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CHAPTER 8 BITMAP INDEXES
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In this example, there are exactly 1,000 rows where column n1 is not null, and it has exactly 1,000 distinct values, and Oracle can see this in the statistics collected by the gather_table_stats() call. Column n2 is defined to be identical to column n1. In the first query, the optimizer will determine that n1 has a density of 1/1,000, with a total of 1,000 non-null rows, and decide that the cardinality will be 1. If you changed n1 to n2 in this query, the calculated cardinality would be the same. But when you run the query with the predicate where n1 = 50000 or n2 = 50000
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the computed cardinality is 1,999. Oracle seems to have applied the density to the total number of rows in the table, and not allowed for the nulls. So (using the standard formula for an and of two predicates) we have 1/1,000 of the million rows accepted for the n1 predicate, plus 1/1,000 of the million rows accepted for the n2 predicate; minus one row in the overlap. Add one is not null predicate, and the computed cardinality drops to 1,000; add the second is not null predicate, and the cardinality drops to the (correct) value of 1.
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Any discussion of bitmap index costing would be incomplete without some notes on what happens when you invoke CPU costing. Given that CPU costing is a strategic feature, it s a good idea to be prepared for the day when you have to make the change. And if you had to guess, you might assume that CPU costing will have more impact on execution plans using bitmap indexes (and the B-tree to bitmap conversion) because of the work that must be done to convert between bits and rowids. We ll use the same system statistics as we did in the 4 (see script bitmap_cost_05.sql in the online code suite): alter session set "_optimizer_cost_model" = cpu; begin dbms_stats.set_system_stats('MBRC',8); dbms_stats.set_system_stats('MREADTIM',20); dbms_stats.set_system_stats('SREADTIM',10); dbms_stats.set_system_stats('CPUSPEED',350); end; / alter system flush shared_pool; With these figures in place, we can run a couple of the queries that we used earlier on in the chapter against our original data set, and see how things change. Here, for example, are the execution plans against column n4 (clustered data, 25 different values, bitmap index) and n6 (clustered data, 25 different values, B-tree index), with their original cost, and with CPU costing all plans are from 9.2.0.6:
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CHAPTER 8 BITMAP INDEXES
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Execution Plan - bitmap index on clustered column ---------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=114 Card=400 Bytes=5600) 1 0 TABLE ACCESS (BY INDEX ROWID) OF 'T1' (Cost=114 Card=400 Bytes=5600) 2 1 BITMAP CONVERSION (TO ROWIDS) 3 2 BITMAP INDEX (SINGLE VALUE) OF 'T1_I4' Execution Plan - bitmap index on scattered column with CPU costing -----------------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=128 Card=400 Bytes=5600) 1 0 TABLE ACCESS (BY INDEX ROWID) OF 'T1' (Cost=128 Card=400 Bytes=5600) 2 1 BITMAP CONVERSION (TO ROWIDS) 3 2 BITMAP INDEX (SINGLE VALUE) OF 'T1_I4' Execution Plan - B-tree index on clustered column ---------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=54 Card=400 Bytes=5600) 1 0 TABLE ACCESS (BY INDEX ROWID) OF 'T1' (Cost=54 Card=400 Bytes=5600) 2 1 INDEX (RANGE SCAN) OF 'T1_I6' (NON-UNIQUE) (Cost=9 Card=400) Execution Plan - B-tree index on clustered column with CPU costing -----------------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=55 Card=400 Bytes=5600) 1 0 TABLE ACCESS (BY INDEX ROWID) OF 'T1' (Cost=55 Card=400 Bytes=5600) 2 1 INDEX (RANGE SCAN) OF 'T1_I6' (NON-UNIQUE) (Cost=10 Card=400) It looks as if our prediction is correct. The cost of the bitmap query has gone up by 14, the cost of the B-tree query has gone up by just 1. Of course, we know that the B-tree query cost has been calculated on the basis of visiting about 45 table blocks, compared to the bitmap query cost that is based on visiting about 100 table blocks but what Oracle actually does by the time it gets to the table is the same irrespective of whether it got there from a B-tree or a bitmap index. So most of the difference in cost must come from the bitmap somehow ... or so we assume! And this is where (a) autotrace really shows up its deficiencies, and (b) we get a nasty surprise. If we do a proper explain plan, we get separate io_cost and cpu_cost columns (we could also look at the 10053 trace for even finer detail). The B-tree cost of 55 comes from io_cost = 54, cpu_cost component = 1 (cpu_cost = 577,401). The bitmap cost of 128 comes from io_cost = 127, cpu_cost component = 1 (cpu_cost = 1,063,986). Two things stand out in these figures: first, the actual CPU cost has roughly doubled, which corresponds roughly to the factor of 2 in the estimated number of I/Os (from 54 to 114) the work involved in acquiring the right to access a buffered block is CPU-intensive. But the CPU required to handle the bitmap expansion is apparently not considered to be dramatically different from the CPU required to binary chop through an index block.
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