Announcing version 1.2 of the MapR Distribution for Hadoop

Today we announced version 1.2 of the MapR Distribution for Apache Hadoop. With this release, MapR continues to push the envelope by making Hadoop more accessible to more users, more languages, and more platforms. This release includes numerous features and capabilities including:
  • Ability to take advantage of next generation resource management framework: MapR users will be able to take advantage of MapReduce 2.0 once it is ready for production use. Although it is expected to take several months for the community to stabilize Hadoop 0.23, users will be able to take advantage of the combined benefits of MapReduce 2.0, such as backward-compatibility and scalability, and other unique capabilities of MapR, such as HA (no lost tasks or jobs during a JobTracker or ApplicationMaster failure) and the high-performance shuffle.
  • High-performance native access library: With Version 1.2, MapR provides a libhdfs implementation that bypasses Java altogether and provides high-performance access to the distributed file system from C/C++ applications and other compatible scripting languages. There is no need to recompile applications that use libhdfs, since the API (header file) is identical.
  • Upgrade of various packages including HBase™, Hive and Pig: The HBase™ package in the MapR distribution has been upgraded to release 0.90.4. In addition, MapR has identified several critical stability and data corruption issues in 0.90.4, which we have addressed by backporting 15 fixes from future HBase™ releases. Versions of Hive and Pig have also been upgraded in the MapR distribution, so users can leverage the latest bug fixes and features available from these Apache projects.
  • MapR Virtual Machine (VM). MapR now provides a VMWare virtual machine that allows users to experiment with the MapR distribution. Although this environment is not suitable for any performance or scale testing, it makes it easy to experiment with some of the unique capabilities of MapR, such as NFS and snapshots. The VM is also a great asset if you are new to Hadoop, because you could be up and running on any environment (e.g., your laptop) within minutes.
  • Additional performance improvements. The MapR distribution is already 2-5x faster than other distributions on typical Hadoop workloads, including the standard DFSIO and Terasort benchmarks, resulting in a significant hardware cost reduction. The 1.2 release continues to push the envelope, with a number of performance improvements in the platform (file system and MapReduce layers).
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