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Meet Hadoop Family: part 2
YARN
• What is it? 

Resource manager platform in a Hadoop cluster, it allows dynamic memory
and CPU sharing between processing frameworks such as MapReduce,
Spark, and others
• Purpose

More predictable performance

Better cluster utilization
• Compared to MapReduce v1

MapReduce v1 starts to break on > 4000 nodes

YARN allows other frameworks to run on it also support multi tenancy

YARN is backward compatible with MapReduce V1
YARN Architecture
Scheduler types
• FIFO Scheduler
• Capacity scheduler

Fixed pools for resources

FIFO scheduling for each pools
• Fair scheduler

Weighted pools for resources

Fair sharing

Capacity Scheduler
• Capacity guaranted on each pool, with hard limits
and soft limits
• Hierarchical pool with a root pool
• Elasticity with preemptive option

Preemption Option
• T1: Time of App2’s submission
• T2: Time of App1 can finish
• T3: Time of App2 can finish
Fair Scheduler
• Each application assigned to a pool, a subpool is possible
• Excess capacity will be spreaded across all pools
• Pools with minimum resources defined received priority
during allocation
• Minimum resources are minimum amount of resources that
must be allocated to the pool before any fair allocation,
often used to satisfy SLA (service level agreement)
• Pools can be assigned a weight
• Preemption types, minimum and fair share
• Resource manager web interface, 

port 8088
• Job history web interface, 

port 19888
Log Aggregation
• Logs can be grouped by application
• Stored in HDFS (was not in Map Reduce v1)
• Gives better load balance when writting logs
• Show applications

yarn application -list

yarn application -list all

yarn application -status <application_id>

yarn application -list -appstates FINISHED
• Kill application

yarn application -kill <application_id>
• Show logs

yarn logs -applicationId <application_id>
• List YARN nodes

yarn node -list
Common Commands
Questions?

https://www.meetup.com/Jakarta-Hadoop-Big-Data/

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Meet Hadoop Family: part 2

  • 1. Meet Hadoop Family: part 2 YARN
  • 2.
  • 3. • What is it? 
 Resource manager platform in a Hadoop cluster, it allows dynamic memory and CPU sharing between processing frameworks such as MapReduce, Spark, and others • Purpose
 More predictable performance
 Better cluster utilization • Compared to MapReduce v1
 MapReduce v1 starts to break on > 4000 nodes
 YARN allows other frameworks to run on it also support multi tenancy
 YARN is backward compatible with MapReduce V1
  • 5. Scheduler types • FIFO Scheduler • Capacity scheduler
 Fixed pools for resources
 FIFO scheduling for each pools • Fair scheduler
 Weighted pools for resources
 Fair sharing

  • 6. Capacity Scheduler • Capacity guaranted on each pool, with hard limits and soft limits • Hierarchical pool with a root pool • Elasticity with preemptive option

  • 7. Preemption Option • T1: Time of App2’s submission • T2: Time of App1 can finish • T3: Time of App2 can finish
  • 8. Fair Scheduler • Each application assigned to a pool, a subpool is possible • Excess capacity will be spreaded across all pools • Pools with minimum resources defined received priority during allocation • Minimum resources are minimum amount of resources that must be allocated to the pool before any fair allocation, often used to satisfy SLA (service level agreement) • Pools can be assigned a weight • Preemption types, minimum and fair share
  • 9.
  • 10.
  • 11. • Resource manager web interface, 
 port 8088 • Job history web interface, 
 port 19888
  • 12. Log Aggregation • Logs can be grouped by application • Stored in HDFS (was not in Map Reduce v1) • Gives better load balance when writting logs
  • 13. • Show applications
 yarn application -list
 yarn application -list all
 yarn application -status <application_id>
 yarn application -list -appstates FINISHED • Kill application
 yarn application -kill <application_id> • Show logs
 yarn logs -applicationId <application_id> • List YARN nodes
 yarn node -list Common Commands