Experimental Study on Concurrency Control Algorithms in In-Memory Databases

被引：0

作者：

Zhao H.-Y. ^{[1
,2
]}

Zhao Z.-H. ^{[1
,2
]}

Yang W.-Q. ^{[1
,2
]}

Lu W. ^{[1
,2
]}

Li H.-X. ^{[3
]}

Du X.-Y. ^{[1
,2
]}

机构：

[1] Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University of China, Ministry of Education, Beijing

[2] School of Information, Renmin University of China, Beijing

[3] Billing Platform Department, Tencent Technology (Beijing) Co. Ltd, Beijing

来源：

Ruan Jian Xue Bao/Journal of Software | 2022年 / 33卷 / 03期

关键词：

3TS; Concurrency control algorithm; Database system; In-memory database; Transaction processing;

D O I：

10.13328/j.cnki.jos.006454

中图分类号：

学科分类号：

摘要：

The concurrency control algorithm is a key component to guarantee the correctness and efficiency of executing transactions. Thus far, substantial effort has been devoted to proposing new concurrency controls algorithms in both database industry and academia. This study abstracts a common paradigm of the state-of-the-art and summarizes the core idea of concurrency control algorithms as “ordering-and-verifying”. Then, the existing concurrency control algorithms are re-presented following the ordering-and-verifying paradigm. Based on extensive experiments under an open-source memory-based distributed transaction testbed called 3TS, it is systematically demonstrated the advantages and disadvantages of the mainstream state-of-the-art concurrency control algorithms. Finally, the preferable application scenario is summarized for each algorithm and some valuable references are provided for the follow-up research of concurrency control algorithms used in in-memory databases. © Copyright 2022, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

引用

页码：867 / 890

页数：23

共 44 条

[1] Wang S, Sa SX., Introduction to Database System, (2014)
[2] Gray J, Reuter A., Transaction Processing: Concepts and Techniques, (1992)
[3] Guo ZH, Wu K, Yan C, Yu XY., Releasing locks as early as you can: Reducing contention of hotspots by violating two-phase locking, Proc. of the 2021 Int’l Conf. on Management of Data, pp. 658-670, (2021)
[4] Gray J, Lorie RA, Putzolu GR, Traiger IL., Granularity of locks and degrees of consistency in a shared data base, Readings in Database Systems, pp. 365-394, (1976)
[5] Bernstein P, Goodman N., Concurrency control in distributed database systems, ACM Computing Surveys, 13, 2, pp. 185-221, (1981)
[6] Mahmoud HA, Arora V, Nawab F, Agrawal D, Abbadi A., MaaT: Effective and scalable coordination of distributed transactions in the cloud, Proc. of the VLDB Endowment, 7, 5, pp. 329-340, (2014)
[7] Kung HT, Robinson JT., On optimistic methods for concurrency control, ACM Trans. on Database Systems, 6, 2, pp. 213-226, (1981)
[8] Yu XY, Xia Y, Pavlo A, Sanchez D, Rudolph L, Devadas S., Sundial: Harmonizing concurrency control and caching in a distributed OLTP database management system, Proc. of the VLDB Endowment, 11, 10, pp. 1289-1302, (2018)
[9] Agrawal R, Carey MJ, Livny M., Concurrency control performance modeling: Alternatives and implications, ACM Trans. on Database Systems, 12, 4, pp. 609-654, (1987)
[10] Carey MJ, Livny M., Distributed concurrency control performance: A study of algorithms, distribution, and replication, Proc. of the 14th Int’l Conf. on Very Large Data Bases (VLDB ‘88), pp. 13-25

← 1 2 3 4 5 →