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For example, a MapReduce distcp job or Cloudera Impala query that does a simple scan on a large table may be heavily disk-bound and require little memory.
Or, an Apache Spark incubating job executing an iterative machine-learning algorithm with complex updates may wish to store the entire dataset in memory and use spurts of CPU to perform complex computation on it. For that reason, the new YARN framework in Hadoop 2 allows workloads to share cluster resources dynamically between a variety of processing frameworks, including MapReduce, Impala, and Spark. Accounting for memory, CPU, and other resources separately confers several advantages: It allows us to treat tenants on a Hadoop cluster more fairly by rationing the resources that are most utilized at a point in time.
It makes resource configuration more straightforward, because a single resource does not need to be used as a proxy for others. It provides more predictable performance by not oversubscribing nodes, and protects higher-priority workloads with better isolation.
Finally, it can increase cluster utilization because all the above mean that resource needs and capacities can be configured less conservatively. Each node was configured with a number of slots, and each map or reduce task occupied a single slot regardless of how much memory or CPU it used. This approach offered the benefit of simplicity but had a few disadvantages.
Initially, YARN improved this situation by switching to memory-based scheduling — in YARN, each node is configured with a set amount of memory and applications such as MapReduce request containers for their tasks with configurable amounts of memory. CPU capacity is configured with the yarn. Dominant Resource Fairness Perhaps the most difficult challenge when managing multiple resources is deciding how to share them fairly.