Explain the role of YARN (Yet Another Resource Negotiator) in Hadoop.

Hadoop is a Java-based framework that is developed in the Apache software environment for storing and processing very large and complex data using the MapReduce technique. YARN or Yet Another Resource Negotiator is one of the components of Hadoop which provides an interface for multiple data processing engines to interact with Hadoop by introducing a separation between resource management and job scheduling/monitoring.

YARN stands for Yet Another Resource Negotiator for Hadoop. Originally developed for Hadoop 2.0, YARN improved the MapReduce implementation and made it possible for Hadoop to handle a greater variety of data processing tasks. In simpler terms, YARN is the means by which clusters, resources, and jobs are managed in Hadoop. This means that the Hadoop can accommodate various data processing engines such as Interactive SQL, real-time streaming, and batch processing besides MapReduce thus expanding the opportunities for use of the platform.

YARN’s architecture comprises three main components:

1. ResourceManager (RM): Acts as the master daemon, managing and allocating cluster resources. It comprises two main parts:
   • Scheduler: Allocates resources based on application requirements and policies.
   • ApplicationManager: Manages job submissions and coordinates with NodeManagers.
2. NodeManager: It is an application that is executed in the data nodes to coordinate the running of containers.
3. ApplicationMaster (AM): This is the component that is tasked with negotiating with the ResourceManager and either the NodeManager(s) to launch and monitor the tasks. It has a data computation framework provided by the ResourceManager and a per-node slave NodeManager. The ApplicationMaster is included in the application framework package.

YARN (Yet Another Resource Negotiator) which is a core part of Hadoop that helps in boosting the architecture by effectively coordinating the resources and job scheduling . Here are the key roles of YARN in Hadoop:Here are the key roles of YARN in Hadoop:

Resource Management:

• Dynamic Allocation: YARN employs adequate strategies for distribution of resources the cluster CPU, memory, disk to a number of applications based on need.
• Centralized Management: It balances the resources in the Hadoop cluster and also avoids the conflict of resources within the control system.

Job Scheduling:

• Flexible Scheduling: In YARN, there are different Scheduling policies that are offered to the users such as FIFO, Capacity Scheduler, Fair Scheduler which helps in proper scheduling of the workload throughout the processing interface.
• Decoupling from MapReduce: Compared to using the concept of a job, YARN provides for more flexible use of resources by the system, as well as the separation of task scheduling from resource allocation, enabling Hadoop to work with frameworks other than MapReduce, including Apache Spark, Apache Flink, and Apache Tez.

Scalability and Flexibility:

• Multi-application Support: YARN also authorizes multiple applications to run-on the same cluster and thus made Hadoop scalable and flexible.
• Efficient Cluster Utilization: With this setup, it allows the various processing engines to run thereby improving the general utilization of the cluster.

Improved Performance:

• Optimal Resource Usage: This frees up the rest of the nodes for resource provisioning to applications as needed without wasting resources hence improving the performance of the system.
• Enhanced Application Management: YARN has ApplicationMaster that is responsible for processing resource allocation and activity tracking and monitoring that apply to the particular application, which are helpful when it comes to the efficient creation and management of tasks relative to the end application.

YARN enabled Hadoop to be utilized for interactive and on-going data processing in addition to the batch processing with MapReduce. It has made it possible for Hadoop to handle more than one application at the same time on the same foundation which has in turn improved the utilization of the resources while at the same time giving the best application optimization between the programs. YARN is an inherent feature of Hadoop for big data in an enterprise environment.

Additional Information Related to the Topic

• YARN Federation: Extends YARN’s scalability by allowing multiple YARN clusters to operate as a single federated cluster, improving resource utilization across large-scale deployments.
• Security: YARN integrates with Hadoop’s security features, providing authentication, authorization, and encryption to secure the cluster.
• Compatibility: YARN supports various data processing frameworks like Apache Spark, Apache Tez, and Apache Flink, making it a versatile resource manager for diverse big data applications.

By efficiently managing resources and scheduling jobs, YARN ensures that Hadoop remains a robust and scalable platform for big data processing.

Explain four main components of YARN.

YARN has four key elements and these are – ResourceManager, NodeManager, ApplicationMaster and Container. There are two primary components in data computation framework, known as ResourceManager and NodeManager. The ApplicationMaster starts as a component of the application and communicates with the ResourceManager to obtain necessary resources and works with NodeManager(s) to run and track the performance of the containers and the resources used by them.

What exactly ResourceManager does in the YARN?

ResourceManager is a component and it exists in the Hadoop cluster to manage the utilization of shared resources in the cluster. It deals with incoming resource requests from the ApplicationMaster and supervises their usage to control resource usage restrictions and limits. It also works hand in hand with the scheduler to assign resources to different applications.

What is NodeManager component in YARN and what does it do?

There is one NodeManager per cluster node where it is started and it is responsible for monitoring containers and reporting to the ResourceManager. It initiates and manages the process of container execution based on the request from an ApplicationMaster in order to minimize resource usage on the given worker node.

How YARN is a major advancement over Hadoop 1 MapReduce ?

YARN seems to provide better scalability since a wider range of applications can be hosted instead of just MapReduce jobs leading to more efficient utilization of the cluster. It is also evident that it decouples resource management from the job scheduling with the advantage of these aspects being able to scale independently depending on the application. Other aspects include cluster utilization where YARN has better capabilities such as resource aware scheduling and optimization.