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Dike: A Benchmark Suite for Distributed Transactional Databases

Published: 05 June 2023 Publication History

Abstract

Distributed relational database management systems (abbr. DDBMSs) for online transaction processing (abbr. OLTP) have been gradually adopted in production environments. With many relevant products vying for the markets, an unbiased benchmark is urgently needed to promote the development of transactional DDBMSs. Current benchmarks for OLTP applications have not taken the challenges encountered during the designs and implementations of a transactional DDBMS into consideration, which expects to provide high elasticity and availability as well as high throughputs. We propose a benchmark suite Dike to evaluate the efforts to tackle these challenges. Dike is designed mainly from three aspects: quantitative control to evaluate scalability, imbalanced distribution to evaluate schedulability, and comprehensive fault injections to evaluate availability. It also provides a dynamic load control to simulate real-world scenarios. In this demonstration, users can experience core features of Dike with user-friendly interfaces.

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MP4 File
Dike is the first benchmark suite to benchmark transactional distributed databases, which provides quantitative controls for critical dimensions, w.r.t contention, transaction distribution, and workload dynamicity. The source code, the detailed designs and representative experiments of popular distributed databases are all available on GitHub.

References

[1]
DBHammer. 2023. How Good is the Distributed Databases in Supporting Transaction Processing. https://github.com/DBHammer/Dike
[2]
Guo Jinwei et al. 2017a. Low-overhead paxos replication. Data Science and Engineering, Vol. 2 (2017), 169--177.
[3]
Harding Rachael et al. 2017b. An evaluation of distributed concurrency control. Proceedings of the VLDB Endowment, Vol. 10, 5 (2017), 553--564.
[4]
Qu Luyi et al. 2022a. Are current benchmarks adequate to evaluate distributed transactional databases? BenchCouncil Transactions on Benchmarks, Standards and Evaluations (2022), 100031.
[5]
Yang Zhenkun et al. 2022b. OceanBase: a 707 million tpmC distributed relational database system. Proceedings of the VLDB Endowment, Vol. 15, 12 (2022), 3385--3397.

Cited By

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  • (2024)CoralDB: A Collaborative Database for Data Sharing Based on Permissioned BlockchainIEEE Transactions on Mobile Computing10.1109/TMC.2024.335749923:9(8886-8901)Online publication date: Sep-2024
  • (2024)Lauca: A Workload Duplicator for Benchmarking Transactional Database PerformanceIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336011636:7(3180-3194)Online publication date: Jul-2024
  • (2024)Mirage: Generating Enormous Databases for Complex Workloads2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00306(3989-4001)Online publication date: 13-May-2024

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  1. Dike: A Benchmark Suite for Distributed Transactional Databases

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    cover image ACM Conferences
    SIGMOD '23: Companion of the 2023 International Conference on Management of Data
    June 2023
    330 pages
    ISBN:9781450395076
    DOI:10.1145/3555041
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    New York, NY, United States

    Publication History

    Published: 05 June 2023

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    Author Tags

    1. benchmark suite
    2. distributed transactional database

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    Dike is the first benchmark suite to benchmark transactional distributed databases, which provides quantitative controls for critical dimensions, w.r.t contention, transaction distribution, and workload dynamicity. The source code, the detailed designs and representative experiments of popular distributed databases are all available on GitHub. https://dl.acm.org/doi/10.1145/3555041.3589710#SIGMOD23-670404.mp4

    Funding Sources

    • Key Laboratory of Performance and Reliability Evaluation of Basic Hardware and Software, Ministry of Industry and Information
    • Ant Group Research Fund
    • National Nature Science Foundation of China

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    SIGMOD/PODS '23
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    Overall Acceptance Rate 785 of 4,003 submissions, 20%

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    Cited By

    View all
    • (2024)CoralDB: A Collaborative Database for Data Sharing Based on Permissioned BlockchainIEEE Transactions on Mobile Computing10.1109/TMC.2024.335749923:9(8886-8901)Online publication date: Sep-2024
    • (2024)Lauca: A Workload Duplicator for Benchmarking Transactional Database PerformanceIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336011636:7(3180-3194)Online publication date: Jul-2024
    • (2024)Mirage: Generating Enormous Databases for Complex Workloads2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00306(3989-4001)Online publication date: 13-May-2024

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