Writing app framworks for hadoop on yarn

Writing Application Frameworkson Apache Hadoop YARNHitesh Shahhitesh@hortonworks.com© Hortonworks Inc. 2011 Page 1

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

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

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

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

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

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

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

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

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

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

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 RPC•  Manage tasks to final completion CM.startContainer•  Inform RM of completion NM NM Architecting the Future of Big Data Page 12 © Hortonworks Inc. 2011

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

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

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

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

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

Client: RegistrationClientRMProtocol 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

Client: App SubmissionApplicationSubmissionContext 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

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

AM: Ask RM for ContainersResourceRequest rsrcRequest;rsrcRequest.setHostName("*”); // hostname, rack, wildcardrsrcRequest.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

AM: Launch ContainersAMResponse 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

AM: Monitoring Containers•  Running ContainersGetContainerStatusRequest statusReq;statusReq.setContainerId(containerId);GetContainerStatusResponse statusResp = cm.getContainerStatus(statusReq);•  Completed ContainersAMResponse 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

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

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Writing app framworks for hadoop on yarn

by Hadoop_Summit on Jun 20, 2012

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