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Frontiers of Computer Science

, Volume 13, Issue 6, pp 1282–1295 | Cite as

Timestamp reassignment: taming transaction abort for serializable snapshot isolation

  • Ningnan Zhou
  • Xiao ZhangEmail author
  • Shan Wang
Research Article
  • 25 Downloads

Abstract

Serializable snapshot isolation (SSI) is a promising technique to exploit parallelism for multi-core databases. However, SSI suffers from excessive transaction aborts. Existing remedies have three drawbacks: 1) tracking prohibitively transitive dependencies; 2) aborting on every write-write conflict; and 3) requiring manual annotation on transaction programs.

In this paper, we propose to suppress transaction aborts by reassigning timestamps. We combine static analysis with augmented query plan. In this way, we save both aborts caused by read-write and write-write conflicts, without tracking transitive dependency and annotating transaction programs. As such, our approach does not exhibit drawbacks of existing methods. Extensive experiments demonstrate the effectiveness and practicality of our approach.

Keywords

serializable snapshot isolation timestamp reassignment static analysis augmented query plan 

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Notes

Acknowledgements

This work was partially supported by the National Key R&D Program of China (2018YFB1004401), and the National Natural Science Foundation of China Key Project (Grant No. 61732014).

Supplementary material

11704_2018_7018_MOESM1_ESM.ppt (302 kb)
Supplementary material, approximately 302 KB.

References

  1. 1.
    Esmaeilzadeh H, Belm E, Amant R S, Sanharalingam K, Burger D. Power challenges may end the multicore era. Communications of ACM, 2013, 56(2): 93–102CrossRefGoogle Scholar
  2. 2.
    Horikawa T. Latch–free data structures for DBMS: design, implementation, and evaluation. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. 2013, 409–420CrossRefGoogle Scholar
  3. 3.
    Ports D R K, Grittner K. Serializable snapshot isolation in PostgreSQL. Proceedings of the VLDB Endowment, 2012, 5(12): 1850–1861CrossRefGoogle Scholar
  4. 4.
    Cahill M J, Röhm U, Fekete A D. Serializable isolation for snapshot databases. ACM Transactions on Database Systems, 2009, 34(4): 20CrossRefGoogle Scholar
  5. 5.
    Revilak S, O’Neil P, O’Neil, E. Precisely serializable snapshot isolation (PSSI). In: Proceedings of the 27th IEEE International Conference on Data Engineering. 2011, 482–493Google Scholar
  6. 6.
    Berenson H, Bernstein P, Gray J, Melton J, O’Neil E, O’Neil P. A critique of ANSI SQL isolation levels. In: Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data. 1995, 1–10Google Scholar
  7. 7.
    Adya A. Weak consistency: a generalized theory and optimistic implementations for distributed transactions. Massachusetts Institute of Technology, 1999Google Scholar
  8. 8.
    Fekete A D, Liarokapis D, O’Neil E, O’Neil P, Shasha D. Making snapshot isolation serializable. ACM Transactions on Database Systems, 2005, 20(2): 492–528CrossRefGoogle Scholar
  9. 9.
    Cahill M J, Röhm U, Fekete A D. Serializable isolation for snapshot databases. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. 2008, 729–738CrossRefGoogle Scholar
  10. 10.
    Finkelstein S. Common expression analysis in database applications. In: Proceedings of the 1982 ACM SIGMOD International Conference on Management of Data. 1982, 235–245CrossRefGoogle Scholar
  11. 11.
    Sellis T K. Multiple–query optimization. ACM Transactions on Database Systems, 1988, 13(1): 23–52CrossRefGoogle Scholar
  12. 12.
    Harizopoulos S, Shkapenyuk V, Ailamaki A. QPipe: a simultaneously pipelined relational query engine. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data. 2005, 383–394CrossRefGoogle Scholar
  13. 13.
    Candea G, Polyzotis N, Vingralek R. A scalable, predictable join operator for highly concurrent data warehouses. Proceedings of the VLDB Endowment, 2009, 2(1): 277–288CrossRefGoogle Scholar
  14. 14.
    Arumugam S, Dobra A, Jermaine C M, Pansare N, Perez L. The DataPath system: a data–centric analytic processing engine for large data warehouses. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data. 2010, 519–530CrossRefGoogle Scholar
  15. 15.
    Giannikis G, Alonso G, Kossmann D. SharedDB: killing one thousand queries with one stone. Proceedings of the VLDB Endowment, 2012, 5(6): 526–537CrossRefGoogle Scholar
  16. 16.
    Chavan M, Guravannavar R, Ramachandra K, Sudarshan S. DBridge: a program rewrite tool for set–oriented query execution. In: Proceedings of the 27th IEEE International Conference on Data Engineering. 2011, 1284–1287Google Scholar
  17. 17.
    Guravannavar R, Sudarshan S. Rewriting procedures for batched bindings. Proceedings of the VLDB Endowment, 2008, 1(1): 1107–1123CrossRefGoogle Scholar
  18. 18.
    Cheung A, Madden S, Solar–Lezama A. Sloth: being lazy is a virtue (when issuing database queries). In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. 2014, 931–942Google Scholar
  19. 19.
    Cheung A, Madden S, Arden, O, Myers A C. Automatic partitioning of database applications. Proceedings of the VLDB Endowment, 2012, 5(11): 1471–1482CrossRefGoogle Scholar
  20. 20.
    Shasha D, Llirbat F, Simon E, Valduriez P. Transaction chopping: algorithms and performance studies. ACM Transactions on Database Systems, 1995, 20(3): 325–363CrossRefGoogle Scholar
  21. 21.
    Bernstein P A, Shipman D W, Rothnie J B. Concurrency control in a system for distributed databases (SDD–1). ACM Transactions on Database Systems, 1980, 5(1): 18–51CrossRefGoogle Scholar
  22. 22.
    Agrawal D, El A A, Jeffers R, Lin L J. Ordered shared locks for realtime databases. The VLDB Journal, 1995, 4(1): 87–126CrossRefGoogle Scholar
  23. 23.
    Xie C, Su C Z, Littley C, Alivisi L, Kapritsos M, Wang Y. Highperformance ACID via modular concurrency control. In: Proceedings of the 25th Symposium on Operating Systems Principles. 2015, 279–294CrossRefGoogle Scholar
  24. 24.
    Yan C, Cheung A. Leveraging lock contention to improve OLTP application performance. Proceedings of the VLDB Endowment, 2016, 9(5): 444–455CrossRefGoogle Scholar
  25. 25.
    Faleiro J M, Thomson A, Abadi D J. Lazy evaluation of transactions in database systems. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. 2014, 15–26Google Scholar
  26. 26.
    Roy S, Kot L, Bender G, Ding B L, Hojjat H, Koch C, Foster N, Gehrke J. The homeostasis protocol: avoiding transaction coordination through program analysis. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. 2015, 1311–1326Google Scholar
  27. 27.
    Bailis P, Fekete A, Franklin M J, Ghodsi A, Hellerstein J M, Stoica I. Coordination avoidance in database systems. Proceedings of the VLDB Endowment, 2014, 8(3): 185–196CrossRefGoogle Scholar
  28. 28.
    Wang Z G, Mu S, Cui Y, Yi H, Chen H B, Li J Y. Scaling multicore databases via constrained parallel execution. In: Proceedings of the 2016 International Conference on Management of Data. 2016, 1643–1658Google Scholar
  29. 29.
    Stonebraker M, Madden S, Abadi D J, Harizopoulos S, Hachem N, Helland P. The end of an architectural era: (it’s time for a complete rewrite). In: Proceedings of the 33rd International Conference on Very Large Data Bases. 2007, 1150–1160Google Scholar
  30. 30.
    Faleiro JM, Abadi D J, Hellerstein JM. High performance transactions via early write visibility. Proceedings of the VLDB Endowment, 2017, 10(5): 613–624CrossRefGoogle Scholar
  31. 31.
    Alomari M, Cahill M, Fekete A, Rohm U. The cost of serializability on platforms that use snapshot isolation. In: Proceedings of the 24th IEEE International Conference on Data Engineering. 2008, 576–585Google Scholar
  32. 32.
    Sirin U, Tözün P, Porobic D, Ailamaki A. Micro–architectural analysis of in–memory OLTP. In: Proceedings of the 2016 International Conference on Management of Data. 2016, 387–402Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.MOE Key Laboratory of DEKERenmin University of ChinaBeijingChina
  2. 2.School of InformationRenmin University of ChinaBeijingChina

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