Big Data: Overview of apache Hadoop

What YARN Does

  • Scalability The processing power in data centers continues to grow quickly. Because YARN ResourceManager focuses exclusively on scheduling, it can manage those larger clusters much more easily.
  • Compatibility with MapReduce Existing MapReduce applications and users can run on top of YARN without disruption to their existing processes.
  • Improved cluster utilization. The ResourceManager is a pure scheduler that optimizes cluster utilization according to criteria such as capacity guarantees, fairness, and SLAs. Also, unlike before, there are no named map and reduce slots, which helps to better utilize cluster resources.
  • Support for workloads other than MapReduceAdditional programming models such as graph processing and iterative modeling are now possible for data processing. These added models allow enterprises to realize near real-time processing and increased ROI on their Hadoop investments.
  • AgilityWith MapReduce becoming a user-land library, it can evolve independently of the underlying resource manager layer and in a much more agile manner.

How YARN Works

  • a global ResourceManager
  • a per-application ApplicationMaster.
  • a per-node slave NodeManager and
  • a per-application Container running on a NodeManager




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