FOSS: A Self-Learned Doctor for Query Optimizer
- URL: http://arxiv.org/abs/2312.06357v1
- Date: Mon, 11 Dec 2023 13:05:51 GMT
- Title: FOSS: A Self-Learned Doctor for Query Optimizer
- Authors: Kai Zhong and Luming Sun and Tao Ji and Cuiping Li and Hong Chen
- Abstract summary: Deep reinforcement learning (DRL) can be used to address the query optimization problem in database system.
We introduce FOSS, a novel DRL-based framework for query optimization.
We show that FOSS outperforms the state-of-the-art methods in terms of latency performance and optimization time.
- Score: 20.54782053709538
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Various works have utilized deep reinforcement learning (DRL) to address the
query optimization problem in database system. They either learn to construct
plans from scratch in a bottom-up manner or guide the plan generation behavior
of traditional optimizer using hints. While these methods have achieved some
success, they face challenges in either low training efficiency or limited plan
search space. To address these challenges, we introduce FOSS, a novel DRL-based
framework for query optimization. FOSS initiates optimization from the original
plan generated by a traditional optimizer and incrementally refines suboptimal
nodes of the plan through a sequence of actions. Additionally, we devise an
asymmetric advantage model to evaluate the advantage between two plans. We
integrate it with a traditional optimizer to form a simulated environment.
Leveraging this simulated environment, FOSS can bootstrap itself to rapidly
generate a large amount of high-quality simulated experiences. FOSS then learns
and improves its optimization capability from these simulated experiences. We
evaluate the performance of FOSS on Join Order Benchmark, TPC-DS, and Stack
Overflow. The experimental results demonstrate that FOSS outperforms the
state-of-the-art methods in terms of latency performance and optimization time.
Compared to PostgreSQL, FOSS achieves savings ranging from 15% to 83% in total
latency across different benchmarks.
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