YARN, which stands for Yet Another Resource Negotiator, is a core component of Hadoop that manages resources and schedules jobs in a Hadoop cluster. It was introduced in Hadoop 2.0 to overcome the limitations of the original MapReduce framework, providing a more flexible and efficient way to handle various types of data processing applications.

1. ResourceManager (RM):

   • The central authority that manages resources and schedules applications.
   • Consists of two main components:
     • Scheduler: Allocates resources to various running applications based on resource availability and scheduling policies.
     • ApplicationManager: Manages the lifecycle of applications, including accepting job submissions, negotiating the first container for executing the application-specific ApplicationMaster, and restarting the ApplicationMaster on failure.

2. NodeManager (NM):

   • Runs on each node in the cluster and is responsible for managing containers, monitoring their resource usage (CPU, memory, disk, network), and reporting this information to the ResourceManager.
   • Manages the execution of individual tasks within the containers.

3. ApplicationMaster (AM):

   • A framework-specific library that negotiates resources from the ResourceManager and works with the NodeManager(s) to execute and monitor tasks.
   • Each application has its own instance of ApplicationMaster.

4. Containers:

   • The fundamental unit of resource allocation in YARN.
   • Encapsulates a fixed amount of resources (memory, CPU) and is used to run a specific task.

The YARN architecture can be visualized as follows:

1. Job Submission:

   • A client submits an application to the ResourceManager.
   • The ResourceManager allocates a container for the ApplicationMaster and starts it.

2. Resource Negotiation:

   • The ApplicationMaster negotiates resources with the ResourceManager.
   • The ResourceManager allocates containers on various NodeManagers based on resource availability and scheduling policies.

3. Task Execution:

   • The ApplicationMaster coordinates with the NodeManagers to launch tasks within the allocated containers.
   • The NodeManagers monitor the resource usage of the containers and report back to the ResourceManager.

4. Job Completion:

   • Once all tasks are completed, the ApplicationMaster informs the ResourceManager.
   • The ResourceManager releases the resources and cleans up the containers.

Let's look at a simple example of how YARN manages a MapReduce job:

1. Job Submission:

   hadoop jar my-mapreduce-job.jar MyMapReduceJob input output
   

2. Resource Allocation:

   • The ResourceManager allocates a container for the ApplicationMaster.
   • The ApplicationMaster is launched and starts negotiating resources for the Map and Reduce tasks.

3. Task Execution:

   • The ApplicationMaster requests containers for Map tasks.
   • The NodeManagers launch the Map tasks in the allocated containers.
   • Once the Map tasks are completed, the ApplicationMaster requests containers for Reduce tasks.
   • The NodeManagers launch the Reduce tasks in the allocated containers.

4. Job Completion:

   • The ApplicationMaster informs the ResourceManager that the job is complete.
   • The ResourceManager releases the resources and cleans up the containers.

Question: Match the following YARN components with their descriptions:

Solution:

Question: Describe the steps involved in the YARN workflow from job submission to job completion.

Solution:

1. Job Submission: A client submits an application to the ResourceManager.
2. Resource Allocation: The ResourceManager allocates a container for the ApplicationMaster and starts it.
3. Resource Negotiation: The ApplicationMaster negotiates resources with the ResourceManager.
4. Task Execution: The ApplicationMaster coordinates with the NodeManagers to launch tasks within the allocated containers.
5. Job Completion: Once all tasks are completed, the ApplicationMaster informs the ResourceManager, which releases the resources and cleans up the containers.

In this section, we explored YARN, a critical component of Hadoop that manages resources and schedules jobs efficiently. We covered its key components, architecture, and workflow, and provided practical examples and exercises to reinforce the concepts. Understanding YARN is essential for effectively managing and optimizing Hadoop clusters, and it sets the foundation for more advanced topics in Hadoop.