Compilation techniques for high throughput transaction processing

  • WANG Dong-hui ,
  • ZHU Tao ,
  • QIAN Wei-ning
Expand
  • Institute for Data Science and Engineering, East China Normal University, Shanghai 200062, China

Received date: 2016-07-01

  Online published: 2016-11-29

Abstract

Because of the problem of low utilization of CPU in memory database, the present research work is focused on improving the execution efficiency and concurrency control by transaction compilation technology to improve the performance of database. This article mainly introduces the following aspects of the memory database transaction compilation technology. First, this paper introduces the general process of transaction processing and analyzes the factors that limit the performance of the system. Second, we analyze the compilation techniques, including Just-in-time Compilation, Dependence Theory and Transaction Chopping. Third, we analyze the database and show how to improve the performance combined with the introduction of typical memory database, such as VoltDB, Hekaton and so on. Finally, the research prospects are given.

Cite this article

WANG Dong-hui , ZHU Tao , QIAN Wei-ning . Compilation techniques for high throughput transaction processing[J]. Journal of East China Normal University(Natural Science), 2016 , 2016(5) : 10 -17 . DOI: 10.3969/j.issn.1000-5641.2016.05.002

References

[1] AILAMAKI A G, DEWITT D J, HILL M D, et al. DBMSs on a modern processor: Where does time go?[C]//Proceedings of International Conference on Very Large Data Bases. UK: VLDB. 1999: 266-277.
[2]哈索, 亚历山大cdot蔡尔. 内存数据库管理 [M]. SAP, 译. 北京: 清华大学出版社,2013.
[3] BERNSTEIN P, BRODIE M, CERI S, et al. The asilomar report on database research[J]. Acm Sigmod Record, 1998, 27(4): 44-113.
[4] GARCIA-MOLINA H, ULLMAN J D, WIDOM J. Database System Implementation [M]. USA: Prentice Hall, 2010, 132-179.
[5] ALEXANDER T, DANIEL I A. The Case for Determimism in Database Systems[J]. VLDB, 2010: 70-80.
[6] WIKIPEDIA. Just-in-time manufacturing [EB/OL]. [2016-06-01]. https://en.wikipedia.org/wiki/Just-in-time{\_]\linebreak manufacturing.
[7] DIACONU C, FREEDMAN C, ISMERT E, et al. Hekaton: SQL server's memory-optimized OLTP engine [J]. SIGMOD, 2013: 1-13.
[8]DIACONU  C, ISMERT E, LARSON P A, et al. Compilation in the microsoft SQL server hekaton engine [J]. IEEE, 2014: 22-32.
[9] NEUMANN T. Efficiently compiling efficient query plans for modern hardware[J]. PVLDB, 2011, 4(9): 539-550.
[10] 吴亚鑫, 孙静. 基于数据库操作的相关性模型及应用[J].计算机工程与设计, 2011, 32(1): 183-187.
[11] SHASHA D, LLIRBAT F, SIMON E, et al. Transaction chopping: Algorithms and performance studies[J]. ACM Transactions on Database Systems (TODS), 1995, 20(3): 325-363.
[12] TU S, ZHENG W T, KOHLER E, et al. Speedy transactions in multicore in-memory databases [J]. Twenty-fourth ACM Symposium on Operating Systems Principles, 2013: 18-34.
[13] STONEBRAKER M, WEISBERG A. The VoltDB main memory DBMS[J]. IEEE DEBull,  2013, 36(2): 21-27.
[14] KALLMAN R, KIMURA H, NATKINS J, et al. H-store: A high-performance, distributed main memory transaction processing system[J]. PVLDB, 2008, 1(2): 1496-1499.
[15] THOMSON A, DIAMOND T, WENG S C, et al. Calvin: Fast distributed transactions for partitioned database systems[C]// ACM SIGMOD International Conference on Management of Data. 2012: 1-12.
[16] WANG Z G, MU S, CUI Y, et al. Scaling multicore databases via constrained parallel execution [C]//\"{O]ICAN F, KOVTRIKE G, MADDEN S. SIGMOD Conference. New York: ACM, 2016: 1643-1658.
[17] PANDIS I, JOHNSON R, HARDAVELLAS N, et al.Data-oriented transaction execution[J]. PVLDB, 2010, 3(1): 928-939.
[18] In-Memory Operational Database, SQL and Scale-Out. VoltDB[EB/OL]. [2016-06-03]. http://voltdb.com.
[19] Introduction to MemSQL. MemSQL[EB/OL]. [2016-06-03]. http://www.dbms2.com/2012/06/18/introduction-to-memsql/
[20] STONEBRAKER M, MADDEN S, ABADI D J, et al.The end of an architectural era it's time for a completerewrite[J].VLDB, 2007: 1150-1160.
[21] HELLERSTEIN J M, STONEBRAKER M, HAMILTON J. Architecture of a database system[J]. Foundations and Trends Databases, 2007, 1(2): 141-259.
[22] KEMPER A, NEUMANN T. HyPer: A hybrid OLTP{\&]OLAP main memory database system based on  virtual memory snapshots[J]. ICDE, 2011: 195-206.
Outlines

/