CooMR: cross-task coordination for efficient data management in MapReduce programs
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Hadoop employs Java-based network transport stack on top of the Java Virtual Machine (JVM) for its data shuffling and merging purposes. Our examination reveals that JVMintroduces a significant amount of overhead to data processing capability of the native interface. Furthermore, JVM constrains the use of high-performance networking mechanisms such as RDMA (Remote Direct Memory Access) which has established itself as an effective data movement technology in many networking environments because of its low-latency, high bandwidth, low CPU utilization, and energy efficiency. In this paper, we introduce a plug-in library called JVM-Bypass Shuffling (JBS) for Hadoopdata shuffling. JBS helps Hadoop data shuffling by avoiding Java-based transport protocols, removing the overhead and limitations of the JVM. In addition, we design JBS as a portable library that can leverage both TCP/IP and RDMA on different network systems such as InfiniBand and 1/10 Gigabit Ethernet. We have designed and implemented JBS as part of Hadoop acceleration. It has been transferred to Mellanox as the software product UDA(Unstructured Data Accelerator) and used to enable our studies on a variety of merging algorithms. Our performance evaluation demonstrates that JBS can effectively reduce the execution time of Hadoop jobs by up to 66.3% and lower the CPU utilization by 48.1%.