|
|
Benchmarking on intensive transaction processing |
Chunxi ZHANG, Yuming LI, Rong ZHANG( ), Weining QIAN, Aoying ZHOU |
1School of Data Science and Engineering, East China Normal University, Shanghai 200062, China |
|
|
Abstract Benchmarks play a crucial role in database performance evaluation, and have been effectively promoting the development of database management systems.With critical transaction processing requirements of new applications, we see an explosion of innovative database technologies for dealing with highly intensive transaction workloads (OLTP) with the obvious characteristics of sharp dynamics, terrific skewness, high contention, or high concurrency (abbr. DSC2), which can not be well described or evaluated by current standard benchmarks. In this paper, based on the representative SecKill applications, we define a pacakge of workloads simulating intensive transactional processing requirements. And we create a general and flexible benchmark framework PeakBench for evaluating intensive OLTP workloads on databases. We are the first work to have full control on simulating DSC2, especially for the fine granularity control for contention generation. With a comprehensive set of experiments conducted on popular open sourced DBMSs compared with the other representative OLTP benchmarks, we completely demonstrate the usefulness of PeakBench.
|
Keywords
benchmark
transaction processing
intensiveworkloads
evaluation
|
Corresponding Author(s):
Rong ZHANG
|
Just Accepted Date: 08 July 2019
Issue Date: 10 March 2020
|
|
1 |
D E Difallah, A Pavlo, C Curino, P CudreMaurouxet. OLTP-bench: an extensible testbed for benchmarking relational databases. Proceedings of the VLDB Endowment, 2013, 7(4): 277–288
https://doi.org/10.14778/2732240.2732246
|
2 |
K Ren, J M Faleiro, i D J Abad. Design principles for scaling multi-core OLTP under high contention. In: Proceedings of the 2016 International Conference on Management of Data. 2016, 1583–1598
https://doi.org/10.1145/2882903.2882958
|
3 |
N Zhou, X Zhou, X Zhang, X Du, S Wang. Reordering transaction execution to boost high-frequency trading applications. Data Science and Engineering, 2017, 2(4): 301–315
https://doi.org/10.1007/s41019-017-0054-0
|
4 |
V Persico, A Pescapé, A Picariello, G Sperli. Benchmarking big data architectures for social networks data processing using public cloud platforms. Future Generation Computer Systems, 2018, 89: 98–109
https://doi.org/10.1016/j.future.2018.05.068
|
5 |
R Harding, D Van Aken, A Pavlo, M Stonebraker. An evaluation of distributed concurrency control. Proceedings of the VLDB Endowment, 2017, 10(5): 553–564
https://doi.org/10.14778/3055540.3055548
|
6 |
S Q L My. For traditional databases, details are published on the MySql home page
|
7 |
S Q L Postgre. Database details are published on the PostgreSQL home page
|
8 |
D B Volt , LLC. Voltdb technical overview. Whitepaper, 2010
|
9 |
D Bitton, D J Dewitt, C Turbyfill. Benchmarking database systems a systematic approach. In: Proceedings of the 9th International Conference on Very Large Data Bases. 1983, 8–19
|
10 |
J Gray. The benchmark handbook for database and transaction systems. In: Proceedings of Sigmod Conference on Gray Hardavellas. 1993
|
11 |
Y C Tay. Data generation for application-specific benchmarking. Proceedings of the VLDB Endowment, 2011, 4(12): 1–4
|
12 |
S Chen, A Ailamaki, M Athanassoulis, P B Gibbons, R Johnson, I Pandis, R Stoica. TPC-E vs. TPC-C: characterizing the new TPC-E benchmark via an I/O comparison study. ACM Sigmod Record, 2011, 39(3): 5–10
https://doi.org/10.1145/1942776.1942778
|
13 |
R Cole, F Funke, L Giakoumakis, W Guy, A Kemper, S Krompass, H Kuno, R Nambiar, T Neumann, M Poess, K Sattler, M Seibold, E Simon, F Waas. The mixed workload CH-benCHmark. In: Proceedings of the 4th International Workshop on Testing Database Systems. 2011
https://doi.org/10.1145/1988842.1988850
|
14 |
Council, Transaction Processing Performance. TPC-H benchmark specification. Tcp.org Website, 2008
|
15 |
M J Cahill, U Röhm, A D Fekete. Serializable isolation for snapshot databases. ACM Transactions on Database Systems, 2009, 34(4): 20
https://doi.org/10.1145/1620585.1620587
|
16 |
A Wolski. TATP Benchmark Description (Version 1.0). 2009
|
17 |
M Stonebraker. A measure of transaction processing power. In: Hellerstein J M, Stonebraker M, Readings in Database Systems, 2nd ed. Morgan Kaufmann Publishers Inc., 1994, 442–454
|
18 |
B F Cooper, A Silberstein, E Tam, R Ramakrishnan, R Sears. Benchmarking cloud serving systems with YCSB. In: Proceedings of the 1st ACM Symposium on Cloud Computing. 2010, 143
https://doi.org/10.1145/1807128.1807152
|
19 |
S Patil, M Polte, R Kai, W Tantisiriroj, X Lin, J López, G Gibson, A Fuchs, B Rinaldi. YCSB++: benchmarking and performance debugging advanced features in scalable table stores. In: Proceedings of the 2nd ACM Symposium on Cloud Computing. 2011
https://doi.org/10.1145/2038916.2038925
|
20 |
J E Gonzalez, R S Xin, A Dave, D Crankshaw, I Stoica. Graphx: graph processing in a distributed dataflow framework. In: Proceedings of the 11th Usenix Conference on Operating Systems Design & Implementation. 2014
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|