RHJoin: A fast and space-efficient join method for log processing in MapReduce

Equi-join is heavily used in MapReduce-based log processing. With the rapid growth of dataset sizes, join methods on MapReduce are extensively studied recently. We find that existing join methods usually cannot get high query performance and affordable storage consumption at the same time when faced with a huge amount of log data. They either only optimize one aspect but significantly sacrifice the other or have limited applications. In this paper, after analyzing characteristics of the workloads and underlying MapReduce, we present a join method with specific optimizations for log processing called RHJoin (Repartition Hash Join) and its implementation on Hadoop. In RHJoin, reference tables are partitioned in the pre-processing step, the log table is partitioned on the map side and hash join is executed on the reduce side. The shuffle procedure of MapReduce is also optimized by removing the sort step and overlapping the execution of mappers and reducers. Comprehensive experiments show that RHJoin achieves high query performance with only a small extra storage cost, and has wide application circumstances for log processing.