Practical and Fast Causal Consistent Partial Geo-Replication

Distributed storage systems are a fundamental component of large-scale Internet services. To keep up with the increasing expectations of users regarding availability and latency, the design of data storage systems has evolved to achieve these properties by exploiting techniques such as partial replication, geo-replication, and weaker consistency models. How to combine all these techniques in a single solution in a practical and efficient way is highly challenging. In this paper we propose a novel replication scheme that can offer causal+ consistency in a geo-distributed scenario with partial replication, where datacenters replicate different portions of the entire database. We leverage on a recently proposed methodology that decouples the propagation of data and causality-tracking metadata. Our solution presents a novel causal consistency tracking and enforcing algorithm, focusing on maximizing parallelism in the execution of remote operations which, as we show, has a significant influence on the performance of a partially replicated system. We also propose and implement a design to integrate our solution in the popular Cassandra database. Experimental results show that, by exploring a new position in the trade-off between throughput and data visibility (by balancing the execution of local and remote operations, respectively), our solution presents overall good performance.