Blind Write Protocol

  • Khairul Anshar
  • Nanna Suryana
  • Noraswaliza Binti Abdullah
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

The current approach to handle interleaved write operation and preserve consistency in relational database system relies on locking protocol. The application system has no other option to deal with interleaved write operation. In other hand, allowing more write operations to be interleaved will increase the throughput of database. Since each application system has its own consistency requirement then database system should provide another protocol to allow more write operation to be interleaved. Therefore, this paper proposes blind write protocol as a complement to the current concurrency control.

Keywords

Concurrency control Interleaved transaction Locking Consistency Availability Deadlock Blind write 
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References

  1. 1.
    Codd, E.F.: A relational model of data for large shared data banks. Commun. ACM 13, 377–387 (1970)CrossRefMATHGoogle Scholar
  2. 2.
    Eswaran, K.P., Gray, J.N., Lorie, R.A., Traiger, I.L.: The notions of consistency and predicate lock in a database system. ACM Comput. Surv. 19, 624–633 (1976)MathSciNetMATHGoogle Scholar
  3. 3.
    Stearns, R.E., Lewis, P.M., Rosenkrantz, D.J.: Concurrency control for database systems. In: Proceedings of 7th Symposium on Foundations of Computer Science, pp. 19–32 (1976)Google Scholar
  4. 4.
    Gray, J., Lorie, R.A., Putzolu, G.R., Traiger, I.L.: Granularity of locks and degrees of consistency in a shared data base. In: IFIP Working Conference on Modelling in Data Base Management Systems, pp. 365–394 (1976)Google Scholar
  5. 5.
    Bernstein, P.A., Shipman, D.W., Wong, W.S.: Formal aspects of serializability in database concurrency control. In: IEEE, vol SE-5, no. 3 (1979)Google Scholar
  6. 6.
    Kung, H.T., Androbinson, J.T.: An optimistic methods for concurrency control. ACM Trans. Database Syst. 6(2), 213–226 (1981)CrossRefGoogle Scholar
  7. 7.
    Stearns, R.E., Rosenkrantz D.J.: Distributed database concurrency controls using before-values. In: Proceedings of the 1981 ACM SIGMOD International Conference on Management of Data (1981)Google Scholar
  8. 8.
    Bernstein, P.A., Goodman, N.: Concurrency control in distributed database systems. ACM Comput. Surv. (CSUR) 13(2), 185–221 (1981)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Bernstein, P.A., Hadzilacos, V., Goodman, N.: Concurrency Control and Recovery in Database Systems. Addison-Wesley Longman Publishing Co., Inc., Boston (1986)Google Scholar
  10. 10.
    das Chagas Mendonca, N., de Oliveira Anido, R.: Using extended hierarchical quorum consensus to control replicated data: from traditional voting to logical structures. In: Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences, Wailea, HI, USA, pp. 303–312 (1994)Google Scholar
  11. 11.
    Berenson, H., Bernstein, P., Gray, J., Melton, J., O’Neil, E., O’Neil, P.: A critique of ANSI SQL isolation levels. In: Carey, M., Schneider, D. (eds.) Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data (SIGMOD 1995), pp. 1–10. ACM, New York (1995)Google Scholar
  12. 12.
    Burger, A., Kumar, V., Hines, M.L.: Performance of multiversion and distributed two-phase locking concurrency control mechanisms in distributed databases. Inf. Sci. 96(1–2), 129–152 (1997)CrossRefGoogle Scholar
  13. 13.
    Kemme, B., Alonso, G.: A new approach to developing and implementing eager database replication protocols. ACM Trans. Database Syst. 25, 333–379 (2000)CrossRefGoogle Scholar
  14. 14.
    Balling, D.J., Lentz, A., Zawodny, J.D., Tkachenko, V., Zaitsev, P., Schwartz, B.: High Performance MySQL, 2nd edn. O’Reilly Media, Sebastopol (2008)Google Scholar
  15. 15.
    Vogels, W.: Eventually consistent. Commun. ACM 52, 40–44 (2009)CrossRefGoogle Scholar
  16. 16.
    Alomari, M., Fekete, A., Röhm, U.: A robust technique to ensure serializable executions with snapshot isolation DBMS. In: IEEE 25th International Conference on Data Engineering, Shanghai, pp. 341–352 (2009)Google Scholar
  17. 17.
    Cahill, J., Rohm, U., Fekete, A.D.: Serializable isolation for snapshot databases. ACM Trans. Database Syst. 34(4), 20 (2009)CrossRefGoogle Scholar
  18. 18.
    Terry, D.: Replicated data consistency explained through baseball. Commun. ACM 56(12), 82–89 (2013)CrossRefGoogle Scholar
  19. 19.
    Bernstein, P.A., Das, S.: Rethinking eventual consistency. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data (SIGMOD 2013), pp. 923–928. ACM, New York (2013)Google Scholar
  20. 20.
    Zhou, X., Yu, Z., Tan, K.L.: Posterior snapshot isolation. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), San Diego, CA, pp. 797–808 (2017)Google Scholar
  21. 21.
    Alomari, M., Fekete, A.: Serializable use of read committed isolation level. In: 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), Marrakech, pp. 1–8 (2015)Google Scholar
  22. 22.
    Zendaoui, F., Hidouci, W.K.: Performance evaluation of serializable snapshot isolation in PostgreSQL. In: 2015 12th International Symposium on Programming and Systems (ISPS), Algiers, pp. 1–11 (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Khairul Anshar
    • 1
  • Nanna Suryana
    • 1
  • Noraswaliza Binti Abdullah
    • 1
  1. 1.Universiti Teknikal Malaysia MelakaAlor GajahMalaysia

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