The document provides an overview of writing application frameworks using Apache Hadoop YARN, detailing its architecture, key components, and essential steps for creating a new framework. It discusses the roles of the resource manager, node manager, and application master, along with concepts like application IDs, containers, and resource requests. Additionally, practical use cases, client functionalities, and relevant APIs are highlighted to facilitate understanding and implementation.

1. Writing Application Frameworks
on Apache Hadoop YARN
Hitesh Shah
hitesh@hortonworks.com
© Hortonworks Inc. 2011 Page 1

2. Hitesh Shah - Background
• Member of Technical Staff at Hortonworks Inc.
• Committer for Apache MapReduce and Ambari
• Earlier, spent 8+ years at Yahoo! building various
infrastructure pieces all the way from data storage
platforms to high throughput online ad-serving
systems.
Architecting the Future of Big Data
Page 2
© Hortonworks Inc. 2011

3. Agenda
•YARN Architecture and Concepts
•Writing a New Framework
Architecting the Future of Big Data
Page 3
© Hortonworks Inc. 2011

4. YARN Architecture
• Resource Manager
–Global resource scheduler
–Hierarchical queues
• Node Manager
–Per-machine agent
–Manages the life-cycle of container
–Container resource monitoring
• Application Master
–Per-application
–Manages application scheduling and task execution
–E.g. MapReduce Application Master
Architecting the Future of Big Data
Page 4
© Hortonworks Inc. 2011

5. YARN Architecture
Node
Manager
Container App Mstr
Client
Resource Node
Manager Manager
Client
App Mstr Container
MapReduce Status Node
Manager
Job Submission
Node Status
Resource Request Container Container
Architecting the Future of Big Data
Page 5
© Hortonworks Inc. 2011

6. YARN Concepts
• Application ID
–Application Attempt IDs
• Container
–ContainerLaunchContext
• ResourceRequest
–Host/Rack/Any match
–Priority
–Resource constraints
• Local Resource
–File/Archive
–Visibility – public/private/application
Architecting the Future of Big Data
Page 6
© Hortonworks Inc. 2011

7. What you need for a new Framework
• Application Submission Client
–For example, the MR Job Client
• Application Master
–The core framework library
• Application History ( optional )
–History of all previously run instances
• Auxiliary Services ( optional )
–Long-running application-specific services running on the
NodeManager
Architecting the Future of Big Data
Page 7
© Hortonworks Inc. 2011

8. Use Case: Distributed Shell
• Take a user-provided script Node
or application and run it on a Manager
set of nodes in the Cluster
DS AppMaster
• Input:
– User Script to execute
– Number of containers to run on Node
Manager
– Variable arguments for each
different container Shell Script
– Memory requirements for the
shell script Node
– Output Location/Dir Manager
Shell Script
Architecting the Future of Big Data
Page 8
© Hortonworks Inc. 2011

9. Client: RPC calls
• Uses ClientRM Protocol
ClientRMProtocol#getNewApplication
• Get a new Application
ID from the RM
ClientRMProtocol#submitApplication
• Application Submission CLIENT
RM
ClientRMProtocol#getApplicationReport
• Application Monitoring
ClientRMProtocol#killApplication
• Kill the Application?
Architecting the Future of Big Data
Page 9
© Hortonworks Inc. 2011

10. Client
• Registration with the RM
–New Application ID
• Application Submission
–User information
–Scheduler queue
–Define the container for the Distributed Shell App Master via
the ContainerLaunchContext
• Application Monitoring
– AppMaster host details with tokens if needed, tracking url
– Application Status (submitted/running/finished)
Architecting the Future of Big Data
Page 10
© Hortonworks Inc. 2011

11. Defining a Container
• ContainerLaunchContext class
–Can run a shell script, a java process or launch a VM
• Command(s) to run
• Local resources needed for the process to run
–Dependent jars, native libs, data files/archives
• Environment to setup
–Java Classpath
• Security-related data
–Container Tokens
Architecting the Future of Big Data
Page 11
© Hortonworks Inc. 2011

12. Application Master: RPC calls
• AMRM and CM protocols
Client
• Register AM with RM AMRM.registerAM
• Ask RM to allocate
resources AMRM.allocate
AM
RM
• Launch tasks on
allocated containers AMRM.
finishAM
App-specific
• Manage tasks to final RPC
completion
CM.startContainer
• Inform RM of completion NM NM
Architecting the Future of Big Data
Page 12
© Hortonworks Inc. 2011

13. Application Master
• Setup RPC to handle requests from Client and/or tasks launched
on Containers
• Register and send regular heartbeats to the RM
• Request resources from the RM.
• Launch user shell script on containers as and when allocated.
• Monitor status of user script of remote containers and manage
failures by retrying if needed.
• Inform RM of completion when application is done.
Architecting the Future of Big Data
Page 13
© Hortonworks Inc. 2011

14. AMRM#allocate
• Request:
– Containers needed
– Not a delta protocol
– Locality constraints: Host/Rack/Any
– Resource constraints: memory
– Priority-based assignments
– Containers to release – extra/unwanted?
– Only non-launched containers
• Response:
– Allocated Containers
– Launch or release
– Completed Containers
– Status of completion
Architecting the Future of Big Data
Page 14
© Hortonworks Inc. 2011

15. YARN Applications
• Data Processing:
– OpenMPI on Hadoop
– Spark (UC Berkeley)
– Shark ( Hive-on-Spark )
– Real-time data processing
– Storm ( Twitter )
– Apache S4
– Graph processing – Apache Giraph
• Beyond data:
– Deploying Apache HBase via YARN (HBASE-4329)
– Hbase Co-processors via YARN (HBASE-4047)
Architecting the Future of Big Data
Page 15
© Hortonworks Inc. 2011

16. References
•Doc on writing new applications:
–WritingYarnApplications.html ( available at
http://hadoop.apache.org/common/docs/r2.0.0-
alpha/ )
Architecting the Future of Big Data
Page 16
© Hortonworks Inc. 2011

17. Questions?
Thank You!
Hitesh Shah
hitesh@hortonworks.com
Architecting the Future of Big Data
Page 17
© Hortonworks Inc. 2011

18. Appendix: Code
Examples
Architecting the Future of Big Data
Page 18
© Hortonworks Inc. 2011

19. Client: Registration
ClientRMProtocol applicationsManager;
YarnConfiguration yarnConf = new YarnConfiguration(conf);
InetSocketAddress rmAddress = NetUtils.createSocketAddr(
yarnConf.get(YarnConfiguration.RM_ADDRESS));
applicationsManager = ((ClientRMProtocol)
rpc.getProxy(ClientRMProtocol.class,
rmAddress, appsManagerServerConf));
GetNewApplicationRequest request =
Records.newRecord(GetNewApplicationRequest.class);
GetNewApplicationResponse response =
applicationsManager.getNewApplication(request);
Architecting the Future of Big Data
Page 19
© Hortonworks Inc. 2011

20. Client: App Submission
ApplicationSubmissionContext appContext;
ContainerLaunchContext amContainer;
amContainer.setLocalResources(Map<String, LocalResource> localResources);
amContainer.setEnvironment(Map<String, String> env);
String command = "${JAVA_HOME}" + /bin/java" + " MyAppMaster " + " arg1 arg2
“;
amContainer.setCommands(List<String> commands);
Resource capability; capability.setMemory(amMemory);
amContainer.setResource(capability);
appContext.setAMContainerSpec(amContainer);
SubmitApplicationRequest appRequest;
appRequest.setApplicationSubmissionContext(appContext);
applicationsManager.submitApplication(appRequest);
Architecting the Future of Big Data
Page 20
© Hortonworks Inc. 2011

21. Client: App Monitoring
• Get Application Status
GetApplicationReportRequest reportRequest =
Records.newRecord(GetApplicationReportRequest.class);
reportRequest.setApplicationId(appId);
GetApplicationReportResponse reportResponse =
applicationsManager.getApplicationReport(reportRequest);
ApplicationReport report = reportResponse.getApplicationReport();
• Kill the application
KillApplicationRequest killRequest =
Records.newRecord(KillApplicationRequest.class);
killRequest.setApplicationId(appId);
applicationsManager.forceKillApplication(killRequest);
Architecting the Future of Big Data
Page 21
© Hortonworks Inc. 2011

22. AM: Ask RM for Containers
ResourceRequest rsrcRequest;
rsrcRequest.setHostName("*”); // hostname, rack, wildcard
rsrcRequest.setPriority(pri);
Resource capability; capability.setMemory(containerMemory);
rsrcRequest.setCapability(capability)
rsrcRequest.setNumContainers(numContainers);
List<ResourceRequest> requestedContainers;
List<ContainerId> releasedContainers;
AllocateRequest req;
req.setResponseId(rmRequestID);
req.addAllAsks(requestedContainers);
req.addAllReleases(releasedContainers);
req.setProgress(currentProgress);
AllocateResponse allocateResponse = resourceManager.allocate(req);
Architecting the Future of Big Data
Page 22
© Hortonworks Inc. 2011

23. AM: Launch Containers
AMResponse amResp = allocateResponse.getAMResponse();
ContainerManager cm = (ContainerManager)rpc.getProxy
(ContainerManager.class, cmAddress, conf);
List<Container> allocatedContainers = amResp.getAllocatedContainers();
for (Container allocatedContainer : allocatedContainers) {
ContainerLaunchContext ctx;
ctx.setContainerId(allocatedContainer .getId());
ctx.setResource(allocatedContainer .getResource());
// set env, command, local resources, …
StartContainerRequest startReq;
startReq.setContainerLaunchContext(ctx);
cm.startContainer(startReq);
}
Architecting the Future of Big Data
Page 23
© Hortonworks Inc. 2011

24. AM: Monitoring Containers
• Running Containers
GetContainerStatusRequest statusReq;
statusReq.setContainerId(containerId);
GetContainerStatusResponse statusResp =
cm.getContainerStatus(statusReq);
• Completed Containers
AMResponse amResp = allocateResponse.getAMResponse();
List<Container> completedContainersStatus =
amResp.getCompletedContainerStatuses();
for (ContainerStatus containerStatus : completedContainers) {
// containerStatus.getContainerId()
// containerStatus.getExitStatus()
// containerStatus.getDiagnostics()
}
Architecting the Future of Big Data
Page 24
© Hortonworks Inc. 2011

25. AM: I am done
FinishApplicationMasterRequest finishReq;
finishReq.setAppAttemptId(appAttemptID);
finishReq.setFinishApplicationStatus
(FinalApplicationStatus.SUCCEEDED); // or FAILED
finishReq.setDiagnostics(diagnostics);
resourceManager.finishApplicationMaster(finishReq);
Architecting the Future of Big Data
Page 25
© Hortonworks Inc. 2011