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New article on statistics and the Oracle SQL optimizer

Glen Godrum, and independent Oracle consultant in Houston has published an excellent DBAZine article on how to influence the cost-based SQL optimizer:


In this article Goodrum demonstrates how to alter the schema statistics (with dbms_stats) to change the execution plans for critical SQL and he also notes the importance of the clustering_factor metric:

To make the index statistics match reality for this query we need to change three things:

·         The number of distinct keys needs to match the number of distinct values that we used for the column statistics earlier. Again, this would be the number of rows in the index (500,000) divided by the expected average number of rows per cycle (25), or 20,000.

·         The number of leaf blocks per key should be the number of leaf blocks in the index divided by the number of keys.

·         The number of data blocks per key should be the index cluster factor divided by the number of distinct keys. This is probably the most important, since it tells the optimizer how many table blocks must be fetched for each key value in the index.

Godrum also concludes that the only safe way to test the actual performance of any statistics change is to test it in a full production environment, and not with a small artificial “proof”:


The only way to know for sure whether a given change will help or not is to try it in a test database containing production-sized tables.  


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