What Does YARN Stand for in Big Data? A Complete Guide

Big Data has become a buzzword in the tech industry lately, and with the ever-growing demand for data processing, Hadoop has emerged as a premier data processing tool. YARN is a central part of the Hadoop ecosystem, which enables multiple processing engines to handle data. In this article, we will delve into what YARN stands for, its use cases, and how it works.

Introduction

The need for data processing and analysis has extended beyond traditional databases and data warehouses. The explosion of data generated through social media, mobile devices, and sensors has created a need for big data processing. Hadoop, an open-source tool, has become the go-to tool for big data processing. In Hadoop, YARN (Yet Another Resource Negotiator) is a central component that manages resources for the Hadoop ecosystem.

What is YARN?

YARN, short for Yet Another Resource Negotiator, is a resource manager and job scheduler for Hadoop. It enables Hadoop to support multiple processing engines like Apache Spark, Apache HBase, and Apache Storm, to name a few. In traditional Hadoop, MapReduce is the only processing engine. With YARN, Hadoop can process different types of data (interactive, stream, graph, batch) and workloads more efficiently.

How does YARN work?

To understand how YARN works, we need to understand the Hadoop ecosystem. The Hadoop ecosystem consists of storage, processing engines, and resource management. YARN is the layer that manages the resources, providing a central platform for scheduling and managing multiple processing engines.

YARN has two main components: the Resource Manager (RM) and the Node Manager (NM). The Resource Manager allocates resources and schedules tasks, while the Node Manager runs tasks on individual nodes and reports resource utilization. The application developer specifies the application requirements, which are then translated into a set of containers with specific resource requirements by the Resource Manager. These containers are automatically distributed across the cluster by the Node Manager. Once the application completes, the resources are released.

Use cases for YARN

YARN’s use cases are not just limited to batch processing. With the advent of new processing engines in the Hadoop ecosystem, YARN’s importance has only increased. Here are some of the use cases for YARN:

Resource Management and Scheduling

YARN enables a central platform for resource management, scheduling, and task coordination, enabling multiple processing engines like MapReduce and Spark to coexist and share resources efficiently.

Dynamic Provisioning and Scaling

With the Node Manager, YARN allows for dynamic resource provisioning and scaling. It can allocate resources as required, allowing for more efficient use of cluster resources.

Multi-Tenancy Support

YARN can support multiple tenants or users, enabling isolation, and sharing computing resources efficiently.

Conclusion

YARN is a critical component of the Hadoop ecosystem, enabling efficient resource management, job scheduling, and coordination of multiple processing engines. Its ability to support different data processing scenarios, including batch, interactive, and stream processing, have made it a go-to platform for big data processing. By understanding the fundamentals of YARN, you can evaluate and select the most appropriate solution for your big data processing needs.