{"id":1232,"date":"2016-10-08T12:35:08","date_gmt":"2016-10-08T20:35:08","guid":{"rendered":"http:\/\/www.developerscloset.com\/?page_id=1232"},"modified":"2019-10-10T08:08:21","modified_gmt":"2019-10-10T16:08:21","slug":"yarn","status":"publish","type":"page","link":"https:\/\/www.developerscloset.com\/?page_id=1232","title":{"rendered":"Yarn"},"content":{"rendered":"

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YARN allows multiple access engines to use Hadoop as the common standard for batch, interactive and real-time engines that can simultaneously access the same data set. Many applications are available to YARN, like MapReduce for batch processing, Storm for real-time stream processing, or Spark for in-memory iterative processing.<\/p>\n

YARN comes bundled with MapReduce 2.0 (MRv2). MapReduce has undergone a complete overhaul in MRv2. The fundamental idea of MRv2’s YARN architecture is to split up the two primary responsibilities of the JobTracker \u2014 resource management and job scheduling\/monitoring \u2014 into separate daemons: a global ResourceManager (RM) and per-application ApplicationMasters (AM). With MRv2, the ResourceManager (RM) and per-node NodeManagers (NM), form the data-computation framework. The ResourceManager service effectively replaces the functions of the JobTracker, and NodeManagers run on slave nodes instead of TaskTracker daemons. The per-application ApplicationMaster is, in effect, a framework specific library and is tasked with negotiating resources from the ResourceManager and working with the NodeManager(s) to execute and monitor the tasks.<\/p>\n

Configure YARN<\/h1>\n

Install YARN<\/h2>\n

Yarn requires the following services:<\/p>\n