Optimistic Execution in Key-Value Store

TL;DR

This paper introduces an optimistic execution approach for key-value stores that leverages monitoring and rollback to improve throughput under weaker consistency models like eventual or causal consistency.

Contribution

It proposes a detect-rollback method allowing correct execution of algorithms designed for sequential consistency on weaker consistency models.

Findings

20-40% throughput improvement with eventual consistency

Monitoring overhead is less than 8%

Violations detected in less than 1 second for 99.9% cases

Abstract

Limitations of CAP theorem imply that if availability is desired in the presence of network partitions, one must sacrifice sequential consistency, a consistency model that is more natural for system design. We focus on the problem of what a designer should do if she has an algorithm that works correctly with sequential consistency but is faced with an underlying key-value store that provides a weaker (e.g., eventual or causal) consistency. We propose a detect-rollback based approach: The designer identifies a correctness predicate, say P , and continue to run the protocol, as our system monitors P . If P is violated (because the underlying key-value store provides a weaker consistency), the system rolls back and resumes the computation at a state where P holds. We evaluate this approach in the Voldemort key-value store. Our experiments with deployment of Voldemort on Amazon AWS shows that using eventual consistency with monitoring can provide 20 - 40% increase in throughput when compared with sequential consistency. We also show that the overhead of the monitor itself is small (typically less than 8%) and the latency of detecting violations is very low. For example, more than 99.9% violations are detected in less than 1 second.