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LEO - DB2's LEarning Optimizer

LEO - DB2's LEarning Optimizer

陈傲天
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By monitoring previously executed queries, LEO compares the optimizer’s estimates with actuals at each step in a QEP, and computes adjustments to cost estimates and statistics that may be used during future query optimizations. This analysis can be done either on-line or off-line on a separate system, and either incrementally or in batches. In this way, LEO introduces a feedback loop to query optimization that enhances the available information on the database where the most queries have occurred, allowing the optimizer to actually learn from its past mistakes.
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