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Hadoop Architecture – YARN

Hadoop ecosystem contain of Hadoop Distributed File System-HDFS and HDFS components, MapReduce, YARN, Hive, Apache Pig, Apache HBase and HBase components, HCatalog, Avro, Thrift, Drill, Apache mahout, Sqoop, Apache Flume, Ambari, Zookeeper and Apache OOzie. YARN is the basic prerequisite for Enterprise Hadoop Infrastructure, providing resource management and a central platform to offer consistent processes, security, and data domination tools across all clusters. It also provides new tools found within the data center so that they can take benefit of cost effective, linear-scale storage and processing. YARN allows different data processing engines containing graph processing, interactive processing, stream processing and batch processing to run, track and process data stored in HDFS.

YARN offers support for additional processing models by implementing a flexible execution engine.
• YARN improves the performance of the Hadoop compute cluster.
• YARN resource manager emphases completely on scheduling making it easy to manage large Hadoop clusters.

Yarn Architecture

Cluster utilization

YARN’s dynamic sharing of cluster resources progresses utilization over more static MapReduce rules used in initial versions of Hadoop

Reservation System

Hadoop YARN also comprises a Reservation System feature that lets users stand-in cluster resources in advance for significant processing jobs to guarantee they run smoothly. To escape overloading a cluster with reservations, administrators can limit the amount of resources that can be reserved by individual users and can set automated policies to discard reservation requests that surpass the limits.


YARN permits multiple access engines (either open-source or proprietary) to practice Hadoop as the common standard for batch, interactive and real-time engines that can concurrently admission the same data set.


ResourceManager (RM) is the master that judges all the available cluster resources and thus aids to manage the distributed applications running on the YARN.


It manages the life-cycle of a job by guiding the NodeManager to make or terminate a container for a job. There is only one ApplicationMaster for a job.


NodeManagers yield orders from the ResourceManager and manage resources accessible on a single node.


YARN helps to significantly improve data center processing power. YARN’s ResourceManager efforts exclusively on scheduling and retains pace as clusters expand to thousands of nodes managing petabytes of data.

For details see Official web site of Hadoop here

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