How Good is My HTAP System?
How Good is My HTAP System?
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我的 HTAP 系统有多好？
DOI：
10.1145/3514221.3526148
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Yu, Xiangyao
中科院分区：
文献类型：
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作者：
Milkai, Elena;Chronis, Yannis;Gaffney, Kevin P.;Guo, Zhihan;Patel, Jignesh M.;Yu, Xiangyao
混合事务处理和分析处理（HTAP）系统最近得到了普及，因为它们结合了联合收割机OLAP和OLTP处理，以减少专用系统之间的管理和同步成本。然而，没有精确的特征，区分一个好的HTAP系统从一个穷人。在本文中，我们试图从性能和新鲜度的角度来解决这个问题。为了同时捕获事务处理和分析处理的性能，我们引入了一个新的概念，称为吞吐量前沿，它在一个二维图中可视化事务和分析吞吐量。吞吐量边界可以捕获关于每个引擎的性能、两个引擎之间的干扰以及各种系统设计决策的信息。为了捕获HTAP系统支持实时分析的程度，我们定义了一个新鲜度度量，该度量量化了每个分析查询所看到的数据快照的最近程度。我们还开发了一种实用的方法来衡量新鲜度在一个真实的系统。我们设计了一个新的混合基准称为HATtrick，它结合了吞吐量前沿和新鲜度作为指标。使用基准测试，我们评估了三个代表HTAP系统在不同的数据大小和系统配置，并展示了如何揭示重要的系统特性和性能信息的指标。
Hybrid Transactional and Analytical Processing (HTAP) systems have recently gained popularity as they combine OLAP and OLTP processing to reduce administrative and synchronization costs between dedicated systems. However, there is no precise characterization of the features that distinguish a good HTAP system from a poor one. In this paper, we seek to solve this problem from the perspectives of both performance and freshness. To simultaneously capture the performance of both transactional and analytical processing, we introduce a new concept called throughput frontier, which visualizes both transactional and analytical throughput in a single 2D graph. The throughput frontier can capture information regarding the performance of each engine, the interference between the two engines, and various system design decisions. To capture how well an HTAP system supports real-time analytics, we define a freshness metric which quantifies how recent is the snapshot of the data seen by each analytical query. We also develop a practical way to measure freshness in a real system. We design a new hybrid benchmark called HATtrick which incorporates both throughput frontier and freshness as metrics. Using the benchmark, we evaluate three representative HTAP systems under various data size and system configurations and demonstrate how the metrics reveal important system characteristics and performance information.
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DOI：
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发表时间：
2017
期刊：
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影响因子：
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作者：
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DOI：
10.1145/3318464.3389764
发表时间：
2020-05
期刊：
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
影响因子：
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DOI：
10.1109/icde.2001.914833
发表时间：
2001
期刊：
Proceedings 17th International Conference on Data Engineering
影响因子：
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作者：
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DOI：
10.1109/icde.2011.5767867
发表时间：
2011
期刊：
2011 IEEE 27th International Conference on Data Engineering
影响因子：
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作者：
A. Kemper;T. Neumann
通讯作者：
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DOI：
10.1145/2882903.2882958
发表时间：
2015-12
期刊：
Proceedings of the 2016 International Conference on Management of Data
影响因子：
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