AI Meets AI

Bailu Ding, Sudipto Das, Ryan Marcus, Wentao Wu, Surajit Chaudhuri, Vivek R. Narasayya
2019 Proceedings of the 2019 International Conference on Management of Data - SIGMOD '19  
State-of-the-art index tuners rely on query optimizer's cost estimates to search for the index configuration with the largest estimated execution cost improvement. Due to wellknown limitations in optimizer's estimates, in a significant fraction of cases, an index estimated to improve a query's execution cost, e.g., CPU time, makes that worse when implemented. Such errors are a major impediment for automated indexing in production systems. We observe that comparing the execution cost of two
more » ... of the same query corresponding to different index configurations is a key step during index tuning. Instead of using optimizer's estimates for such comparison, our key insight is that formulating it as a classification task in machine learning results in significantly higher accuracy. We present a study of the design space for this classification problem. We further show how to integrate this classifier into the stateof-the-art index tuners with minimal modifications, i.e., how artificial intelligence (AI) can benefit automated indexing (AI). Our evaluation using industry-standard benchmarks and a large number of real customer workloads demonstrates up to 5× reduction in the errors in identifying the cheaper plan in a pair, which eliminates almost all query execution cost regressions when the model is used in index tuning. * Ryan Marcus performed the work while at Microsoft Research.
doi:10.1145/3299869.3324957 dblp:conf/sigmod/DingDM0CN19 fatcat:kaho47sbtbc6tc57c6j3edwwzi