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CHAPTER 10 JOIN CARDINALITY
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Intermediate to T3: the table with 150 rows: Join Selectivity = (120 - 10) / 120) * -- the CBO uses the t2 figures at one end (150 - 10) / 150) / -- of the join, and the t3 at the other. greater(11, 14) = 0.0611111 Join Cardinality = 0.061111 * 900 * 150 = 8,250 But the new-style 9i calculations look like this: T1 to T2: the tables with 100 and 120 rows respectively: Join Selectivity = 1 / greater(9, 11) = 0.09090909 Join Cardinality = 0.09090909 * 90 * 110 = 900 Intermediate to T3: the table with 150 rows: Join Selectivity = 1 / greater(11, 14) = 0.0714285 Join Cardinality = 0.0714285 * 900 * 140 = 9,000
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Note, with 9i, how we have factored the effects of the is not null predicates into the join cardinality line as the filtered cardinality of each of the individual tables. This gives us a more appropriate answer than the 8i strategy, which has factored the null count of n1 into the calculations twice, once in the first join and then again in the second join, and so produced a final cardinality that is too low. Of course, as you upgrade from 8i to 9i, queries involving multiple tables joining along the same nullable columns may suddenly change their execution plans because the computed cardinality goes up. If the computed cardinality has increased for one step of a query, the optimizer may decide that the next step should be a hash or merge join rather than a nested loop join. I can t think of a rationale why this change only applies when the null count exceeds 5% of the row count, as it seems to be reasonable to invoke the rule across the board. But perhaps the limit has been imposed to reduce the number of queries that might change path on upgrade.
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CHAPTER 10 JOIN CARDINALITY
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There are many ways to implement Oracle systems badly, and as a general rule, anything that hides useful information from the optimizer is a bad idea. One of the simple, and highly popular, strategies for doing this is to stick all your reference data into a single table with a type column. The results can be catastrophic as far as the optimizer is concerned. Unless you are very lucky, the optimizer will calculate ridiculously inappropriate cardinalities for most simple joins to this reference table. For example (see script type_demo.sql in the online code suite): create table t1 as with generator as ( select --+ materialize rownum id from all_objects where rownum <= 3000 ) select trunc(dbms_random.value(0,20)) trunc(dbms_random.value(0,25)) rownum lpad(rownum,10,'0') from generator v1, generator v2 where rownum <= 500000 ;
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class1_code, class2_code, id, small_vc
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We create a table of 500,000 rows that requires two lookups to expand meaningless code numbers into recognizable descriptions. I have restricted the test case to just a pair of reference sets with similar numbers of entries in each set to avoid some of the oddities that I mention in the next section, and to keep the arithmetic straightforward. Consider the following query: select t1.small_vc, type1.description from t1, type1 where and and and ; t1.id type1.id type1.type type1.description between 1000 and 1999 = t1.class1_code = 'CURRENCY' = 'GBP'
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CHAPTER 10 JOIN CARDINALITY
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All this does is select some rows from my large table, and join to a reference table to translate a code into a description to eliminate data based on that description. This is typical of the way in which an end-user query would have to make use of the type table. (The SQL in the test case in the download does not use the literal values 'CURRENCY' and 'GBP', but I thought that a couple of meaningful code samples would help make the point of the example more clearly.) So what does the execution plan look like It depends on what you ve done with your reference tables. Here s one option for the reference table the currency data (or 'CLASS1' as it really was) is stored in a table all by itself: create table type1 as select rownum-1 'CLASS1' lpad(rownum-1,10,'0') from all_objects where rownum <= 20 ;
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