<转>YARN源码分析(四)-----Journalnode

前言

最近在排查公司Hadoop集群性能问题时,发现Hadoop集群整体处理速度非常缓慢,平时只需要跑几十分钟的任务时间一下子上张到了个把小时,起初怀疑是网络原因,后来证明的确是有一部分这块的原因,但是过了没几天,问题又重现了,这次就比较难定位问题了,后来分析hdfs请求日志和Ganglia的各项监控指标,发现namenode的挤压请求数持续比较大,说明namenode处理速度异常,然后进而分析出是因为写journalnode的editlog速度慢问题导致的,后来发现的确是journalnode的问题引起的,后来的原因是因为journalnode的editlog目录没创建,导致某台节点写edillog一直抛FileNotFoundException,所以在这里提醒大家一定要重视一些小角色,比如JournalNode.在问题排查期间,也对YARN的JournalNode相关部分的代码做了学习,下面是一下学习心得,可能有些地方分析有误,敬请谅解.

JournalNode

可能有些同学没有听说过JournalNode,只听过Hadoop的Datanode,Namenode,因为这个概念是在MR2也就是Yarn中新加的,journalNode的作用是存放EditLog的,在MR1中editlog是和fsimage存放在一起的然后SecondNamenode做定期合并,Yarn在这上面就不用SecondNamanode了.下面是目前的Yarn的架构图,重点关注一下JournalNode的角色.

上面在Active Namenode与StandBy Namenode之间的绿色区域就是JournalNode,当然数量不一定只有1个,作用相当于NFS共享文件系统.Active Namenode往里写editlog数据,StandBy再从里面读取数据进行同步.

QJM

下面从Yarn源码的角度分析一下JournalNode的机制,在配置中定义JournalNode节点的个数是可多个的,所以一定会存在一个类似管理者这样的角色存在,而这个管理者就是QJM,全程QuorumJournalManager.下面是QJM的变量定义:

 /**
  * A JournalManager that writes to a set of remote JournalNodes,
  * requiring a quorum of nodes to ack each write.
  * JournalManager可以写很多记录数据给多个远程JournalNode节点
  */
 @InterfaceAudience.Private
 public class QuorumJournalManager implements JournalManager {
   static final Log LOG = LogFactory.getLog(QuorumJournalManager.class);

   // Timeouts for which the QJM will wait for each of the following actions.
   private final int startSegmentTimeoutMs;
   private final int prepareRecoveryTimeoutMs;
   private final int acceptRecoveryTimeoutMs;
   private final int finalizeSegmentTimeoutMs;
   private final int selectInputStreamsTimeoutMs;
   private final int getJournalStateTimeoutMs;
   private final int newEpochTimeoutMs;
   private final int writeTxnsTimeoutMs;

   // Since these don't occur during normal operation, we can
   // use rather lengthy timeouts, and don't need to make them
   // configurable.
   private static final int FORMAT_TIMEOUT_MS            = 60000;
   private static final int HASDATA_TIMEOUT_MS           = 60000;
   private static final int CAN_ROLL_BACK_TIMEOUT_MS     = 60000;
   private static final int FINALIZE_TIMEOUT_MS          = 60000;
   private static final int PRE_UPGRADE_TIMEOUT_MS       = 60000;
   private static final int ROLL_BACK_TIMEOUT_MS         = 60000;
   private static final int UPGRADE_TIMEOUT_MS           = 60000;
   private static final int GET_JOURNAL_CTIME_TIMEOUT_MS = 60000;
   private static final int DISCARD_SEGMENTS_TIMEOUT_MS  = 60000;

   private final Configuration conf;
   private final URI uri;
   private final NamespaceInfo nsInfo;
   private boolean isActiveWriter;

   //远程节点存在于AsyncLoggerSet集合中
   private final AsyncLoggerSet loggers;

   private int outputBufferCapacity = 512 * 1024;
   private final URLConnectionFactory connectionFactory;

上面定义了很多的操作超时时间,这个过程也是走RPC的方式的.所有JournalNode客户端的代理被包含在了AsyncLoggerSet对象中,在此对象中包含了AsyncLogger对象列表,每个logger对象管控一个独立的Journalnode,下面是QJM中从配置动态创建logger对象

 static List createLoggers(Configuration conf,
       URI uri, NamespaceInfo nsInfo, AsyncLogger.Factory factory)
           throws IOException {
     List ret = Lists.newArrayList();
     List addrs = getLoggerAddresses(uri);
     String jid = parseJournalId(uri);
     for (InetSocketAddress addr : addrs) {
       ret.add(factory.createLogger(conf, nsInfo, jid, addr));
     }
     return ret;
   }

然后设置到AsyncLoggerSet集合类中:

 QuorumJournalManager(Configuration conf,
      URI uri, NamespaceInfo nsInfo,
      AsyncLogger.Factory loggerFactory) throws IOException {
    Preconditions.checkArgument(conf != null, "must be configured");

    this.conf = conf;
    this.uri = uri;
    this.nsInfo = nsInfo;
    this.loggers = new AsyncLoggerSet(createLoggers(loggerFactory));
    ...

AsyncLoggerSet集合类的定义很简单,就是Logger对象的包装类.

 /**
  * Wrapper around a set of Loggers, taking care of fanning out
  * calls to the underlying loggers and constructing corresponding
  * {@link QuorumCall} instances.
  */
 class AsyncLoggerSet {
   static final Log LOG = LogFactory.getLog(AsyncLoggerSet.class);

   private final List loggers;

   private static final long INVALID_EPOCH = -1;
   private long myEpoch = INVALID_EPOCH;

   public AsyncLoggerSet(List loggers) {
     this.loggers = ImmutableList.copyOf(loggers);
   }

重新回到Logger对象类中,AsyncLogger对象是一个抽象类,实际起作用的是下面这个管道类

 /**
  * Channel to a remote JournalNode using Hadoop IPC.
  * All of the calls are run on a separate thread, and return
  * {@link ListenableFuture} instances to wait for their result.
  * This allows calls to be bound together using the {@link QuorumCall}
  * class.
  */
 @InterfaceAudience.Private
 public class IPCLoggerChannel implements AsyncLogger {

   private final Configuration conf;
   //JournalNode通信地址
   protected final InetSocketAddress addr;
   private QJournalProtocol proxy;

   /**
    * Executes tasks submitted to it serially, on a single thread, in FIFO order
    * (generally used for write tasks that should not be reordered).
    * 单线程串行操作线程池
    */
   private final ListeningExecutorService singleThreadExecutor;
   /**
    * Executes tasks submitted to it in parallel with each other and with those
    * submitted to singleThreadExecutor (generally used for read tasks that can
    * be safely reordered and interleaved with writes).
    * 并行操作线程池
    */
   private final ListeningExecutorService parallelExecutor;
   private long ipcSerial = 0;
   private long epoch = -1;
   private long committedTxId = HdfsConstants.INVALID_TXID;

   private final String journalId;
   private final NamespaceInfo nsInfo;

   private URL httpServerURL;
   //journalnode线程metric统计操作
   private final IPCLoggerChannelMetrics metrics;

正如这个类的名称一样,作用就是服务端与客户端执行类的连接类,注意,这个类并不是直接执行类.在这个管道类中,定义了许多有用的监控信息变量,ganglia上的journal监控指标就是取自于这里

 ...
 /**
    * The number of bytes of edits data still in the queue.
    * 积压的editlog记录数
    */
   private int queuedEditsSizeBytes = 0;

   /**
    * The highest txid that has been successfully logged on the remote JN.
    * 最高位的事物Id数量
    */
   private long highestAckedTxId = 0;

   /**
    * Nanotime of the last time we successfully journaled some edits
    * to the remote node.
    */
   private long lastAckNanos = 0;

   /**
    * Nanotime of the last time that committedTxId was update. Used
    * to calculate the lag in terms of time, rather than just a number
    * of txns.
    */
   private long lastCommitNanos = 0;

   /**
    * The maximum number of bytes that can be pending in the queue.
    * This keeps the writer from hitting OOME if one of the loggers
    * starts responding really slowly. Eventually, the queue
    * overflows and it starts to treat the logger as having errored.
    */
   private final int queueSizeLimitBytes;

   /**
    * If this logger misses some edits, or restarts in the middle of
    * a segment, the writer won't be able to write any more edits until
    * the beginning of the next segment. Upon detecting this situation,
    * the writer sets this flag to true to avoid sending useless RPCs.
    * 非同步状态指标,判断JournalNode是否掉线
    */
   private boolean outOfSync = false;
 ...

因为管道类方法与真正客户端方法继承了相同的协议,方法定义是相同的,下面列举几个常见方法:

开始执行记录写操作

 @Override
   public ListenableFuture startLogSegment(final long txid,
       final int layoutVersion) {
     return singleThreadExecutor.submit(new Callable() {
       @Override
       public Void call() throws IOException {
         getProxy().startLogSegment(createReqInfo(), txid, layoutVersion);
         synchronized (IPCLoggerChannel.this) {
           if (outOfSync) {
             outOfSync = false;
             QuorumJournalManager.LOG.info(
                 "Restarting previously-stopped writes to " +
                 IPCLoggerChannel.this + " in segment starting at txid " +
                 txid);
           }
         }
         return null;
       }
     });
   }

写完之后,执行记录确认finalize操作

 @Override
   public ListenableFuture finalizeLogSegment(
       final long startTxId, final long endTxId) {
     return singleThreadExecutor.submit(new Callable() {
       @Override
       public Void call() throws IOException {
         throwIfOutOfSync();

         getProxy().finalizeLogSegment(createReqInfo(), startTxId, endTxId);
         return null;
       }
     });
   }

singleThreadExecutor单线程线程池一般执行的是写操作相关,而并行线程池则进行的是读操作,而且所有的这些操作采用的异步执行的方式,保证了高效性.服务端执行操作函数后,立刻得到一个call列表,并等待回复值

 @Override
   public void finalizeLogSegment(long firstTxId, long lastTxId)
       throws IOException {
     QuorumCall q = loggers.finalizeLogSegment(
         firstTxId, lastTxId);
     loggers.waitForWriteQuorum(q, finalizeSegmentTimeoutMs,
         String.format("finalizeLogSegment(%s-%s)", firstTxId, lastTxId));
   }

JournalNode和Journal

与服务端对应的客户端,对每个JournalNode进行操作执行的类是JournalNode

 /**
  * The JournalNode is a daemon which allows namenodes using
  * the QuorumJournalManager to log and retrieve edits stored
  * remotely. It is a thin wrapper around a local edit log
  * directory with the addition of facilities to participate
  * in the quorum protocol.
  */
 @InterfaceAudience.Private
 public class JournalNode implements Tool, Configurable, JournalNodeMXBean {
   public static final Log LOG = LogFactory.getLog(JournalNode.class);
   private Configuration conf;
   private JournalNodeRpcServer rpcServer;
   private JournalNodeHttpServer httpServer;
   private final Map journalsById = Maps.newHashMap();
   private ObjectName journalNodeInfoBeanName;
   private String httpServerURI;
   private File localDir;

   static {
     HdfsConfiguration.init();
   }

   /**
    * When stopped, the daemon will exit with this code.
    */
   private int resultCode = 0;

里面定义了与服务端对应的log记录操作方法

 ...
 public void discardSegments(String journalId, long startTxId)
       throws IOException {
     getOrCreateJournal(journalId).discardSegments(startTxId);
   }

   public void doPreUpgrade(String journalId) throws IOException {
     getOrCreateJournal(journalId).doPreUpgrade();
   }

   public void doUpgrade(String journalId, StorageInfo sInfo) throws IOException {
     getOrCreateJournal(journalId).doUpgrade(sInfo);
   }

   public void doFinalize(String journalId) throws IOException {
     getOrCreateJournal(journalId).doFinalize();
   }
 ...

而这些方法间接调用的方法又是Journal这个方法,并不约而同的传入了方法journald,journalId难道指的是所在JournalNode节点的标识?起初我也是这么想的,后来证明是错的.

 File[] journalDirs = localDir.listFiles(new FileFilter() {
       @Override
       public boolean accept(File file) {
         return file.isDirectory();
       }
     });
     for (File journalDir : journalDirs) {
       String jid = journalDir.getName();
       if (!status.containsKey(jid)) {
         Map jMap = new HashMap();
         jMap.put("Formatted", "true");
         status.put(jid, jMap);
       }
     }

答案其实是目标写目录,从hadoop-yarn-project的测试代码中也能知道

 /**
    * Set up the given Configuration object to point to the set of JournalNodes
    * in this cluster.
    */
   public URI getQuorumJournalURI(String jid) {
     List addrs = Lists.newArrayList();
     for (JNInfo info : nodes) {
       addrs.add("127.0.0.1:" + info.ipcAddr.getPort());
     }
     String addrsVal = Joiner.on(";").join(addrs);
     LOG.debug("Setting logger addresses to: " + addrsVal);
     try {
       return new URI("qjournal://" + addrsVal + "/" + jid);
     } catch (URISyntaxException e) {
       throw new AssertionError(e);
     }
   }

JournalUri的格式是下面这种,qjournal://host/jid

 dfs.namenode.shared.edits.dir
 qjournal://had1:8485;had2:8485;had3:8485/mycluster

JournalNode中保存了Journal的map图映射对象可以使得不同的节点可以写不同的editlog目录.Journal对象才是最终的操作执行者,并且拥有直接操作editlog输出文件的EditLogOutputStream类.下面是其中一个方法

 /**
    * Start a new segment at the given txid. The previous segment
    * must have already been finalized.
    */
   public synchronized void startLogSegment(RequestInfo reqInfo, long txid,
       int layoutVersion) throws IOException {
     assert fjm != null;
     checkFormatted();
     checkRequest(reqInfo);

     if (curSegment != null) {
       LOG.warn("Client is requesting a new log segment " + txid +
           " though we are already writing " + curSegment + ". " +
           "Aborting the current segment in order to begin the new one.");
       // The writer may have lost a connection to us and is now
       // re-connecting after the connection came back.
       // We should abort our own old segment.
       abortCurSegment();
     }

     // Paranoid sanity check: we should never overwrite a finalized log file.
     // Additionally, if it's in-progress, it should have at most 1 transaction.
     // This can happen if the writer crashes exactly at the start of a segment.
     EditLogFile existing = fjm.getLogFile(txid);
     if (existing != null) {
       if (!existing.isInProgress()) {
         throw new IllegalStateException("Already have a finalized segment " +
             existing + " beginning at " + txid);
       }
 ...

具体代码的写逻辑,读者可自行查阅,本文只从整体上梳理一下整个JournalNode的写流程,下面是准备的一张简单架构图,帮助大家理解.

全部代码的分析请点击链接https://github.com/linyiqun/hadoop-yarn,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)