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When you get to 9i, you have the option of enabling system statistics, and this makes a difference to execution plans. We examined how the new costing mechanisms work in 2,
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but we can now consider the impact the feature can have even on very simple index-based execution plans. As far as indexes are concerned, the arithmetic is basically unchanged. As we saw in the earlier chapter, the critical features of CPU costing are the time component for multiblock I/Os and the CPU utilization. But typical indexed access is done in single-block I/Os so the time component is irrelevant, and the CPU utilization walking an index to locate and acquire a few rows from a table is usually relatively small. The big difference that appears when you enable CPU costing is that tablescans suddenly become more expensive. I ll demonstrate this with an example. To start, here s some code to create some system statistics (the code was specifically designed for 9i, but is valid on 10g, where there are a couple of other statistics): alter session set "_optimizer_cost_model" = cpu; begin dbms_stats.set_system_stats('CPUSPEED',350); dbms_stats.set_system_stats('MREADTIM',20); dbms_stats.set_system_stats('MBRC',8); dbms_stats.set_system_stats('SREADTIM',10); end; /
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-- MHz -- millisec -- millisec
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In this code fragment, we are telling the optimizer to assume that our CPU speed is 350MHz (or probably 350 million Oracle operations per second), that a multiblock I/O will take 20 milliseconds and will typically be fetching 8 blocks, whereas a single block I/O takes 10 milliseconds. The deliberate setting of the optimizer cost model to use CPU costing is to standardize the effect between 9i and 10g. The default value for this parameter is choose, and 9i will choose to use CPU costing only if the system statistics exist, but 10g will always choose to use CPU costing and then synthesize some statistics if there aren t any in place (and I can t get the delete_ system_stats call to drop the system statistics in 10g). If we then run the following query against our base data set, we can see the effects of CPU costing (see script btree_cost_05.sql in the online code suite): select small_vc from t1 where and and ; n1 = 2 ind_pad = rpad('x',40) n2 in (5,6,7)
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In my test script, I ran the query with an index() hint, a full() hint, and unhinted. The costs behaved as shown in Table 4-2.
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Table 4-2. Effects of System Statistics
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With index() hint With full() hint Path when not hinted
Original Cost
68 58 Full tablescan
Cost with System Statistics 9i / (10g)
69 (68) 96 (95) Index iterator
In the absence of the system statistics, the optimizer estimated that the index-based path would visit 68 blocks individually for a total of 68 I/O requests, compared to 1 + ( 371 / 6.59 ) for the tablescan. (Remember that for tablescans and index fast full scans, and ignoring ASSM tablespaces, the optimizer divides the high water mark by an adjusted value of the db_file_multiblock_read_count.) When we enable system statistics, the optimizer decides that the cost for the indexed path is still 68 for the I/O component, plus a small extra cost to cater to the CPU cost of acquiring blocks, and making a few data comparisons.
SYSTEM STATISTICS VS. OPTIMIZER_INDEX_COST_ADJ
One of the most important features of system statistics (or CPU costing) is that it allows the optimizer to balance the costs of single-block and multiblock reads properly by scaling up the cost of a multiblock read. As an early fix to this imbalance, the parameter optimizer_index_cost_adj appeared in 8i. In effect, this parameter appeared to be a percentage that would be used to scale down the cost of single block reads compared to multiblock reads. (See Tim Gorman s paper The Search for Intelligent Life in the Cost Based Optimizer at www.evdbt.com for the first useful paper published about this parameter.) The flaw built into this fix was that it reduced the cost of single block reads, rather than increasing the cost of multiblock reads. On the plus side, this did tend to stop the optimizer from using excessive tablescans. On the minus side, the rounding built into the calculations could cause the optimizer to switch from a good index to a bad index, because their costs became identical after adjustment and rounding. When you scale down, rounding errors become more significant.
The cost for the tablescan path changes more dramatically when we switch to CPU costing. To work out the I/O cost component, we divide the high water mark by the actual stored MBRC, and round up: ceiling(371/8) = 47. Then we double it, because our mreadtim is twice our sreadtim, to get 94. We then add a little for the amount of CPU that will be used examining every row of the table. The upshot of this is that if your system statistics really do represent the typical behavior of your system, then the cost based optimizer can produce a cost that is a more realistic reflection of actual resource usage and time to completion before it starts running the query.
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