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.
翻译:在这个漫长的抽象中,我们提出了一个新的查询时间安排技术,其明确目标是减少磁盘读数,从而间接提高查询性能。我们引入了SmartQuue,这是一个学习的定时器,它利用了在收到的查询中重复的数据,并学习了改进缓存点击率的时间安排战略。SmartQuue依靠深入的强化学习,以产生注重长期业绩效益、同时适应以往不见数据访问模式的具体工作量时间安排战略。我们介绍了一个验证概念原型的结果,表明学习的定时器可以大大改进手动排时法的性能。最终,我们证明这是机器学习和数据库交汇处的一个有希望的研究方向。