uReplicator实现分析

MirrorMakerWorker分析

是整个同步机制的主入口，主要组织的逻辑有：

• 配置数据的传入与处理，ConsumerConfig对象的构建
• 度量对象的准备，定时上报的度量数据收集线程的定义与启动
• CompactConsumerFetcherManager实例的创建与startConnections
• 根据fetchNum创建KafkaConnector实例，KafkaConnector实例中会关联CompactConsumerFetcherManager实例
• 添加Helix Controller
• 添加优雅关闭的钩子
• 构造producer的config producerProps
• 根据维护KafkaConnector实例的connectorMap来创建MirrorMakerThread实例并启动
• 通过shutdownLatch: CountDownLatch来等待关闭退出main方法

与Helix关联的地方

实现HelixWorkerOnlineOfflineStateModelFactory和OnlineOfflineStateModel，OnlineOfflineStateModel可以理解成是一个监听器。实例在上下线切换时可以监听到。

helixZkManager = HelixManagerFactory.getZKHelixManager(helixClusterName, instanceId, InstanceType.PARTICIPANT, zkServer)
val stateMachineEngine: StateMachineEngine = helixZkManager.getStateMachineEngine()
// register the MirrorMaker worker
val stateModelFactory = new HelixWorkerOnlineOfflineStateModelFactory(instanceId, fetchNum, connectorMap)
stateMachineEngine.registerStateModelFactory("OnlineOffline", stateModelFactory)
helixZkManager.connect()
helixAdmin = helixZkManager.getClusterManagmentTool

class HelixWorkerOnlineOfflineStateModelFactory(final val instanceId: String, final val fetchNum: Int,
                                                final val connectorMap: ConcurrentHashMap[String, KafkaConnector]) extends StateModelFactory[StateModel] {

  override def createNewStateModel(partitionName: String) = new OnlineOfflineStateModel(instanceId, connectorMap)

  // register mm instance
  class OnlineOfflineStateModel(final val instanceId: String, final val connectors: ConcurrentHashMap[String, KafkaConnector]) extends StateModel {

    def onBecomeOnlineFromOffline(message: Message, context: NotificationContext) = {
      // add topic partition on the instance
      connectorMap.get(getFetcherId(message.getResourceName, message.getPartitionName.toInt)).addTopicPartition(message.getResourceName, message.getPartitionName.toInt)
    }

    def onBecomeOfflineFromOnline(message: Message, context: NotificationContext) = {
      // delete topic partition on the instance
      connectorMap.get(getFetcherId(message.getResourceName, message.getPartitionName.toInt)).deleteTopicPartition(message.getResourceName, message.getPartitionName.toInt)
    }

    def onBecomeDroppedFromOffline(message: Message, context: NotificationContext) = {
      // do nothing
    }

    private def getFetcherId(topic: String, partitionId: Int): String = {
      "" + Utils.abs(31 * topic.hashCode() + partitionId) % fetchNum
    }
  }

}

run方法逻辑

• 通过KafkaConnector拿到KafkaStream，通过KafkaStream拿到ConsumerIterator
• 在没有关闭时，一直迭代ConsumerIterator
• 拿到迭代器中的数据，就是取到的消息(为什么迭代器中能一直有消息，因为这样反推iter-->KafkaStream-->KafkaConnector+Queue-->PartitionTopicInfo-->fetcherManager.partitionAddMap-->fetcherManager.partitionInfoMap-->fetcherManager.createFetcherThread-->CompactConsumerFetcherThread.partitionInfoMap-->CompactConsumerFetcherThread.processPartitionData-->CompactConsumerFetcherThread.doWork-->ShutdownableThread.run//spin)
• 经过MirrorMakerMessageHandler处理消息形成ProducerRecord数组实例，主要是分区对齐
• 用producer发到目标集群
• 用maybeFlushAndCommitOffsets方法flush并提交offset
• 真正commit offset的动作由自行实现的KafkaConnector完成，记录在ZK上，提交是定时提交

CompactConsumerFetcherThread分析

概述

CompactConsumerFetcherThread是继承自Kafka提供的ShutdownableThread，ShutdownableThread内部会在isRunning标志位ok的情况下以spin的形式一直调用doWork方法。

  override def run(): Unit = {
    info("Starting ")
    try{
      while(isRunning.get()){
        doWork()
      }
    } catch{
      case e: Throwable =>
        if(isRunning.get())
          error("Error due to ", e)
    }
    shutdownLatch.countDown()
    info("Stopped ")
  }

doWork方法分析

• 锁定partitionMapLock
• 锁定updateMapLock
• 将partitionAddMap中的数据放到partitionMap，然后清空partitionAddMap
• 将partitionDeleteMap中的数据从partitionMap中移除并移除fetcherLagStats中对应的stat，然后清空partitionDeleteMap
• 迭代partitionMap将需要拉取的topic、partition、fetchoffset、fetchsize等信息加入fetchRequestBuilder
• 用fetchRequestBuilder构造出FetchRequest实例
• 如果fetchRequest.requestInfo.isEmpty是空的，那么等待fetchBackOffMs
• 对于两次拉取间隔是否过大做日志输出(DUMP_INTERVAL_MS = 5 * 60 * 1000)
• processFetchRequest 处理拉的请求

processFetchRequest方法分析

当doWork方法准备好了FetchRequest实例就要靠processFetchRequest方法来拉数据给partitionInfoMap中的PartitionTopicInfo实例中的队列了。简单过程如下：

• 迭代响应中的每条数据，按每个分区维度处理
• 拿到消息
• 根据拿到的消息算出下一次的new offset，并更新到partitionMap中
• 更新度量信息，计算堆积
• 将取到的消息在PartitionTopicInfo实例中放入队列。 PartitionTopicInfo实例的队列来自Connect中的构造KafkaStream实例时传递的同一个队列。 这样能打通连接器和stream