave the histograms unchanged if the data keeps changing in a way that moves the high (or low) values, as the size of the overlap is the thing that you are trying to capture with the histogram.
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Note It is an interesting point that the optimizer can use the density from user_tab_columns instead of 1/num_distinct when applying filter predicates but doesn t seem to do so when applying join predicates (at least not directly; it is possible that the join selectivity is derived from the density in some way that I haven t yet worked out).
It s a difficult problem to address essentially it comes down to knowing your data, recognizing the few critical cases, and tailoring a specific mechanism to each of them.
Transitive Closure
It doesn t matter how much you know, there are always new examples that need further investigation. What happens if your query includes an extra predicate against the join columns, such as the following example
CHAPTER 10 JOIN CARDINALITY
select t1.v1, t2.v1 from t1, t2 where and and ; t1.join1 = 20 t2.join1 = t1.join1 t2.join2 = t1.join2 -- 30 distinct values -- 40 / 30 distinct values -- 40 / 50 distinct values
This is the data set that we used in join_card_04.sql, but the actual test script is join_card_07.sql in the online code suite. The comments show the number of distinct values in each column, in the order that the column names appear on the line. The original execution plan (i.e., without the constant predicate t1.join1 = 20) looked like this: Execution Plan (9.2.0.6 autotrace) ---------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=60 Card=50000 Bytes=1700000) 1 0 HASH JOIN (Cost=60 Card=50000 Bytes=1700000) 2 1 TABLE ACCESS (FULL) OF 'T1' (Cost=28 Card=10000 Bytes=170000) 3 1 TABLE ACCESS (FULL) OF 'T2' (Cost=28 Card=10000 Bytes=170000) With the extra predicate in place, we now have a plan like this: Execution Plan (9.2.0.6 autotrace) ---------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=57 Card=1667 Bytes=56678) 1 0 HASH JOIN (Cost=57 Card=1667 Bytes=56678) 2 1 TABLE ACCESS (FULL) OF 'T2' (Cost=28 Card=250 Bytes=4250) 3 1 TABLE ACCESS (FULL) OF 'T1' (Cost=28 Card=333 Bytes=5661) How has the optimizer managed to produce such a dramatic difference in the cardinalities all the way through the plan (They are the right values, by the way.) The trick is revealed when you look at a more detailed execution plan (from the dbms_xplan package, for example). -------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost | -------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1667 | 56678 | 57 | |* 1 | HASH JOIN | | 1667 | 56678 | 57 | |* 2 | TABLE ACCESS FULL | T2 | 250 | 4250 | 28 | |* 3 | TABLE ACCESS FULL | T1 | 333 | 5661 | 28 | -------------------------------------------------------------------Predicate Information (identified by operation id):
CHAPTER 10 JOIN CARDINALITY
1 - access("T2"."JOIN2"="T1"."JOIN2") 2 - filter("T2"."JOIN1"=20) 3 - filter("T1"."JOIN1"=20) Note particularly the second line of Predicate Information. There is a predicate, t2.join1 = 20 ..., but we didn t have that one in the original SQL, the optimizer derived it using a mechanism known as transitive closure. You will also note that there is no longer a predicate t2.join1 = t1.join1; in the process of transitive closure, this predicate became redundant and was thrown away. Transitive closure means the optimizer is allowed to infer the following: If colB = colA and colA = {constant X} then colB = {constant X} In our case, we have t2.join1 = t1.join1 and t1.join1 = 20 so t2.join1 = 20 and the join condition can be discarded. So we have changed our SQL from a two-column join with one filter predicate to a singlecolumn join with two filter predicates. So let s reprise the standard form of the basic formula, and put the numbers in remembering that in our example, both the join columns are called join2, and both the filtering columns are called join1: Join Selectivity = ((num_rows(t1) - num_nulls(t1.c1)) / num_rows(t1)) * ((num_rows(t2) - num_nulls(t2.c2)) / num_rows(t2)) / greater(num_distinct(t1.c1), num_distinct(t2.c2)) Join Cardinality = join selectivity * filtered cardinality(t1) * filtered cardinality(t2) Join Selectivity = ((10000 - 0)/ 10000) * ((10000 - 0)/ 10000) / greater(40, 50) = 1/50
-- t1.join2, t2.join2 (num_distinct)
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