Bao: Learning to Steer Query Optimizers Ryan Marcus12, Parimarjan Negi1, Hongzi Mao1, Nesime Tatbul12, Mohammad Alizadeh1, Tim Kraska1 1MIT CSAIL 2Intel Labs fryanmarcus, pnegi, hongzi, tatbul, alizadeh,
[email protected] ABSTRACT 60.2s PostgreSQL Query optimization remains one of the most challenging 60 PostgreSQL (no loop join) problems in data management systems. Recent efforts to apply machine learning techniques to query optimization 40 challenges have been promising, but have shown few prac- tical gains due to substantive training overhead, inability 21.2s to adapt to changes, and poor tail performance. Motivated 20 19.7s by these difficulties and drawing upon a long history of re- search in multi-armed bandits, we introduce Bao (the Bandit 0.4s optimizer). Bao takes advantage of the wisdom built into Query latency (seconds) 0 existing query optimizers by providing per-query optimiza- 16b 24b tion hints. Bao combines modern tree convolutional neu- JOB Query ral networks with Thompson sampling, a decades-old and well-studied reinforcement learning algorithm. As a result, Figure 1: Disabling the loop join operator in PostgreSQL Bao automatically learns from its mistakes and adapts to can improve (16b) or harm (24b) a particular query's per- changes in query workloads, data, and schema. Experimen- formance. These example queries are from the Join Order tally, we demonstrate that Bao can quickly (an order of mag- Benchmark (JOB) [30]. nitude faster than previous approaches) learn strategies that improve end-to-end query execution performance, including tail latency. In cloud environments, we show that Bao can 2. Brittleness. While performing expensive training oper- offer both reduced costs and better performance compared ations once may already be impractical, changes in query with a sophisticated commercial system.