The potential dangers of causal consistency and an explicit solution

  • Authors:

  • Peter Bailis;Alan Fekete;Ali Ghodsi;Joseph M. Hellerstein;Ion Stoica

  • Affiliations:

  • University of California, Berkeley;University of Sydney;University of California, Berkeley and KTH/Royal Institute of Technology;University of California, Berkeley;University of California, Berkeley

  • Venue:
  • Proceedings of the Third ACM Symposium on Cloud Computing
  • Year:
  • 2012

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Abstract

Causal consistency is the strongest consistency model that is available in the presence of partitions and provides useful semantics for human-facing distributed services. Here, we expose its serious and inherent scalability limitations due to write propagation requirements and traditional dependency tracking mechanisms. As an alternative to classic potential causality, we advocate the use of explicit causality, or application-defined happens-before relations. Explicit causality, a subset of potential causality, tracks only relevant dependencies and reduces several of the potential dangers of causal consistency.