论文标题
缓冲池意识到查询调度通过深度加固学习
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning
论文作者
论文摘要
在这个扩展的摘要中，我们提出了一种用于查询调度的新技术，其明确的目标是减少磁盘读取并隐含地提高查询性能。我们介绍了Smartqueue，这是一种学习的调度程序，利用重叠的数据在传入的查询中读取，并学习了改善缓存命中的调度策略。 Smartqueue依靠深度强化学习来生成针对长期绩效效益的特定工作负载的调度策略，同时适应以前未见的数据访问模式。我们提供了概念验证原型的结果，表明学习的调度程序可以在手工制作的计划启发式方面提供显着的性能改进。最终，我们认为这是机器学习与数据库交集的有希望的研究方向。
In this extended abstract, we propose a new technique for query scheduling with the explicit goal of reducing disk reads and thus implicitly increasing query performance. We introduce SmartQueue, a learned scheduler that leverages overlapping data reads among incoming queries and learns a scheduling strategy that improves cache hits. SmartQueue relies on deep reinforcement learning to produce workload-specific scheduling strategies that focus on long-term performance benefits while being adaptive to previously-unseen data access patterns. We present results from a proof-of-concept prototype, demonstrating that learned schedulers can offer significant performance improvements over hand-crafted scheduling heuristics. Ultimately, we make the case that this is a promising research direction at the intersection of machine learning and databases.
