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Elsewhere in this book, I have said that there are cases where the optimizer uses 1/num_rows when the selectivity derived from multiple predicates falls below a critical limit. This may be true some of the time, but as you can see from the outer(0.5) in the 10g trace extract, sometimes the optimizer will use a selectivity that is smaller than 1/num_rows. It is possible that my earlier assumption is actually wrong, and only appears to be true because of variations in the rounding strategy. It is possible that what I have observed is the optimizer rounding an intermediate result up to 1 giving the impression that there was a lower limit of 1/num_rows on a computed selectivity.

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Of course, when things go wrong because of odd data distributions, like that type column, we can always fall back on generating histograms we hope. Clearly the problem has appeared because we have two types in the same table, so let s create a cumulative frequency histogram on the type column and see what happens: Execution Plan (9.2.0.6 autotrace) ---------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=287 Card=22 Bytes=858) 1 0 HASH JOIN (Cost=287 Card=22 Bytes=858) 2 1 TABLE ACCESS (FULL) OF 'TYPE2' (Cost=2 Card=1 Bytes=20) 3 1 TABLE ACCESS (FULL) OF 'T1' (Cost=284 Card=1001 Bytes=19019) Would you believe it, the result gets worse. The optimizer has recognized that there is an uneven distribution on the type column, and knows that there are 20 rows with type = 'CLASS1' and 25 rows with type = 'CLASS2', but it still has no information to tell it that the description we are supplying belongs in 'CLASS1', and therefore should be treated as one of 20 rather than one of 45. (In effect, because we have amalgamated several sets of data into a single set with a type column, we have caught ourselves in the trap of dependent data columns described in 6 in the section Correlated Columns. ) In the test case I used for this chapter, the calculations are only out by a factor of two but I engineered the test data by combining just two sets of reference data that were both about the same size. If you combine several sets of reference data, and the number of rows in the sets vary significantly, then the error can be huge.

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Consider, for example, how bad the error could be when one reference data set has 3 entries, and another has 6,000 with an average number of rows per data set in the region of a couple of hundred. The cardinality of a simple join could easily be out by a factor of 100 or more. If you find yourself in this trap, there is a simple (though possibly expensive) solution. Recreate the table as a list partitioned table, partitioning on the type column. If you do this, and if every query against the table references the type column as a literal value watch out for cursor_sharing then you have effectively turned the single table into one table per data set as the optimizer will use the partition level statistics to do its arithmetic. The expense, of course, comes from the license fee for the partitioning option.

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