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.
Actian CWI Research Grant
ACM SIGMOD International Conference on Management of Data
Database Architectures

Raducanu, B., Boncz, P., & Zukowski, M. (2013). Micro Adaptivity in Vectorwise.