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Conflict-Free Replicated Data Types CRDTs

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Encyclopedia of Big Data Technologies

Definitions

A conflict-free replicated data type (CRDT) is an abstract data type, with a well-defined interface, designed to be replicated at multiple processes and exhibiting the following properties: (i) any replica can be modified without coordinating with other replicas and (ii) when any two replicas have received the same set of updates, they reach the same state, deterministically, by adopting mathematically sound rules to guarantee state convergence.

Overview

Internet-scale distributed systems often replicate data at multiple geographic locations to provide low latency and high availability, despite outages and network failures. To this end, these systems must accept updates at any replica and propagate these updates asynchronously to the other replicas. This approach allows replicas to temporarily diverge and requires a mechanism for merging concurrent updates into a common state. CRDTs provide a principled approach to address this issue.

As any abstract data type, a CRDT...

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Acknowledgements

This work was partially supported by NOVA LINCS (UID/CEC/04516/2013), EU H2020 LightKone project (732505), and SMILES line in project TEC4Growth (NORTE-01-0145-FEDER-000020).

Author information

Authors and Affiliations

  1. NOVA LINCS and DI, FCT, Universidade NOVA de Lisboa, Caparica, Portugal

    Nuno Preguiça

  2. HASLab/INESC TEC and Universidade do Minho, Braga, Portugal

    Carlos Baquero

  3. Sorbonne Université, LIP6 and INRIA, Paris, France

    Marc Shapiro

Authors
  1. Nuno Preguiça
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  2. Carlos Baquero
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  3. Marc Shapiro
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Corresponding author

Correspondence to Nuno Preguiça .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Sherif Sakr

  2. School of Information Technologies, Sydney University, Sydney, Australia

    Albert Y. Zomaya

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Preguiça, N., Baquero, C., Shapiro, M. (2019). Conflict-Free Replicated Data Types CRDTs. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_185

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