Probabilistic Consistency Guarantee in Partial Quorum-based Data Store

Xin Yao, Cho-Li Wang. Probabilistic Consistency Guarantee in Partial Quorum-based Data Store

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Abstract: Many NoSQL databases support quorum-based protocols, which require a subset of replicas (called a quorum) respond to each write/read operation. These systems configure the quorum size to tune the operation latency and adopt multiple consistency levels. Some recent works illustrate that using probability models to quantify the chance of reading the last update is important because it could avoid returning stale values under eventual consistency. There are two challenging issues: (1) from inconsistent replicas, how to determine the minimum quorum size (i.e., the lowest access latency) to read the newest data at a specified probability; (2) node failure frequently happens in large-scale systems, how to guarantee the probability-based consistent reads. This paper presents Probabilistic Consistency Guarantee (PCG), which is the first dynamic quorum decision and failure-aware quantification model. PCG model respectively quantifies the server-side consistency after the latest write, which reflects the object’s time-varying update progress, and the possibility of reading this update when responding to the end-users. Our theoretical analysis derives several formulas to determine the quorum size of a read quorum and the consensus result selected from this quorum is the data updated by the last write at the user-specified probability. When some replicas are unavailable, our model knows how to rescale the quorum and read values from surviving replicas could reduce the stale reads caused by node failures. The experimental results in Cassandra demonstrate that the PCG model can achieve up to 77.7% more accurate predictions and reduce up to 48.9% read latency than those of the previous model.