SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Mars: a MapReduce framework on graphics processors
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
CLOUDLET: towards mapreduce implementation on virtual machines
Proceedings of the 18th ACM international symposium on High performance distributed computing
Evaluating MapReduce on Virtual Machines: The Hadoop Case
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Improving MapReduce performance in heterogeneous environments
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Hunting for problems with Artemis
WASL'08 Proceedings of the First USENIX conference on Analysis of system logs
X-trace: a pervasive network tracing framework
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
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MapReduce programming model is emerging as an efficient tool for data-intensive applications. Hadoop, an open-source implementation of MapReduce, has been widely adopted and experienced by both academia and enterprise. Recently, lots of efforts have been done on improving the performance of MapReduce system and on analyzing the MapReduce process based on the log files generated during the Hadoop execution. Visualizing log files seems to be a very useful tool to understand the behavior of the Hadoop process. In this paper, we present MR-Scope, a real-time MapReduce tracing tool. MR-Scope provides a real-time insight of the MapReduce process, including the ongoing progress of every task hosted in Task Tracker. In addition, it displays the health of the Hadoop cluster data nodes, the distribution of the file system's blocks and their replicas and the content of the different block splits of the file system. We implement MR-Scope in native Hadoop 0.1. Experimental results demonstrat that MR-Scope's overhead is less than 4% when running wordcount benchmark.