Apache Hadoop YARN: Moving beyond MapReduce and Batch by Arun C. Murthy, Vinod Kumar Vavilapalli, Doug Eadline,

By Arun C. Murthy, Vinod Kumar Vavilapalli, Doug Eadline, Joseph Niemiec, Jeff Markham

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Scaling the JobTracker to clusters containing more than about 4000 nodes would prove to be extremely difficult. Part of the problem arose from the fact that the JobTracker was keeping in memory data from user jobs that could potentially be unbounded. Despite the innumerable limits that were put in place, the JobTracker would eventually run into some other part of the data structure that wasn’t limited. For example, a user job might generate so many counters (which were then not limited) that TaskTrackers would spend all their time uploading those counters.

Requirement 4] Locality Awareness: The next-generation compute platform should support locality awareness—moving computation to the data is a major win for many applications. [Requirement 5] High Cluster Utilization: The next-generation compute platform should enable high utilization of the underlying physical resources. [Requirement 6] Secure and Auditable Operation: The next-generation compute platform should continue to enable secure and auditable usage of cluster resources. [Requirement 7] Reliability and Availability: The next-generation compute platform should have a very reliable user interaction and support high availability.

This server off loaded the responsibility of serving web requests about completed jobs away from the JobTracker. x releases. Central Scheduler When HOD was abandoned, the central scheduler that worked in unison with a traditional resource manager also went away. Trying to integrate existing schedulers with the newly proposed JobTracker-based architecture was a nonstarter due to the engineering challenges involved. It was proposed to extend the JobTracker itself to support queues of jobs. Users would interact with the queues, which are configured appropriately.

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