research-article

An analysis of concurrency control protocols for in-memory databases with CCBench

Authors:

Takayuki Tanabe,

Takashi Hoshino,

Hideyuki Kawashima,

Osamu TatebeAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 13, Issue 13

Pages 3531 - 3544

https://doi.org/10.14778/3424573.3424575

Published: 01 September 2020 Publication History

Abstract

This paper presents yet another concurrency control analysis platform, CCBench. CCBench supports seven protocols (Silo, TicToc, MOCC, Cicada, SI, SI with latch-free SSN, 2PL) and seven versatile optimization methods and enables the configuration of seven workload parameters. We analyzed the protocols and optimization methods using various workload parameters and a thread count of 224. Previous studies focused on thread scalability and did not explore the space analyzed here. We classified the optimization methods on the basis of three performance factors: CPU cache, delay on conflict, and version lifetime. Analyses using CCBench and 224 threads, produced six insights. The performance of optimistic concurrency control protocol for a read-only workload rapidly degrades as cardinality increases even without L3 cache misses. (I2) Silo can outperform TicToc for some write-intensive workloads by using invisible reads optimization. (I3) The effectiveness of two approaches to coping with conflict (wait and no-wait) depends on the situation. (I4) OCC reads the same record two or more times if a concurrent transaction interruption occurs, which can improve performance. (I5) Mixing different implementations is inappropriate for deep analysis. (I6) Even a state-of-the-art garbage collection method cannot improve the performance of multi-version protocols if there is a single long transaction mixed into the workload. On the basis of I4, we defined the read phase extension optimization in which an artificial delay is added to the read phase. On the basis of I6, we defined the aggressive garbage collection optimization in which even visible versions are collected. The code for CCBench and all the data in this paper are available online at GitHub.

References

[1]

CCBench Developer Guide. https://github.com/thawk105/ccbench/tree/master/cc_format.

[2]

CCBench Experimental Data. https://github.com/thawk105/ccdata.

[3]

CCBench OCC. https://github.com/thawk105/ccbench/tree/master/occ.

[4]

Code of Cavalia. https://github.com/Cavalia.

[5]

Code of CCBench. https://github.com/thawk105/ccbench.

[6]

Code of DBx1000. https://github.com/yxymit/DBx1000.

[7]

Code of Masstree. https://github.com/kohler/masstree-beta.

[8]

Code of Peloton. https://pelotondb.io.

[9]

gflags. https://github.com/gflags/gflags.

[10]

How to Extend CCBench. https://medium.com/@jnmt.

[11]

mimalloc. https://github.com/microsoft/mimalloc.

[12]

The Transaction Processing Council. TPC-C Benchmark (Revision 5.11), February 2011.

[13]

R. Appuswamy, A. G. Anadiotis, D. Porobic, M. Iman, and A. Ailamaki. Analyzing the Impact of System Architecture on the Scalability of OLTP Engines for High-Contention Workloads. PVLDB, 11(2):121--134, 2017.

Digital Library

[14]

T. Bang, N. May, I. Petrov, and C. Binnig. The Tale of 1000 Cores: An Evaluation of Concurrency Control on Real(Ly) Large Multi-Socket Hardware. In DaMoN, 2020.

Digital Library

[15]

R. Bayer, H. Heller, and A. Reiser. Parallelism and Recovery in Database Systems. ACM TODS, 5(2):139--156, 1980.

Digital Library

[16]

H. Berenson, P. Bernstein, J. Gray, J. Melton, E. O'Neil, and P. O'Neil. A Critique of ANSI SQL Isolation Levels. In SIGMOD Record, volume 24, pages 1--10, 1995.

Digital Library

[17]

P. A. Bernstein and N. Goodman. Concurrency Control in Distributed Database Systems. ACM Comput. Surv., 13(2):185--221, 1981.

Digital Library

[18]

P. A. Bernstein, V. Hadzilacos, and N. Goodman. Concurrency control and recovery in database systems. 1987.

Digital Library

[19]

A. T. Clements, M. F. Kaashoek, and N. Zeldovich. RadixVM: Scalable address spaces for multithreaded applications. In EuroSys, pages 211--224, 2013.

Digital Library

[20]

M. Dashti, S. Basil John, A. Shaikhha, and C. Koch. Transaction Repair for Multi-Version Concurrency Control. In SIGMOD Conf., pages 235--250, 2017.

Digital Library

[21]

D. E. Difallah, A. Pavlo, C. Curino, and P. Cudre-Mauroux. OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases. PVLDB, 7(4):277--288, 2013.

Digital Library

[22]

B. Ding, L. Kot, and J. Gehrke. Improving Optimistic Concurrency Control through Transaction Batching and Operation Reordering. PVLDB, 12(2):169--182, 2018.

Digital Library

[23]

D. Durner and T. Neumann. No False Negatives: Accepting All Useful Schedules in a Fast Serializable Many-Core System. In ICDE, pages 734--745, 2019.

[24]

K. P. Eswaran, J. N. Gray, R. A. Lorie, and I. L. Traiger. The Notions of Consistency and Predicate Locks in a Database System. Comm. ACM, 19(11):624--633, 1976.

Digital Library

[25]

J. M. Faleiro, D. J. Abadi, and J. M. Hellerstein. High Performance Transactions via Early Write Visibility. PVLDB, 10(5):613--624, 2017.

Digital Library

[26]

J. Gray, P. Sundaresan, S. Englert, K. Baclawski, and P. J. Weinberger. Quickly generating billion-record synthetic databases. In SIGMOD Record, volume 23, pages 243--252, 1994.

Digital Library

[27]

J. Guo, P. Cai, J. Wang, W. Qian, and A. Zhou. Adaptive Optimistic Concurrency Control for Heterogeneous Workloads. PVLDB, 12(5):584--596, 2019.

Digital Library

[28]

R. Harding, D. Van Aken, A. Pavlo, and M. Stonebraker. An Evaluation of Distributed Concurrency Control. PVLDB, 10(5):553--564, 2017.

Digital Library

[29]

Y. Huang, W. Qian, E. Kohler, B. Liskov, and L. Shrira. Opportunities for Optimism in Contended Main-Memory Multicore Transactions. PVLDB, 13(5):629--642, 2020.

[30]

R. Johnson, I. Pandis, R. Stoica, M. Athanassoulis, and A. Ailamaki. Aether: a Scalable Approach to Logging. PVLDB, 3(1-2):681--692, 2010.

Digital Library

[31]

H. Jung, H. Han, A. Fekete, U. Röhm, and H. Y. Yeom. Performance of Serializable Snapshot Isolation on Multicore Servers. In DASFAA, pages 416--430, 2013.

[32]

K. Kim, T. Wang, R. Johnson, and I. Pandis. ERMIA: Fast Memory-Optimized Database System for Heterogeneous Workloads. In SIGMOD Conf., pages 1675--1687, 2016.

Digital Library

[33]

H. Kimura. FOEDUS: OLTP engine for a thousand cores and NVRAM. In SIGMOD Conf., pages 691--706, 2015.

Digital Library

[34]

P. Larson, S. Blanas, C. Diaconu, C. Freedman, J. M. Patel, and M. Zwilling. High-Performance Concurrency Control Mechanisms for Main-Memory Databases. PVLDB, 5(4):298--309, 2011.

Digital Library

[35]

H. Lim, M. Kaminsky, and D. G. Andersen. Cicada: Dependably fast multi-core in-memory transactions. In SIGMOD Conf., pages 21--35, 2017.

Digital Library

[36]

Y. Mao, E. Kohler, and R. T. Morris. Cache Craftiness for Fast Multicore Key-Value Storage. In EuroSys, pages 183--196, 2012.

Digital Library

[37]

V. J. Marathe, W. N. Scherer, and M. L. Scott. Design Tradeoffs in Modern Software Transactional Memory Systems. In LCR, pages 1--7, 2004.

Digital Library

[38]

J. M. Mellor-Crummey and M. L. Scott. Scalable Reader-Writer Synchronization for Shared-Memory Multiprocessors. In SIGPLAN Notices, volume 26, pages 106--113, 1991.

Digital Library

[39]

T. Morzy. The Correctness of Concurrency Control for Multiversion Database Systems with Limited Number of Versions. In ICDE, pages 595--604, 1993.

Digital Library

[40]

Y. Nakamura, H. Kawashima, and O. Tatebe. Integration of TicToc Concurrency Control Protocol with Parallel Write Ahead Logging Protocol. Journal of Network Computing, 9(2):339--353, 2019.

[41]

S. Nakazono, H. Uchiyama, Y. Fujiwara, Y. Nakamura, and H. Kawashima. NWR: Rethinking Thomas Write Rule for Omittable Write Operations. http://arxiv.org/abs/1904.08119, 2020.

[42]

T. Neumann, T. Mühlbauer, and A. Kemper. Fast Serializable Multi-Version Concurrency Control for Main-Memory Database Systems. In SIGMOD Conf., pages 677--689, 2015.

Digital Library

[43]

A. Pavlo, G. Angulo, J. Arulraj, H. Lin, J. Lin, L. Ma, P. Menon, T. C. Mowry, M. Perron, I. Quah, S. Santurkar, A. Tomasic, S. Toor, D. V. Aken, Z. Wang, Y. Wu, R. Xian, and T. Zhang. Self-Driving Database Management Systems. In CIDR, 2017.

[44]

G. Prasaad, A. Cheung, and D. Suciu. Handling Highly Contended OLTP Workloads Using Fast Dynamic Partitioning. In SIGMOD Conf., pages 527--542, 2020.

Digital Library

[45]

D. J. Rosenkrantz, R. E. Stearns, and P. M. Lewis. System Level Concurrency Control for Distributed Database Systems. ACM TODS, 3(2):178--198, 1978.

Digital Library

[46]

M. L. Scott and W. N. Scherer. Scalable Queue-based Spin Locks with Timeout. In SIGPLAN Notices, volume 36, pages 44--52, 2001.

Digital Library

[47]

Y. Sheng, A. Tomasic, T. Zhang, and A. Pavlo. Scheduling OLTP Transactions via Learned Abort Prediction. In aiDM, 2019.

Digital Library

[48]

R. E. Stearns and D. J. Rosenkrantz. Distributed Database Concurrency Controls Using Before-Values. In SIGMOD Conf., pages 74--83, 1981.

Digital Library

[49]

T. Tanabe, T. Hoshino, H. Kawashima, and O. Tatebe. An Analysis of Concurrency Control Protocols for In-Memory Databases with CCBench (Extended Version). http://arxiv.org/abs/2009.11558, 2020.

[50]

T. Tanabe, H. Kawashima, and O. Tatebe. Integration of Parallel Write Ahead Logging and Cicada Concurrency Control Method. In BITS, pages 291--296, 2018.

[51]

D. Tang, H. Jiang, and A. J. Elmore. Adaptive Concurrency Control: Despite the Looking Glass, One Concurrency Control Does Not Fit All. In CIDR, 2017.

[52]

R. H. Thomas. A Majority Consensus Approach to Concurrency Control for Multiple Copy Databases. ACM TODS, 4(2):180--209, 1979.

Digital Library

[53]

S. Tu, W. Zheng, E. Kohler, B. Liskov, and S. Madden. Speedy Transactions in Multicore in-Memory Databases. In SOSP, pages 18--32, 2013.

Digital Library

[54]

C. Wang, K. Huang, and X. Qian. A Comprehensive Evaluation of RDMA-enabled Concurrency Control Protocols. http://arxiv.org/abs/2002.12664, 2020.

[55]

T. Wang, R. Johnson, A. Fekete, and I. Pandis. The Serial Safety Net: Efficient Concurrency Control on Modern Hardware. In DaMoN, 2015.

Digital Library

[56]

T. Wang, R. Johnson, A. Fekete, and I. Pandis. Efficiently Making (Almost) Any Concurrency Control Mechanism Serializable. VLDB Journal, 26(4):537--562, 2017.

Digital Library

[57]

T. Wang and H. Kimura. Mostly-Optimistic Concurrency Control for Highly Contended Dynamic Workloads on a Thousand Cores. PVLDB, 10(2):49--60, 2016.

Digital Library

[58]

Z. Wang, S. Mu, Y. Cui, H. Yi, H. Chen, and J. Li. Scaling Multicore Databases via Constrained Parallel Execution. In SIGMOD Conf., pages 1643--1658, 2016.

Digital Library

[59]

G. Weikum and G. Vossen. Transactional Information Systems. Elsevier, 2001.

Digital Library

[60]

Y. Wu, J. Arulraj, J. Lin, R. Xian, and A. Pavlo. An empirical evaluation of in-memory multi-version concurrency control. PVLDB, 10(7):781--792, 2017.

Digital Library

[61]

Y. Wu, C.-Y. Chan, and K.-L. Tan. Transaction Healing: Scaling Optimistic Concurrency Control on Multicores. In SIGMOD Conf., pages 1689--1704, 2016.

Digital Library

[62]

Y. Wu and K.-L. Tan. Scalable In-Memory Transaction Processing with HTM. In ATC, pages 365--377, 2016.

Digital Library

[63]

X. Yu, G. Bezerra, A. Pavlo, S. Devadas, and M. Stonebraker. Staring into the Abyss: An Evaluation of Concurrency Control with One Thousand Cores. PVLDB, 8(3):209--220, 2014.

Digital Library

[64]

X. Yu, A. Pavlo, D. Sanchez, and S. Devadas. Tictoc: Time Traveling Optimistic Concurrency Control. In SIGMOD Conf., pages 1629--1642, 2016.

Digital Library

[65]

Y. Yuan, K. Wang, R. Lee, X. Ding, J. Xing, S. Blanas, and X. Zhang. BCC: Reducing False Aborts in Optimistic Concurrency Control with Low Cost for in-Memory Databases. PVLDB, 9(6):504--515, 2016.

Digital Library

Cited By

Doki KHoshino TKawashima H(2024)WoundDie: Concurrency Control Protocol with Lightweight Priority ControlProceedings of the 15th ACM SIGOPS Asia-Pacific Workshop on Systems10.1145/3678015.3680480(130-135)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3678015.3680480
Zhao HLi JLu WZhang QYang WZhong JZhang MLi HDu XPan A(2024)RCBench: an RDMA-enabled transaction framework for analyzing concurrency control algorithmsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00821-033:2(543-567)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00778-023-00821-0
Wang RWang JIdreos SÖzsu MAref W(2022)The case for distributed shared-memory databases with RDMA-enabled memory disaggregationProceedings of the VLDB Endowment10.14778/3561261.356126316:1(15-22)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.14778/3561261.3561263
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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 13, Issue 13

September 2020

100 pages

ISSN:2150-8097

Editors:
Magdalena Balazinska
University of Washington
,
Xiaofang Zhou
University of Queensland, Australia

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VLDB Endowment

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Published: 01 September 2020

Published in PVLDB Volume 13, Issue 13

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Cited By

Doki KHoshino TKawashima H(2024)WoundDie: Concurrency Control Protocol with Lightweight Priority ControlProceedings of the 15th ACM SIGOPS Asia-Pacific Workshop on Systems10.1145/3678015.3680480(130-135)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3678015.3680480
Zhao HLi JLu WZhang QYang WZhong JZhang MLi HDu XPan A(2024)RCBench: an RDMA-enabled transaction framework for analyzing concurrency control algorithmsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00821-033:2(543-567)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00778-023-00821-0
Wang RWang JIdreos SÖzsu MAref W(2022)The case for distributed shared-memory databases with RDMA-enabled memory disaggregationProceedings of the VLDB Endowment10.14778/3561261.356126316:1(15-22)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.14778/3561261.3561263
Freitag MKemper ANeumann T(2022)Memory-optimized multi-version concurrency control for disk-based database systemsProceedings of the VLDB Endowment10.14778/3551793.355183215:11(2797-2810)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.14778/3551793.3551832
Bang TMay NPetrov IBinnig C(2022)The full story of 1000 coresThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-022-00742-431:6(1185-1213)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1007/s00778-022-00742-4
Nakamori TNemoto JHoshino TKawashima H(2022)Removing Performance Bottleneck of Timestamp Allocation in Two-Phase Locking Based ProtocolDatabases Theory and Applications10.1007/978-3-031-15512-3_18(201-208)Online publication date: 3-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-15512-3_18

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