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If you have been collecting frequency histograms, you may find that dbms_stats doesn t work very well for you when you decide that it s time to stop using the analyze command. The analyze command will build a frequency histogram on N distinct values if you specify N buckets. In 9i and 10.1, the equivalent dbms_stats call may need many more buckets specified before it notices the option for building a frequency histogram. In fact, somewhere around the 200 mark you may find that you can no longer create a frequency histogram because you can t specify a large enough bucket count. In cases like this, you may have to write your own SQL to collect suitable figures, and use the dbms_stats.set_column_stats() procedure to get those figures into the data dictionary. (This defect has been addressed in 10.2.)
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There are two options for CPU costing (system statistics) in 10g. Unless you are already familiar with the use of system statistics in 9i, you will find that 10g will force your system into CPU costing using some special noworkload statistics. This will affect the cost of tablescans significantly but the scale of the change is different from the changes caused by the normal CPU costing algorithm used by 9i. In particular, you will also notice a greater variation in costs if you have code that modifies the db_file_multiblock_read_count for different processes. If you decide to start gathering workload system statistics in 10g, the effects could be quite surprising when compared with noworkload system statistics. Moreover, the dbms_stats.delete_system_stats() procedure doesn t seem to work, so the only way to get rid of the gathered statistics if you decide you don t want them is to do an explicit delete from the sys.aux_stats$ table. (This is fixed in 10.2 so you do have a legal way of getting back to noworkload system statistics after experimenting with gathered system statistics.)
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The way in which the optimizer deals with rounding varies from version to version. Some results are rounded properly, some are truncated, and for some values the ceiling is used. This can lead to odd changes in estimated costs and cardinalities which could result in changes in execution plan as you upgrade. Most significantly, perhaps, as you upgrade from 8i to 9i, there are cases where 8i rounds intermediate results, but 9i only rounds at the end of the entire calculation. This can make a big difference to the final result. One particular point to watch out for with rounding: the parameter optimizer_index_ cost_adj can be used to scale down the cost of index-driven single-block reads, but this increases the relative impact of rounding errors. If you are running 9i, you should be using system statistics (CPU costing) to deal with any imbalance between tablescans and indexed access paths. CPU costing scales up the cost of multiblock reads, which means it reduces the impact of rounding errors. If you still have to upgrade from 8i, the change in rounding strategy may cause you particular problems if you have systems that store multiple sets of reference data in a single table with a type column. If you do run into this problem, re-creating the table as a list-partitioned table should help.
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Bind variables always cause confusion. A bounded range using bind variables is given a selectivity of 0.0025 (0.25% or 1 in 400), which introduces a bias towards indexed access. An unbounded range using bind variables is given a selectivity of 0.05 (5% or 1 in 20), which tends to introduce a bias towards tablescans. An equality using a bind variable uses the value of user_tab_columns.density. In all three cases, histograms are ignored and the resources used to build them will have been wasted. (The density may have been modified as a histogram was created, so the effort isn t entirely pointless.) As of 9i, the optimizer usually peeks at the actual values whenever it has to optimize a statement using bind variables, and uses the actual values to evaluate an execution path. So when you upgrade from 8i to 9i, you may find some SQL statements change their execution
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