Adaptive query processing in the looking glass
A great deal of work on adaptive query processing has been done over the last few years: Adaptive query processing has been used to detect and correct optimizer errors due to incorrect statistics or simplified cost metrics; it has been applied to long-running continuous queries over data streams whose characteristics vary over time; and routing-based adaptive query processing does away with the optimizer altogether. Despite this large body of interrelated work, no unifying comparison of adaptive query processing techniques or systems has been attempted; we tackle this problem. We identify three families of systems (plan-based, CQ-based, and routingbased), and compare them in detail with respect to the most important aspects of adaptive query processing: plan quality, statistics monitoring and re-optimization, plan migration, and scalability. We also suggest two new approaches to adaptive query processing that address some of the shortcomings revealed by our in-depth analysis: (1) Proactive re-optimization, where the optimizer chooses query plans with the expectation of reoptimization; and (2) Plan logging, where optimizer decisions under different conditions are logged over time, enabling plan reuse as well as analysis of relevant statistics and benefits of adaptivity.
