YARN was introduced as part of Hadoop 2.0 to address limitations in the original MapReduce (MR1) architecture like scalability bottlenecks and underutilization of resources. YARN introduces a global ResourceManager and per-node NodeManagers to allocate cluster resources to distributed applications. It allows various distributed processing frameworks beyond MapReduce to share common cluster resources. Applications request containers for ApplicationMasters that then negotiate resources from YARN to run application components in containers across nodes. Existing MapReduce jobs can also run unchanged on YARN.

1. Bhupesh Chawda
bhupesh@apache.org
DataTorrent
Introduction to YARN
Next Gen Hadoop

2. Image Source: https://memegenerator.net/instance/64508420

3. Why YARN
Hadoop v1 (MR1) Architecture
● Job Tracker
○ Manages cluster resources
○ Job scheduling
○ Bottleneck
● Task Tracker
○ Per-node Agent
○ Manages tasks
○ Map / Reduce task slots
MapReduce Status
Job Submission
Job
Tracker
Task Task
Task Task
Client
Client
Task
Tracker
Task Task
Task
Tracker
Task
Tracker

4. Limitations with MR1
• Scalability
Maximum cluster size: 4,000 nodes
Maximum concurrent tasks: 40,000
• Availability - Job Tracker is a SPOF
• Resource Utilization - Map / Reduce slots
• Runs only MapReduce applications
Why YARN (Cont…)

6. Introducing YARN
● YARN - Yet Another Resource Negotiator
● Framework that facilitates writing arbitrary distributed processing
frameworks and applications.
● YARN Applications/frameworks:
e.g. MapReduce2, Apache Spark, Apache Giraph, Apache Apex etc.
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7. Hadoop beyond Batch
YARN for better
resource utilization
More applications
than MapReduce

8. Comparing MapReduce with YARN
MapReduce
YARN
≈
≈
≈
8Proprietary and Confidential
Job Tracker
Resource Manager
Application Master
Task Tracker Node Manager
Map Slot
Reduce Slot
Backward Compatibility
Maintained!
● Existing Map Reduce
jobs run as is on the
YARN framework
● No Job Tracker and
Task Tracker processes

9. • Resource Manager
Manages and allocates cluster resources
Application scheduling
Applications Manager
• Node Manager
Per-machine agent
Manages life-cycle of container
Monitors resources
• Application Master
Per-application
Manages application scheduling and task execution
Hadoop v2 (YARN) Architecture
Image Source: hadoop.apache.org

10. Application Submission workflow
YarnClient
Node RM
(ApplicationsManagers +
Scheduler)
Resource Manager
Node
NM
Node Manager
Node
NM
Node Manager
Application
Master
Container
Container
1) Submit application
2) Launch application Master
RM = Resource Manager
NM = Node Manager
AM = Application Master
= Heartbeats
3) AM registers with RM
4) AM negotiates for containers
5) Launch Container

11. Application Masters - One for each Application Type
MapReduce Application
MapReduce
Application Master
Apex Application
Apex
Application Master
(StrAM)
Flink Application
Flink
Application Master
Giraph Application
Giraph
Application Master
Already provided by
Hadoop as a backward
compatibility option for
MapReduce
Provided by Apache
Apex

12. ●YARN enables non-MapReduce applications to run in a distributed fashion
●Each Application first asks for a container for the Application Master
○The Application Master then talks to YARN to get resources needed by
the application
○Once YARN allocates containers as requested to the Application Master,
it starts the application components in those containers.
●Hadoop is no more just batch processing!!
Key Takeaways

14. References
● Simple Yarn code example
○ https://github.com/hortonworks/simple-yarn-app
● Document references
○ https://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html
○ http://hortonworks.com/blog/apache-hadoop-yarn-concepts-and-applications/
○ http://www.slideshare.net/
● Acknowledgements
○ Priyanka Gugale, DataTorrent - Slide deck

15. Thank You!!
Please send your questions at:
bhupesh@apache.org / bhupesh@datatorrent.com