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Extra resources for Apache Hadoop YARN: Moving beyond MapReduce and Batch Processing with Apache Hadoop 2 (Addison-Wesley Data & Analytics Series)

Sample text

We considered multiple solutions—for example, the host operating system facilitating user limits that are both static and dynamic, putting caps on individual tasks, and setting a cumulative limit on the overall usage across all tasks. We eventually settled on the ability to control individual tasks by killing any process trees that surpass predetermined memory limits. The TaskTracker uses a default admin configuration or a per-job user-specified configuration, continuously monitors tasks’ memory usage in regular cycles, and shoots down any process tree that has overrun the memory limits.

In addition, they all pointed to the fundamental limitation of the traditional resource management software—that is, the ability to understand data locality as a first-class dimension. This aspect of [Requirement 4] Locality Awareness is a key requirement for YARN. n [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. Cluster Utilization MapReduce jobs consist of multiple stages: a map stage followed by a shuff le and a reduce stage.

Isolation on Individual Nodes Many times, tasks of user Map/Reduce applications would get extremely memory intensive. This could occur due to many reasons—for example, due to inadvertent bugs in the users’ map or reduce code, because of incorrectly configured jobs that would unnecessarily process huge amounts of data, or because of mappers/reducers spawning children processes whose memory/CPU utilization couldn’t be controlled by the task JVM. The last issue was very possible with the Hadoop streaming framework, Phase 2: Dawn of the Shared Compute Clusters which enabled users to write their MapReduce code in an arbitrary language that was then run under separate children processes of task JVMs.

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