Skip to main content

Quorum Systems

  • Reference work entry
  • First Online:
  • 29 Accesses

Synonyms

Continuous availability; Tolerance to network partitions

Definition

Data replication is a technique to provide high availability and scalability by introducing redundancy. The data remains available as long as some replicas are accessible and, as the load can be distributed across replicas, adding more replicas potentially allows for increased throughput. Challenges arise when the data has to be updated as the replicas must be kept consistent. The most intuitive approach is to always execute all write operations at all replicas. Then, all replicas always have the same state and a read operation can read any replica. The main problem with this Read-One-Write-All (ROWA) approach is that as soon as one replica is no more available write operations cannot be performed anymore. A further problem is that executing all updates always on all replicas makes write operations very expensive.

Quorum systems address both these issues. They allow write operations to succeed if they execute...

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   6,499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Recommended Reading

  1. Agrawal D, El Abbadi A. The generalized tree quorum protocol: an efficient approach for managing replicated data. ACM Trans Database Syst. 1992;17(4):689–717.

    Article  MathSciNet  Google Scholar 

  2. Amir Y, Wool A. Optimal availability quorums systems: theory and practice. Inf Process Lett. 1998;65(5):223–28.

    Article  MathSciNet  MATH  Google Scholar 

  3. Barbara D, Garcia-Molina H. The reliability of vote mechanisms. IEEE Trans Comput Syst. 1987;36(10):1197–1208.

    Article  Google Scholar 

  4. Bernstein PA, Hadzilacos V, Goodman N. Concurrency control and recovery in database systems. Reading: Addison Wesley; 1987.

    Google Scholar 

  5. Cheung SY, Ahamad M, Ammar MH. The grid protocol: a high performance scheme for maintaining replicated data. In: Proceedings of the 6th International Conference on Data Engineering; 1990. p. 438–45.

    Google Scholar 

  6. Corbett JC, Dean J, Epstein M, Fikes A, Frost C, Furman JJ, Ghemawat S, Gubarev A, Heiser C, Hochschild P, Hsieh WC, Kanthak S, Kogan E, Li H, Lloyd A, Melnik S, Mwaura D, Nagle D, Quinlan S, Rao R, Rolig L, Saito Y, Szymaniak M, Taylor C, Wang R, Woodford D. Spanner: Google’s globally distributed database. ACM Trans Comput Syst. 2013;31(3):8.

    Article  Google Scholar 

  7. Gifford DK. Weighted voting for replicated data. In: Proceedings of the seventh ACM symposium on Operating systems principles; 1979. p. 150–62.

    Google Scholar 

  8. Jiménez-Peris R, Patiño-Martínez M, Alonso G, Kemme B. Are quorums an alternative for data replication. ACM Trans Database Syst. 2003;28(3): 257–294.

    Article  Google Scholar 

  9. Kumar A. Hierarchical quorum consensus: a new algorithm for managing replicated data. IEEE Trans Comput. 1991;40(9):996–1004.

    Article  Google Scholar 

  10. Lakshman A, Malik P. Cassandra: a decentralized structured storage system. Op Syst Rev. 2010;44(2):35–40.

    Article  Google Scholar 

  11. Lamport L. The part-time parliament. ACM Trans Comput Syst. 1998;16(2):133–69.

    Article  Google Scholar 

  12. Maekawa M. A \(\sqrt {N}\) algorithm for mutual exclusion in decentralized systems. ACM Trans Comput Syst. 1985;3(2):145–59.

    Article  Google Scholar 

  13. Mahmoud HA, Nawab F, Pucher A, Agrawal D, El Abbadi A. Low-latency multi-datacenter databases using replicated commit. Proc VLDB Endow. 2013;6(9):661–72.

    Article  Google Scholar 

  14. Malkhi D, Reiter MK, Wool A. The load and availability of Byzantine quorum systems. SIAM J Comput. 2000;29(6):1889–1906.

    Article  MathSciNet  MATH  Google Scholar 

  15. Naor M, Wool A. The load, capacity, and availability of quorum systems. SIAM J Comput. 1998;27(2):423–47.

    Article  MathSciNet  MATH  Google Scholar 

  16. Peleg D, Wool A. The availability of quorum systems. Inf Comput. 1995;123(2):210–23

    Article  MathSciNet  MATH  Google Scholar 

  17. Rao J, Shekita EJ, Tata S. Using paxos to build a scalable, consistent, and highly available datastore. Proc VLDB Endow. 2011;4(4):243–54.

    Article  Google Scholar 

  18. Thomas RH. A majority consensus approach to concurrency control for multiple copy databases. ACM Trans Database Syst. 1979;4(9):180–209.

    Article  Google Scholar 

  19. Tong Z, Kain RY. Vote assignments in weighted voting mechanisms. In: IEEE international symposium on reliable distributed systems (SRDS). West Lafayette: IEEE Computer Society Press; 1988.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marta Patiño-Martínez .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Patiño-Martínez, M., Kemme, B. (2018). Quorum Systems. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_299

Download citation

Publish with us

Policies and ethics