The Role of YARN in Apache Spark: Resource Management and Architecture Explained

YARN is a crucial part of the Apache Hadoop ecosystem that serves as a resource management framework for large-scale distributed applications like Apache Spark. In Spark, YARN acts as a cluster manager to deploy, scale, and manage resources required for Spark applications. The YARN architecture has two components: the ResourceManager and the NodeManager. The ResourceManager manages resource allocation in the cluster, and the NodeManager runs on each worker node and manages resources on that node.

When a Spark application is submitted to the cluster, YARN assigns resources like CPU, memory, and storage to the application based on its requirements. YARN monitors the application’s progress and resource usage while it runs on the allocated resources. YARN also schedules and prioritizes jobs in the cluster to enable multiple applications to run simultaneously and efficiently use available resources.

YARN also includes an Application Master that manages the lifecycle of a Spark application, such as requesting resources from the Resource Manager, monitoring the application’s progress, and reporting the results back to the user. The YARN architecture in Spark enables efficient processing of large-scale datasets in a distributed computing environment by providing a reliable way to manage resource allocation on a Hadoop cluster.