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随着业务量的急速膨胀和又一年双11的到来,我们会对现有的Kafka集群进行扩容,以应对更大的流量和业务尖峰。当然,扩容之后的新Kafka Broker默认是不会有任何Topic和Partition的,需要手动利用分区重分配命令kafka-reassign-partitions将现有的Partition/Replica平衡到新的Broker上去。那么Kafka具体是如何执行重分配流程的呢?本文就来简单解读一下。

生成、提交重分配方案

我们知道,使用kafka-reassign-partitions命令分为三步,一是根据指定的Topic生成JSON格式的重分配方案(--generate),二是将生成的方案提交执行(--execute),三是观察重分配的进度(--verify),它们分别对应kafka.admin.ReassignPartitionsCommand类中的generateAssignment()、executeAssignment()和verifyAssignment()方法。

generateAssignment()方法会调用AdminUtils#assignReplicasToBrokers()方法生成Replica分配方案。源码就不再读了,其原则简述如下:

  • 将Replica尽量均匀地分配到各个Broker上去;
  • 一个Partition的所有Replica必须位于不同的Broker上;
  • 如果Broker有机架感知(rack aware)的信息,将Partition的Replica尽量分配到不同的机架。

executeReassignment()方法调用了reassignPartitions()方法,其源码如下。

def reassignPartitions(throttle: Throttle = NoThrottle, timeoutMs: Long = 10000L): Boolean = {
  maybeThrottle(throttle)
  try {
    val validPartitions = proposedPartitionAssignment.filter { case (p, _) => validatePartition(zkUtils, p.topic, p.partition) }
    if (validPartitions.isEmpty) false
    else {
      if (proposedReplicaAssignment.nonEmpty) {
        val adminClient = adminClientOpt.getOrElse(
          throw new AdminCommandFailedException("bootstrap-server needs to be provided in order to reassign replica to the specified log directory"))
        val alterReplicaDirResult = adminClient.alterReplicaLogDirs(
          proposedReplicaAssignment.asJava, new AlterReplicaLogDirsOptions().timeoutMs(timeoutMs.toInt))
        alterReplicaDirResult.values().asScala.foreach { case (replica, future) => {
            try {
              future.get()
              throw new AdminCommandFailedException(s"Partition ${replica.topic()}-${replica.partition()} already exists on broker ${replica.brokerId()}." +
                s" Reassign replica to another log directory on the same broker is currently not supported.")
            } catch {
              case t: ExecutionException =>
                t.getCause match {
                  case e: ReplicaNotAvailableException => // It is OK if the replica is not available
                  case e: Throwable => throw e
                }
            }
        }}
      }
      val jsonReassignmentData = ZkUtils.formatAsReassignmentJson(validPartitions)
      zkUtils.createPersistentPath(ZkUtils.ReassignPartitionsPath, jsonReassignmentData)
      true
    }
  } catch {
    // ......
  }
}

在进行必要的Partition校验之后,创建ZK持久节点/admin/reassign_partitions,并将JSON格式的重分配方案写进去。如果该节点存在,就表示已经在进行重分配,不能再启动新的重分配流程(相关的判断在executeReassignment()方法中)。

监听并处理重分配事件

在之前讲解Kafka Controller时,笔者提到Controller会注册多个ZK监听器,将监听到的事件投递到内部的事件队列,并由事件处理线程负责处理。监听ZK中/admin/reassign_partitions节点的监听器为PartitionReassignmentListener,并产生PartitionReassignment事件,处理逻辑如下。

case object PartitionReassignment extends ControllerEvent {
  def state = ControllerState.PartitionReassignment

  override def process(): Unit = {
    if (!isActive) return
    val partitionReassignment = zkUtils.getPartitionsBeingReassigned()
    val partitionsToBeReassigned = partitionReassignment.filterNot(p => controllerContext.partitionsBeingReassigned.contains(p._1))
    partitionsToBeReassigned.foreach { case (partition, context) =>
      if(topicDeletionManager.isTopicQueuedUpForDeletion(partition.topic)) {
        error(s"Skipping reassignment of partition $partition since it is currently being deleted")
        removePartitionFromReassignedPartitions(partition)
      } else {
        initiateReassignReplicasForTopicPartition(partition, context)
      }
    }
  }
}

该方法先取得需要重分配的Partition列表,然后从中剔除掉那些已经被标记为删除的Topic所属的Partition,再调用initiateReassignReplicasForTopicPartition()方法:

def initiateReassignReplicasForTopicPartition(topicAndPartition: TopicAndPartition,
                                              reassignedPartitionContext: ReassignedPartitionsContext) {
  val newReplicas = reassignedPartitionContext.newReplicas
  val topic = topicAndPartition.topic
  val partition = topicAndPartition.partition
  try {
    val assignedReplicasOpt = controllerContext.partitionReplicaAssignment.get(topicAndPartition)
    assignedReplicasOpt match {
      case Some(assignedReplicas) =>
        if (assignedReplicas == newReplicas) {
          throw new KafkaException("Partition %s to be reassigned is already assigned to replicas".format(topicAndPartition) +
            " %s. Ignoring request for partition reassignment".format(newReplicas.mkString(",")))
        } else {
          info("Handling reassignment of partition %s to new replicas %s".format(topicAndPartition, newReplicas.mkString(",")))
          // first register ISR change listener
          watchIsrChangesForReassignedPartition(topic, partition, reassignedPartitionContext)
          controllerContext.partitionsBeingReassigned.put(topicAndPartition, reassignedPartitionContext)
          // mark topic ineligible for deletion for the partitions being reassigned
          topicDeletionManager.markTopicIneligibleForDeletion(Set(topic))
          onPartitionReassignment(topicAndPartition, reassignedPartitionContext)
        }
      case None => throw new KafkaException("Attempt to reassign partition %s that doesn't exist"
        .format(topicAndPartition))
    }
  } catch {
    case e: Throwable => error("Error completing reassignment of partition %s".format(topicAndPartition), e)
    // remove the partition from the admin path to unblock the admin client
    removePartitionFromReassignedPartitions(topicAndPartition)
  }
}

该方法的执行逻辑如下:

  1. 判断Partition的原有Replica是否与即将重分配的新Replica相同,如果相同则抛出异常;
  2. 注册即将被重分配的Partition的ISR变化监听器;
  3. 把即将被重分配的Partition/Replica记录在Controller上下文中的partitionsBeingReassigned集合中;
  4. 把即将被重分配的Topic标记为不可删除;
  5. 调用onPartitionReassignment()方法真正触发重分配流程。

执行重分配流程

onPartitionReassignment()方法的代码如下。

def onPartitionReassignment(topicAndPartition: TopicAndPartition, reassignedPartitionContext: ReassignedPartitionsContext) {
  val reassignedReplicas = reassignedPartitionContext.newReplicas
  if (!areReplicasInIsr(topicAndPartition.topic, topicAndPartition.partition, reassignedReplicas)) {
    info("New replicas %s for partition %s being ".format(reassignedReplicas.mkString(","), topicAndPartition) +
      "reassigned not yet caught up with the leader")
    val newReplicasNotInOldReplicaList = reassignedReplicas.toSet -- controllerContext.partitionReplicaAssignment(topicAndPartition).toSet
    val newAndOldReplicas = (reassignedPartitionContext.newReplicas ++ controllerContext.partitionReplicaAssignment(topicAndPartition)).toSet
    //1. Update AR in ZK with OAR + RAR.
    updateAssignedReplicasForPartition(topicAndPartition, newAndOldReplicas.toSeq)
    //2. Send LeaderAndIsr request to every replica in OAR + RAR (with AR as OAR + RAR).
    updateLeaderEpochAndSendRequest(topicAndPartition, controllerContext.partitionReplicaAssignment(topicAndPartition),
      newAndOldReplicas.toSeq)
    //3. replicas in RAR - OAR -> NewReplica
    startNewReplicasForReassignedPartition(topicAndPartition, reassignedPartitionContext, newReplicasNotInOldReplicaList)
    info("Waiting for new replicas %s for partition %s being ".format(reassignedReplicas.mkString(","), topicAndPartition) +
      "reassigned to catch up with the leader")
  } else {
    //4. Wait until all replicas in RAR are in sync with the leader.
    val oldReplicas = controllerContext.partitionReplicaAssignment(topicAndPartition).toSet -- reassignedReplicas.toSet
    //5. replicas in RAR -> OnlineReplica
    reassignedReplicas.foreach { replica =>
      replicaStateMachine.handleStateChanges(Set(PartitionAndReplica(topicAndPartition.topic, topicAndPartition.partition,
        replica)), OnlineReplica)
    }
    //6. Set AR to RAR in memory.
    //7. Send LeaderAndIsr request with a potential new leader (if current leader not in RAR) and
    //   a new AR (using RAR) and same isr to every broker in RAR
    moveReassignedPartitionLeaderIfRequired(topicAndPartition, reassignedPartitionContext)
    //8. replicas in OAR - RAR -> Offline (force those replicas out of isr)
    //9. replicas in OAR - RAR -> NonExistentReplica (force those replicas to be deleted)
    stopOldReplicasOfReassignedPartition(topicAndPartition, reassignedPartitionContext, oldReplicas)
    //10. Update AR in ZK with RAR.
    updateAssignedReplicasForPartition(topicAndPartition, reassignedReplicas)
    //11. Update the /admin/reassign_partitions path in ZK to remove this partition.
    removePartitionFromReassignedPartitions(topicAndPartition)
    info("Removed partition %s from the list of reassigned partitions in zookeeper".format(topicAndPartition))
    controllerContext.partitionsBeingReassigned.remove(topicAndPartition)
    //12. After electing leader, the replicas and isr information changes, so resend the update metadata request to every broker
    sendUpdateMetadataRequest(controllerContext.liveOrShuttingDownBrokerIds.toSeq, Set(topicAndPartition))
    // signal delete topic thread if reassignment for some partitions belonging to topics being deleted just completed
    topicDeletionManager.resumeDeletionForTopics(Set(topicAndPartition.topic))
  }
}

官方JavaDoc比较详细,给出了3个方便解释流程的定义,列举如下:

  • RAR(Re-assigned replicas):重分配的Replica集合,记为reassignedReplicas;
  • OAR(Original assigned replicas):重分配之前的原始Replica集合,通过controllerContext.partitionReplicaAssignment()方法取得;
  • AR(Assigned replicas):当前的Replica集合,随着重分配的进行不断变化。

根据上文的代码和注释,我们可以很容易地梳理出重分配的具体流程:

(0) 检查RAR是否都已经在Partition的ISR集合中(即是否已经同步),若否,则计算RAR与OAR的差集,也就是需要被创建或者重分配的Replica集合;

(1) 计算RAR和OAR的并集,即所有Replica的集合,并将ZK中的AR更新;

(2) 增加Partition的Leader纪元值,并向AR中的所有Replica所在的Broker发送LeaderAndIsrRequest;

(3) 更新RAR与OAR的差集中Replica的状态为NewReplica,以触发这些Replica的创建或同步;

(4) 计算OAR和RAR的差集,即重分配过程中需要被下线的Replica集合;

(5) 等待RAR都已经在Partition的ISR集合中,将RAR中Replica的状态设置为OnlineReplica,表示同步完成;

(6) 将迁移现场的AR更新为RAR;

(7) 检查Partition的Leader是否在RAR中,如果没有,则触发新的Leader选举。然后增加Partition的Leader纪元值,发送LeaderAndIsrRequest更新Leader的结果;

(8~9) 将OAR和RAR的差集中的Replica状态设为Offline->NonExistentReplica,这些Replica后续将被删除;

(10) 将ZK中的AR集合更新为RAR;

(11) 一个Partition重分配完成,更新/admin/reassign_partitions节点中的执行计划,删掉完成的Partition;

(12) 发送UpdateMetadataRequest给所有Broker,刷新元数据缓存;

(13) 如果有一个Topic已经重分配完成并且将被删除,就将它从不可删除的Topic集合中移除。

The End

最后一个小问题:Partition重分配往往会涉及大量的数据交换,有可能会影响正常业务的运行,如何避免呢?ReassignPartitionsCommand也提供了throttle功能用于限流,在代码和帮助文档中都可以看到它,就不多讲了。当然,一旦启用了throttle,我们一定要定期进行verify操作,防止因为限流导致重分配的Replica一直追不上Leader的情况发生。

民那晚安晚安。

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