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Yarn Own BD'

YARN (Yet Another Resource Negotiator) is a Big Data framework that manages resources like memory and CPU for applications using components such as Resource Containers, Scheduler, ApplicationsManager, and ApplicationMaster. It includes features like a Reservation System for pre-booking resources and Federation for connecting multiple clusters. The application workflow involves submitting an application, resource allocation, and monitoring, with potential failure points including task, application master, resource manager, and node manager failures.

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SUJITHA M
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8 views3 pages

Yarn Own BD'

YARN (Yet Another Resource Negotiator) is a Big Data framework that manages resources like memory and CPU for applications using components such as Resource Containers, Scheduler, ApplicationsManager, and ApplicationMaster. It includes features like a Reservation System for pre-booking resources and Federation for connecting multiple clusters. The application workflow involves submitting an application, resource allocation, and monitoring, with potential failure points including task, application master, resource manager, and node manager failures.

Uploaded by

SUJITHA M
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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1)YARN:

YARN (Yet Another Resource Negotiator) is a framework in Big Data that helps manage
resources like memory, CPU, disk, and network for various applications. It does this using
different components:

Components of yarn:

1. Resource Containers: These are like boxes that hold the required resources
(memory, CPU, etc.) for an application.
2. Scheduler:
o It decides how to divide cluster resources among applications and queues.
o It uses customizable policies. For example, the CapacityScheduler and
FairScheduler are two popular scheduling policies.
3. ApplicationsManager:
o It handles job submissions.
o It starts and restarts the ApplicationMaster, which is a manager for individual
applications.
4. ApplicationMaster:
o This is specific to each application.
o It requests resource containers from the Scheduler and monitors the progress
of its application.
5. Reservation System:
o This feature allows users to book resources in advance by specifying time-
related requirements (like deadlines).
o It ensures that critical jobs run on time by reserving the needed resources.
6. Federation:
o This feature connects multiple smaller YARN clusters into one large, unified
cluster.
o It helps scale YARN to handle very large jobs or allows sharing resources
across multiple clusters.

YARN diagram:
Application workflow in yarn:
Application workflow in Hadoop
YARN
Client submits an application
The Resource Manager allocates a container to start the
Application Manager.
The Application Manager registers itself with the Resource
Manager.
The Application Manager negotiates containers from the Resource
Manager.
The Application Manager notifies the Node Manager to launch
containers.
. Application code is executed in the container.
Client contacts Resource Manager/Application Manager to monitor
application’s status.
Once the processing is complete, the Application Manager un-
registers with the Resource Manager
Failure in yarn:

Task failure

Application master failure

Resource manager failure

Node manager failure

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