计算机科学

面向高冲突事务处理的架构设计和优化

  • 连薛超 ,
  • 刘维 ,
  • 王清帅 ,
  • 张蓉
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  • 1. 华东师范大学 数据科学与工程学院, 上海 200062
    2. 工业和信息化部电子第五研究所, 广州 511300

收稿日期: 2022-07-17

  网络出版日期: 2023-11-23

基金资助

国家自然科学基金(62072179); 2021 CCF-华为数据库创新研究计划; 基础软硬件性能与可靠性测评工业和信息化部重点实验室开放课题

Design and optimization of high-contention transaction processing architecture

  • Xuechao LIAN ,
  • Wei LIU ,
  • Qingshuai WANG ,
  • Rong ZHANG
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  • 1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
    2. The Fifth Electronics Research Institute of the Ministry of Industry and Information Technology, Guangzhou 511300, China

Received date: 2022-07-17

  Online published: 2023-11-23

摘要

无共享架构的分布式数据库是为了应对互联网业务的高可扩展性和高可用性而诞生的. 目前无共享架构的分布式数据库虽然已经有了长足的进展, 但是对于一些采用无状态计算层的无共享架构分布式数据库而言, 往往存在着冲突检测链路过长的问题, 这一问题在高冲突的场景下会更加突出. 针对这一问题, 设计了预先加锁和本地缓存两个策略, 并开发了配套的高冲突检测模块, 可以实现快速检测冲突并启动对应的高冲突处理策略. 实验表明, 对高冲突事务处理的架构设计和优化可以提高分布式数据库系统在高冲突负载下的性能.

本文引用格式

连薛超 , 刘维 , 王清帅 , 张蓉 . 面向高冲突事务处理的架构设计和优化[J]. 华东师范大学学报(自然科学版), 2023 , 2023(6) : 28 -38 . DOI: 10.3969/j.issn.1000-5641.2023.06.003

Abstract

Shared-nothing distributed databases are designed for the high scalability and high availability request of Internet-based applications. There have been significant achievements in shared-nothing distributed databases, but for some shared-nothing databases with stateless computation layers, long conflict-detection paths challenge database performance under high-contention workloads. To solve this problem, we design two methods, pre-lock and local cache, together with a high-contention detection module that allow high-contention to be quickly detected and the corresponding high-contention-handling strategy applied. Experiments show that our design and optimization for high-contention transaction-processing architecture can improve the performance of distributed databases under high-contention workloads.

参考文献

1 PAVLO A, ASLETT M.. What’s really new with NewSQL?. SIGMOD Record, 2016, 45 (2): 45- 55.
2 BINNIG C, CROTTY A, GALAKATOS A, et al.. The end of slow networks: It’s time for a redesign. Proceedings of the VLDB Endowment, 2016, 9 (7): 528- 539.
3 ZAMANIAN E, SHUN J, BINNIG C, et al. Chiller: Contention-centric transaction execution and data partitioning for modern networks [C]// Proceedings of the 2020 International Conference on Management of Data. 2020: 511-526.
4 FARBER F, MAY N, LEHNER W, et al. The SAP HANA database – an architecture overview [J]. IEEE Data Engineering Bulletin, 2012, 35(1): 28-33.
5 KALLMAN R, KIMURA H, NATKINS J, et al. H-store: A high-performance, distributed main memory transaction processing system [J]. Proceedings of the VLDB Endowment, 2008, 1(2): 1496–1499.
6 LOMET D, FEKETE A, WEIKUM G, et al. Unbundling transaction services in the cloud [EB/OL]. (2009-09-09)[2022-06-11]. https://arxiv.org/ftp/arxiv/papers/0909/0909.1768.pdf.
7 HUANG D X, LIU Q, CUI Q, et al.. TiDB: A raft-based HTAP database. Proceedings of VLDB Endowment, 2020, 13 (12): 3072- 3084.
8 TAFT R, SHARIF I, MATEI A, et al. CockroachDB: The resilient geo-distributed SQL database [C]// Proceedings of the 2020 International Conference on Management of Data. 2020: 1493–1509.
9 CORBETT J C, DEAN J, EPSTEIN M, et al. Spanner: Google’s globally-distributed database [C]// Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation. 2012: 251–264.
10 ZHOU J Y, XU M, SHRAER A, et al. FoundationDB: A distributed unbundled transactional key value store [C]// Proceedings of the 2021 International Conference on Management of Data. 2021: 2653–2666.
11 PENG D, DABEK F. Large-scale incremental processing using distributed transactions and notifications [C]// Proceedings of the 9th USENIX Conference on Operating Systems Design and Implementation. 2010: 251–264.
12 WANG T Z, KIMURA H.. Mostly-optimistic concurrency control for highly contended dynamic workloads on a thousand cores. Proceedings of the VLDB Endowment, 2016, 10 (2): 49- 60.
13 GUO Z H, WU K, YAN C, et al. Releasing locks as early as you can: Reducing contention of hotspots by violating two-phase locking [C]// Proceedings of the 2021 International Conference on Management of Data. 2021: 658–670.
14 WANG Z G, MU S, CAI Y, et al. Scaling multicore databases via constrained parallel execution [C]// Proceedings of the 2016 International Conference on Management of Data. 2016: 1643–1658.
15 THOMSON A, DIAMOND T, WENG S C, et al. Calvin: Fast distributed transactions for partitioned database systems [C]//Proceedings of the ACM SIGMOD International Conference on Management of Data. 2012. https://doi.org/10.1145/2213836.2213838.
16 GUO H, ZHOU X, CAI L. Lock violation for fault-tolerant distributed database system [C]// 2021 IEEE 37th IEEE International Conference on Data Engineering. 2021: 1416–1427.
17 TAFT R, MANSOUR E, SERAFINI M, et al.. E-Store: Fine-grained elastic partitioning for distributed transaction processing systems. Proceedings of the VLDB Endowment, 2014, 8 (3): 245- 256.
18 TIAN B Y, HUANG J M, MOZAFARI B Y, et al.. Contention-aware lock scheduling for transactional databases. Proceedings of the VLDB Endowment, 2018, 11 (5): 648- 662.
19 COOPER B F, SILBERSTEIN A, TAM E, et al. Benchmarking cloud serving systems with YCSB [C]// Proceedings of the 1st ACM Symposium on Cloud Computing. 2010: 143–154.
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