] val stopped: AtomicBoolean = new AtomicBoolean(false) //断言存活的SparkCntext private[spark] def assertNotStopped...")).foreach { v => executorEnvs("SPARK_PREPEND_CLASSES") = v } // The Mesos scheduler backend...is stopped if the user forgets about it....shutdown hook") _shutdownHookRef = ShutdownHookManager.addShutdownHook( ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY...stop() } catch { case e: Throwable => logWarning("Ignoring Exception while
periodicGCService: ScheduledExecutorService = ThreadUtils.newDaemonSingleThreadScheduledExecutor("context-cleaner-periodic-gc...stopped：该ContextCleaner是否停止的标记。...Context Cleaner") cleaningThread.start() periodicGCService.scheduleAtFixedRate(new Runnable...o.a.s.ContextCleaner.keepCleaning()方法 private def keepCleaning(): Unit = Utils.tryOrStopSparkContext(sc) { while...rddId) } } } } catch { case ie: InterruptedException if stopped
该线程池内的线程数由spark.rpc.netty.dispatcher.numThreads配置项决定，默认值为1或2（取决于服务器是否只有一个可用的核心）。...private class MessageLoop extends Runnable { override def run(): Unit = { try { while...= null) { numActiveThreads += 1 } else { return } } while (true)...) => try { endpoint.receiveAndReply(context).applyOrElse[Any, Unit](content...总结 本文从Dispatcher类入手，首先介绍了其内部的属性，进而引申出Spark RPC环境内消息调度的逻辑。
and static. */ STOPPED, /** * The reader is running and generated...while polling"); } logger.trace("Polling for next batch of records"); List<...(Clock.SYSTEM, Temporals.max(pollInterval, ConfigurationDefaults.RETURN_CONTROL_INTERVAL)); while...duration); throw new ConnectException("Timed out after " + actualSeconds + " seconds while...waiting to connect to MySQL at " + connectionContext.hostname() + ":" +
and static. */ STOPPED, /** * The reader is running and generated...while polling"); } logger.trace("Polling for next batch of records"); List...(Clock.SYSTEM, Temporals.max(pollInterval, ConfigurationDefaults.RETURN_CONTROL_INTERVAL)); while...duration); throw new ConnectException("Timed out after " + actualSeconds + " seconds while...waiting to connect to MySQL at " + connectionContext.hostname() + ":" +
: Invoking stop() from shutdown hook 17/09/16 10:23:33 INFO server.AbstractConnector: Stopped Spark@8ab78bc...{HTTP/1.1,[http/1.1]}{0.0.0.0:4040} 17/09/16 10:23:33 INFO ui.SparkUI: Stopped Spark web UI at http:/...stopped!...17/09/16 10:23:34 INFO spark.SparkContext: Successfully stopped SparkContext 17/09/16 10:23:34 INFO util.ShutdownHookManager...--principal PRINCIPAL Principal to be used to login to KDC, while running on
runnable stages 18/11/03 16:20:26 INFO DAGScheduler: running: Set() 18/11/03 16:20:26 INFO DAGScheduler: waiting...stopped!...03 16:20:26 INFO MemoryStore: MemoryStore cleared 18/11/03 16:20:26 INFO BlockManager: BlockManager stopped...$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!...context Web UI available at http://192.168.5.182:4040 Spark context available as 'sc' (master = spark
= nil { // While normally we might "return err" here we're not going to // because if we can't stop...return error because we only care that container is stopped, not what function stopped it } } daemon.LogContainerEvent...= nil { // While normally we might "return err" here we're not going to // because if we can't stop...return error because we only care that container is stopped, not what function stopped it } } daemon.LogContainerEvent...eventually processed. // Doing this has the side effect that if no event was ever going to come we are waiting
getSimpleName(), timeout); } /** * Start a Client or Server in a fully blocking fashion, not only waiting...handler, null); } /** * Start a Client or Server in a fully blocking fashion, not only waiting...(); context.onClose() .doOnError(e -> LOG.error("Stopped {} on {} with an error...{}", description, context.address(), e)) .doOnTerminate(() -> LOG.info("Stopped {} on...(); context.onClose() .doOnError(e -> LOG.error("Stopped {} on {} with an error
return result; } @Override public void flush() throws IOException { // The maximum waiting...long end = System.currentTimeMillis() + 500; while (traceContextQueue.size() > 0 || appenderQueue.size...trace/AsyncTraceDispatcher.java class AsyncRunnable implements Runnable { private boolean stopped...; @Override public void run() { while (!...) { this.stopped = true; } } } } AsyncRunnable
In the PySparkling driver program, the Spark Context, which uses Py4J to start the driver JVM and the...Java Spark Context, is used to create the H2O Context (hc)...._jhc field = hc.getClass().getDeclaredField("stopped") field.setAccessible(True)..._jhc.toString() else: return "H2OContext has been stopped or hasn't been created....Figure 2 shows a data pipeline benefiting from H2O’s parallel data load and parse capabilities, while
5 waiting def status(self,param): if not self....= "command sent after session stopped" waiting_status = "session timed out while waiting for...stopped状态表示该会话已经彻底结束，我们可以退出该会话了。waiting状态在调用非常耗时的操作时会出现。 ...通过Xdebug获取所有栈上的变量要分为三步： 获取调用堆栈深度 获取context_names 获取指定堆栈深度的指定context_names下的所有变量 这一系列操作通过如下操作完成...def _get_context(self, depth_id, context_id): query = 'context_get -d ' + str(depth_id) +
return result; } @Override public void flush() throws IOException { // The maximum waiting...long end = System.currentTimeMillis() + 500; while (traceContextQueue.size() > 0 || appenderQueue.size...() + " " + appenderQueue.size()); } @Override public void shutdown() { this.stopped...; @Override public void run() { while (!...) { this.stopped = true; } } } } AsyncRunnable
Replica artisan is stopped. Waiting. Replica artisan is stopped. Waiting....Replica artisan is stopped. Waiting. Replica artisan is stopped. Waiting....Replica artisan is stopped. Waiting. Replica artisan is stopped. Waiting....Replica artisan is stopped. Waiting. Replica artisan is stopped. Waiting....Replica artisan is stopped. Waiting. Replica artisan is stopped. Waiting.
MainThreadInterface::DispatchMessages() { // 遍历请求队列 requests_.swap(dispatching_message_queue_); while...void Dispatcher::wire(UberDispatcher* uber, Backend* backend){ std::unique_ptr dispatcher...DispatcherImpl(FrontendChannel* frontendChannel, Backend* backend) : DispatcherBase(frontendChannel...) , m_backend(backend) { m_dispatchMap["NodeWorker.sendMessageToWorker"] = &DispatcherImpl...::Scope context_scope(env_->context()); MaybeLocal v8string = String::NewFromTwoByte(isolate
started,datadir is -D "/opt/gaussdb/install/data/db1" [2020-07-12 00:16:01.535][65296][][gs_ctl]: waiting...gs_ctl]: waitpid 65306 failed, exitstatus is 256, ret is 2 [2020-07-12 00:16:02.538][65296][][gs_ctl]: stopped...waiting [2020-07-12 00:16:02.538][65296][][gs_ctl]: could not start server [2020-07-12 00:16:02.538]...openGauss ~]$ gs_om -t stop -mf Stopping cluster. ========================================= Successfully stopped...server_version'; name | setting | unit | category | short_desc | extra_desc | context
是如何启动的： override def start() { backend.start() if (!...backend是一个SchedulerBackend接口。SchedulerBackend接口由CoarseGrainedSchedulerBackend类实现。...= null) { executorRef.address } else { context.senderAddress...shuffledOffers, availableCpus, tasks) launchedAnyTask |= launchedTaskAtCurrentMaxLocality } while...我们来看下TaskSchedulerImpl的初始化就能发现： def initialize(backend: SchedulerBackend) { this.backend = backend
restart [2022-04-12 16:47:25.231][3508][][gs_ctl]: gs_ctl restarted ,datadir is /var/lib/opengauss/data waiting...for server to shut down... done server stopped [2022-04-12 16:47:32.254][3508][][gs_ctl]: waiting for...line: 55 2022-04-12 16:47:32.556 [unknown] [unknown] localhost 140364734101056 0[0:0#0] 0 [BACKEND]...:47:32.595 [unknown] [unknown] localhost 140364734101056 0[0:0#0] 0 [BACKEND] LOG: Set max backend...] LOG: shared memory 50 Mbytes, memory context 12221 Mbytes, max process memory 12288 Mbytes 2022-04
Livy是一个开源的REST 接口，用于与Spark进行交互，它同时支持提交执行代码段和完整的程序。 ? image.png Livy封装了spark-submit并支持远端执行。...stopped!"...", "15/10/20 16:32:21 INFO storage.BlockManagerMaster: BlockManagerMaster stopped", "..., "15/10/20 16:32:21 INFO spark.SparkContext: Successfully stopped SparkContext", "15...including 384 MB overhead", "15/10/21 01:37:27 INFO yarn.Client: Setting up container launch context
启动线程来调用用户定义的spark分析程序。...processEvent(event)){ submitWaitingStages() } else{ resubmissionTask.cancel() context.stop(self) } }...waiting(stage)&& !running(stage)&& !...backend.reviveOffers() } 9.1TaskSetManager的实例生成: private[spark]classTaskSetManager( sched: TaskSchedulerImpl...Locality级别中接着运行其他的task的分配， 假设这个值为true,不放大maxLocality的级别，从下一个worker中接着分配剩余的task launchedTask= true } } } while
领取专属 10元无门槛券
手把手带您无忧上云
云服务器
ICP备案
对象存储
实时音视频
即时通信 IM
