What is Hadoop Yarn? | Hadoop Yarn Tutorial | Hadoop Yarn Architecture | COSO IT

Summary

This tutorial covers the fundamentals of YARN (Yet Another Resource Negotiator) and its role in the Hadoop ecosystem. The video explains how YARN enhances Hadoop's performance by separating resource management and processing components, overcoming limitations of the original MapReduce framework. It discusses YARN's architecture, including components like the Resource Manager, Application Master, and Node Manager, and explains how YARN enables running various distributed applications beyond MapReduce. The tutorial also provides an overview of MapReduce 2, demonstrating how a word count application runs on YARN with improved scalability and efficiency.

Takeaways

• 📊 YARN (Yet Another Resource Negotiator) is a core component of Hadoop 2, improving performance over the classic MapReduce engine.
• 🔍 YARN separates the resource management layer from the processing components layer, unlike in Hadoop 1 where they were bound together.
• 🚀 The motivation behind YARN was to solve scalability bottlenecks faced by Hadoop 1's single JobTracker system, especially in large clusters.
• 📉 Limitations of MapReduce 1 include poor resource utilization and lack of support for non-MapReduce applications.
• 🌐 YARN supports non-MapReduce distributed applications, opening up Hadoop to a wider range of use cases.
• 🛠 In YARN, the responsibilities of the JobTracker are split into the Resource Manager (for resource allocation) and the Application Master (for application-specific processing).
• 📦 A container is the basic unit of resource allocation in YARN, consisting of resources like CPU, memory, and storage.
• 💡 YARN's Resource Manager handles global resource allocation across the cluster, while Node Managers manage containers and their tasks at the node level.
• 📁 MapReduce 2 is the updated version of MapReduce that runs on YARN, allowing each job to manage its own resources through its own Application Master.
• 🖥 A Word Count application in MapReduce 2 involves a Resource Manager, Node Managers, Application Masters, and containers coordinating to process data stored in HDFS.

Q & A

What is the focus of this tutorial series?

The tutorial series focuses on Big Data and Hadoop, specifically covering MapReduce and YARN (Yet Another Resource Negotiator).

What is YARN, and why was it introduced in Hadoop?

YARN is a core component of Hadoop introduced to improve performance by separating the resource management layer from the processing components layer. It addresses the limitations of the classic MapReduce engine by offering better resource management and scalability.

What are the main limitations of MapReduce version 1 that YARN aims to overcome?

MapReduce version 1 has scalability bottlenecks due to a single job tracker, inefficient resource utilization, and is limited to running only MapReduce jobs. YARN resolves these issues by allowing multiple distributed applications to run and improving resource management.

How does YARN improve resource management compared to MapReduce version 1?

In YARN, the job tracker's responsibilities are split into two components: the Resource Manager, which handles resource allocation across the cluster, and the Application Master, which manages the resources needed for individual applications. This allows for more efficient resource management and scalability.

What is the role of the Resource Manager in YARN?

The Resource Manager is responsible for managing and allocating resources across the cluster. It oversees the entire cluster's resource usage and coordinates resource allocation for different applications.

What does the Application Master do in YARN?

The Application Master manages the lifecycle of an application, including resource requests and task scheduling. It communicates with both the Resource Manager and Node Managers to allocate resources and ensure the application runs smoothly.

How are containers used in YARN?

Containers in YARN are units of resource allocation that bundle resources like CPU, RAM, and network. They run application-specific processes, including MapReduce tasks, under the management of Node Managers.

How is the MapReduce version 2 framework different from version 1?

MapReduce version 2 operates on YARN, where each job has its own Application Master responsible for managing resources and tasks. Unlike version 1, where a single Job Tracker handled all tasks, version 2 distributes the responsibilities for better scalability and fault tolerance.

What is the function of Node Managers in YARN?

Node Managers are responsible for managing the containers on each node in the cluster. They monitor and control the resources allocated to each container and ensure that no container exceeds its allocated resources.

How does YARN enable Hadoop to support more than just MapReduce jobs?

YARN separates the resource management layer from the application layer, allowing it to support various types of distributed applications, not just MapReduce. This flexibility makes Hadoop more versatile and scalable.