Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Multi-datacenter Consistency Properties

  • Peter BailisEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80643


Consistency; Geo-replication


Multi-datacenter consistency refers to the integrity of application data that is stored in multiple, possibly geographically distant locations. Due to large communication delays between sites, traditional protocols for enforcing integrity despite concurrent operation on separate copies of data may be prohibitively expensive. As a result, multi-datacenter storage systems increasingly implement a range of techniques that avoid coordination. The most basic strategy foregoes all coordination and only provides users with the guarantee that eventually all replicas agree (i.e., eventual consistency). However, many systems avoid coordination while still preserving various integrity criteria. Generally, the more application semantics that are made available to a storage engine, the more that coordination can be safely avoided without compromising application integrity. A growing set of abstract data type implementations (e.g., counters) designed...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Alpern B, Schneider FB. Defining liveness. Inf Process Lett. 1985;21(4):181–5.MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Ameloot TJ, Neven F, Van Den Bussche J. Relational transducers for declarative networking. J ACM. 2013;60(2):15:1–15:38.MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Bailis P, Davidson A, Fekete A, Ghodsi A, Hellerstein JM, Stoica I. Highly available transactions: virtues and limitations. In: Proceedings of the 40th International Conference on Very Large Data Bases; 2014.Google Scholar
  4. 4.
    Bailis P, Fekete A, Franklin MJ, Ghodsi A, Hellerstein JM, Stoica I. Feral concurrency control: an empirical investigation of modern application integrity. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2015.Google Scholar
  5. 5.
    Bailis P, Fekete A, Franklin MJ, Hellerstein JM, Ghodsi A, Stoica I. Coordination avoidance in database systems. In: Proceedings of the 41st International Conference on Very Large Data Bases; 2015.Google Scholar
  6. 6.
    Bailis P, Fekete A, Ghodsi A, Hellerstein JM, Stoica I. Scalable atomic visibility with RAMP transactions. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2014.Google Scholar
  7. 7.
    Bailis P, Ghodsi A. Eventual consistency today: limitations, extensions, and beyond. ACM Queue. 2013;11(3):20–32.CrossRefGoogle Scholar
  8. 8.
    Bailis P, Venkataraman S, Franklin MJ, Hellerstein JM, Stoica I. VLDB J. 2014;23(2):279–302CrossRefGoogle Scholar
  9. 9.
    Barbará-Millá D, Garcia-Molina H. The demarcation protocol: a technique for maintaining constraints in distributed database systems. VLDB J. 1994;3(3):325–53.CrossRefGoogle Scholar
  10. 10.
    Bernstein PA, Hadzilacos V, Goodman N. Concurrency control and recovery in database systems, vol. 370. New York: Addison-wesley; 1987.Google Scholar
  11. 11.
    Bernstein PA, Shipman DW, Rothnie JB Jr. Concurrency control in a system for distributed databases (SDD-1). ACM Trans Database Syst. 1980;5(1): 18–51CrossRefGoogle Scholar
  12. 12.
    Brewer E. CAP twelve years later: how the “rules” have changed. Computer 2012;45(2):23–9.CrossRefGoogle Scholar
  13. 13.
    Das S, Agrawal D, El Abbadi A. G-store: a scalable data store for transactional multi key access in the cloud. In: Proceedings of the 1st ACM Symposium on Cloud Computing; 2010.Google Scholar
  14. 14.
    Davidson SB, Garcia-Molina H, Skeen D. Consistency in partitioned networks. ACM Comput Surv. 1985;17(3):341–70.CrossRefGoogle Scholar
  15. 15.
    Fekete A, Gupta D, Luchangco V, Lynch N, Shvartsman A. Eventually-serializable data services. In: Proceedings of the ACM SIGACT-SIGOPS 15th Symposium on the Principles of Distributed Computing; 1996. p. 300–9.Google Scholar
  16. 16.
    Fox A, Gribble SD, Chawathe Y, Brewer EA, Gauthier P. Cluster-based scalable network services. ACM SIGOPS Oper Syst Rev. 1997;31(5):78–91.CrossRefGoogle Scholar
  17. 17.
    Garcia-Molina H, Salem K. Sagas. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1987.Google Scholar
  18. 18.
    Gray JN, Lorie RA, Putzolu GR, Traiger IL. Granularity of locks and degrees of consistency in a shared data base. Technical report, IBM, 1976.Google Scholar
  19. 19.
    Haerder T, Reuter A. Principles of transaction-oriented database recovery. ACM Comput Surv. 1983;15(4):287–317.MathSciNetCrossRefGoogle Scholar
  20. 20.
    Helland P, Campbell D. Building on quicksand. In: Proceedings of the 4th Biennial Conference on Innovative Data Systems Research; 2009.Google Scholar
  21. 21.
    Johnson PR, Thomas RH. Rfc 667: the maintenance of duplicate databases. Technical report, 1 1975.Google Scholar
  22. 22.
    Kung H-T, Papadimitriou CH. An optimality theory of concurrency control for databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1979.Google Scholar
  23. 23.
    Li C, Porto D, Clement A, Gehrke J, Preguiça N, Rodrigues R. Making geo-replicated systems fast as possible, consistent when necessary. In: Proceedings of the 10th USENIX Symposium on Operating System Design and Implementation; 2012.Google Scholar
  24. 24.
    Li C, Leitao J, Clement A, Preguiça N, Rodrigues R et al. Automating the choice of consistency levels in replicated systems. In: Proceedings of the USENIX 2014 Annual Technical Conference; 2014.Google Scholar
  25. 25.
    Lu H, Veeraraghavan K, Ajoux P, Hunt J, Song YJ, Tobagus W, Kumar S, Lloyd W. Existential consistency: measuring and understanding consistency at facebook. In: Proceedings of the 25th ACM Symposium on Operating System Principles; 2015.Google Scholar
  26. 26.
    O’Neil PE. The Escrow transactional method. ACM Trans Database Syst. 1986;11(4):405–30.CrossRefGoogle Scholar
  27. 27.
    Recht B, Ré C, Wright S, Niu F. Hogwild: a lock-free approach to parallelizing stochastic gradient descent. In: Advances in Neural Information Proceedings of the Systems 24, Proceedings of the 25th Annual Conference on Neural Information Proceedings of the Systems; 2011.Google Scholar
  28. 28.
    Roy S, Kot L, Bender G, Ding B, Hojjat H, Koch C, Foster N, Gehrke J. The homeostasis protocol: avoiding transaction coordination through program analysis. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2015.Google Scholar
  29. 29.
    Shapiro M, Preguiça N, Baquero C, Zawirski M. A comprehensive study of convergent and commutative replicated data types. INRIA TR 7506. 2011.Google Scholar
  30. 30.
    Wada H, Fekete A, Zhao L, Lee K, Liu A. Data consistency properties and the trade-offs in commercial cloud storage: the consumers’ perspective. In: Proceedings of the 5th Biennial Conference on Innovative Data Systems Research; 2011.Google Scholar
  31. 31.
    Weihl W. Specification and implementation of atomic data types. PhD thesis, Massachusetts Institute of Technology, 1984.Google Scholar
  32. 32.
    Yu H, Vahdat A. Design and evaluation of a conit-based continuous consistency model for replicated services. ACM Trans Comput Syst. 2002;20(3):239–82.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Computer ScienceStanford UniversityPalo AltoUSA