Apache Hadoop YARN is a next-generation distributed operating system designed to enhance resource management and scalability for various data processing applications beyond MapReduce. It separates resource management and job lifecycle management to improve efficiency and cluster utilization, targeting high availability and historical data services. YARN is actively being developed with contributions from a vibrant community, and it is currently in production at major companies like Yahoo and eBay.

1. Apache Hadoop YARN: The Next-
generation Distributed Operating
System
Zhijie Shen & Jian He @ Hortonworks

2. About Us
● Two hats
o Software Engineer @ Hortonworks, Inc.
o Hadoop Committer @ The Apache Foundation
● We’re doing Apache YARN!

3. Agenda
● What Is YARN
● YARN Basics
● Recent Development
● Writing Your YARN Applications

4. What is YARN (1)
Frequent questions on mailing list:
● What is Hadoop 2.0?
● What is YARN?
Hadoop 2.0
= YARN
= the operating systems to run various
distributed data processing applications

5. What is YARN (2) - Motivation
● Hadoop is 8yr old
● World is significantly changed
o The hardware
o diverse data processing model: interaction,
streaming

6. What is YARN (3) - Motivation
Flexibility - Enabling data processing model
more than MapReduce

7. What is YARN (5) - Motivation
● Scalability - Splitting
resource management
and job life cycle
management and
mointoring
● Efficiency - Improve
cluster utilization
o distinct map/reduce
slots on a host

8. What Is YARN (4) - Motivation
● Resource Sharing - Multiple workloads in
cluster

9. What Is YARN (5) - Status
● YARN is shipped
● YARN is going to be better
● Hadoop 2.4 is coming this month!
● ResourceManager high availability
● Application historic data services
● Long-running applications optimization

10. What Is YARN (6) - Status
YARN ecosystem

11. What is YARN (7) - Status
YARN community
Active committers working on YARN 10+
Active other contributors 20+
Total Jira tickets 1800+
Jira tickets that are resolved/closed 1100+

12. Agenda
● What Is YARN
● YARN Basics
● Recent Development
● Writing Your YARN Applications

13. YARN Basics (1) - Concept
● ResourceManager -
Master of a cluster
● NodeManager - Slave
to take care of one host
● ApplicationMaster -
Master of an application
● Container - Resource
abstraction, process to
complete a task

14. YARN Basics (2) - RM
Component interfacing RM to the
clients:
● ClientRMService
Component interacting with the
per-application AMs:
● ApplicationMasterService
Component connecting RM to the
nodes:
● ResourceTrackerService
Core of the ResourceManager
● Scheduler, managing apps

15. YARN Basics (3) - NM
Component for NM-RM
communication:
● NodeStatusUpdater
Core component managing
containers on the node:
● ContainerManager
Component interacting with OS to
start/stop the container process:
● ContainerExecutor

16. YARN Basics (4) - Workflow
Execution Sequence:
1. Client submits an application
2. RM allocates a container to start AM
3. AM registers with RM
4. AM asks containers from RM
5. AM notifies NM to launch containers
6. Application code is executed in
container
7. Client contacts RM/AM to monitor
application’s status
8. AM unregisters with RM
Client RM NM AM
1
2
3
4
5
7
8
6

17. YARN Basics (5) - Scheduler
● FIFO
Simply first come, first serve
● Fair
o evenly share the resource across queues, among
applications
● Capacity
o allowing certain resource capacity to queues

18. Agenda
● What Is YARN
● YARN Basics
● Recent Development
● Writing Your YARN Applications

19. Apache Hadoop release
● Hadoop 2.4 is coming out very soon
● ResourceManager high availability
o RM Restart
o RM failover
● Application Historic Data Service
o MRv1 JobHisotoryServer.

20. ResourceManager High Availability
● RM is potentially a single point of failure.
o Restart for various reasons: Bugs, hardware failures, deliberate down-
time for upgrades
● Goal: RM down-time invisible to end-users.
● Includes two parts:
o RM Restart
o RM Failover

21. ResourceManager Restart
● RMState includes:
o static state (appSubmissionContext, credentials) , belongs to RM
o running state - per AM
 scheduler state, i.e. per-app scheduling state, resource
consumptions
● Overly complex in MRv1 for the fact that JobTracker has to save too much
information: both static state and running state.
● YARN RM only persists static state, because AM and RM are running on
different machines on YARN
o Persist application submission metadata and gathers running state
from AM and NM

22. ResourceManager FailOver
● Active/Standby
o Leader election
(ZooKeeper)
● Standby on transition to
Active loads all the state
from the state store.
● NM, AM, clients, redirect
to the new RM
o RMProxy

23. Application Historic Data Service
● Motivation: MRv1 JobHisotoryServer
o MR job writes history data into HDFS.
o When job finishes and removes from memory, Clients request will be
redirected to JHS.
o Reads the this persistent history data in a on-demand way to serve
the requests.

24. Application Historic Data Service
● Goal: Solve the application-history problem in a generic
and more scalable way.
● A single application history server to serve all
applications’ requests.
● ResourceManager records generic application information
o Application, ApplicationAttempt, Container
● ApplicationMaster writes framework specific information
o Free for users to define
● Multiple interfaces to inquiry the historic information
o RPC, RESTful Services
o History server Web UI

25. Application Historic Server

26. Long Running Service(YARN-896)
● YARN is intended to be generic purpose.
o Short-lived (e.g.MR) apps vs long-lived apps
● Goal: disruptive impact minimum to running
applications.
o Work-preserving AM restart.
 Single point of failure for application’s point of view
 Not killing containers
 AM rebind with previous running containers.
o Work-preserving NM restart.
 containers process
 Previous running containers rebind with new NM.

27. Agenda
● What Is YARN
● YARN Basics
● Recent Development
● Writing Your YARN Applications

28. Write a Yarn Applicaition

29. Write client and AM
Client
● Client – RM --- YarnClient
o submitApplication
ApplicationMaster
● AM – RM --- AMRMClient
o registerApplicationMaster
o allocate
o finishApplicationMaster
● AM – NM --- NMClient
o startContainer
o getContainerStatus
o stopContainer

30. Writing the YarnClient
1. Get the application Id from RM
1. Construct ApplicationSubmissionContext
a. Shell command to run the AM
b. Environment (class path, env-variable)
c. LocalResources (Job jars downloaded from HDFS)
1. Submit the request to RM
a. submitApplication

31. Example: client submit an application
1. YarnClient yarnClient = YarnClient.createYarnClient();
1. YarnClientApplication app = yarnClient.createApplication();
1. appContext = app.getAppSubmissionContext()
…….client logic …..
set command: "$JAVA_HOME/bin/java" + "ApplicationMaster" + command
set environments: class path etc.
set jars:
…………………………..
1. yarnClient.submitApplication(appContext);

32. Writing ApplicationMaster
● The ApplicationMaster is started.
● In AM code, for each task, to start the container, we
repeat a similar procedure as starting the master.
o construct ContainerLaunchContext
 commands
 env
 jars

33. Writing ApplicationMaster
1. AM registers with RM (registerApplicationMaster)
1. HeartBeats(allocate) with RM (asynchronously)
a. send the Request
i. Request new containers.
ii. Release containers.
b. Received containers and send request to NM to start the
container
1. Unregisters with RM (finishApplicationMaster)

34. Writing ApplicationMaster
● RM assigns containers asynchronously
● Containers are likely not returned immediately at current
call.
● User needs to give empty requests until it gets the
containers it requested.
● ResourceRequest is incremental.

35. ContainerManagementProtocol
1. Start container (task) on NM
a. startContainer
1. Monitor the container(task) status
a. getContainerStatus
1. Stop the the container (task)
a. stopContainer

36. Example - AM: request containers
AMRMClient rmClient = AMRMClient.createAMRMClient();
ContainerRequest containerAsk = new ContainerRequest(capability)
rmClient.addContainerRequest(containerAsk);
Wait until get the containers:
do {
AllocateResponse response = rmClient.allocate(0);
Thread.sleep(100);
} while (response.getAllocatedContainers().size() > 0)

37. Example - AM: start/stop containers
NMClient nmClient = NMClient.createNMClient();
for (Container container : response.getAllocatedContainers()) {
ContainerLaunchContext ctx =
ContainerLaunchContext.newInstace();
ctx.setCommands(command);
ctx.setEnvironment(envs);
nmClient.startContainer(container, ctx);
}
-monitor container: nmClient.getContainerStatus(containerId)
-stop container: nmClient.stopContainer(ContainerId)

38. Takeaway
1. YARN is a resource-management platform that can host different data-
processing frameworks (beyond MapReduce).
o No longer limited by MR, (batch process, interactive query, stream)
o All applications can now co-exist and share the same data on HDFS.
1. YARN is in production now.
o Yahoo, Ebay.
1. We welcome your contributions.
o patches, tests, writing applications on YARN
o https://issues.apache.org/jira/browse/YARN
o https://github.com/hortonworks/simple-yarn-app

39. The book is out !
http://yarn-book.com/
http://yarn-book.com/