Cherry: A Distributed Task-Aware Shuffle Service for Serverless Analytics

Abstract

While there has been a lot of effort in recent years in optimising Big Data systems like Apache Spark and Hadoop, the all-to-all transfer of data between a MapReduce computation step, i.e., the shuffle data mechanism between cluster nodes remains always a serious bottleneck. In this work, we present Cherry, an open-source distributed task-aware Caching sHuffle sErvice for seRveRless analYtics. Our thorough experiments on a cloud testbed using realistic and synthetic workloads showcase that Cherry can achieve an almost 23% to 39% reduction in completion of the reduce stage with small shuffle block sizes, a 10% reduction in execution time on real workloads, while it can efficiently handle Spark execution failures with a constant task time re-computation overhead compared to existing approaches. © 2021 IEEE.