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Hadoop-Yarn-NodeManager是如何监控容器的
   

Hadoop-Yarn-NodeManager是如何监控容器的

一、源码下载

下面是hadoop官方源码下载地址,我下载的是hadoop-3.2.4,那就一起来看下吧

Index of /dist/hadoop/core

二、上下文

在我的博客中的ContainerLaunch  prepareForLaunch()会触发ContainerEventType.CONTAINER_LAUNCHED事件,ContainerImpl会处理该事件,监控该容器的资源使用以及处理后续操作,下面让我们把源码捋起来吧。

三、开始捋源码

1、ContainerImpl

public class ContainerImpl implements Container {
  private static StateMachineFactory
           
        stateMachineFactory =
      new StateMachineFactory(ContainerState.NEW).
    //......省略其他事件处理......
    addTransition(ContainerState.SCHEDULED, ContainerState.RUNNING,
        ContainerEventType.CONTAINER_LAUNCHED, new LaunchTransition())
    //......省略其他事件处理......
    .installTopology();
  static class LaunchTransition extends ContainerTransition {
    @SuppressWarnings("unchecked")
    @Override
    public void transition(ContainerImpl container, ContainerEvent event) {
      //发送容器监控事件,去监控容器的使用
      container.sendContainerMonitorStartEvent();
      container.metrics.runningContainer();
      container.wasLaunched  = true;
      if (container.isReInitializing()) {
        NMAuditLogger.logSuccess(container.user,
            AuditConstants.FINISH_CONTAINER_REINIT, "ContainerImpl",
            container.containerId.getApplicationAttemptId().getApplicationId(),
            container.containerId);
      }
      container.setIsReInitializing(false);
      // Check if this launch was due to a re-initialization.
      // If autocommit == true, then wipe the re-init context. This ensures
      // that any subsequent failures do not trigger a rollback.
      if (container.reInitContext != null
          && !container.reInitContext.canRollback()) {
        container.reInitContext = null;
      }
      if (container.recoveredAsKilled) {
        LOG.info("Killing " + container.containerId
            + " due to recovered as killed");
        container.addDiagnostics("Container recovered as killed.\n");
        container.dispatcher.getEventHandler().handle(
            new ContainersLauncherEvent(container,
                ContainersLauncherEventType.CLEANUP_CONTAINER));
      }
    }
  }
  private void sendContainerMonitorStartEvent() {
    long launchDuration = clock.getTime() - containerLaunchStartTime;
    metrics.addContainerLaunchDuration(launchDuration);
    long pmemBytes = getResource().getMemorySize() * 1024 * 1024L;
    float pmemRatio = daemonConf.getFloat(
        YarnConfiguration.NM_VMEM_PMEM_RATIO,
        YarnConfiguration.DEFAULT_NM_VMEM_PMEM_RATIO);
    long vmemBytes = (long) (pmemRatio * pmemBytes);
    int cpuVcores = getResource().getVirtualCores();
    long localizationDuration = containerLaunchStartTime -
        containerLocalizationStartTime;
    //这里会触发 ContainersMonitorEventType.START_MONITORING_CONTAINER
    //该事件由ContainersMonitorImpl处理
    dispatcher.getEventHandler().handle(
        new ContainerStartMonitoringEvent(containerId,
        vmemBytes, pmemBytes, cpuVcores, launchDuration,
        localizationDuration));
  }
}

2、ContainersMonitorImpl

监视收集资源使用情况的容器,并在容器超出限制时抢占容器

public class ContainersMonitorImpl extends AbstractService implements
    ContainersMonitor {
  private final static Logger LOG =
       LoggerFactory.getLogger(ContainersMonitorImpl.class);
  private final static Logger AUDITLOG =
       LoggerFactory.getLogger(ContainersMonitorImpl.class.getName()+".audit");
  private long monitoringInterval;
  private MonitoringThread monitoringThread;
  private int logCheckInterval;
  private LogMonitorThread logMonitorThread;
  private long logDirSizeLimit;
  private long logTotalSizeLimit;
  private CGroupElasticMemoryController oomListenerThread;
  private boolean containerMetricsEnabled;
  private long containerMetricsPeriodMs;
  private long containerMetricsUnregisterDelayMs;
  @VisibleForTesting
  final Map trackingContainers =
      new ConcurrentHashMap<>();
  private final ContainerExecutor containerExecutor;
  private final Dispatcher eventDispatcher;
  private final Context context;
  private ResourceCalculatorPlugin resourceCalculatorPlugin;
  private Configuration conf;
  private static float vmemRatio;
  //用于获取进程资源使用情况的接口类
  //注意:此类不应由外部用户使用,而只能由外部开发人员使用,以扩展和包括他们自己的流程树实现,尤其是对于Linux和Windows以外的平台。
  private Class processTreeClass;
  private long maxVmemAllottedForContainers = UNKNOWN_MEMORY_LIMIT;
  private long maxPmemAllottedForContainers = UNKNOWN_MEMORY_LIMIT;
  private boolean pmemCheckEnabled;
  private boolean vmemCheckEnabled;
  private boolean elasticMemoryEnforcement;
  private boolean strictMemoryEnforcement;
  private boolean containersMonitorEnabled;
  private boolean logMonitorEnabled;
  private long maxVCoresAllottedForContainers;
  private static final long UNKNOWN_MEMORY_LIMIT = -1L;
  private int nodeCpuPercentageForYARN;
  /**
   * 容器度量的类型
   */
  @Private
  public enum ContainerMetric {
    CPU, MEMORY
  }
  //ResourceUtilization对集群中一组计算机资源的利用率进行建模
  private ResourceUtilization containersUtilization;
  private volatile boolean stopped = false;
  public ContainersMonitorImpl(ContainerExecutor exec,
      AsyncDispatcher dispatcher, Context context) {
    super("containers-monitor");
    this.containerExecutor = exec;
    this.eventDispatcher = dispatcher;
    this.context = context;
    this.monitoringThread = new MonitoringThread();
    this.logMonitorThread = new LogMonitorThread();
	//ResourceUtilization.newInstance(物理内存, 虚拟内存, cpu利用率)
    this.containersUtilization = ResourceUtilization.newInstance(0, 0, 0.0f);
  }
  @Override
  protected void serviceInit(Configuration myConf) throws Exception {
    this.conf = myConf;
	//监视容器的频率
	//获取 yarn.nodemanager.container-monitor.interval-ms 的值 
	//如果未设置,则将使用yarn.nodemanager.resource-monitor.interval-ms的值。如果为0或为负数,则禁用容器监视。
	//监视节点和容器的频率
	//获取 yarn.nodemanager.resource-monitor.interval-ms 的值 默认值 3000ms 即 3s 如果为0或为负数,则禁用监视
    this.monitoringInterval =
        this.conf.getLong(YarnConfiguration.NM_CONTAINER_MON_INTERVAL_MS,
            this.conf.getLong(YarnConfiguration.NM_RESOURCE_MON_INTERVAL_MS,
                YarnConfiguration.DEFAULT_NM_RESOURCE_MON_INTERVAL_MS));
	//检查容器日志目录使用情况的频率(以毫秒为单位)
	//获取 yarn.nodemanager.container-log-monitor.interval-ms 的值 默认值 60000ms 即 1min
    this.logCheckInterval =
        conf.getInt(YarnConfiguration.NM_CONTAINER_LOG_MON_INTERVAL_MS,
            YarnConfiguration.DEFAULT_NM_CONTAINER_LOG_MON_INTERVAL_MS);
	//单个容器日志目录的磁盘空间限制(以字节为单位)1GB = 1024MB = 1024*1024KB = 1024*1024*1024B B就是字节
	//获取 yarn.nodemanager.container-log-monitor.dir-size-limit-bytes 的值 默认值 1000000000L 约等于 1G 
    this.logDirSizeLimit =
        conf.getLong(YarnConfiguration.NM_CONTAINER_LOG_DIR_SIZE_LIMIT_BYTES,
            YarnConfiguration.DEFAULT_NM_CONTAINER_LOG_DIR_SIZE_LIMIT_BYTES);
	//容器所有日志的磁盘空间限制(以字节为单位)
	//获取 yarn.nodemanager.container-log-monitor.total-size-limit-bytes 的值 默认值 10000000000L 即 10G
    this.logTotalSizeLimit =
        conf.getLong(YarnConfiguration.NM_CONTAINER_LOG_TOTAL_SIZE_LIMIT_BYTES,
            YarnConfiguration.DEFAULT_NM_CONTAINER_LOG_TOTAL_SIZE_LIMIT_BYTES);
	//用于计算系统上的资源信息的插件,如果未配置插件,此方法将尝试返回可用于此系统的内存计算器插件。
	//先获取 yarn.nodemanager.container-monitor.resource-calculator.class (计算当前资源利用率的类) 的值 默认空
	//再获取 yarn.nodemanager.resource-calculator.class (计算当前资源利用率的类) 的值 默认空
	//如果都为空会判断操作系统,LINUX 返回 SysInfoLinux WINDOWS 返回 SysInfoWindows
    this.resourceCalculatorPlugin =
        ResourceCalculatorPlugin.getContainersMonitorPlugin(this.conf);
		
    LOG.info(" Using ResourceCalculatorPlugin : "
        + this.resourceCalculatorPlugin);
	//获取 yarn.nodemanager.container-monitor.process-tree.class (用于计算进程树资源利用率) 的值 默认为空
    processTreeClass = this.conf.getClass(
            YarnConfiguration.NM_CONTAINER_MON_PROCESS_TREE, null,
            ResourceCalculatorProcessTree.class);
    LOG.info(" Using ResourceCalculatorProcessTree : "
        + this.processTreeClass);
	//启用容器度量的标志
	//获取 yarn.nodemanager.container-metrics.enable 的值 默认 true 
    this.containerMetricsEnabled =
        this.conf.getBoolean(YarnConfiguration.NM_CONTAINER_METRICS_ENABLE,
            YarnConfiguration.DEFAULT_NM_CONTAINER_METRICS_ENABLE);
	//容器度量刷新周期(毫秒)。设置为-1表示完成时刷新
	//获取 yarn.nodemanager.container-metrics.period-ms 的值 默认为-1
    this.containerMetricsPeriodMs =
        this.conf.getLong(YarnConfiguration.NM_CONTAINER_METRICS_PERIOD_MS,
            YarnConfiguration.DEFAULT_NM_CONTAINER_METRICS_PERIOD_MS);
	//完成后注销容器度量的延迟时间ms
	//获取 yarn.nodemanager.container-metrics.unregister-delay-ms 的值 默认 10000ms 即 10s
    this.containerMetricsUnregisterDelayMs = this.conf.getLong(
        YarnConfiguration.NM_CONTAINER_METRICS_UNREGISTER_DELAY_MS,
        YarnConfiguration.DEFAULT_NM_CONTAINER_METRICS_UNREGISTER_DELAY_MS);
	//NodeManagerHardwareUtils:用于确定与硬件相关的特性,例如节点上的处理器数量和内存量
	//函数返回应该为YARN容器留出多少内存。如果在配置文件中指定了一个数字,则会返回该数字。如果未指定任何内容,则为-1。
	//如果操作系统是“未知”操作系统(我们没有为其实现ResourceCalculatorPlugin),则返回默认的NodeManager物理内存。
	//如果操作系统实现了ResourceCalculatorPlugin,则计算为0.8*(RAM-2*JVM内存),即在考虑了DataNode和NodeManager使用的内存后,使用80%的内存。
	//如果数字小于1GB,请记录一条警告消息
	//获取 yarn.nodemanager.resource.detect-hardware-capabilities (启用节点功能的自动检测,如内存和CPU) 的值 默认 false
	//如果为 false ,即默认会 获取配置文件中的数字 yarn.nodemanager.resource.memory-mb (可分配给容器的内存量(MB)) 
	//这里 源码 和 官方文档 有出入 ,官方文档默认值为-1 源码默认值为 8 * 1024 MB 即 8G ,如果设置为 -1 源码还是会更改为 8G ,可以设置其他值
	//返回的值是 8*1024 这里又 * 1024 * 1024L 即为 转换为 8G 对应的字节 B 
    long configuredPMemForContainers =
        NodeManagerHardwareUtils.getContainerMemoryMB(
            this.resourceCalculatorPlugin, this.conf) * 1024 * 1024L;
	//函数返回系统上可用于YARN容器的vcore数。如果在配置文件中指定了一个数字,则会返回该数字。如果未指定任何内容,则为-1。
	//如果操作系统是“未知”操作系统(我们没有为其实现ResourceCalculatorPlugin),则返回默认的NodeManager内核。
	//2.如果配置变量yarn.nodemanager.cpu.use_logical_processers设置为true,则返回逻辑处理器计数(将超线程计数为核心),否则返回物理核心计数。
	//获取 yarn.nodemanager.resource.cpu-vcores (可分配给容器的虚拟CPU内核数) 的值 
	//可以分配给容器的vcore数。这是RM调度程序在为容器分配资源时使用的。这并不用于限制YARN容器使用的CPU数量。如果它设置为-1,
	//并且yarn.nodemanager.resource.detect-hardware-cability为true,则在Windows和Linux的情况下,它将自动从硬件中确定。
	//在其他情况下,默认情况下vcore的数量为8。
    long configuredVCoresForContainers =
        NodeManagerHardwareUtils.getVCores(this.resourceCalculatorPlugin,
            this.conf);
    //无论是否启用检查,都要设置这些。UI中必需
    // / 物理内存配置 //
	//maxPmemAllottedForContainers = 8G 
	//maxVCoresAllottedForContainers = 8个虚拟核
	//这样看来 默认的容器能申请到的最多的资源为 8vc 8G
    this.maxPmemAllottedForContainers = configuredPMemForContainers;
    this.maxVCoresAllottedForContainers = configuredVCoresForContainers;
    // / 虚拟内存配置 //
	//获取 yarn.nodemanager.vmem-pmem-ratio 的值 默认 2.1
	//为容器设置内存限制时,虚拟内存与物理内存之间的比率。容器分配是以物理内存的形式表示的,虚拟内存的使用率可以超过此分配比例。
    vmemRatio = this.conf.getFloat(YarnConfiguration.NM_VMEM_PMEM_RATIO,
        YarnConfiguration.DEFAULT_NM_VMEM_PMEM_RATIO);
	//校验 为容器设置的内存限制比率,必须大于 0.99
    Preconditions.checkArgument(vmemRatio > 0.99f,
        YarnConfiguration.NM_VMEM_PMEM_RATIO + " should be at least 1.0");
	//容器可分配的最大虚拟默认为 : 2.1 * 8 = 16.8 G
    this.maxVmemAllottedForContainers =
        (long) (vmemRatio * configuredPMemForContainers);
	//是否将对容器强制执行物理内存限制
	//获取 yarn.nodemanager.pmem-check-enabled 的值 默认 true 
    pmemCheckEnabled = this.conf.getBoolean(
        YarnConfiguration.NM_PMEM_CHECK_ENABLED,
        YarnConfiguration.DEFAULT_NM_PMEM_CHECK_ENABLED);
	//是否将对容器强制执行虚拟内存限制
	//获取 yarn.nodemanager.vmem-check-enabled 的值 默认 true 
    vmemCheckEnabled = this.conf.getBoolean(
        YarnConfiguration.NM_VMEM_CHECK_ENABLED,
        YarnConfiguration.DEFAULT_NM_VMEM_CHECK_ENABLED);
	//启用弹性内存控制。这是Linux独有的功能。启用后,如果所有容器都超过了限制,则节点管理器会添加一个侦听器来接收事件。
	//限制由yarn.nodemanager.resource.memory-mb指定。如果未设置此项,则会根据功能设置限制。
	//有关详细信息,请参阅yarn.nodemanager.resource.detect-hardware-cability。该限制适用于物理或虚拟(rss+交换)内存,
	//具体取决于是否设置了yarn.nodemanager.pmem-check-enabled或yarn.node manager.vmem-check-enabled。
	//获取 yarn.nodemanager.elastic-memory-control.enabled 的值 默认 false 
    elasticMemoryEnforcement = this.conf.getBoolean(
        YarnConfiguration.NM_ELASTIC_MEMORY_CONTROL_ENABLED,
        YarnConfiguration.DEFAULT_NM_ELASTIC_MEMORY_CONTROL_ENABLED);
	//是否启用YARN CGroups严格内存强制,顾名思义就是资源一旦超过设置的限制就会里面kill掉
	//获取 yarn.nodemanager.resource.memory.enforced 的值 默认 true
    strictMemoryEnforcement = conf.getBoolean(
        YarnConfiguration.NM_MEMORY_RESOURCE_ENFORCED,
        YarnConfiguration.DEFAULT_NM_MEMORY_RESOURCE_ENFORCED);
    LOG.info("Physical memory check enabled: " + pmemCheckEnabled);
    LOG.info("Virtual memory check enabled: " + vmemCheckEnabled);
    LOG.info("Elastic memory control enabled: " + elasticMemoryEnforcement);
    LOG.info("Strict memory control enabled: " + strictMemoryEnforcement);
	//默认不开启弹性内存控制,这段逻辑不走
    if (elasticMemoryEnforcement) {
      if (!CGroupElasticMemoryController.isAvailable()) {
        // Test for availability outside the constructor
        // to be able to write non-Linux unit tests for
        // CGroupElasticMemoryController
        throw new YarnException(
            "CGroup Elastic Memory controller enabled but " +
            "it is not available. Exiting.");
      } else {
        this.oomListenerThread = new CGroupElasticMemoryController(
            conf,
            context,
            ResourceHandlerModule.getCGroupsHandler(),
            pmemCheckEnabled,
            vmemCheckEnabled,
            pmemCheckEnabled ?
                maxPmemAllottedForContainers : maxVmemAllottedForContainers
        );
      }
    }
	//isContainerMonitorEnabled() 默认为 true 
	//monitoringInterval 默认 3000ms 即 3s
	//因此 containersMonitorEnabled 默认为 true 容器监视默认是开启的
    containersMonitorEnabled =
        isContainerMonitorEnabled() && monitoringInterval > 0;
    LOG.info("ContainersMonitor enabled: " + containersMonitorEnabled);
	//用于启用容器日志监视器的标志,该监视器强制执行容器日志目录大小限制
	//获取 yarn.nodemanager.container-log-monitor.enable 的值 默认 false
    logMonitorEnabled =
            conf.getBoolean(YarnConfiguration.NM_CONTAINER_LOG_MONITOR_ENABLED,
                    YarnConfiguration.DEFAULT_NM_CONTAINER_LOG_MONITOR_ENABLED);
    LOG.info("Container Log Monitor Enabled: "+ logMonitorEnabled);
	//获取为YARN容器配置的物理CPU的百分比。返回值是 0 ~ 100
	//可以分配给容器的CPU百分比。此设置允许用户限制YARN容器使用的CPU数量。目前仅在使用cgroups的Linux上运行。默认情况是使用100%的CPU。
	//获取 yarn.nodemanager.resource.percentage-physical-cpu-limit 的值 默认值 100
	//nodeCpuPercentageForYARN 默认为 100
    nodeCpuPercentageForYARN =
        NodeManagerHardwareUtils.getNodeCpuPercentage(this.conf);
	//默认为 true 对容器强制执行物理内存限制
    if (pmemCheckEnabled) {
      //如果无法确定实际设备,则记录下
      long totalPhysicalMemoryOnNM = UNKNOWN_MEMORY_LIMIT;
	  //默认操作系统是LINUX resourceCalculatorPlugin = SysInfoLinux
      if (this.resourceCalculatorPlugin != null) {
		//SysInfoLinux 只读取/proc/meminfo、解析和计算一次内存信息。给  ramSize、hardwareCorruptSize、hugePagesTotal、hugePageSize赋值
		//totalPhysicalMemoryOnNM =  (ramSize - hardwareCorruptSize - (hugePagesTotal * hugePageSize)) * 1024
		//totalPhysicalMemoryOnNM =  (ram磁盘空间 - ram已损坏空间 - (保留的标准大页 * 每个标准大页的大小)) * 1024
		//可以参考我的这篇  博客中了解
		//ramSize : ram 磁盘空间
		//hardwareCorruptSize : RAM已损坏且不可用大小
		//hugePagesTotal : 保留的标准大页
		//hugePageSize : 每个标准大页的大小
        totalPhysicalMemoryOnNM = this.resourceCalculatorPlugin
            .getPhysicalMemorySize();
        if (totalPhysicalMemoryOnNM <= 0) {
          LOG.warn("NodeManager's totalPmem could not be calculated. "
              + "Setting it to " + UNKNOWN_MEMORY_LIMIT);
          totalPhysicalMemoryOnNM = UNKNOWN_MEMORY_LIMIT;
        }
      }
	  //分配给容器的物理内存,占可用物理内存总量的80%以上可能会发生Thrashing
      if (totalPhysicalMemoryOnNM != UNKNOWN_MEMORY_LIMIT &&
          this.maxPmemAllottedForContainers > totalPhysicalMemoryOnNM * 0.80f) {
        LOG.warn("NodeManager configured with "
            + TraditionalBinaryPrefix.long2String(maxPmemAllottedForContainers,
                "", 1)
            + " physical memory allocated to containers, which is more than "
            + "80% of the total physical memory available ("
            + TraditionalBinaryPrefix.long2String(totalPhysicalMemoryOnNM, "",
                1) + "). Thrashing might happen.");
      }
    }
    super.serviceInit(this.conf);
  }
  //是否启用容器监视器
  //获取 yarn.nodemanager.container-monitor.enabled 的值 默认 true
  private boolean isContainerMonitorEnabled() {
    return conf.getBoolean(YarnConfiguration.NM_CONTAINER_MONITOR_ENABLED,
        YarnConfiguration.DEFAULT_NM_CONTAINER_MONITOR_ENABLED);
  }
  /**
   * 获取最佳进程树计算器
   * @param pId container process id
   * @return process tree calculator
   */
  private ResourceCalculatorProcessTree
      getResourceCalculatorProcessTree(String pId) {
    return ResourceCalculatorProcessTree.
        getResourceCalculatorProcessTree(
            pId, processTreeClass, conf);
  }
  private boolean isResourceCalculatorAvailable() {
    if (resourceCalculatorPlugin == null) {
      LOG.info("ResourceCalculatorPlugin is unavailable on this system. " + this
          .getClass().getName() + " is disabled.");
      return false;
    }
    if (getResourceCalculatorProcessTree("0") == null) {
      LOG.info("ResourceCalculatorProcessTree is unavailable on this system. "
          + this.getClass().getName() + " is disabled.");
      return false;
    }
    return true;
  }
  @Override
  protected void serviceStart() throws Exception {
	//containersMonitorEnabled 默认为 true 容器监视默认是开启的
    if (containersMonitorEnabled) {
	  //起一个线程对容器进行监视
      this.monitoringThread.start();
    }
	//默认不开启弹性内存控制
    if (oomListenerThread != null) {
	  //如果开启基于cgroups的一种弹性内存控制,允许某些container可以使用超过设定值的资源,只要不超过整体的阈值。
	  //因此会启动这个线程oomListenerThread监控是否超过了整体的阈值
      oomListenerThread.start();
    }
	//容器日志监视器默认关闭
    if (logMonitorEnabled) {
      this.logMonitorThread.start();
    }
    super.serviceStart();
  }
  
  private class MonitoringThread extends Thread {
    MonitoringThread() {
      super("Container Monitor");
    }
    @Override
    public void run() {
      while (!stopped && !Thread.currentThread().isInterrupted()) {
        // 打印processTrees以进行调试
        if (LOG.isDebugEnabled()) {
          StringBuilder tmp = new StringBuilder("[ ");
          for (ProcessTreeInfo p : trackingContainers.values()) {
            tmp.append(p.getPID());
            tmp.append(" ");
          }
          LOG.debug("Current ProcessTree list : "
              + tmp.substring(0, tmp.length()) + "]");
        }
        //用于计算容器的总资源利用率的临时结构
        ResourceUtilization trackedContainersUtilization  =
            ResourceUtilization.newInstance(0, 0, 0.0f);
        //现在对trackingContainers进行监视,检查内存使用情况并杀死任何溢出的容器
		//每个容器在启动时都会将本容器信息放入trackingContainers中,详细看onStartMonitoringContainer()
        long vmemUsageByAllContainers = 0;
        long pmemByAllContainers = 0;
        long cpuUsagePercentPerCoreByAllContainers = 0;
        for (Entry entry : trackingContainers
            .entrySet()) {
          ContainerId containerId = entry.getKey();
          ProcessTreeInfo ptInfo = entry.getValue();
          try {
            //初始化未初始化的进程树
            initializeProcessTrees(entry);
            String pId = ptInfo.getPID();
            if (pId == null || !isResourceCalculatorAvailable()) {
              continue; //无法跟踪该 processTree
            }
            if (LOG.isDebugEnabled()) {
              LOG.debug("Constructing ProcessTree for : PID = " + pId
                  + " ContainerId = " + containerId);
            }
            ResourceCalculatorProcessTree pTree = ptInfo.getProcessTree();
            pTree.updateProcessTree();    // 更新 process-tree
			//获取进程树中所有进程使用的虚拟内存。
            long currentVmemUsage = pTree.getVirtualMemorySize();
			//获取进程树中所有进程使用的常驻集大小(rss)内存
			//rss 是 Resident Set Size 的缩写 表示驻留内存大小,是进程当前实际使用物理内存大小(包含共享库占用的内存)
            long currentPmemUsage = pTree.getRssMemorySize();
            if (currentVmemUsage < 0 || currentPmemUsage < 0) {
              // YARN-6862/YARN-5021 If the container just exited or for
              // another reason the physical/virtual memory is UNAVAILABLE (-1)
              // the values shouldn't be aggregated.
              LOG.info("Skipping monitoring container {} because "
                  + "memory usage is not available.", containerId);
              continue;
            }
            // if machine has 6 cores and 3 are used,
            // cpuUsagePercentPerCore should be 300%
			//基于样本之间的平均值,获取进程树中所有进程的CPU使用率,作为与顶部相似的总CPU周期的比率。因此,如果使用四分之二的核心,则返回200.0。
			//注意:在CPU使用率不可用的情况下,将返回UNAVAILABLE。不建议返回任何其他错误代码。
            float cpuUsagePercentPerCore = pTree.getCpuUsagePercent();
            if (cpuUsagePercentPerCore < 0) {
              // CPU usage is not available likely because the container just
              // started. Let us skip this turn and consider this container
              // in the next iteration.
              LOG.info("Skipping monitoring container " + containerId
                  + " since CPU usage is not yet available.");
              continue;
            }
			
			//记录使用情况指标
            recordUsage(containerId, pId, pTree, ptInfo, currentVmemUsage,
                    currentPmemUsage, trackedContainersUtilization);
			//检查资源限制,如果超出限制,请采取措施
            checkLimit(containerId, pId, pTree, ptInfo,
                    currentVmemUsage, currentPmemUsage);
            //计算所有容器的总内存使用情况
            vmemUsageByAllContainers += currentVmemUsage;
            pmemByAllContainers += currentPmemUsage;
            //计算所有容器的总cpu使用量
            cpuUsagePercentPerCoreByAllContainers += cpuUsagePercentPerCore;
			//向时间线服务报告使用情况指标
            reportResourceUsage(containerId, currentPmemUsage,
                    cpuUsagePercentPerCore);
          } catch (Exception e) {
            // Log the exception and proceed to the next container.
            LOG.warn("Uncaught exception in ContainersMonitorImpl "
                + "while monitoring resource of {}", containerId, e);
          }
        }
        if (LOG.isDebugEnabled()) {
          LOG.debug("Total Resource Usage stats in NM by all containers : "
              + "Virtual Memory= " + vmemUsageByAllContainers
              + ", Physical Memory= " + pmemByAllContainers
              + ", Total CPU usage(% per core)= "
              + cpuUsagePercentPerCoreByAllContainers);
        }
        //保存容器的聚合利用率
        setContainersUtilization(trackedContainersUtilization);
        //将容器利用率度量发布到节点管理器度量系统
        NodeManagerMetrics nmMetrics = context.getNodeManagerMetrics();
        if (nmMetrics != null) {
          nmMetrics.setContainerUsedMemGB(
              trackedContainersUtilization.getPhysicalMemory());
          nmMetrics.setContainerUsedVMemGB(
              trackedContainersUtilization.getVirtualMemory());
          nmMetrics.setContainerCpuUtilization(
              trackedContainersUtilization.getCPU());
        }
        try {
		  //监视容器的频率 默认3s
          Thread.sleep(monitoringInterval);
        } catch (InterruptedException e) {
          LOG.warn(ContainersMonitorImpl.class.getName()
              + " is interrupted. Exiting.");
          break;
        }
      }
    }
   
    private void recordUsage(ContainerId containerId, String pId,
                             ResourceCalculatorProcessTree pTree,
                             ProcessTreeInfo ptInfo,
                             long currentVmemUsage, long currentPmemUsage,
                             ResourceUtilization trackedContainersUtilization) {
      // if machine has 6 cores and 3 are used,
      // cpuUsagePercentPerCore should be 300% and
      // cpuUsageTotalCoresPercentage should be 50%
      float cpuUsagePercentPerCore = pTree.getCpuUsagePercent();
      float cpuUsageTotalCoresPercentage = cpuUsagePercentPerCore /
              resourceCalculatorPlugin.getNumProcessors();
      //乘以1000以避免在转换为int时丢失数据
	  //cpu 核数利用率 * 1000 * 8 / 100 
	  //比如 0.5 * 1000 * 8 / 100 = 40
      int milliVcoresUsed = (int) (cpuUsageTotalCoresPercentage * 1000
              * maxVCoresAllottedForContainers /nodeCpuPercentageForYARN);
	  //进程树的虚拟内存限制(字节)
      long vmemLimit = ptInfo.getVmemLimit();
	  //进程树的物理内存限制(字节)
      long pmemLimit = ptInfo.getPmemLimit();
      if (AUDITLOG.isDebugEnabled()) {
        int vcoreLimit = ptInfo.getCpuVcores();
        long cumulativeCpuTime = pTree.getCumulativeCpuTime();
        AUDITLOG.debug(String.format(
            "Resource usage of ProcessTree %s for container-id %s:" +
                " %s %%CPU: %f %%CPU-cores: %f" +
                " vCores-used: %d of %d Cumulative-CPU-ms: %d",
            pId, containerId.toString(),
            formatUsageString(
                currentVmemUsage, vmemLimit,
                currentPmemUsage, pmemLimit),
            cpuUsagePercentPerCore,
            cpuUsageTotalCoresPercentage,
            milliVcoresUsed / 1000, vcoreLimit,
            cumulativeCpuTime));
      }
      //添加此容器的资源利用率
      trackedContainersUtilization.addTo(
              (int) (currentPmemUsage >> 20),
              (int) (currentVmemUsage >> 20),
              milliVcoresUsed / 1000.0f);
      //将使用情况添加到容器指标
      if (containerMetricsEnabled) {
        ContainerMetrics.forContainer(
                containerId, containerMetricsPeriodMs,
                containerMetricsUnregisterDelayMs).recordMemoryUsage(
                (int) (currentPmemUsage >> 20));
        ContainerMetrics.forContainer(
                containerId, containerMetricsPeriodMs,
                containerMetricsUnregisterDelayMs).recordCpuUsage((int)
                cpuUsagePercentPerCore, milliVcoresUsed);
      }
    }
    
    private void checkLimit(ContainerId containerId, String pId,
                            ResourceCalculatorProcessTree pTree,
                            ProcessTreeInfo ptInfo,
                            long currentVmemUsage,
                            long currentPmemUsage) {
      Optional isMemoryOverLimit = Optional.empty();
      String msg = "";
      int containerExitStatus = ContainerExitStatus.INVALID;
		//strictMemoryEnforcement 默认 true elasticMemoryEnforcement默认 false
		//因此不走这个逻辑 elasticMemoryEnforcement 开启 
      if (strictMemoryEnforcement && elasticMemoryEnforcement) {
        //弹性内存控制和严格内存控制都是通过cgroups实现的。如果容器超过其请求,它会被弹性内存控制机制冻结,所以我们在这里检查并杀死它。
        //否则,如果节点从未超过其限制,并且基于procfs的内存核算与基于cgroup的核算不同,则不会杀死容器。
		//默认为 CGroupsMemoryResourceHandlerImpl
		//处理程序类来处理内存控制器。YARN已经在Java中提供了一个物理内存监视器,但它不如CGroups。
		//此处理程序设置软内存和硬内存限制。软限制设置为硬限制的90%。
        MemoryResourceHandler handler =
            ResourceHandlerModule.getMemoryResourceHandler();
        if (handler != null) {
		  //检查容器是否处于OOM状态
          isMemoryOverLimit = handler.isUnderOOM(containerId);
          containerExitStatus = ContainerExitStatus.KILLED_EXCEEDED_PMEM;
          msg = containerId + " is under oom because it exceeded its" +
              " physical memory limit";
        }
      } else if (strictMemoryEnforcement || elasticMemoryEnforcement) {
        //如果启用了基于cgroup的内存控制
        isMemoryOverLimit = Optional.of(false);
      }
      if (!isMemoryOverLimit.isPresent()) {
        long vmemLimit = ptInfo.getVmemLimit();
        long pmemLimit = ptInfo.getPmemLimit();
        //当流程从1开始时,我们想看看是否有超过1次迭代的流程。
        long curMemUsageOfAgedProcesses = pTree.getVirtualMemorySize(1);
        long curRssMemUsageOfAgedProcesses = pTree.getRssMemorySize(1);
		//默认为 true 对容器强制执行虚拟内存限制
        if (isVmemCheckEnabled()
            && isProcessTreeOverLimit(containerId.toString(),
            currentVmemUsage, curMemUsageOfAgedProcesses, vmemLimit)) {
          //当前使用率(年龄=0)始终高于过期使用率。我们不在消息中显示老化的大小,而是根据当前使用情况进行增量
          long delta = currentVmemUsage - vmemLimit;
          // 容器(根进程)仍处于活动状态,内存溢出
          // 转储流程树,然后进行清理
          msg = formatErrorMessage("virtual",
              formatUsageString(currentVmemUsage, vmemLimit,
                  currentPmemUsage, pmemLimit),
              pId, containerId, pTree, delta);
          isMemoryOverLimit = Optional.of(true);
          containerExitStatus = ContainerExitStatus.KILLED_EXCEEDED_VMEM;
		//默认为 true 对容器强制执行物理内存限制
		//isProcessTreeOverLimit():
		//检查容器的进程树的当前内存使用量是否超过限制
		
		//当java进程exec是一个程序时,它可能会暂时占据其内存大小的两倍,因为JVM执行fork()+exec(),在fork时间创建父内存的副本。
		//如果监视线程在同一个实例中检测到容器树使用的内存,它可能会认为它超出了限制并杀死该树,因为进程本身没有故障。
		
		//我们通过采用启发式检查来解决这个问题:如果进程树超过内存限制两倍以上,它将立即被杀死;如果进程树的进程比监控间隔早,
		//甚至超过内存限制1倍,它将被杀死。否则,它会被赋予怀疑的标志,可以再进行一次迭代。
        } else if (isPmemCheckEnabled()
            && isProcessTreeOverLimit(containerId.toString(),
            currentPmemUsage, curRssMemUsageOfAgedProcesses,
            pmemLimit)) {
          //当前使用率(年龄=0)始终高于过期使用率。我们不在消息中显示老化的大小,而是根据当前使用情况进行增量
          long delta = currentPmemUsage - pmemLimit;
          //容器(根进程)仍处于活动状态,内存溢出
          //转储流程树,然后进行清理
          msg = formatErrorMessage("physical",
              formatUsageString(currentVmemUsage, vmemLimit,
                  currentPmemUsage, pmemLimit),
              pId, containerId, pTree, delta);
          isMemoryOverLimit = Optional.of(true);
          containerExitStatus = ContainerExitStatus.KILLED_EXCEEDED_PMEM;
        }
      }
      if (isMemoryOverLimit.isPresent() && isMemoryOverLimit.get()
          && trackingContainers.remove(containerId) != null) {
        //虚拟内存或物理内存超出限制。使容器失败并删除相应的流程树
        LOG.warn(msg);
        //警告(如果不是领导者)
        if (!pTree.checkPidPgrpidForMatch()) {
          LOG.error("Killed container process with PID " + pId
                  + " but it is not a process group leader.");
        }
        //杀掉容器
        eventDispatcher.getEventHandler().handle(
                new ContainerKillEvent(containerId,
                      containerExitStatus, msg));
        LOG.info("Removed ProcessTree with root " + pId);
      }
    }
    
  private void onStopMonitoringContainer(
      ContainersMonitorEvent monitoringEvent, ContainerId containerId) {
    LOG.info("Stopping resource-monitoring for " + containerId);
    updateContainerMetrics(monitoringEvent);
    trackingContainers.remove(containerId);
  }
  private void onStartMonitoringContainer(
      ContainersMonitorEvent monitoringEvent, ContainerId containerId) {
    ContainerStartMonitoringEvent startEvent =
        (ContainerStartMonitoringEvent) monitoringEvent;
    LOG.info("Starting resource-monitoring for " + containerId);
    updateContainerMetrics(monitoringEvent);
    trackingContainers.put(containerId,
        new ProcessTreeInfo(containerId, null, null,
            startEvent.getVmemLimit(), startEvent.getPmemLimit(),
            startEvent.getCpuVcores()));
  }
}

四、总结

1、启动容器触发ContainerEventType.CONTAINER_LAUNCHED事件

2、ContainerImpl会处理1中事件,启动容器的同时触发容器监控事件ContainersMonitorEventType.START_MONITORING_CONTAINER

3、该事件由ContainersMonitorImpl调用onStartMonitoringContainer()处理2中事件

4、将启动的容器id、虚拟内存限制、物理内存限制、cpu核数限制封装成ProcessTreeInfo,并放到跟踪所有容器的trackingContainers中

5、ContainersMonitorImpl初始化时会获取监控容器的频率(默认3s一次)、监控容器日志目录大小频率(默认1min一次)、容器磁盘大小限制(默认1G)、全部容器总磁盘大小限制(默认10G)、系统资源计算插件(可以自己实现,默认LINUX 使用SysInfoLinux,WINDOWS 使用SysInfoWindows)、计算processTree资源利用率的类、系统为YARN容器留内存大小、YARN容器可用vcore数、虚拟内存和物理内存比率、内存控制策略等

6、ContainersMonitorImpl启动时会启动一个线程(monitoringThread)对容器的资源使用进行监控,如果超过限制就杀掉容器。默认只开启这一个线程,oomListenerThread和logMonitorThread默认不开启

 
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文章名称:《Hadoop-Yarn-NodeManager是如何监控容器的》
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