Flume之核心架构深入解析

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我们一起来了解Source、Channel和Sink的全链路过程。

一、Flume架构分析

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这个图中核心的组件是:

Source,ChannelProcessor,Channel,Sink。他们的关系结构如下:

Source  {
    ChannelProcessor  {
             Channel  ch1
             Channel  ch2
             …
    }
} 
Sink  {
   Channel  ch; 
} 
SinkGroup {
   Channel ch;
   Sink s1;
   Sink s2;
   …
}

二、各组件详细介绍

1、Source组件

Source是数据源的总称,我们往往设定好源后,数据将源源不断的被抓取或者被推送。

常见的数据源有:ExecSource,KafkaSource,HttpSource,NetcatSource,JmsSource,AvroSource等等。

所有的数据源统一实现一个接口类如下:

@InterfaceAudience.Public
@InterfaceStability.Stable
public interface Source extends LifecycleAware, NamedComponent {

  /**
   * Specifies which channel processor will handle this source‘s events.
   *
   * @param channelProcessor
   */
  public void setChannelProcessor(ChannelProcessor channelProcessor);

  /**
   * Returns the channel processor that will handle this source‘s events.
   */
  public ChannelProcessor getChannelProcessor();

}

Source提供了两种机制: PollableSource(轮询拉取)和EventDrivenSource(事件驱动):

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上图展示的Source继承关系类图。

通过类图我们可以看到NetcatSource,ExecSource和HttpSource属于事件驱动模型。KafkaSource,SequenceGeneratorSource和JmsSource属于轮询拉取模型。

Source接口继承了LifecycleAware接口,它的的所有逻辑的实现在接口的start和stop方法中进行。

下图是类关系方法图:

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Source接口定义的是最终的实现过程,比如通过日志抓取日志,这个抓取的过程和实际操作就是在对应的Source实现中,比如:ExecSource。那么这些Source实现由谁来驱动的呢?现在我们将介绍SourceRunner类。看一下类继承结构图:

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我们看一下PollableSourceRunner和EventDrivenSourceRunner的具体实现:

//PollableSourceRunner:
public void start() {
  PollableSource source = (PollableSource) getSource();
  ChannelProcessor cp = source.getChannelProcessor();
  cp.initialize();
  source.start();

  runner = new PollingRunner();

  runner.source = source; //Source实现类就在这里被赋与。
  runner.counterGroup = counterGroup;
  runner.shouldStop = shouldStop;

  runnerThread = new Thread(runner);
  runnerThread.setName(getClass().getSimpleName() + "-" + 
      source.getClass().getSimpleName() + "-" + source.getName());
  runnerThread.start();

  lifecycleState = LifecycleState.START;
}

//EventDrivenSourceRunner:
@Override
public void start() {
  Source source = getSource();
  ChannelProcessor cp = source.getChannelProcessor();
  cp.initialize();
  source.start();
  lifecycleState = LifecycleState.START;
}

注:其实所有的Source实现类内部都维护着线程,执行source.start()其实就是启动了相应的线程。

刚才我们看代码,代码中一直都在展示channelProcessor这个类,同时最上面架构设计图里面也提到了这个类,那它到底是干什么呢,下面我们就对其分解。

2、Channel组件

Channel用于连接Source和Sink,Source将日志信息发送到Channel,Sink从Channel消费日志信息;Channel是中转日志信息的一个临时存储,保存有Source组件传递过来的日志信息。

先看代码如下:

ChannelSelectorConfiguration selectorConfig = config.getSelectorConfiguration();

ChannelSelector selector = ChannelSelectorFactory.create(sourceChannels, selectorConfig);

ChannelProcessor channelProcessor = new ChannelProcessor(selector);
Configurables.configure(channelProcessor, config);

source.setChannelProcessor(channelProcessor);

ChannelSelectorFactory.create方法实现如下:

public static ChannelSelector create(List<Channel> channels,
    ChannelSelectorConfiguration conf) {
  String type = ChannelSelectorType.REPLICATING.toString();
  if (conf != null){
    type = conf.getType();
  }
  ChannelSelector selector = getSelectorForType(type);
  selector.setChannels(channels);
  Configurables.configure(selector, conf);
  return selector;
}

其中我们看一下ChannelSelectorType这个枚举类,包括了几种类型:

public enum ChannelSelectorType {

  /**
   * Place holder for custom channel selectors not part of this enumeration.
   */
  OTHER(null),

  /**
   * 复用通道选择器
   */
  REPLICATING("org.apache.flume.channel.ReplicatingChannelSelector"),

  /**
   *  多路通道选择器
   */
  MULTIPLEXING("org.apache.flume.channel.MultiplexingChannelSelector");
}

ChannelSelector的类结构图如下所示:

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注:RelicatingChannelSelector和MultiplexingChannelSelector是二个通道选择器,第一个是复用型通道选择器,也就是的默认的方式,会把接收到的消息发送给其他每个channel。第二个是多路通道选择器,这个会根据消息header中的参数进行通道选择。

说完通道选择器,正式来解释Channel是什么,先看一个接口类:

public interface Channel extends LifecycleAware, NamedComponent {  
  public void put(Event event) throws ChannelException;  
  public Event take() throws ChannelException;  
  public Transaction getTransaction();  
}

注:put方法是用来发送消息,take方法是获取消息,transaction是用于事务操作。

类结构图如下:

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3、Sink组件

Sink负责取出Channel中的消息数据,进行相应的存储文件系统,数据库,或者提交到远程服务器。

Sink在设置存储数据时,可以向文件系统中,数据库中,hadoop中储数据,在日志数据较少时,可以将数据存储在文件系中,并且设定一定的时间间隔保存数据。在日志数据较多时,可以将相应的日志数据存储到Hadoop中,便于日后进行相应的数据分析。

Sink接口类内容如下:

public interface Sink extends LifecycleAware, NamedComponent {  
  public void setChannel(Channel channel);  
  public Channel getChannel();  
  public Status process() throws EventDeliveryException;  
  public static enum Status {  
    READY, BACKOFF  
  }  
}

Sink是通过如下代码进行的创建:

Sink sink = sinkFactory.create(comp.getComponentName(),  comp.getType());

DefaultSinkFactory.create方法如下:

public Sink create(String name, String type) throws FlumeException {
  Preconditions.checkNotNull(name, "name");
  Preconditions.checkNotNull(type, "type");
  logger.info("Creating instance of sink: {}, type: {}", name, type);
  Class<? extends Sink> sinkClass = getClass(type);
  try {
    Sink sink = sinkClass.newInstance();
    sink.setName(name);
    return sink;
  } catch (Exception ex) {
    System.out.println(ex);
    throw new FlumeException("Unable to create sink: " + name
        + ", type: " + type + ", class: " + sinkClass.getName(), ex);
  }
}

注:Sink是通过SinkFactory工厂来创建,提供了DefaultSinkFactory默认工厂,程序会查找org.apache.flume.conf.sink.SinkType这个枚举类找到相应的Sink处理类,比如:org.apache.flume.sink.LoggerSink,如果没找到对应的处理类,直接通过Class.forName(className)进行直接查找实例化实现类。

Sink的类结构图如下:

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与ChannelProcessor处理类对应的是SinkProcessor,由SinkProcessorFactory工厂类负责创建,SinkProcessor的类型由一个枚举类提供,看下面代码:

public enum SinkProcessorType {
  /**
   * Place holder for custom sinks not part of this enumeration.
   */
  OTHER(null),

  /**
   * 故障转移 processor
   *
   * @see org.apache.flume.sink.FailoverSinkProcessor
   */
  FAILOVER("org.apache.flume.sink.FailoverSinkProcessor"),

  /**
   * 默认processor
   *
   * @see org.apache.flume.sink.DefaultSinkProcessor
   */
  DEFAULT("org.apache.flume.sink.DefaultSinkProcessor"),

  /**
   * 负载processor
   *
   * @see org.apache.flume.sink.LoadBalancingSinkProcessor
   */
  LOAD_BALANCE("org.apache.flume.sink.LoadBalancingSinkProcessor");

  private final String processorClassName;

  private SinkProcessorType(String processorClassName) {
    this.processorClassName = processorClassName;
  }

  public String getSinkProcessorClassName() {
    return processorClassName;
  }
}

SinkProcessor的类结构图如下:

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说明:

1、FailoverSinkProcessor是故障转移处理器,当sink从通道拿数据信息时出错进行的相关处理,代码如下:

public Status process() throws EventDeliveryException {
  // 经过了冷却时间,再次发起重试
  Long now = System.currentTimeMillis();
  while(!failedSinks.isEmpty() && failedSinks.peek().getRefresh() < now) {
    //从失败队列中获取sink节点
    FailedSink cur = failedSinks.poll(); 
    Status s;
    try {
      //调用相应sink进行处理,比如将channel的数据读取存放到文件中,
      //这个存放文件的动作就在process中进行。
      s = cur.getSink().process();
      if (s  == Status.READY) {
        //如果处理成功,则放到存活队列中
        liveSinks.put(cur.getPriority(), cur.getSink());
        activeSink = liveSinks.get(liveSinks.lastKey());
        logger.debug("Sink {} was recovered from the fail list",
                cur.getSink().getName());
      } else {
        // if it‘s a backoff it needn‘t be penalized.
        //如果处理失败,则继续放到失败队列中
        failedSinks.add(cur);
      }
      return s;
    } catch (Exception e) {
      cur.incFails();
      failedSinks.add(cur);
    }
  }

  Status ret = null;
  while(activeSink != null) {
    try {
      ret = activeSink.process();
      return ret;
    } catch (Exception e) {
      logger.warn("Sink {} failed and has been sent to failover list",
              activeSink.getName(), e);
      activeSink = moveActiveToDeadAndGetNext();
    }
  }

2、LoadBalancingSinkProcessor是负载Sink处理器

首先我们和ChannelProcessor一样,我们也要重点说明一下SinkSelector这个选择器。

先看一下SinkSelector.configure方法的部分代码:

if (selectorTypeName.equalsIgnoreCase(SELECTOR_NAME_ROUND_ROBIN)) {
  selector = new RoundRobinSinkSelector(shouldBackOff);
} else if (selectorTypeName.equalsIgnoreCase(SELECTOR_NAME_RANDOM)) {
  selector = new RandomOrderSinkSelector(shouldBackOff);
} else {
  try {
    @SuppressWarnings("unchecked")
    Class<? extends SinkSelector> klass = (Class<? extends SinkSelector>)
        Class.forName(selectorTypeName);

    selector = klass.newInstance();
  } catch (Exception ex) {
    throw new FlumeException("Unable to instantiate sink selector: "
        + selectorTypeName, ex);
  }
}

结合上面的代码,再看类结构图如下:

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注:RoundRobinSinkSelector是轮询选择器,RandomOrderSinkSelector是随机分配选择器。

最后我们以KafkaSink为例看一下Sink里面的具体实现:

public Status process() throws EventDeliveryException {
  Status result = Status.READY;
  Channel channel = getChannel();
  Transaction transaction = null;
  Event event = null;
  String eventTopic = null;
  String eventKey = null;

  try {
    long processedEvents = 0;

    transaction = channel.getTransaction();
    transaction.begin();

    messageList.clear();
    for (; processedEvents < batchSize; processedEvents += 1) {
      event = channel.take();

      if (event == null) {
        // no events available in channel
        break;
      }

      byte[] eventBody = event.getBody();
      Map<String, String> headers = event.getHeaders();

      if ((eventTopic = headers.get(TOPIC_HDR)) == null) {
        eventTopic = topic;
      }

      eventKey = headers.get(KEY_HDR);

      if (logger.isDebugEnabled()) {
        logger.debug("{Event} " + eventTopic + " : " + eventKey + " : "
          + new String(eventBody, "UTF-8"));
        logger.debug("event #{}", processedEvents);
      }

      // create a message and add to buffer
      KeyedMessage<String, byte[]> data = new KeyedMessage<String, byte[]>
        (eventTopic, eventKey, eventBody);
      messageList.add(data);

    }

    // publish batch and commit.
    if (processedEvents > 0) {
      long startTime = System.nanoTime();
      producer.send(messageList);
      long endTime = System.nanoTime();
      counter.addToKafkaEventSendTimer((endTime-startTime)/(1000*1000));
      counter.addToEventDrainSuccessCount(Long.valueOf(messageList.size()));
    }

    transaction.commit();

  } catch (Exception ex) {
    String errorMsg = "Failed to publish events";
    logger.error("Failed to publish events", ex);
    result = Status.BACKOFF;
    if (transaction != null) {
      try {
        transaction.rollback();
        counter.incrementRollbackCount();
      } catch (Exception e) {
        logger.error("Transaction rollback failed", e);
        throw Throwables.propagate(e);
      }
    }
    throw new EventDeliveryException(errorMsg, ex);
  } finally {
    if (transaction != null) {
      transaction.close();
    }
  }

  return result;
}

注:方法从channel中不断的获取数据,然后通过Kafka的producer生产者将消息发送到Kafka里面。

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