List of usage examples for org.apache.hadoop.yarn.conf YarnConfiguration getLong
public long getLong(String name, long defaultValue)
name property as a long. From source file:com.cloudera.llama.am.LlamaAMServer.java
License:Apache License
@Override protected void startService() { startHttpServer();//from ww w .jav a2 s . c o m try { Security.loginToHadoop(getServerConf()); Class<? extends NodeMapper> klass = getServerConf().getNodeMappingClass(); nodeMapper = ReflectionUtils.newInstance(klass, getConf()); clientNotificationService = new ClientNotificationService(getServerConf(), nodeMapper, getMetricRegistry()); clientNotificationService.addListener(this); clientNotificationService.start(); clientNotificationService.addListener(restData); // For mapping reservations to queues and checking queue ACLs YarnConfiguration yarnConf = new YarnConfiguration(); // Check the token renew interval and set the default here so that we can // renew the RMConnectors properly. long renewInterval = yarnConf.getLong(YarnConfiguration.DELEGATION_TOKEN_MAX_LIFETIME_KEY, YarnConfiguration.DELEGATION_TOKEN_RENEW_INTERVAL_DEFAULT); LlamaAM.RM_CONNECTOR_RECYCLE_INTERVAL_DEFAULT = renewInterval * 3 / 4; allocConf = new AtomicReference<AllocationConfiguration>(); allocsLoader = new AllocationFileLoaderService(); allocsLoader.init(yarnConf); allocsLoader.setReloadListener(new AllocationFileLoaderService.Listener() { @Override public void onReload(AllocationConfiguration allocs) { allocConf.set(allocs); } }); try { allocsLoader.reloadAllocations(); allocsLoader.start(); } catch (Exception ex) { LOG.warn("Failed to load queue allocations"); } if (allocConf.get() == null) { allocConf.set(new AllocationConfiguration(yarnConf)); } getConf().set(YarnRMConnector.ADVERTISED_HOSTNAME_KEY, ThriftEndPoint.getServerAddress(getServerConf())); getConf().setInt(YarnRMConnector.ADVERTISED_PORT_KEY, ThriftEndPoint.getServerPort(getServerConf())); getConf().set(YarnRMConnector.ADVERTISED_TRACKING_URL_KEY, getHttpLlamaUI()); llamaAm = LlamaAM.create(getConf()); asyncListener = new AsyncLlamaAMListener(restData); asyncListener.setMetricRegistry(getMetricRegistry()); asyncListener.start(); llamaAm.addListener(asyncListener); llamaAm.setMetricRegistry(getMetricRegistry()); llamaAm.start(); } catch (Exception ex) { throw new RuntimeException(ex); } }
From source file:com.ibm.bi.dml.yarn.ropt.YarnClusterAnalyzer.java
License:Open Source License
/** * Analyzes properties of Yarn cluster and Hadoop configurations. *//*from w w w.ja v a2s .c o m*/ public static void analyzeYarnCluster(YarnClient yarnClient, YarnConfiguration conf, boolean verbose) { try { List<NodeReport> nodesReport = yarnClient.getNodeReports(); if (verbose) System.out.println("There are " + nodesReport.size() + " nodes in the cluster"); if (nodesReport.isEmpty()) throw new YarnException("There are zero available nodes in the yarn cluster"); nodesMaxPhySorted = new ArrayList<Long>(nodesReport.size()); clusterTotalMem = 0; clusterTotalCores = 0; clusterTotalNodes = 0; minimumMRContainerPhyMB = -1; for (NodeReport node : nodesReport) { Resource resource = node.getCapability(); Resource used = node.getUsed(); if (used == null) used = Resource.newInstance(0, 0); int mb = resource.getMemory(); int cores = resource.getVirtualCores(); if (mb <= 0) throw new YarnException("A node has non-positive memory " + mb); int myMinMRPhyMB = mb / cores / CPU_HYPER_FACTOR; if (minimumMRContainerPhyMB < myMinMRPhyMB) minimumMRContainerPhyMB = myMinMRPhyMB; // minimumMRContainerPhyMB needs to be the largest among the mins clusterTotalMem += (long) mb * 1024 * 1024; nodesMaxPhySorted.add((long) mb * 1024 * 1024); clusterTotalCores += cores; clusterTotalNodes++; if (verbose) System.out.println("\t" + node.getNodeId() + " has " + mb + " MB (" + used.getMemory() + " MB used) memory and " + resource.getVirtualCores() + " (" + used.getVirtualCores() + " used) cores"); } Collections.sort(nodesMaxPhySorted, Collections.reverseOrder()); nodesMaxBudgetSorted = new ArrayList<Double>(nodesMaxPhySorted.size()); for (int i = 0; i < nodesMaxPhySorted.size(); i++) nodesMaxBudgetSorted.add(ResourceOptimizer.phyToBudget(nodesMaxPhySorted.get(i))); _remotePar = nodesReport.size(); if (_remotePar == 0) throw new YarnException("There are no available nodes in the yarn cluster"); // Now get the default cluster settings _remoteMRSortMem = (1024 * 1024) * conf.getLong("io.sort.mb", 100); //100MB //handle jvm max mem (map mem budget is relevant for map-side distcache and parfor) //(for robustness we probe both: child and map configuration parameters) String javaOpts1 = conf.get("mapred.child.java.opts"); //internally mapred/mapreduce synonym String javaOpts2 = conf.get("mapreduce.map.java.opts", null); //internally mapred/mapreduce synonym String javaOpts3 = conf.get("mapreduce.reduce.java.opts", null); //internally mapred/mapreduce synonym if (javaOpts2 != null) //specific value overrides generic _remoteJVMMaxMemMap = extractMaxMemoryOpt(javaOpts2); else _remoteJVMMaxMemMap = extractMaxMemoryOpt(javaOpts1); if (javaOpts3 != null) //specific value overrides generic _remoteJVMMaxMemReduce = extractMaxMemoryOpt(javaOpts3); else _remoteJVMMaxMemReduce = extractMaxMemoryOpt(javaOpts1); //HDFS blocksize String blocksize = conf.get(MRConfigurationNames.DFS_BLOCK_SIZE, "134217728"); _blocksize = Long.parseLong(blocksize); minimalPhyAllocate = (long) 1024 * 1024 * conf.getInt(YarnConfiguration.RM_SCHEDULER_MINIMUM_ALLOCATION_MB, YarnConfiguration.DEFAULT_RM_SCHEDULER_MINIMUM_ALLOCATION_MB); maximumPhyAllocate = (long) 1024 * 1024 * conf.getInt(YarnConfiguration.RM_SCHEDULER_MAXIMUM_ALLOCATION_MB, YarnConfiguration.DEFAULT_RM_SCHEDULER_MAXIMUM_ALLOCATION_MB); mrAMPhy = (long) conf.getInt("yarn.app.mapreduce.am.resource.mb", 1536) * 1024 * 1024; } catch (Exception e) { throw new RuntimeException("Unable to analyze yarn cluster ", e); } /* * This is for AppMaster to query available resource in the cluster during heartbeat * AMRMClient<ContainerRequest> rmClient = AMRMClient.createAMRMClient(); rmClient.init(conf); rmClient.start(); AllocateResponse response = rmClient.allocate(0); int nodeCount = response.getNumClusterNodes(); Resource resource = response.getAvailableResources(); List<NodeReport> nodeUpdate = response.getUpdatedNodes(); LOG.info("This is a " + nodeCount + " node cluster with totally " + resource.getMemory() + " memory and " + resource.getVirtualCores() + " cores"); LOG.info(nodereport.size() + " updatedNode reports received"); for (NodeReport node : nodeUpdate) { resource = node.getCapability(); LOG.info(node.getNodeId() + " updated with " + resource.getMemory() + " memory and " + resource.getVirtualCores() + " cores"); }*/ }
From source file:org.apache.flink.yarn.YarnFlinkResourceManager.java
License:Apache License
/** * Creates the props needed to instantiate this actor. * //from w w w. ja v a2 s. c o m * Rather than extracting and validating parameters in the constructor, this factory method takes * care of that. That way, errors occur synchronously, and are not swallowed simply in a * failed asynchronous attempt to start the actor. * @param actorClass * The actor class, to allow overriding this actor with subclasses for testing. * @param flinkConfig * The Flink configuration object. * @param yarnConfig * The YARN configuration object. * @param applicationMasterHostName * The hostname where this application master actor runs. * @param webFrontendURL * The URL of the tracking web frontend. * @param taskManagerParameters * The parameters for launching TaskManager containers. * @param taskManagerLaunchContext * The parameters for launching the TaskManager processes in the TaskManager containers. * @param numInitialTaskManagers * The initial number of TaskManagers to allocate. * @param log * The logger to log to. * * @return The Props object to instantiate the YarnFlinkResourceManager actor. */ public static Props createActorProps(Class<? extends YarnFlinkResourceManager> actorClass, Configuration flinkConfig, YarnConfiguration yarnConfig, LeaderRetrievalService leaderRetrievalService, String applicationMasterHostName, String webFrontendURL, ContaineredTaskManagerParameters taskManagerParameters, ContainerLaunchContext taskManagerLaunchContext, int numInitialTaskManagers, Logger log) { final int yarnHeartbeatIntervalMS = flinkConfig.getInteger(ConfigConstants.YARN_HEARTBEAT_DELAY_SECONDS, DEFAULT_YARN_HEARTBEAT_INTERVAL_MS / 1000) * 1000; final long yarnExpiryIntervalMS = yarnConfig.getLong(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, YarnConfiguration.DEFAULT_RM_AM_EXPIRY_INTERVAL_MS); if (yarnHeartbeatIntervalMS >= yarnExpiryIntervalMS) { log.warn( "The heartbeat interval of the Flink Application master ({}) is greater " + "than YARN's expiry interval ({}). The application is likely to be killed by YARN.", yarnHeartbeatIntervalMS, yarnExpiryIntervalMS); } final int maxFailedContainers = flinkConfig.getInteger(ConfigConstants.YARN_MAX_FAILED_CONTAINERS, numInitialTaskManagers); if (maxFailedContainers >= 0) { log.info("YARN application tolerates {} failed TaskManager containers before giving up", maxFailedContainers); } return Props.create(actorClass, flinkConfig, yarnConfig, leaderRetrievalService, applicationMasterHostName, webFrontendURL, taskManagerParameters, taskManagerLaunchContext, yarnHeartbeatIntervalMS, maxFailedContainers, numInitialTaskManagers); }
From source file:org.apache.sysml.yarn.ropt.YarnClusterAnalyzer.java
License:Apache License
/** * Analyzes properties of Yarn cluster and Hadoop configurations. * //w ww .ja v a 2 s . c o m * @param yarnClient hadoop yarn client * @param conf hadoop yarn configuration * @param verbose output info to standard output */ public static void analyzeYarnCluster(YarnClient yarnClient, YarnConfiguration conf, boolean verbose) { try { List<NodeReport> nodesReport = yarnClient.getNodeReports(); if (verbose) System.out.println("There are " + nodesReport.size() + " nodes in the cluster"); if (nodesReport.isEmpty()) throw new YarnException("There are zero available nodes in the yarn cluster"); nodesMaxPhySorted = new ArrayList<>(nodesReport.size()); clusterTotalMem = 0; clusterTotalCores = 0; clusterTotalNodes = 0; minimumMRContainerPhyMB = -1; for (NodeReport node : nodesReport) { Resource resource = node.getCapability(); Resource used = node.getUsed(); if (used == null) used = Resource.newInstance(0, 0); int mb = resource.getMemory(); int cores = resource.getVirtualCores(); if (mb <= 0) throw new YarnException("A node has non-positive memory " + mb); int myMinMRPhyMB = mb / cores / CPU_HYPER_FACTOR; if (minimumMRContainerPhyMB < myMinMRPhyMB) minimumMRContainerPhyMB = myMinMRPhyMB; // minimumMRContainerPhyMB needs to be the largest among the mins clusterTotalMem += (long) mb * 1024 * 1024; nodesMaxPhySorted.add((long) mb * 1024 * 1024); clusterTotalCores += cores; clusterTotalNodes++; if (verbose) System.out.println("\t" + node.getNodeId() + " has " + mb + " MB (" + used.getMemory() + " MB used) memory and " + resource.getVirtualCores() + " (" + used.getVirtualCores() + " used) cores"); } Collections.sort(nodesMaxPhySorted, Collections.reverseOrder()); nodesMaxBudgetSorted = new ArrayList<>(nodesMaxPhySorted.size()); for (int i = 0; i < nodesMaxPhySorted.size(); i++) nodesMaxBudgetSorted.add(ResourceOptimizer.phyToBudget(nodesMaxPhySorted.get(i))); _remotePar = nodesReport.size(); if (_remotePar == 0) throw new YarnException("There are no available nodes in the yarn cluster"); // Now get the default cluster settings _remoteMRSortMem = (1024 * 1024) * conf.getLong(MRConfigurationNames.MR_TASK_IO_SORT_MB, 100); //100MB //handle jvm max mem (map mem budget is relevant for map-side distcache and parfor) //(for robustness we probe both: child and map configuration parameters) String javaOpts1 = conf.get(MRConfigurationNames.MR_CHILD_JAVA_OPTS); //internally mapred/mapreduce synonym String javaOpts2 = conf.get(MRConfigurationNames.MR_MAP_JAVA_OPTS, null); //internally mapred/mapreduce synonym String javaOpts3 = conf.get(MRConfigurationNames.MR_REDUCE_JAVA_OPTS, null); //internally mapred/mapreduce synonym if (javaOpts2 != null) //specific value overrides generic _remoteJVMMaxMemMap = extractMaxMemoryOpt(javaOpts2); else _remoteJVMMaxMemMap = extractMaxMemoryOpt(javaOpts1); if (javaOpts3 != null) //specific value overrides generic _remoteJVMMaxMemReduce = extractMaxMemoryOpt(javaOpts3); else _remoteJVMMaxMemReduce = extractMaxMemoryOpt(javaOpts1); //HDFS blocksize String blocksize = conf.get(MRConfigurationNames.DFS_BLOCKSIZE, "134217728"); _blocksize = Long.parseLong(blocksize); minimalPhyAllocate = (long) 1024 * 1024 * conf.getInt(YarnConfiguration.RM_SCHEDULER_MINIMUM_ALLOCATION_MB, YarnConfiguration.DEFAULT_RM_SCHEDULER_MINIMUM_ALLOCATION_MB); maximumPhyAllocate = (long) 1024 * 1024 * conf.getInt(YarnConfiguration.RM_SCHEDULER_MAXIMUM_ALLOCATION_MB, YarnConfiguration.DEFAULT_RM_SCHEDULER_MAXIMUM_ALLOCATION_MB); mrAMPhy = (long) conf.getInt(MRConfigurationNames.YARN_APP_MR_AM_RESOURCE_MB, 1536) * 1024 * 1024; } catch (Exception e) { throw new RuntimeException("Unable to analyze yarn cluster ", e); } /* * This is for AppMaster to query available resource in the cluster during heartbeat * AMRMClient<ContainerRequest> rmClient = AMRMClient.createAMRMClient(); rmClient.init(conf); rmClient.start(); AllocateResponse response = rmClient.allocate(0); int nodeCount = response.getNumClusterNodes(); Resource resource = response.getAvailableResources(); List<NodeReport> nodeUpdate = response.getUpdatedNodes(); LOG.info("This is a " + nodeCount + " node cluster with totally " + resource.getMemory() + " memory and " + resource.getVirtualCores() + " cores"); LOG.info(nodereport.size() + " updatedNode reports received"); for (NodeReport node : nodeUpdate) { resource = node.getCapability(); LOG.info(node.getNodeId() + " updated with " + resource.getMemory() + " memory and " + resource.getVirtualCores() + " cores"); }*/ }