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CHAPTER 8 BITMAP INDEXES
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Sum(index range scan * 1.4) blocks for 20% scattered rows blocks for 80% packed rows 41 * 1.4 * 2 + 80 + 5.7
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This is a lot closer to our target of 206, and well within the limits of that ever-present variation we get by changing the value of db_file_multiblock_read_count, so I m happy to leave the approximation there, and move on to the other bitmap transformation. The second bitmap transformation is something I ve only come across very recently (in fact, in a question on the AskTom web site, at http://asktom.oracle.com). Like many execution plans in Oracle, it was obvious after I saw it that I should have known that it could happen (a case of 20-20 hindsight ), it was just that I had never expected that it would happen. If Oracle can convert between bitmap entries and rowids, there is no technical reason why it shouldn t do so at any point in an execution plan, so the following execution plan is perfectly legal (see script bitmap_cost_08.sql in the online code suite): select d1, count(*) from t1 where and group by d1 ; Execution Plan (9.2.0.6) ---------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=48 Card=4 Bytes=44) 1 0 SORT (GROUP BY) (Cost=48 Card=4 Bytes=44) 2 1 VIEW OF 'index$_join$_001' (Cost=41 Card=801 Bytes=8811) 3 2 HASH JOIN 4 3 BITMAP CONVERSION (TO ROWIDS) 5 4 BITMAP INDEX (RANGE SCAN) OF 'T1_D1' 6 3 BITMAP CONVERSION (TO ROWIDS) 7 6 BITMAP INDEX (SINGLE VALUE) OF 'T1_N1' Note especially how we start with two bitmap indexes, acquire some leaf block data from each in turn, and then effectively turn the results into an in-memory B-tree index. Once we have two B-tree index sections, we can do an index hash join between them. This example, of course, is exactly the opposite to the previous example, where we started with B-tree indexes, acquired some leaf block data, and converted to in-memory bitmap indexes. As ever, we can put together the stuff we have learned so far to work out how this plan achieves its final cost. And in this case, the bit we are most interested in is the working that gets us to the result of the hash join (the index$_join$_001 view in line 2). n1 = 2 d1 between to_date('&m_today', 'DD-MON-YYYY') and to_date('&m_future','DD-MON-YYYY')
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CHAPTER 8 BITMAP INDEXES
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Whenever you have to work with problems like this, there are usually several different approaches you could take, and the easiest demonstration of what s going on here is to run the query with the 10053 trace event enabled. When you examine the trace file, you find that Oracle is simply working out the usual index access cost to get at the leaf blocks of the two indexes, with no special scaling factor, and no little extra add-on to allow for the bitmap conversion (to rowids). After that, the cost of the hash join is simply the normal costing for hash joins (which you will see in 12). An alternative strategy to determine that Oracle is simply using the normal index costing is to use dbms_stats.set_index_stats to adjust the index statistics. Add 10 to the blevel of one index, and the total cost of the query goes up by 10; adjust the number of leaf_blocks in an index by an amount that should add 10 to the index cost, and it adds 10 to the total cost of the query.
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Bitmap indexes lose information about data scattering, so the optimizer has to invent some numbers. As soon as the optimizer uses hard-coded constants in place of real information, it is inevitable that some of your queries will do the wrong thing. I am still not sure of the exact formulae used by Oracle for costing bitmap access there may even be bugs in the costing algorithms that make the costing unstable. I think the notes and approximations of this chapter should be sufficient to give you a reasonable idea of how the optimizer is going to behave, but there seems to be a surprising fudge factor that depends on the value of db_file_multiblock_read_count. When you move from traditional costing to CPU costing, you may see some execution plans change dramatically, and others stay largely the same but run more slowly because an extra bitmap index has been used (perhaps unnecessarily) to filter data out. When you combine bitmap indexes, the optimizer seems to report a cost based on the cost of just the cheapest relevant index instead of the cost of the indexes actually used. This has some odd side effects that may mean some queries do too much work because an inappropriate set of indexes has been picked. It is possible that the apparent bugs in the calculations are actually a deliberate design choice that is supposed to incur high numbers of logical I/Os against bitmap indexes to save on small numbers of physical I/Os against tables. In effect, the costing model may be assuming that you have your bitmap indexes in a large KEEP pool and the corresponding tables into a small RECYCLE pool. (Warning: this comment is highly speculative, so don t depend on it.) Keep a close eye on the patch list for any bugs relating to costing of bitmap indexes. Some fixes might have a serious impact on your databases performance.
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