CausalTester: Measuring the Consistency of Replicated Services via Causality Semantics

Abstract

Cloud and Big Data systems often replicate data and prefer weak consistency such as eventual consistency for better scalability and availability. Such weak consistency may produce unexpected and harmful system behaviors, for example, stale reads and conflicting writes. In order to measure the consistency levels and help developers understand the harmful degree, we propose a testing framework called CausalTester to evaluate the causality semantics of replicated systems, including 12 real test cases collected from Twitter, Flickr, Amazon, the corresponding benchmark services, and the automatic detection of causality violation with crash injection. We implement the testing framework and measure the consistency of three widely-used distributed databases. The experimental results show that it is effective to detect the consistency violations for the weak consistency and helpful to find consistency-related bugs if the strong consistency is violated.