PREDATOR: a resource for database research
ACM SIGMOD Record
Efficient mid-query re-optimization of sub-optimal query execution plans
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Robust query processing through progressive optimization
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
StreaMon: an adaptive engine for stream query processing
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Dynamic plan generation for parameterized queries
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
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Traditional query optimizers rely on the accuracy of estimated statistics of intermediate subexpressions to choose good query execution plans. This design often leads to suboptimal plan choices for complex queries since errors in estimates grow exponentially in the presence of skewed and correlated data distributions. We propose to demonstrate the Rio prototype database system that uses proactive re-optimization to address the problems with traditional optimizers. Rio supports three new techniques:1. Intervals of uncertainty are considered around estimates of statistics during plan enumeration and costing2. These intervals are used to pick execution plans that are robust to deviations of actual values of statistics from estimated values, or to defer the choice of execution plan until the uncertainty in estimates can be resolved3. Statistics of intermediate subexpressions are collected quickly, accurately, and efficiently during query executionThese three features are fully functional in the current Rio prototype which is built using the Predator open-source DBMS [5]. In this proposal, we first describe the novel features of Rio, then we use an example query to illustrate the main aspects of our demonstration.