Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning
Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning
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演示 UDO：通过强化学习优化交易代码、物理设计和系统参数的统一方法
DOI：
10.1145/3448016.3452754
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Basu, Debabrota
中科院分区：
文献类型：
--
作者：
Wang, Junxiong;Trummer, Immanuel;Basu, Debabrota
UDO是一个多功能的工具，用于针对特定工作负载离线调优数据库系统。UDO可以考虑各种调优选择，从选择事务代码变量到索引选择，一直到数据库系统参数调优。UDO使用强化学习来收敛到接近最优的配置，通过实际的查询执行来创建和评估不同的配置（而不是依赖于简化的成本模型）。为了适应不同的参数类型，UDO将重参数（更改成本高，例如物理设计参数）与轻参数区分开来。特别是为了优化重参数，UDO使用强化学习算法，允许延迟奖励反馈可用的时间点。这使我们能够自由地优化时间点以及创建和评估不同配置的顺序（通过对工作负载样本进行基准测试）。UDO使用基于成本的计划器来最小化配置切换开销。例如，它旨在通过连续评估使用它们的配置来分摊昂贵数据结构的创建。我们在Postgres和MySQL以及TPC-H和TPC-C上演示了UDO，同时优化了各种轻型和重型参数。
UDO is a versatile tool for offline tuning of database systems for specific workloads. UDO can consider a variety of tuning choices, reaching from picking transaction code variants over index selections up to database system parameter tuning. UDO uses reinforcement learning to converge to near-optimal configurations, creating and evaluating different configurations via actual query executions (instead of relying on simplifying cost models). To cater to different parameter types, UDO distinguishes heavy parameters (which are expensive to change, e.g. physical design parameters) from light parameters. Specifically for optimizing heavy parameters, UDO uses reinforcement learning algorithms that allow delaying the point at which reward feedback becomes available. This gives us the freedom to optimize the point in time and the order in which different configurations are created and evaluated (by benchmarking a workload sample). UDO uses a cost-based planner to minimize configuration switching overheads. For instance, it aims to amortize the creation of expensive data structures by consecutively evaluating configurations using them. We demonstrate UDO on Postgres as well as MySQL and on TPC-H as well as TPC-C, optimizing a variety of light and heavy parameters concurrently.
数据库的索引选择：硬度研究和原则启发式解决方案
影响因子：
8.9
作者：
S. Chaudhuri;Mayur Datar;Vivek R. Narasayya
通讯作者：
Vivek R. Narasayya