Efficient data access strategies for Hadoop and Spark on HPC cluster with heterogeneous storage

The most popular Big Data processing frameworks of these days are Hadoop MapReduce and Spark. Hadoop Distributed File System (HDFS) is the primary storage for these frameworks. Big Data frameworks like Hadoop MapReduce and Spark launch tasks based on data locality. In the presence of heterogeneous storage devices, when different nodes have different storage characteristics, only locality-aware data access cannot always guarantee optimal performance. Rather, storage type becomes important, specially when high performance SSD and in-memory storage devices along with high performance interconnects are available. Therefore, in this paper, we propose efficient data access strategies (e.g. Greedy (prioritizes storage type over locality), Hybrid (balances the load for locality and high performance storage), etc.) for Hadoop and Spark considering both data locality and storage types. We re-design HDFS to accommodate the enhanced access strategies. Our evaluations show that, the proposed data access strategies can improve the read performance of HDFS by up to 33% compared to the default locality-aware data access. The execution times of Hadoop and Spark Sort are also reduced by up to 32% and 17%. The performances of Hadoop and Spark TeraSort are also improved by up to 11% through our design.