Consistency in Distributed Data Stores

TL;DR

This paper reviews various consistency models in distributed data stores, analyzing their trade-offs, implementations like COPS, GentleRain, and Dynamo, and discusses scenarios where each model is appropriate.

Contribution

It provides a comprehensive overview of consistency models, compares implementations, and offers insights into their practical applications and future research directions.

Findings

Strong consistency is simple but incompatible with availability during partitions.

Causal and eventual consistency models are more flexible but less strict.

Different data stores are suitable for different consistency requirements.

Abstract

This paper focuses on the problem of consistency in distributed data stores.We define strong consistency model which provides a simple semantics for application programmers, but impossible to achieve with availability and partition-tolerance. We also define weaker consistency models including causal and eventual consistency. We review COPS and GentleRain as two causally consistent data stores as well as Dynamo as an eventually consistent data store. We provide insights about scenarios where each of these methods is suitable, and some future research directions.