DB Paper Reading 这周继续来营业啦！我们希望通过对业界学术论文的分享，带大家了解数据库学术界最新的研究方向。8 月 3 日晚 皇家墨尔本理工博士一年级的兰海将为大家解读 Steering Query Optimizers: A Practical Take on Big Data Workloads（在大数据负载下指导查询优化器）。该论文将 Bao 思想应用到 Scope 优化器中，并解决多个在 Cascade 优化器中遇到的新挑战。感兴趣的朋友不要错过，赶紧报名约起来~
时间：2021 年 8 月 3 日晚 19:00-20:00
Steering Query Optimizers: A Practical Take on Big Data Workloads
皇家墨尔本理工博士一年级，喜欢数据库大部分内容，特别是优化器和索引。
Content：
本次主要想和大家分享学习方法如何应用到实际数据库优化器中。
In recent years, there has been tremendous interest in research that applies machine learning to database systems. Being one of the most complex components of a DBMS, query optimizers could benefit from adaptive policies that are learned systematically from the data and the query workload. Recent research has brought up novel ideas towards a learned query optimizer, however these ideas have not been evaluated on a commercial query processor or on large scale, real-world workloads. In this paper, we take the approach used by Marcus et al. in Bao and adapt it to SCOPE, a big data system used internally at Microsoft. Along the way, we solve multiple new challenges: we define how optimizer rules affect final query plans by introducing the concept of a rule signature, we devise a pipeline computing interesting rule configurations for recurring jobs, and we define a new learning problem allowing us to apply such interesting rule configurations to previously unseen jobs. We evaluate the efficacy of the approach on production workloads that include 150K daily jobs. Our results show that alternative rule configurations can generate plans with lower costs, and this can translate to runtime latency savings of 7-30% on average and up to 90% for a non-trivial subset of the workload.
💡 更多 DB Paper reading 敬请期待～
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KVSSD: Close integration of LSM trees and flash translation layer for write-efficient KV store
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