Asymptotically efficient adaptive control in stochastic regression models
Advances in Applied Mathematics
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Block Oriented Processing of Relational Database Operations in Modern Computer Architectures
Proceedings of the 17th International Conference on Data Engineering
Selection conditions in main memory
ACM Transactions on Database Systems (TODS)
Improving hash join performance through prefetching
ACM Transactions on Database Systems (TODS)
Foundations and Trends in Databases
Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Vectorization vs. compilation in query execution
Proceedings of the Seventh International Workshop on Data Management on New Hardware
Multi-armed bandit algorithms and empirical evaluation
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Performance of query processing functions in a DBMS can be affected by many factors, including the hardware platform, data distributions, predicate parameters, compilation method, algorithmic variations and the interactions between these. Given that there are often different function implementations possible, there is a latent performance diversity which represents both a threat to performance robustness if ignored (as is usual now) and an opportunity to increase the performance if one would be able to use the best performing implementation in each situation. Micro Adaptivity, proposed here, is a framework that keeps many alternative function implementations (flavors) in a system. It uses a learning algorithm to choose the most promising flavor potentially at each function call, guided by the actual costs observed so far. We argue that Micro Adaptivity both increases performance robustness, and saves development time spent in finding and tuning heuristics and cost model thresholds in query optimization. In this paper, we (i) characterize a number of factors that cause performance diversity between primitive flavors, (ii) describe an e-greedy learning algorithm that casts the flavor selection into a multi-armed bandit problem, and (iii) describe the software framework for Micro Adaptivity that we implemented in the Vectorwise system. We provide micro-benchmarks, and an overall evaluation on TPC-H, showing consistent improvements.