Application lifecycle in YARN in Hadoop

Introduction

YARN manages how applications run on a cluster. Understanding its lifecycle helps you know how your programs start, run, and finish.

When you want to run a big data job on a Hadoop cluster.

When you need to track the progress of your data processing application.

When you want to troubleshoot why a job failed or stopped.

When you want to optimize resource use for your applications.

When you are learning how Hadoop manages multiple users and jobs.

Syntax

Hadoop

1. Application Submission
2. Application Master Initialization
3. Resource Request and Allocation
4. Container Launch
5. Application Execution
6. Application Completion and Cleanup

This is a high-level sequence of steps in YARN's application lifecycle.

Each step involves communication between YARN components like ResourceManager and NodeManager.

Examples

This shows the start of the lifecycle: submission and master setup.

Hadoop

1. Submit your job to YARN using a client command.
2. YARN starts an Application Master for your job.

Resource allocation is how YARN manages cluster resources for your job.

Hadoop

3. Application Master asks ResourceManager for containers.
4. ResourceManager allocates containers on nodes.

Execution and cleanup ensure your job runs and finishes properly.

Hadoop

5. Containers run your tasks.
6. After tasks finish, Application Master reports completion and cleans up.

Sample Program

This simple code shows the main steps of a YARN application lifecycle in order.

Hadoop

# This is a conceptual Python simulation of YARN application lifecycle steps

class YARNApplication:
    def __init__(self, app_id):
        self.app_id = app_id
        self.status = 'NEW'

    def submit(self):
        self.status = 'SUBMITTED'
        print(f'Application {self.app_id} submitted.')

    def start_am(self):
        self.status = 'AM_RUNNING'
        print(f'Application Master for {self.app_id} started.')

    def request_resources(self):
        print('Requesting containers from ResourceManager...')

    def allocate_containers(self):
        print('ResourceManager allocated containers.')

    def run_tasks(self):
        self.status = 'RUNNING'
        print('Containers are running tasks...')

    def finish(self):
        self.status = 'FINISHED'
        print(f'Application {self.app_id} finished and cleaned up.')


app = YARNApplication('app_001')
app.submit()
app.start_am()
app.request_resources()
app.allocate_containers()
app.run_tasks()
app.finish()

Important Notes

The Application Master is unique per application and manages its tasks.

ResourceManager controls the cluster resources and assigns containers.

NodeManagers run containers on individual cluster nodes.

Summary

YARN manages applications by coordinating submission, resource allocation, execution, and cleanup.

Understanding the lifecycle helps you monitor and troubleshoot big data jobs.

Each step involves different YARN components working together smoothly.