HadoopM: A message-enabled data processing system on large clusters

Abstract

MapReduce as a popular platform for solving embarrassingly parallel problems has been extensively used on large commodity clusters. However constrained by embarrassingly parallel assumption, some computation patterns are not easy to express in MapReduce, and in some cases performance and efficiency can not be achieved without communication between tasks, such as iteration and map phase filtration from a holistic perspective. This paper presents HadoopM, a message-enhanced version of Hadoop MapReduce architecture that it breaks the key embarrassingly parallel assumption and can execute the MR jobs in a more efficient and elegant way. HadoopM allows user-defined message to be passed between mappers or reducers by two message passing mechanisms: lightweight and heavyweight, and asynchronous and synchronous message passing are both supported by system. HadoopM retains the scalability and fault-tolerance of Hadoop and is binary compatible with Hadoop Mapreduce. Our experimental results demonstrate the superiority of modified version over original Hadoop MapReduce on a range of algorithms. In some cases, such as PageRank and Skyline, HadoopM significantly boosts the job performance up to 50 %. © 2014 Springer-Verlag Berlin Heidelberg.