Adaptive Query Processing in the Looking Glass,
Authors
Abstract
A great deal of work on adaptive query pro-cessing has been done over the last few years:
Adaptive query processing has been used to de-
tect and correct optimizer errors due to incor-
rect 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 process-
ing does away with the optimizer altogether. De-
spite this large body of interrelated work, no uni-
fying 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 routing-
based), 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 adap-
tive 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 re-
optimization; and (2) Plan logging, where op-
timizer decisions under different conditions are
logged over time, enabling plan reuse as well as
analysis of relevant statistics and benefits of adap-
tivity.
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