Compactionary: A Dictionary for LSM Compactions
Compactionary: A Dictionary for LSM Compactions
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Compactionary：LSM 压缩字典
DOI：
10.1145/3514221.3520169
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Athanassoulis, Manos
中科院分区：
文献类型：
--
作者：
Sarkar, Subhadeep;Chen, Kaijie;Zhu, Zichen;Athanassoulis, Manos
日志结构合并（LSM）树被广泛用作现代NoSQL数据存储的存储层，因为它们提供了高效的摄取性能。为了实现有竞争力的读取性能并减少空间放大，LSM树通过压缩迭代地重新组织磁盘上的数据布局。压缩是每个基于LSM的存储引擎的核心，从根本上影响其性能。然而，LSM引擎中的压缩过程通常被视为一个黑匣子，很少被暴露为一个调谐旋钮。在本文中，我们展示了Compactionary，一个用于LSM压缩的字典，它有助于可视化压缩对不同工作负载和LSM调优性能的影响。Compactionary打破了LSM压缩黑盒，将压缩表示为四个一阶设计选择的集合：（i）何时压缩，（ii）压缩后如何组织数据，（iii）压缩多少数据，以及（iv）压缩哪些数据。我们配置Compactionary来演示几种最先进的LSM压缩策略的操作流程以及每种策略如何影响性能。参与者可以（i）自定义工作负载，（ii）配置LSM调优，以及（iii）在高级压缩选项之间切换，以单独了解不同因素对性能的影响。此外，为了吸引感兴趣的参与者，我们通过允许参与者创建自定义混合压缩策略来扩展演示，以及在单个分析阶段为每个策略单独配置设置。该演示可在https://disc-projects.bu.edu/compactionary/#interactiveDemo上获得。
Log-structured merge (LSM) trees are widely used as the storage layer of modern NoSQL data stores, as they offer efficient ingestion performance. To enable competitive read performance and reduce space amplification, LSM-trees re-organize data layout on disk iteratively, through compactions. Compactions are at the heart of every LSM-based storage engine, fundamentally influencing their performance. However, the process of compaction in LSM-engines is often treated as a black-box that is rarely exposed as a tuning knob. In this paper, we demonstrate Compactionary, a dictionary for LSM compactions, that helps to visualize the implications of compactions on performance for different workloads and LSM tunings. Compactionary breaks down the LSM compaction black-box, expressing compactions as an ensemble of four first-order design choices: (i) when to compact, (ii) how to organize the data after compaction, (iii) how much data to compact, and (iv) which data to compact. We configure Compactionary to demonstrate the operational flow of several state-of-the-art LSM compaction strategies and how each strategy affects performance. The participants can (i) customize the workload, (ii) configure the LSM tuning, and (iii) switch between advanced compaction options, to understand individually the impact of the different factors on performance. Further, to engage the interested participants, we extend the demonstration by allowing the participants to create custom hybrid compaction strategies, as well as to configure the settings separately for each strategy in an individual analysis phase. The demo is available at https://disc-projects.bu.edu/compactionary/#interactiveDemo.
DOI：
10.1145/3514221.3522563
发表时间：
2022-06
期刊：
Proceedings of the 2022 International Conference on Management of Data
影响因子：
--
作者：
Subhadeep Sarkar;Manos Athanassoulis
通讯作者：
Subhadeep Sarkar;Manos Athanassoulis
DOI：
10.1145/3318464.3389757
发表时间：
2020-05
期刊：
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
影响因子：
--
作者：
Subhadeep Sarkar;Tarikul Islam Papon;Dimitris Staratzis;Manos Athanassoulis
通讯作者：
Subhadeep Sarkar;Tarikul Islam Papon;Dimitris Staratzis;Manos Athanassoulis