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Schema Statistics Management

Many infrastructure issues must be addressed in order to avoid surprises with SQL optimization. Shops that do not create this infrastructure are plagued by constantly changing SQL execution plans and poor database performance.


It is very rare for the fundamental nature of a schema to change.  Large tables remain large, and index columns rarely change distribution, cardinality, and skew. The DBA should only consider periodically re-analyzing the total schema statistics if the database matches the following criteria:

§       CPU-intensive databases: Many scientific systems load a small set of experimental data, analyze the data, produce reports, and then truncate and reload a new set of experiments. There are also Oracle databases with super large data buffer caches, with reduce physical I/O at the expense of higher CPU consumption.  For these types of systems, it may be necessary to re-analyze the schema each time the database is reloaded.

§       Highly volatile databases: In these rare cases, the size of tables and the characteristics of index column data changes radically. For example, Laboratory Information Management Systems  (LIMS) load, analyze, and purge experimental data so frequently that it is very difficult to always have optimal CBO statistics.  If a database has a table that has 100 rows one week and 10,000 rows the next week, the DBA may want to consider using Oracle10g dynamic sampling or a periodic reanalysis of statistics.


The following section will show how Oracle SQL optimization can be adjusted to evaluate I/O costs of CPU costs.



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