AKARA: A Flexible Clustering Protocol for Demanding Transactional Workloads

  • A. CorreiaJr.
  • J. Pereira
  • R. Oliveira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5331)


Shared-nothing clusters are a well known and cost-effective approach to database server scalability, in particular, with highly intensive read-only workloads typical of many 3-tier web-based applications. The common reliance on a centralized component and a simplistic propagation strategy employed by mainstream solutions however conduct to poor scalability with traditional on-line transaction processing (OLTP), where the update ratio is high. Such approaches also pose an additional obstacle to high availability while introducing a single point of failure.

More recently, database replication protocols based on group communication have been shown to overcome such limitations, expanding the applicability of shared-nothing clusters to more demanding transactional workloads. These take simultaneous advantage of total order multicast and transactional semantics to improve on mainstream solutions. However, none has already been widely deployed in a general purpose database management system.

In this paper, we argue that a major hurdle for their acceptance is that these proposals have disappointing performance with specific subsets of real-world workloads. Such limitations are deep-rooted and working around them requires in-depth understanding of protocols and changes to applications. We address this issue with a novel protocol that combines multiple transaction execution mechanisms and replication techniques and then show how it avoids the identified pitfalls. Experimental results are obtained with a workload based on the industry standard TPC-C benchmark.


Abort Rate Active Replication Optimistic Execution Snapshot Isolation Replication Protocol 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Baldoni, R., Cimmino, S., Marchetti, C., Termini, A.: Performance Analisys of Java Group Toolkits: a Case Study. In: Guelfi, N., Astesiano, E., Reggio, G. (eds.) FIDJI 2002. LNCS, vol. 2604, pp. 49–60. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Bernstein, P., Hadzilacos, V., Goodman, N.: Concurrency Control and Recovery in Database Systems. Addison-Wesley, Reading (1987)Google Scholar
  3. 3.
    Cecchet, E., Marguerite, J., Zwaenepoel, W.: C-JDBC: Flexible Database Clustering Middleware. In: USENIX Annual Technical Conference (2004)Google Scholar
  4. 4.
    Continuent. Sequoia 4.x (2008),
  5. 5.
    Correia, A., Orlando, J., Rodrigues, L., Carvalho, N., Oliveira, R., Guedes, S.: Gorda: An Open Architecture for Database Replication. In: IEEE NCA (2007)Google Scholar
  6. 6.
    Gray, J., Helland, P., O’Neil, P., Shasha, D.: The Dangers of Replication and a Solution. In: ACM SIGMOD (1996)Google Scholar
  7. 7.
    Correia Jr., A., Sousa, A., Soares, L., Pereira, J., Moura, F., Oliveira, R.: Group-based Replication of On-line Transaction Processing Servers. In: Maziero, C.A., Gabriel Silva, J., Andrade, A.M.S., de Assis Silva, F.M. (eds.) LADC 2005. LNCS, vol. 3747, pp. 245–260. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Kemme, B., Alonso, G.: Don’t Be Lazy, Be Consistent: Postgres-R, A New Way to Implement Database Replication. In: VLDB Conference (2000)Google Scholar
  9. 9.
    Lin, Y., Kemme, B., Jiménez Peris, R., Patiño Martíne, M.: Middleware based Data Replication providing Snapshot Isolation. In: ACM SIGMOD (2005)Google Scholar
  10. 10.
    Matos Jr., M., Correia, A., Pereira, J., Oliveira, R.: Serpentine: adaptive middleware for complex heterogeneous distributed systems. In: ACM SAC (2008)Google Scholar
  11. 11.
    Milan-Franco, J., PatioMartnez, M., Jimenez-Peri, R., Kemme, B.: Adaptive middleware for data replication. In: USENIX International Conference on Middleware (2004)Google Scholar
  12. 12.
    Oliveira, R., Pereira, J., Correia Jr., A., Archibald, E.: Revisiting 1-Copy Equivalence in Clustered Databases. In: ACM SAC (2006)Google Scholar
  13. 13.
    Pedone, F., Guerraoui, R., Schiper, A.: The Database State Machine Approach. Journal of Distributed and Parallel Databases and Technology (2003)Google Scholar
  14. 14.
    Jiménez Peris, R., Patiño Martínez, M., Kemme, B., Alonso, G.: Improving the Scalability of Fault-Tolerant Database Clusters. In: IEEE ICDCS (2002)Google Scholar
  15. 15.
    Plattner, C., Alonso, G.: Ganymed: scalable replication for transactional web applications. In: USENIX International Conference on Middleware (2004)Google Scholar
  16. 16.
    Schneider, F.: Replication management using the state-machine approach. In: Distributed Systems. ACM Press/Addison-Wesley Publishing Co. (1993)Google Scholar
  17. 17.
    Schroeder, B., Harchol-Balter, M., Iyengar, A., Nahum, E., Wiernam, A.: How to determine a good multi-programming level for external scheduling. In: IEEE ICDE (2006)Google Scholar
  18. 18.
    Schroeder, B., Wierman, A., Harchol-Balter, M.: Open Versus Closed: A Cautionary Tale. In: NSDI (2006)Google Scholar
  19. 19.
    Sousa, A., Pedone, F., Oliveira, R., Moura, F.: Partial Replication in the Database State Machine. In: IEEE NCA (2001)Google Scholar
  20. 20.
    Sousa, A., Pereira, J., Soares, L., Correia Jr., A., Rocha, L., Oliveira, R., Moura, F.: Testing the Dependability and Performance of GCS-Based Database Replication Protocols. In: IEEE DSN (2005)Google Scholar
  21. 21.
    Transaction Processing Performance Council (TPC). TPC benchmark C Standard Specification Revision 5.0 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • A. CorreiaJr.
    • 1
  • J. Pereira
    • 1
  • R. Oliveira
    • 1
  1. 1.Computer Science and Technology Center (CCTC)University of MinhoPortugal

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