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Online Recovery in Parallel Database Systems

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Encyclopedia of Database Systems
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Synonyms

High availability; Continuous availability; 24x7 operation

Definition

Replication (also known as clustering) is a technique to provide high availability in parallel and distributed databases. High availability aims to provide continuous service operation. High availability has two faces. On one hand, it provides fault-tolerance by introducing redundancy in the form of replication, that is, having multiple copies or replicas of the data at different sites. On the other hand, since sites holding the replicas may crash and/or fail, in order to keep a given degree of availability, failed or new replicas should be reintroduced into the system. Introducing new replicas requires transferring to them the current state in a consistent fashion (known as recovery). A simple solution to this problem is offline recovery, that is, in order to obtain a quiescent state, request processing is suspended, then the state is transferred from a working replica (termed recoverer replica) to the new...

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Recommended Reading

  1. Bernstein P.A., Hadzilacos V., and Goodman N. Concurrency Control and Recovery in Database Systems. Addison Wesley, 1987.

    Google Scholar 

  2. Castro M. and Liskov B. Practical byzantine fault tolerance and proactive recovery. ACM Trans. Comput. Syst., 20(4):398–461, 2002.

    Article  Google Scholar 

  3. Gançarski S. , Naacke H., Pacitti E., and Valduriez P. The leganet system: Freshness-aware transaction routing in a database cluster. Inform. Syst., 32(2):320–343, 2007.

    Google Scholar 

  4. Gashi I., Popov P., and Strigini L. Fault Tolerance via Diversity for Off-The-Shelf Products: a Study with SQL Database Servers. IEEE Trans. Depend. Secur. Comput., 4(4):280–294, 2007.

    Google Scholar 

  5. Jiménez-Peris R., M. Patiño-Martínez, and Alonso G. Non-Intrusive, Parallel Recovery of Replicated Data. In Proc. 21st Symp. on Reliable Distributed Syst., 2002, pp. 150–159.

    Google Scholar 

  6. Kemme B. and Alonso G. Don’t be lazy, be consistent: Postgres-R, a new way to implement database replication. In Proc. 26th Int. Conf. on Very Large Data Bases, 2000, pp. 134–143.

    Google Scholar 

  7. Kemme B. and Alonso G. A New Approach to Developing and Implementing Eager Database Replication Protocols. ACM Trans. Database Syst., 25(3):333–379, 2000.

    Google Scholar 

  8. Kemme B., Bartoli A., and Babaoglu O. Online Reconfiguration in Replicated Databases Based on Group Communication. In Proc. Int. Conf. on Dependable Systems and Networks, 2001, pp. 117–130.

    Google Scholar 

  9. Lau E. and Madden S. An Integrated Approach to Recovery and High Availability in an Updatable, Distributed Data Warehouse. In Proc. 32nd Int. Conf. on Very Large Data Bases. 2006, pp. 703–714.

    Google Scholar 

  10. Manassiev K. and Amza C. Scaling and Continuous Availability in Database Server Clusters through Multiversion Replication. In Proc. Int. Conf. on Dependable Systems and Networks, 2007, pp. 666–676.

    Google Scholar 

  11. Özsu M.T. and Valduriez P. Principles of Distributed Database Systems. Prentice-Hall, 2nd ed., 1999.

    Google Scholar 

  12. Pacitti E. and Simon E. Update Propagation Strategies to Improve Freshness in Lazy Master Replicated Databases. VLDB J., 8(3):305–318, 2000.

    Google Scholar 

  13. Patiño-Martínez M., Jiménez-Peris R., Kemme B., and Alonso G. Middle-R: Consistent Database Replication at the Middleware Level. ACM Trans. Comput. Syst., 23(4):375–423, 2005.

    Google Scholar 

  14. Pedone F., Guerraoui R., and Schiper A. The Database State Machine Approach. Distributed and Parallel Databases, 14(1):71–98, 2003.

    Article  Google Scholar 

  15. Plattner C. and Alonso G. Ganymed: Scalable Replication for Transactional Web Applications. In Proc. ACM/IFIP/USENIX Int. Middleware Conf., 2004, pp. 155–174.

    Google Scholar 

  16. PostgreSQL PostgreSQL Point in Time Recovery. http://www.postgresql.org/docs/8.0/interactive/backup-online.html.

  17. Vandiver B., Balakrishnan H., Liskov B., and Madden S. Tolerating Byzantine Faults in Database Systems using Commit Barrier Scheduling. In Proc. 21st ACM Symp. on Operating System Principles, 2007, pp. 59–72.

    Google Scholar 

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Jimenez-Peris, R. (2009). Online Recovery in Parallel Database Systems. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1089

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