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Giraph源码分析(九)Aggregators原理解析

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2016-06-07 15:56:381705검색

本人原创,转载请注明出处!欢迎大家加入 Giraph 技术交流群 : 228591158 Giraph中Aggregator的用法请参考官方文档:http://giraph.apache.org/aggregators.html ,本文重点在解析Giraph如何实现 Aggregators 。 基本原理 :在每个超级步中,每个Worker计算

本人原创,转载请注明出处!欢迎大家加入Giraph 技术交流群: 228591158

Giraph中Aggregator的用法请参考官方文档:http://giraph.apache.org/aggregators.html ,本文重点在解析Giraph如何实现Aggregators

基本原理:在每个超级步中,每个Worker计算本地的聚集值。超级步计算完成后,把本地的聚集值发送给Master汇总。在MasterCompute()执行后,把全局的聚集值回发给所有的Workers。

缺点:当某个应用(或算法)使用了多个聚集器(Aggregators),Master要完成所有聚集器的计算。因为Master要接受、处理、发送大量的数据,无论是在计算方面还是网络通信层次,都会导致Master成为系统瓶颈。

改进:采用分片聚集 (sharded aggregators) . 在每个超级步的最后,每个聚集器被派发给一个Worker,该Worker接受和聚集其他Workers发送给该聚集器的值。然后Workers把自己的所有的聚集器发送给Master,这样Master就无需执行任何聚集,只是接收每个聚集器的最终值。在MasterCompute.compute执行后,Master不是直接把所有的聚集器发送给所有的Workers,而是发送给聚集器所属的Worker,然后每个Worker再把其上的聚集器发送给所有的Workers.

首先给出Master Worker间, Worker Worker间通信协议,在每个类中的doRequest(ServerData serverData)方法中会解析并存储收到的消息。
1). org.apache.giraph.comm.requests.SendWorkerAggregatorsRequest 类 . Worker --> Worker Owner
功能:每个worker把当前超步的局部 aggregated values 发送到该Aggregator的拥有者。
2). org.apache.giraph.comm.requests.SendAggregatorsToMasterRequest 类. Worker Owner--> Master
功能:每个Worker把自己所拥有的Aggregator的最终 aggregated values 发送给 master。
3). org.apache.giraph.comm.requests.SendAggregatorsToOwnerRequest 类. Master --> Worker Owner.
功能:master把最终的 aggregated values 或aggregators 发送给该Aggregator的拥有者。
4). org.apache.giraph.comm.requests.SendAggregatorsToWorkerRequest 类。 Worker Owner--> Worker
功能: 发送最终的 aggregated values 到 其他workers。发送者为该Aggregator的拥有者,接受者为除发送者之外的所有workers。

\

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finishSuperStep(MasterClient masterClient) 方法核心内容如下:

喎?http://www.2cto.com/kf/ware/vc/" target="_blank" class="keylink">vcD4KPHByZSBjbGFzcz0="brush:sql;"> /** * Finalize aggregators for current superstep and share them with workers */ public void finishSuperstep(MasterClient masterClient) { for (AggregatorWrapper aggregator : aggregatorMap.values()) { if (aggregator.isChanged()) { // if master compute changed the value, use the one he chose aggregator.setPreviousAggregatedValue( aggregator.getCurrentAggregatedValue()); // reset aggregator for the next superstep aggregator.resetCurrentAggregator(); } } /** * 把聚集器发送给所属的Worker。发送内容: * 1). Name of the aggregator * 2). Class of the aggregator * 3). Value of the aggretator */ try { for (Map.Entry> entry : aggregatorMap.entrySet()) { masterClient.sendAggregator(entry.getKey(), entry.getValue().getAggregatorClass(), entry.getValue().getPreviousAggregatedValue()); } masterClient.finishSendingAggregatedValues(); } catch (IOException e) { throw new IllegalStateException("finishSuperstep: " + "IOException occurred while sending aggregators", e); } }
问题1:如何确定aggregator的Worker Owner ?

答:根据aggregator的Name来确定它所属的Worker,计算方法如下:

/**
 * 根据aggregatorName和所有的workers列表来计算aggregator所属的Worker
 * 参数aggregatorName:Name of the aggregator
 * 参数workers: Workers的list列表
 * 返回值:Worker which owns the aggregator
 */
public static WorkerInfo getOwner(String aggregatorName,List<WorkerInfo> workers) {
    //用aggregatorName的HashCode()值模以 Workers的总数目
    int index = Math.abs(aggregatorName.hashCode() % workers.size());
    return workers.get(index);  //返回aggregator所属的Worker
}
问题2:Worker 如何判断自身是否接收完自己所拥有的aggregators?

答:Master给某个Worker发送aggregators时,同时发送到该Worker的aggregators数目。使用的 SendAggregatorsToOwnerRequest类对消息进行封装和解析。

2. Worker接受Master发送的Aggregator,Worker把接收到的聚集体值发送给其他所有Workers,然后每个Workers就会得到上一个超级步的全局聚集值。

由前文知道,每个Worker都有一个ServerData对象,ServerData类中关于Aggregator的两个成员变量如下:

// 保存Worker在当前超步拥有的aggregators
private final OwnerAggregatorServerData ownerAggregator;
// 保存前一个超步的aggregators
private final AllAggregatorServerData allAggregatorData;

可以看到,ownerAggregatorData用来存储在当前超步Master发送给Worker的聚集器,allAggregatorData用来保存上一个超级步全局的聚集值。ownerAggregatorData和allAggregatorData值的初始化在SendAggregatorsToOwnerRequest 类中的doRequest(ServerData serverData)方法中,如下:

public void doRequest(ServerData serverData) {
    DataInput input = getDataInput();
    AllAggregatorServerData aggregatorData = serverData.getAllAggregatorData();
    try {
      //收到的Aggregators数目。在CountingOutputStream类中有计数器counter,
      //每向输出流中添加一个聚集器对象,计数加1. 发送时,在flush方法中把该值插入到输出流最前面。
      int numAggregators = input.readInt();
      for (int i = 0; i < numAggregators; i++) {
        String aggregatorName = input.readUTF();
        String aggregatorClassName = input.readUTF();
        if (aggregatorName.equals(AggregatorUtils.SPECIAL_COUNT_AGGREGATOR)) {
          LongWritable count = new LongWritable(0);
          //Master发送给该Worker的requests总数目.
          count.readFields(input);
          aggregatorData.receivedRequestCountFromMaster(count.get(),
              getSenderTaskId());
        } else {
          Class<Aggregator<Writable>> aggregatorClass =
              AggregatorUtils.getAggregatorClass(aggregatorClassName);
          aggregatorData.registerAggregatorClass(aggregatorName,
              aggregatorClass);
          Writable aggregatorValue =
              aggregatorData.createAggregatorInitialValue(aggregatorName);
          aggregatorValue.readFields(input);
          //把收到的上一次全局聚集的值赋值给allAggregatorData
          aggregatorData.setAggregatorValue(aggregatorName, aggregatorValue);
          //ownerAggregatorData只接受聚集器
          serverData.getOwnerAggregatorData().registerAggregator(
              aggregatorName, aggregatorClass);
        }
      }
    } catch (IOException e) {
      throw new IllegalStateException("doRequest: " +
          "IOException occurred while processing request", e);
    }
    //接受一个 request,计数减1,同时把收到的Data添加到allAggregatorServerData的List<byte[]> masterData中
    aggregatorData.receivedRequestFromMaster(getData());
 }

每个Worker在开始计算前,会调用BspServiceWorker类的prepareSuperStep()方法来进行聚集器值的派发和接受其他Workers发送的聚集器值。调用关系如下:

\

BspServiceWorker类的prepareSuperStep()方法如下:

@Override
public void prepareSuperstep() {
   if (getSuperstep() != INPUT_SUPERSTEP) {
	  /*
	   * aggregatorHandler为WorkerAggregatorHandler类型.可参考上文中MasterAggregatorHandler的类继承关系
	   * workerAggregatorRequestProcessor声明为WorkerAggregatorRequestProcessor(接口)类型,
	   * 实际为NettyWorkerAggregatorRequestProcessor的实例,用于Worker间发送聚集器的值。
	   */
      aggregatorHandler.prepareSuperstep(workerAggregatorRequestProcessor);
   }
}

WorkerAggregatorHandler类的prepareSuperstep( WorkerAggregatorRequestProcessor requestProcessor)方法如下:

public void prepareSuperstep(WorkerAggregatorRequestProcessor requestProcessor) {
    AllAggregatorServerData allAggregatorData =
        serviceWorker.getServerData().getAllAggregatorData();
    /**
     * 等待直到Master发送给该Worker的聚集器都已接受完,
     * 返回值为Master发送给该Worker的所有Data(聚集器)
     */
    Iterable<byte[]> dataToDistribute =
        allAggregatorData.getDataFromMasterWhenReady(
            serviceWorker.getMasterInfo());
  
    // 把从Master收到的Data(聚集器)发送给其他所有Workers
    requestProcessor.distributeAggregators(dataToDistribute);

    // 等待直到接受完其他Workers发送给该Workers的聚集器
    allAggregatorData.fillNextSuperstepMapsWhenReady(
        getOtherWorkerIdsSet(), previousAggregatedValueMap,
        currentAggregatorMap);
    // 只是清空allAggregatorServerData的List<byte[]> masterData对象
    // 为下一个超级步接受Master发送的聚集器做准备
    allAggregatorData.reset();
}
下面详述Worker如何判定已接收完所有Master发送的所有Request ? 主要目的在于描述分布式环境下线程间如何协作。在AllAggregatorServerData类中定义了TaskIdsPermitBarrier类型的变量masterBarrier,用来判断是否接收完Master发送的Request. TaskIdsPermitBarrier类中主要使用wait()、notifyAll()等方法来控制,当获得的aggregatorName等于AggregatorUtils.SPECIAL_COUNT_AGGREGATOR时,会调用requirePermits(long permits,int taskId)来增加接收的arrivedTaskIds和需要等待的request数目waitingOnPermits. 接受一个Request

  /**
   * Require more permits. This will increase the number of times permits
   * were required. Doesn&#39;t wait for permits to become available.
   *
   * @param permits Number of permits to require
   * @param taskId Task id which required permits
   */
  public synchronized void requirePermits(long permits, int taskId) {
    arrivedTaskIds.add(taskId);
    waitingOnPermits += permits;
    notifyAll();
  }

\ 接受一个Request后,会调用releaseOnePermit()方法把waitingOnPermits减1。 \

3. 在Vertex.compute()方法中,每个Worker聚集自身的值。计算完成后,调用WorkerAggregatorHandler类的finishSuperstep( WorkerAggregatorRequestProcessor requestProcessor)方法,把本地的聚集器的值给句聚集器的aggregatorName发送给该aggregator所属的Worker. Aggregator的属主Worker接受其他所有Workers发送的本地聚集值进行汇总,汇总完毕后发送给Master,供下一次超级步的MasterCompute.compute()方法使用。finishSuperstep方法如下:

 /**
   * Send aggregators to their owners and in the end to the master
   *
   * @param requestProcessor Request processor for aggregators
   */
  public void finishSuperstep(
      WorkerAggregatorRequestProcessor requestProcessor) {
    OwnerAggregatorServerData ownerAggregatorData =
        serviceWorker.getServerData().getOwnerAggregatorData();
    // First send partial aggregated values to their owners and determine
    // which aggregators belong to this worker
    for (Map.Entry<String, Aggregator<Writable>> entry :
        currentAggregatorMap.entrySet()) {
        boolean sent = requestProcessor.sendAggregatedValue(entry.getKey(),
            entry.getValue().getAggregatedValue());
        if (!sent) {
          // If it&#39;s my aggregator, add it directly
          ownerAggregatorData.aggregate(entry.getKey(),
              entry.getValue().getAggregatedValue());
        }
    }
    // Flush
    requestProcessor.flush();
    // Wait to receive partial aggregated values from all other workers
    Iterable<Map.Entry<String, Writable>> myAggregators =
        ownerAggregatorData.getMyAggregatorValuesWhenReady(
            getOtherWorkerIdsSet());

    // Send final aggregated values to master
    AggregatedValueOutputStream aggregatorOutput =
        new AggregatedValueOutputStream();
    for (Map.Entry<String, Writable> entry : myAggregators) {
        int currentSize = aggregatorOutput.addAggregator(entry.getKey(),
            entry.getValue());
        if (currentSize > maxBytesPerAggregatorRequest) {
          requestProcessor.sendAggregatedValuesToMaster(
              aggregatorOutput.flush());
        }   
    }
    requestProcessor.sendAggregatedValuesToMaster(aggregatorOutput.flush());
    // Wait for master to receive aggregated values before proceeding
    serviceWorker.getWorkerClient().waitAllRequests();
    ownerAggregatorData.reset();
  }

调用关系如下:

\

4. 大同步后,Master调用MasterAggregatorHandler类的prepareSusperStep(masterClient)方法,收集聚集器的值。方法内容如下:

  public void prepareSuperstep(MasterClient masterClient) {

    // 收集上次超级步的聚集值,为master compute 做准备
    for (AggregatorWrapper<Writable> aggregator : aggregatorMap.values()) {
	// 如果是 Persistent Aggregator,则累加
	if (aggregator.isPersistent()) {
        aggregator.aggregateCurrent(aggregator.getPreviousAggregatedValue());
      }
      aggregator.setPreviousAggregatedValue(
          aggregator.getCurrentAggregatedValue());
      aggregator.resetCurrentAggregator();
      progressable.progress();
    }
  }
然后调用MasterCompute.compute()方法(可能会修改聚集器的值),在该方法内若根据聚集器的值调用了MasterCompute类的haltCompute()方法来终止MaterCompute,则表明要结束整个Job。那么Master就会通知所有Workers要结束整个作业;在该方法内若没有调用MasterCompute类的haltCompute()方法,则回到步骤1继续进行迭代。

备注:Job迭代结束条件有三,满足其一就行:
1) 达到最大迭代次数
2) 没有活跃顶点且没有消息在传递
3) 终止MasterCompute计算

总结:为解决在多个Aggregator条件下,Master成为系统瓶颈的问题。采取了把所有Aggregator派发给某一部分Workers,由这些Workers完成全局的聚集值的计算与发送,Master只需要与这些Workers进行简单数据通信即可,大大降低了Master的工作量。

追加:下面用图示方法说明上述执行过程。

实验条件:

1). 一个Master,四个Worker

2). 两个Aggregators,记为A1和A2。

1. Master把Aggregators发送给Workers,收到Aggregator的Worker就作为该Aggregator的Owner。下图中Master把A1发送给Worker1,A2发送给Worker3.那么Worker1就作为A1的Owner,Worker3就是A2的Owner。该步骤在MasterAggregatorHandler类的finishSuperStep(MasterClient masterClient) 方法中完成,使用的是SendAggregatorsToOwnerRequest 通信协议。注:每个Owner Worker 可能有多个聚集器。

\

图1 Master分发Aggregator

2. Workers接受Master发送的Aggregator,然后把Aggregator发送给其他Workers。Worker1要把A1分别发送给Worker2、Worker3和Worker4;Worker3要把A2分别发送给Worker1、Worker2和Worker4。该步骤在WorkerAggregatorHandler类的prepareSuperstep( WorkerAggregatorRequestProcessor requestProcessor)方法中完成,使用的是SendAggregatorsToMasterRequest 通信协议。此步骤完成后,每个Worker上都有了聚集器A1和A2(具体为上一个超步的全局最终聚集值)。

\

3. 每个Worker调用Vertex.compute()方法开始计算,收集本地的Aggregator聚集值。对聚集体A1来说,Worker1、Worker2、Worker3、Worker4的本地聚集值依次记为:

A11 、A12、 A13、A14;对聚集器A2来说,Worker1、Worker2、Worker3、Worker4的本地聚集值依次记为:

A21 、A22、 A23、A24 。计算完成后,每个Worker就要把本地的聚集值发送给聚集器的Owner,聚集器的Owner在接受的时候会合并聚集。那么A11 、A12、 A13、A14要发送给Worker1进行全局聚集得到A1,A21 、A22、 A23、A24 要发送给Worker3进行全局聚集得到A2’ 。

公式如下:

\

此部分采用的是SendWorkerAggregatorsRequest通信协议。Worker1和Worker3要把汇总的A1和A2的新值:A1’ 和A2’发送给Master,供下一次超级步的MasterCompute.compute()方法使用采用的是SendAggregatorsToMasterRequest通信协议。此部分在WorkerAggregatorHandler类的finishSuperstep( WorkerAggregatorRequestProcessor requestProcessor)方法中完成。过程如下图所示:

\

4. Master收到Worker1发送的A1’ 和Woker3发送的A2’后,此步骤在MasterAggregatorHandler类的prepareSusperStep(masterClient)方法中完成。然后调用MasterCompute.compute()方法,此方法可能会修改聚集器的值,如得到A1’’和A2’’。在masterCompute.compute()方法内若根据聚集器的值调用了MasterCompute类的haltCompute()方法来终止MaterCompute,则表明要结束整个Job。那么Master就会通知所有Workers要结束整个作业;在该方法内若没有调用MasterCompute类的haltCompute()方法,则回到步骤1继续进行迭代,继续把A1’’发送给Worker1,A2’’发送给Worker3。



完!

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