HARP: Towards enhancing data recency for eventually consistent data stores

To attain high performance and remain available during network partitions or node failures, modern distributed systems often sacrifice recency guarantees, which can provide a uniform view on recent versions of data items for different clients. In this work, we consider the problem of increasing the probability of data recency while preserving low response latency and maintaining high availability on top of an eventually consistent data store. To solve the problem, we propose HARP, an approach that can enhance data recency in a highly available way. Based on HARP, we implement an agent layer to detect stale reads and resolve the conflicts, and by leveraging widely deployed data store technologies, we build a data storage system. We compare the prototype system to Cassandra, and experimentally prove that our method produces low overhead (less than 10%) based on the eventually consistent configuration and, for most workloads, achieves better performance than the Cassandra's strong “read your writes” configurations.