YARN源码学习(五)-----NN,DN,RM在Ganglia上的监控实现机理

前言

任何分布式系统在不断演变的过程中,必然都会经过有小变到大的过程,中间也必定会由不稳定到逐步稳定的过程.在所有的这些系统能够稳定运行的一个前提是,完整的监控和报警系统.这个模块是系统保持稳定最最基础的模块服务.只有在这块功能完善的情况下,才会让系统的维护者更高效的定位到问题所在,减少不必要的时间消耗,才会有更多的时间去做其他方面的一些优化.今天我所主要描述的就是对于Hadoop的强大监控工具Ganglia.


Ganglia

先来说说什么是Ganglia,Ganglia是开源的集群监控项目,代码可以在github社区进行下载.Ganglia的架构设计也是类似于Client-Server的模式,Client端会开启gmond进程进行客户端监控数据的收集,然后发给Server端,server端对数据进行收集并进行页面的展示,下面是一张结构图,大家只需做大致了解即可.


那么Ganglia与Hadoop,Yarn有什么关系呢.应该说Ganglia对Hadoop进行了很完美的支持,看过Ganglia监控界面的同学,应该知道Hadoop在Ganglia上定义了非常多细粒度的指标,基本涉及了非常多方面的统计信息.这里还需提到一点,Ganglia对HBase的支持也十分完美.


Hadoop的Ganglia统计指标

下面是文章描述的重点,Hadoop如何将统计指标注册到Ganglia上的呢,而且还能定期发送最新的数据.以DataNode数据节点为例.与DataNode统计相关的维度列表信息显示如下:


这个名称定义也是有来头的,这个名称是与相应的Metric类是相关的.比如dfs.datanode metrics ,以点分为2个部分,第一个dfs为上下文,第二个为具体的统计名称.上下文由下面的行定义

/**
 *
 * This class is for maintaining  the various DataNode statistics
 * and publishing them through the metrics interfaces.
 * This also registers the JMX MBean for RPC.
 * <p>
 * This class has a number of metrics variables that are publicly accessible;
 * these variables (objects) have methods to update their values;
 *  for example:
 *  <p> {@link #blocksRead}.inc()
 *
 */
@InterfaceAudience.Private
@Metrics(about="DataNode metrics", context="dfs")
public class DataNodeMetrics {
...
而注册统计类是由下面的注册代码决定:

@InterfaceAudience.Private
@Metrics(about="DataNode metrics", context="dfs")
public class DataNodeMetrics {

  ....

  final MetricsRegistry registry = new MetricsRegistry("datanode");
因此有了上面的统计大类名.

那么再在Ganglia上点击详情指标统计图,就呈现了各式各样的指标统计.


这些指标其实对应的就是DataNodeMetrics中定义的类变量.比如第二个变量blockChecsum检查操作次数为:

  MutableCounterLong datanodeNetworkErrors;

  @Metric MutableRate readBlockOp;
  @Metric MutableRate writeBlockOp;
  @Metric MutableRate blockChecksumOp;
其他变量的指标也是对应到metrics中的一个局部变量,那么这些变量如何进行改变呢,所有的这些变量改变值的方法都比较像.

...
public void incrBlocksReplicated(int delta) {
    blocksReplicated.incr(delta);
  }

  public void incrBlocksWritten() {
    blocksWritten.incr();
  }

  public void incrBlocksRemoved(int delta) {
    blocksRemoved.incr(delta);
  }

  public void incrBytesWritten(int delta) {
    bytesWritten.incr(delta);
  }
...
delta就是增加值,如果什么都不传入就是默认1.这个操作有点类似于Atomic的类对象的CAS操作.那么DataNodeMetrics的这些统计变量类都是在哪里被调用的呢?比如blockWritten的block写次数的统计方法

public void incrBlocksWritten() {
    blocksWritten.incr();
  }
是在下面这个方法中调用的

void receiveBlock(
      DataOutputStream mirrOut, // output to next datanode
      DataInputStream mirrIn,   // input from next datanode
      DataOutputStream replyOut,  // output to previous datanode
      String mirrAddr, DataTransferThrottler throttlerArg,
      DatanodeInfo[] downstreams,
      boolean isReplaceBlock) throws IOException {

      syncOnClose = datanode.getDnConf().syncOnClose;
      ....

      // If this write is for a replication or transfer-RBW/Finalized,
      // then finalize block or convert temporary to RBW.
      // For client-writes, the block is finalized in the PacketResponder.
      if (isDatanode || isTransfer) {
        // Hold a volume reference to finalize block.
        try (ReplicaHandler handler = claimReplicaHandler()) {
          // close the block/crc files
          close();
          block.setNumBytes(replicaInfo.getNumBytes());

          if (stage == BlockConstructionStage.TRANSFER_RBW) {
            // for TRANSFER_RBW, convert temporary to RBW
            datanode.data.convertTemporaryToRbw(block);
          } else {
            // for isDatnode or TRANSFER_FINALIZED
            // Finalize the block.
            datanode.data.finalizeBlock(block);
          }
        }
        datanode.metrics.incrBlocksWritten();
      }
...
而这个方法的所属类是BlockReceiver,而这个类又是DataNode的1个内部变量DataXceiver中会用到创建这个类.再结合其他的调用,基本这些DataNodeMetrics中的统计变量是被DataNode类直接调用以及他的子变量调用.

下面来看看另外一个NameNode的Ganglia指标统计.在NameNode中,存在同样对应的统计类NameNodeMetrics.

/**
 * This class is for maintaining  the various NameNode activity statistics
 * and publishing them through the metrics interfaces.
 */
@Metrics(name="NameNodeActivity", about="NameNode metrics", context="dfs")
public class NameNodeMetrics {
  final MetricsRegistry registry = new MetricsRegistry("namenode");
...
里面同样会有一堆相关的统计变量

/**
 * This class is for maintaining  the various NameNode activity statistics
 * and publishing them through the metrics interfaces.
 */
@Metrics(name="NameNodeActivity", about="NameNode metrics", context="dfs")
public class NameNodeMetrics {
  final MetricsRegistry registry = new MetricsRegistry("namenode");

  @Metric MutableCounterLong createFileOps;
  @Metric MutableCounterLong filesCreated;
  @Metric MutableCounterLong filesAppended;
  @Metric MutableCounterLong getBlockLocations;
  @Metric MutableCounterLong filesRenamed;
  @Metric MutableCounterLong filesTruncated;
  @Metric MutableCounterLong getListingOps;
  @Metric MutableCounterLong deleteFileOps;
  @Metric MutableCounterLong requestOps;
  
  @Metric("Number of files/dirs deleted by delete or rename operations")
  MutableCounterLong filesDeleted;
  @Metric MutableCounterLong fileInfoOps;
  @Metric MutableCounterLong addBlockOps;
  @Metric MutableCounterLong getAdditionalDatanodeOps;
  @Metric MutableCounterLong createSymlinkOps;
  @Metric MutableCounterLong getLinkTargetOps;
  @Metric MutableCounterLong filesInGetListingOps;
  @Metric("Number of allowSnapshot operations")
然而这里面的许多的变量统计方法不是单单在NameNode上调用的,而是在以一个大类FSNameSystem中为首的许多NameNode自变量中,包括其他一些NameNodeRpcServer.

最后重点说说RM,ResourceManager的监控,为什么说是重点说说呢,因为这个类在Hadoop原有的代码中是不存在的.我自己新加的,就是ResourceManagerMetrics,取这个名称是保持之前的一致性.RM的统计在目前的Ganglia上非常缺乏.因此我加了许多RM相关关键指标的统计.主要包含如下几点.

1.Container容器allocated申请与released释放成功失败次数.

2.Application应用注册与注销次数.

3.关闭应用次数统计.

父类的名称为rpc.ResourceManager metrics 

@InterfaceAudience.Private
@Metrics(about="ResourceManager metrics", context="rpc")
public class ResourceManagerMetrics {
	final MetricsRegistry registry = new MetricsRegistry("Resourcemanager");
	String name;
这些代码是照着之前代码进行修改的.所有的统计操作类型如下,是从RMAuditLogger中抠出来的.

public static final String KILL_APP_REQUEST = "Kill Application Request";
    public static final String SUBMIT_APP_REQUEST = "Submit Application Request";
    public static final String MOVE_APP_REQUEST = "Move Application Request";
    public static final String FINISH_SUCCESS_APP = "Application Finished - Succeeded";
    public static final String FINISH_FAILED_APP = "Application Finished - Failed";
    public static final String FINISH_KILLED_APP = "Application Finished - Killed";
    public static final String REGISTER_AM = "Register App Master";
    public static final String AM_ALLOCATE = "App Master Heartbeats";
    public static final String UNREGISTER_AM = "Unregister App Master";
    public static final String ALLOC_CONTAINER = "AM Allocated Container";
    public static final String RELEASE_CONTAINER = "AM Released Container";
定义了如下的11对指标

@Metric MutableCounterLong cmAllocatedSuccessOps;
	@Metric MutableCounterLong cmReleasedSuccessOps;
	@Metric MutableCounterLong killAppRequestSuccessOps;
	@Metric MutableCounterLong submitAppRequestSuccessOps;
	@Metric MutableCounterLong moveAppRequestSuccessOps;
	@Metric MutableCounterLong registerAMSuccessOps;
	@Metric MutableCounterLong unRegisterAMSuccessOps;
	@Metric MutableCounterLong amAllocatedSuccessOps;
	@Metric MutableCounterLong finishSucceedAppSuccessOps;
	@Metric MutableCounterLong finishFailedAppSuccessOps;
	@Metric MutableCounterLong finishKilledAppSuccessOps;
	
	@Metric MutableCounterLong cmAllocatedFailedOps;
	@Metric MutableCounterLong cmReleasedFailedOps;
	@Metric MutableCounterLong killAppRequestFailedOps;
	@Metric MutableCounterLong submitAppRequestFailedOps;
	@Metric MutableCounterLong moveAppRequestFailedOps;
	@Metric MutableCounterLong registerAMFailedOps;
	@Metric MutableCounterLong unRegisterAMFailedOps;
	@Metric MutableCounterLong amAllocatedFailedOps;
	@Metric MutableCounterLong finishSucceedAppFailedOps;
	@Metric MutableCounterLong finishFailedAppFailedOps;
	@Metric MutableCounterLong finishKilledAppFailedOps;
累加统计变量示例方法,容器申请分配方法

public void incrContainerAllcatedOpr(String opr, String result){
		if(opr.equals(ALLOC_CONTAINER)){
			if(result.equals("SUCCESS")){
				cmAllocatedSuccessOps.incr();
			}else if (result.equals("FAILED")){
				cmAllocatedFailedOps.incr();
			}
		}
	}
在RMAuditLogger中被调用

/**
   * A helper api for creating an audit log for a failure event.
   */
  static String createFailureLog(String user, String operation, String perm,
      String target, String description, ApplicationId appId,
      ApplicationAttemptId attemptId, ContainerId containerId) {
	rmm.incrFailedOpr(operation);
这个RMAuditLogger类非常的强大,他能记录每个用户对RM资源的使用记录,所以在这个总入口加这样的统计分析代码是非常高效的.最后在Ganglia上的展现效果就是下面这个样子.


OK,分析到这里,最后看一下与本文所贯穿的主要结构图.


同时对ResourceManagerMetrics代码有兴趣的同学,请看下面的链接.


全部代码的分析请点击链接https://github.com/linyiqun/hadoop-yarn/tree/master/RMMetric,后续将会继续更新YARN其他方面的代码分析。


参考源代码

Apach-hadoop-2.7.1(hadoop-hdfs-project)


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原文地址：https://www.cnblogs.com/bianqi/p/12183860.html