Quantifying eventual consistency with PBS

Abstract

Data store replication results in a fundamental trade-off between operation latency and data consistency. At the weak end of the consistency spectrum is eventual consistency providing no limit to the staleness of data returned. However, anecdotally, eventual consistency is often “good enough” for practitioners given its latency and availability benefits. In this work, we explain why eventually consistent systems are regularly acceptable in practice, analyzing both the staleness of data they return and the latency benefits they offer. We introduce Probabilistically Bounded Staleness (PBS), a consistency model which provides expected bounds on data staleness with respect to both versions and wall clock time. We derive a closed-form solution for versioned staleness as well as model real-time staleness under Internet-scale production workloads for a large class of quorum-replicated, Dynamo-style stores. Using PBS, we measure the latency–consistency trade-off for partial, non-overlapping quorum systems, including limited multi-object operations. We quantitatively demonstrate how and why eventually consistent systems frequently return consistent data within tens of milliseconds while offering significant latency benefits.