Fast log replication in highly available data store

Modern large-scale data stores widely adopt consensus protocols to achieve high availability and throughput. The recently proposed Raft algorithm has better understandability and widely implemented in large amount of open source projects. In these consensus algorithms including Raft, log replication is a common and frequently used operation which has significant impact on the system performance. Especially, since the commit latency is capped by the slowest follower out of the majority followers responded to the leader, it’s important to design a fast scheme to process the replicated logs by follower nodes. Based on the analysis on how the follower node handles the received log entries in Raft algorithm, we figure out the main factors influencing the duration time from when the follower receives the log and to when it acknowledges the leader this log was received. In terms of these factors we propose an effective log replication scheme to optimize the process of flushing logs to disk and replaying them, referred to as Raft with Fast Followers (FRaft). Finally, we compare the performance of Raft and FRaft using YCSB benchmark and Sysbench test tools, and experimental results demonstrate FRaft has lower latency and higher throughput than the Raft only using straightforward pipeline and batch optimization for log replication.