Yarn Tutorial for Beginners | Hadoop Yarn Training Video | Hadoop Yarn Architecture

YARN is the architectural centre of Hadoop that allows multiple data processing engines such as interactive SQL, real-time streaming, data science and batch processing to handle data stored in a single platform, unlocking an entirely new approach to analytics.

YARN is the foundation of the new generation of Hadoop and is enabling organizations everywhere to realize a modern data architecture.

WHAT YARN DOES: YARN is the prerequisite for Enterprise Hadoop, providing resource management and a central platform to deliver consistent operations, security, and data governance tools across Hadoop clusters.
YARN also extends the power of Hadoop to incumbent and new technologies found within the data centre so that they can take advantage of cost effective, linear-scale storage and processing. It provides ISVs and developers a consistent framework for writing data access applications that run IN Hadoop
YARN’s original purpose was to split up the two major responsibilities of the JobTracker/TaskTracker into separate entities:
• a global ResourceManager
• a per-application ApplicationMaster
• a per-node slave NodeManager
• a per-application Container running on a NodeManager

The ResourceManager and the NodeManager formed the new generic system for managing applications in a distributed manner. The ResourceManager is the ultimate authority that arbitrates resources among all applications in the system. The ApplicationMaster is a framework-specific entity that negotiates resources from the ResourceManager and works with the NodeManager(s) to execute and monitor the component tasks.
The ResourceManager has a scheduler, which is responsible for allocating resources to the various applications running in the cluster, according to constraints such as queue capacities and user limits. The scheduler schedules based on the resource requirements of each application.

Each ApplicationMaster has responsibility for negotiating appropriate resource containers from the scheduler, tracking their status, and monitoring their progress. From the system perspective, the ApplicationMaster runs as a normal container.
The NodeManager is the per-machine slave, which is responsible for launching the applications’ containers, monitoring their resource usage (cpu, memory, disk, network) and reporting the same to the ResourceManager.

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