Optimizing MapReduce Based on Locality of K-V Pairs and Overlap between Shuffle and Local Reduce

Abstract

At present, MapReduce is the most popular programming model for Big Data processing. As a typical open source implementation of MapReduce, Hadoop is divided into map, shuffle, and reduce. In the mapping phase, according to the principle moving computation towards data, the load is basically balanced and network traffic is relatively small. However, shuffle is likely to result in the outburst of network communication. At the same time, reduce without considering data skew will lead to an imbalanced load, and then performance degradation. This paper proposes a Locality-Enhanced Load Balance (LELB) algorithm, and then extends the execution flow of MapReduce to Map, Local reduce, Shuffle and final Reduce (MLSR), and proposes a corresponding MLSR algorithm. Use of the novel algorithms can share the computation of reduce and overlap with shuffle in order to take full advantage of CPU and I/O resources. The actual test results demonstrate that the execution performance using the LELB algorithm and the MLSR algorithm outperforms the execution performance using hadoop by up to 9.2% (for Merge Sort) and 14.4% (for Word Count).