本篇算是 Controller 部分的最后一篇,在前面讲述 ReplicaManager 时,留一个地方没有讲解,是关于 Broker 对 Controller 发送的 LeaderAndIsr 请求的处理,这个请求的处理实现会稍微复杂一些,本篇文章主要就是讲述 Kafka Server 是如何处理 LeaderAndIsr 请求的。

LeaderAndIsr 请求

LeaderAndIsr 请求是在一个 Topic Partition 的 leader、isr、assignment replicas 变动时,Controller 向 Broker 发送的一种请求,有时候是向这个 Topic Partition 的所有副本发送,有时候是其中的某个副本,跟具体的触发情况有关系。在一个 LeaderAndIsr 请求中,会封装多个 Topic Partition 的信息,每个 Topic Partition 会对应一个 PartitionState 对象,这个对象主要成员变量如下:

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public class PartitionState {
public final int controllerEpoch;
public final int leader;
public final int leaderEpoch;
public final List<Integer> isr;
public final int zkVersion;
public final Set<Integer> replicas;
}

由此可见,在 LeaderAndIsr 请求中,会包含一个 Partition 的以下信息:

  1. 当前 Controller 的 epoch(Broker 收到这个请求后,如果发现是过期的 Controller 请求,就会拒绝这个请求);
  2. leader,Partition 的 leader 信息;
  3. leader epoch,Partition leader epoch 信息(leader、isr、AR 变动时,这个 epoch 都会加1);
  4. isr 列表;
  5. zkVersion,;
  6. AR,所有的 replica 列表。

LeaderAndIsr 请求处理

处理整体流程

LeaderAndIsr 请求可谓是包含了一个 Partition 的所有 metadata 信息,Server 在接收到 Controller 发送的这个请求后,其处理的逻辑如下:

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//KafkaApis
//note: LeaderAndIsr 请求的处理
def handleLeaderAndIsrRequest(request: RequestChannel.Request) {
// ensureTopicExists is only for client facing requests
// We can't have the ensureTopicExists check here since the controller sends it as an advisory to all brokers so they
// stop serving data to clients for the topic being deleted
val correlationId = request.header.correlationId
val leaderAndIsrRequest = request.body.asInstanceOf[LeaderAndIsrRequest]
try {
def onLeadershipChange(updatedLeaders: Iterable[Partition], updatedFollowers: Iterable[Partition]) {
// for each new leader or follower, call coordinator to handle consumer group migration.
// this callback is invoked under the replica state change lock to ensure proper order of
// leadership changes
//note: __consumer_offset 是 leader 的情况,读取相应 group 的 offset 信息
updatedLeaders.foreach { partition =>
if (partition.topic == Topic.GroupMetadataTopicName)
coordinator.handleGroupImmigration(partition.partitionId)
}
//note: __consumer_offset 是 follower 的情况,如果之前是 leader,那么移除这个 partition 对应的信息
updatedFollowers.foreach { partition =>
if (partition.topic == Topic.GroupMetadataTopicName)
coordinator.handleGroupEmigration(partition.partitionId)
}
}
val leaderAndIsrResponse =
if (authorize(request.session, ClusterAction, Resource.ClusterResource)) {//note: 有权限的情况下
//note: replicaManager 进行相应的处理
val result = replicaManager.becomeLeaderOrFollower(correlationId, leaderAndIsrRequest, metadataCache, onLeadershipChange)
new LeaderAndIsrResponse(result.errorCode, result.responseMap.mapValues(new JShort(_)).asJava)
} else {
val result = leaderAndIsrRequest.partitionStates.asScala.keys.map((_, new JShort(Errors.CLUSTER_AUTHORIZATION_FAILED.code))).toMap
new LeaderAndIsrResponse(Errors.CLUSTER_AUTHORIZATION_FAILED.code, result.asJava)
}
requestChannel.sendResponse(new Response(request, leaderAndIsrResponse))
} catch {
case e: KafkaStorageException =>
fatal("Disk error during leadership change.", e)
Runtime.getRuntime.halt(1)
}
}

上述处理逻辑分为以下两步:

  1. ReplicaManager 调用 becomeLeaderOrFollower() 方法对这个请求进行相应的处理;
  2. 如果请求中包含 __consumer_offset 的 Partition(对应两种情况:之前是 fllower 现在变成了 leader、之前是 leader 现在变成了 follower),那么还需要调用这个方法中定义的 onLeadershipChange() 方法进行相应的处理。

becomeLeaderOrFollower() 的整体处理流程如下:

LeaderAndIsr 请求的处理

becomeLeaderOrFollower

这里先看下 ReplicaManager 的 becomeLeaderOrFollower() 方法,它是 LeaderAndIsr 请求处理的实现,如下所示:

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//note: 处理 LeaderAndIsr 请求
def becomeLeaderOrFollower(correlationId: Int,leaderAndISRRequest: LeaderAndIsrRequest,
metadataCache: MetadataCache,
onLeadershipChange: (Iterable[Partition], Iterable[Partition]) => Unit): BecomeLeaderOrFollowerResult = {
leaderAndISRRequest.partitionStates.asScala.foreach { case (topicPartition, stateInfo) =>
stateChangeLogger.trace("Broker %d received LeaderAndIsr request %s correlation id %d from controller %d epoch %d for partition [%s,%d]"
.format(localBrokerId, stateInfo, correlationId,
leaderAndISRRequest.controllerId, leaderAndISRRequest.controllerEpoch, topicPartition.topic, topicPartition.partition))
}
replicaStateChangeLock synchronized {
val responseMap = new mutable.HashMap[TopicPartition, Short]
//note: 1. 验证 controller 的 epoch,如果是来自旧的 controller,就拒绝这个请求
if (leaderAndISRRequest.controllerEpoch < controllerEpoch) {
stateChangeLogger.warn(("Broker %d ignoring LeaderAndIsr request from controller %d with correlation id %d since " +
"its controller epoch %d is old. Latest known controller epoch is %d").format(localBrokerId, leaderAndISRRequest.controllerId,
correlationId, leaderAndISRRequest.controllerEpoch, controllerEpoch))
BecomeLeaderOrFollowerResult(responseMap, Errors.STALE_CONTROLLER_EPOCH.code)
} else { //note: 当前 controller 的请求
val controllerId = leaderAndISRRequest.controllerId
controllerEpoch = leaderAndISRRequest.controllerEpoch
// First check partition's leader epoch
//note: 2. 检查 leader epoch,得到一个 partitionState map,epoch 满足条件并且有副本在本地的集合
val partitionState = new mutable.HashMap[Partition, PartitionState]()
leaderAndISRRequest.partitionStates.asScala.foreach { case (topicPartition, stateInfo) =>
val partition = getOrCreatePartition(topicPartition) //note: 对应的 tp 如果没有 Partition 实例的话,就新建一个
val partitionLeaderEpoch = partition.getLeaderEpoch //note: 更新 leader epoch
// If the leader epoch is valid record the epoch of the controller that made the leadership decision.
// This is useful while updating the isr to maintain the decision maker controller's epoch in the zookeeper path
if (partitionLeaderEpoch < stateInfo.leaderEpoch) {
if(stateInfo.replicas.contains(localBrokerId))
partitionState.put(partition, stateInfo) //note: 更新 replica 的 stateInfo
else {
stateChangeLogger.warn(("Broker %d ignoring LeaderAndIsr request from controller %d with correlation id %d " +
"epoch %d for partition [%s,%d] as itself is not in assigned replica list %s")
.format(localBrokerId, controllerId, correlationId, leaderAndISRRequest.controllerEpoch,
topicPartition.topic, topicPartition.partition, stateInfo.replicas.asScala.mkString(",")))
responseMap.put(topicPartition, Errors.UNKNOWN_TOPIC_OR_PARTITION.code)
}
} else { //note: 忽略这个请求,因为请求的 leader epoch 小于缓存的 epoch
// Otherwise record the error code in response
stateChangeLogger.warn(("Broker %d ignoring LeaderAndIsr request from controller %d with correlation id %d " +
"epoch %d for partition [%s,%d] since its associated leader epoch %d is not higher than the current leader epoch %d")
.format(localBrokerId, controllerId, correlationId, leaderAndISRRequest.controllerEpoch,
topicPartition.topic, topicPartition.partition, stateInfo.leaderEpoch, partitionLeaderEpoch))
responseMap.put(topicPartition, Errors.STALE_CONTROLLER_EPOCH.code)
}
}
//note: 3. 过滤出本地副本设置为 leader 的 Partition 列表
val partitionsTobeLeader = partitionState.filter { case (_, stateInfo) =>
stateInfo.leader == localBrokerId
}
//note: 4. 过滤出本地副本设置为 follower 的 Partition 列表
val partitionsToBeFollower = partitionState -- partitionsTobeLeader.keys //note: 这些 tp 设置为了 follower
//note: 5. 将为 leader 的副本设置为 leader
val partitionsBecomeLeader = if (partitionsTobeLeader.nonEmpty)
makeLeaders(controllerId, controllerEpoch, partitionsTobeLeader, correlationId, responseMap)
else
Set.empty[Partition]
//note: 6. 将为 follower 的副本设置为 follower
val partitionsBecomeFollower = if (partitionsToBeFollower.nonEmpty)
makeFollowers(controllerId, controllerEpoch, partitionsToBeFollower, correlationId, responseMap, metadataCache)
else
Set.empty[Partition]
//note: 7. 如果 hw checkpoint 的线程没有初始化,这里需要进行一次初始化
// we initialize highwatermark thread after the first leaderisrrequest. This ensures that all the partitions
// have been completely populated before starting the checkpointing there by avoiding weird race conditions
if (!hwThreadInitialized) {
startHighWaterMarksCheckPointThread()
hwThreadInitialized = true
}
//note: 8. 检查 replica fetcher 是否需要关闭(有些副本需要关闭因为可能从 follower 变为 leader)
replicaFetcherManager.shutdownIdleFetcherThreads()
//note: 9. 检查是否 __consumer_offset 的 Partition 的 leaderAndIsr 信息,有的话进行相应的操作
onLeadershipChange(partitionsBecomeLeader, partitionsBecomeFollower)
BecomeLeaderOrFollowerResult(responseMap, Errors.NONE.code)
}
}
}

上述实现,其处理逻辑总结如下:

  1. 检查 Controller 的 epoch,如果是来自旧的 Controller,那么就拒绝这个请求;
  2. 获取请求的 Partition 列表的 PartitionState 信息,在遍历的过程中,会进行一个检查,如果 leader epoch 小于缓存中的 epoch 值,那么就过滤掉这个 Partition 信息,如果这个 Partition 在本地不存在,那么会初始化这个 Partition 的对象(这时候并不会初始化本地副本);
  3. 获取出本地副本为 leader 的 Partition 列表(partitionsTobeLeader);
  4. 获取出本地副本为 follower 的 Partition 列表(partitionsToBeFollower);
  5. 调用 makeLeaders() 方法将 leader 的副本设置为 leader;
  6. 调用 makeFollowers() 方法将 leader 的副本设置为 follower;
  7. 检查 HW checkpoint 的线程是否初始化,如果没有,这里需要进行一次初始化;
  8. 检查 ReplicaFetcherManager 是否有线程需要关闭(如果这个线程上没有分配要拉取的 Topic Partition,那么在这里这个线程就会被关闭,下次需要时会再次启动);
  9. 检查是否有 __consumer_offset Partition 的 leaderAndIsr 信息,有的话进行相应的操作。

这其中,比较复杂的部分是第 5、6、9步,也前面图中标出的 1、2、4步,文章下面接着分析这三部分。

makeLeaders

ReplicaManager 的 makeLeaders() 的作用是将指定的这批 Partition 列表设置为 Leader,并返回是新 leader 对应的 Partition 列表(之前不是 leader,现在选举为了 leader),其实实现如下:

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//note: 选举当前副本作为 partition 的 leader,处理过程:
//note: 1. 停止这些 partition 的 副本同步请求;
//note: 2. 更新缓存中的 partition metadata;
//note: 3. 将这些 partition 添加到 leader partition 集合中。
private def makeLeaders(controllerId: Int,
epoch: Int,
partitionState: Map[Partition, PartitionState],
correlationId: Int,
responseMap: mutable.Map[TopicPartition, Short]): Set[Partition] = {
partitionState.keys.foreach { partition =>
stateChangeLogger.trace(("Broker %d handling LeaderAndIsr request correlationId %d from controller %d epoch %d " +
"starting the become-leader transition for partition %s")
.format(localBrokerId, correlationId, controllerId, epoch, partition.topicPartition))
}
for (partition <- partitionState.keys)
responseMap.put(partition.topicPartition, Errors.NONE.code)
val partitionsToMakeLeaders: mutable.Set[Partition] = mutable.Set()
try {
// First stop fetchers for all the partitions
//note: 1. 停止这些副本同步请求
replicaFetcherManager.removeFetcherForPartitions(partitionState.keySet.map(_.topicPartition))
// Update the partition information to be the leader
//note: 2. 更新这些 partition 的信息(这些 partition 成为 leader 了)
partitionState.foreach{ case (partition, partitionStateInfo) =>
//note: 在 partition 对象将本地副本设置为 leader
if (partition.makeLeader(controllerId, partitionStateInfo, correlationId))
partitionsToMakeLeaders += partition //note: 成功选为 leader 的 partition 集合
else
//note: 本地 replica 已经是 leader replica,可能是接收了重试的请求
stateChangeLogger.info(("Broker %d skipped the become-leader state change after marking its partition as leader with correlation id %d from " +
"controller %d epoch %d for partition %s since it is already the leader for the partition.")
.format(localBrokerId, correlationId, controllerId, epoch, partition.topicPartition))
}
partitionsToMakeLeaders.foreach { partition =>
stateChangeLogger.trace(("Broker %d stopped fetchers as part of become-leader request from controller " +
"%d epoch %d with correlation id %d for partition %s")
.format(localBrokerId, controllerId, epoch, correlationId, partition.topicPartition))
}
} catch {
case e: Throwable =>
partitionState.keys.foreach { partition =>
val errorMsg = ("Error on broker %d while processing LeaderAndIsr request correlationId %d received from controller %d" +
" epoch %d for partition %s").format(localBrokerId, correlationId, controllerId, epoch, partition.topicPartition)
stateChangeLogger.error(errorMsg, e)
}
// Re-throw the exception for it to be caught in KafkaApis
throw e
}
//note: LeaderAndIsr 请求处理完成
partitionState.keys.foreach { partition =>
stateChangeLogger.trace(("Broker %d completed LeaderAndIsr request correlationId %d from controller %d epoch %d " +
"for the become-leader transition for partition %s")
.format(localBrokerId, correlationId, controllerId, epoch, partition.topicPartition))
}
partitionsToMakeLeaders
}

实现逻辑如下:

  1. 调用 ReplicaFetcherManager 的 removeFetcherForPartitions() 方法移除这些 Partition 的副本同步线程;
  2. 遍历这些 Partition,通过 Partition 的 makeLeader() 方法将这个 Partition 设置为 Leader,如果设置成功(如果 leader 没有变化,证明这个 Partition 之前就是 leader,这个方法返回的是 false,这种情况下不会更新到缓存中),那么将 leader 信息更新到缓存中。

下面来看下在 Partition 中是如何真正初始化一个 Partition 的 leader?其实现如下:

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//note: 将本地副本设置为 leader, 如果 leader 不变,向 ReplicaManager 返回 false
def makeLeader(controllerId: Int, partitionStateInfo: PartitionState, correlationId: Int): Boolean = {
val (leaderHWIncremented, isNewLeader) = inWriteLock(leaderIsrUpdateLock) {
val allReplicas = partitionStateInfo.replicas.asScala.map(_.toInt)
// record the epoch of the controller that made the leadership decision. This is useful while updating the isr
// to maintain the decision maker controller's epoch in the zookeeper path
controllerEpoch = partitionStateInfo.controllerEpoch
// add replicas that are new
//note: 为了新的 replica 创建副本实例
allReplicas.foreach(replica => getOrCreateReplica(replica))
//note: 获取新的 isr 列表
val newInSyncReplicas = partitionStateInfo.isr.asScala.map(r => getOrCreateReplica(r)).toSet
// remove assigned replicas that have been removed by the controller
//note: 将已经在不在 AR 中的副本移除
(assignedReplicas.map(_.brokerId) -- allReplicas).foreach(removeReplica)
inSyncReplicas = newInSyncReplicas
leaderEpoch = partitionStateInfo.leaderEpoch
zkVersion = partitionStateInfo.zkVersion
//note: 判断是否是新的 leader
val isNewLeader =
if (leaderReplicaIdOpt.isDefined && leaderReplicaIdOpt.get == localBrokerId) {//note: leader 没有更新
false
} else {
leaderReplicaIdOpt = Some(localBrokerId)
true
}
val leaderReplica = getReplica().get //note: 获取在当前上的副本,也就是 leader replica
val curLeaderLogEndOffset = leaderReplica.logEndOffset.messageOffset //note: 获取 leader replica 的 the end offset
val curTimeMs = time.milliseconds
// initialize lastCaughtUpTime of replicas as well as their lastFetchTimeMs and lastFetchLeaderLogEndOffset.
(assignedReplicas - leaderReplica).foreach { replica => //note: 对于 isr 中的 replica,更新 LastCaughtUpTime
val lastCaughtUpTimeMs = if (inSyncReplicas.contains(replica)) curTimeMs else 0L
replica.resetLastCaughtUpTime(curLeaderLogEndOffset, curTimeMs, lastCaughtUpTimeMs)
}
// we may need to increment high watermark since ISR could be down to 1
if (isNewLeader) { //note: 如果是新的 leader,那么需要
// construct the high watermark metadata for the new leader replica
//note: 为新的 leader 构造 replica 的 HW metadata
leaderReplica.convertHWToLocalOffsetMetadata()
// reset log end offset for remote replicas
//note: 更新远程副本的副本同步信息(设置为 unKnown)
assignedReplicas.filter(_.brokerId != localBrokerId).foreach(_.updateLogReadResult(LogReadResult.UnknownLogReadResult))
}
//note: 如果满足更新 isr 的条件,就更新 HW 信息
(maybeIncrementLeaderHW(leaderReplica), isNewLeader)
}
// some delayed operations may be unblocked after HW changed
if (leaderHWIncremented) //note: HW 更新的情况下
tryCompleteDelayedRequests()
isNewLeader
}

简单总结一下上述的实现:

  1. 首先更新这个 Partition 的相应信息,包括:isr、AR、leader epoch、zkVersion 等,并为每个副本创建一个 Replica 对象(如果不存在该对象的情况下才会创建,只有本地副本才会初始化相应的日志对象);
  2. 如果这个 Partition 的 leader 本来就是本地副本,那么返回的结果设置为 false,证明这个 leader 并不是新的 leader;
  3. 对于 isr 中的所有 Replica,更新 LastCaughtUpTime 值,即最近一次赶得上 leader 的时间;
  4. 如果是新的 leader,那么为 leader 初始化相应的 HighWatermarkMetadata 对象,并将所有副本的副本同步信息更新为 UnknownLogReadResult;
  5. 检查一下是否需要更新 HW 值。

如果这个本地副本是新选举的 leader,那么它所做的事情就是初始化 Leader 应该记录的相关信息。

makeFollowers

ReplicaManager 的 makeFollowers() 方法,是将哪些 Partition 设置为 Follower,返回的结果是那些新的 follower 对应的 Partition 列表(之前是 leader,现在变成了 follower),其实现如下:

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private def makeFollowers(controllerId: Int,
epoch: Int,
partitionState: Map[Partition, PartitionState],
correlationId: Int,
responseMap: mutable.Map[TopicPartition, Short],
metadataCache: MetadataCache) : Set[Partition] = {
partitionState.keys.foreach { partition =>
stateChangeLogger.trace(("Broker %d handling LeaderAndIsr request correlationId %d from controller %d epoch %d " +
"starting the become-follower transition for partition %s")
.format(localBrokerId, correlationId, controllerId, epoch, partition.topicPartition))
}
for (partition <- partitionState.keys)
responseMap.put(partition.topicPartition, Errors.NONE.code)
//note: 1. 统计 follower 的集合
val partitionsToMakeFollower: mutable.Set[Partition] = mutable.Set()
try {
// TODO: Delete leaders from LeaderAndIsrRequest
partitionState.foreach{ case (partition, partitionStateInfo) =>
val newLeaderBrokerId = partitionStateInfo.leader
metadataCache.getAliveBrokers.find(_.id == newLeaderBrokerId) match { //note: leader 是可用的 Partition
// Only change partition state when the leader is available
case Some(_) => //note: 2. 将 Partition 的本地副本设置为 follower
if (partition.makeFollower(controllerId, partitionStateInfo, correlationId))
partitionsToMakeFollower += partition
else //note: 这个 partition 的本地副本已经是 follower 了
stateChangeLogger.info(("Broker %d skipped the become-follower state change after marking its partition as follower with correlation id %d from " +
"controller %d epoch %d for partition %s since the new leader %d is the same as the old leader")
.format(localBrokerId, correlationId, controllerId, partitionStateInfo.controllerEpoch,
partition.topicPartition, newLeaderBrokerId))
case None =>
// The leader broker should always be present in the metadata cache.
// If not, we should record the error message and abort the transition process for this partition
stateChangeLogger.error(("Broker %d received LeaderAndIsrRequest with correlation id %d from controller" +
" %d epoch %d for partition %s but cannot become follower since the new leader %d is unavailable.")
.format(localBrokerId, correlationId, controllerId, partitionStateInfo.controllerEpoch,
partition.topicPartition, newLeaderBrokerId))
// Create the local replica even if the leader is unavailable. This is required to ensure that we include
// the partition's high watermark in the checkpoint file (see KAFKA-1647)
partition.getOrCreateReplica()
}
}
//note: 3. 移除这些 Partition 的副本同步线程,这样在 MakeFollower 期间,这些 Partition 就不会进行副本同步了
replicaFetcherManager.removeFetcherForPartitions(partitionsToMakeFollower.map(_.topicPartition))
partitionsToMakeFollower.foreach { partition =>
stateChangeLogger.trace(("Broker %d stopped fetchers as part of become-follower request from controller " +
"%d epoch %d with correlation id %d for partition %s")
.format(localBrokerId, controllerId, epoch, correlationId, partition.topicPartition))
}
//note: 4. Truncate the partition logs to the specified offsets and checkpoint the recovery point to this offset
logManager.truncateTo(partitionsToMakeFollower.map { partition =>
(partition.topicPartition, partition.getOrCreateReplica().highWatermark.messageOffset)
}.toMap)
//note: 5. 完成那些延迟请求的处理(Produce 和 FetchConsumer 请求)
partitionsToMakeFollower.foreach { partition =>
val topicPartitionOperationKey = new TopicPartitionOperationKey(partition.topicPartition)
tryCompleteDelayedProduce(topicPartitionOperationKey)
tryCompleteDelayedFetch(topicPartitionOperationKey)
}
partitionsToMakeFollower.foreach { partition =>
stateChangeLogger.trace(("Broker %d truncated logs and checkpointed recovery boundaries for partition %s as part of " +
"become-follower request with correlation id %d from controller %d epoch %d").format(localBrokerId,
partition.topicPartition, correlationId, controllerId, epoch))
}
if (isShuttingDown.get()) {
partitionsToMakeFollower.foreach { partition =>
stateChangeLogger.trace(("Broker %d skipped the adding-fetcher step of the become-follower state change with correlation id %d from " +
"controller %d epoch %d for partition %s since it is shutting down").format(localBrokerId, correlationId,
controllerId, epoch, partition.topicPartition))
}
}
else {
// we do not need to check if the leader exists again since this has been done at the beginning of this process
//note: 6. 启动副本同步线程
val partitionsToMakeFollowerWithLeaderAndOffset = partitionsToMakeFollower.map(partition =>
partition.topicPartition -> BrokerAndInitialOffset(
metadataCache.getAliveBrokers.find(_.id == partition.leaderReplicaIdOpt.get).get.getBrokerEndPoint(config.interBrokerListenerName),
partition.getReplica().get.logEndOffset.messageOffset)).toMap //note: leader 信息+本地 replica 的 offset
replicaFetcherManager.addFetcherForPartitions(partitionsToMakeFollowerWithLeaderAndOffset)
partitionsToMakeFollower.foreach { partition =>
stateChangeLogger.trace(("Broker %d started fetcher to new leader as part of become-follower request from controller " +
"%d epoch %d with correlation id %d for partition %s")
.format(localBrokerId, controllerId, epoch, correlationId, partition.topicPartition))
}
}
} catch {
case e: Throwable =>
val errorMsg = ("Error on broker %d while processing LeaderAndIsr request with correlationId %d received from controller %d " +
"epoch %d").format(localBrokerId, correlationId, controllerId, epoch)
stateChangeLogger.error(errorMsg, e)
// Re-throw the exception for it to be caught in KafkaApis
throw e
}
partitionState.keys.foreach { partition =>
stateChangeLogger.trace(("Broker %d completed LeaderAndIsr request correlationId %d from controller %d epoch %d " +
"for the become-follower transition for partition %s")
.format(localBrokerId, correlationId, controllerId, epoch, partition.topicPartition))
}
partitionsToMakeFollower
}
//note: 遍历所有的 partition 对象,检查其 isr 是否需要抖动
private def maybeShrinkIsr(): Unit = {
trace("Evaluating ISR list of partitions to see which replicas can be removed from the ISR")
allPartitions.values.foreach(partition => partition.maybeShrinkIsr(config.replicaLagTimeMaxMs))
}
private def updateFollowerLogReadResults(replicaId: Int, readResults: Seq[(TopicPartition, LogReadResult)]) {
debug("Recording follower broker %d log read results: %s ".format(replicaId, readResults))
readResults.foreach { case (topicPartition, readResult) =>
getPartition(topicPartition) match {
case Some(partition) =>
//note: 更新副本的相关信息
partition.updateReplicaLogReadResult(replicaId, readResult)
// for producer requests with ack > 1, we need to check
// if they can be unblocked after some follower's log end offsets have moved
tryCompleteDelayedProduce(new TopicPartitionOperationKey(topicPartition))
case None =>
warn("While recording the replica LEO, the partition %s hasn't been created.".format(topicPartition))
}
}
}

简单总结一下上述的逻辑过程:

  1. 首先遍历所有的 Partition,获到那些 leader 可用、并且 Partition 可以成功设置为 Follower 的 Partition 列表(partitionsToMakeFollower);
  2. 在上面遍历的过程中,会调用 Partition 的 makeFollower() 方法将 Partition 设置为 Follower(在这里,如果该 Partition 的本地副本不存在,会初始化相应的日志对象,如果该 Partition 的 leader 已经存在,并且没有变化,那么就返回 false,只有 leader 变化的 Partition,才会返回 true,才会加入到 partitionsToMakeFollower 集合中,这是因为 leader 没有变化的 Partition 是不需要变更副本同步线程的);
  3. 移除这些 Partition 的副本同步线程,这样在 MakeFollower 期间,这些 Partition 就不会进行副本同步了;
  4. Truncate the partition logs to the specified offsets and checkpoint the recovery point to this offset,因为前面已经移除了这个 Partition 的副本同步线程,所以这里在 checkpoint 后可以保证所有缓存的数据都可以刷新到磁盘;
  5. 完成那些延迟请求的处理(Produce 和 FetchConsumer 请求);
  6. 启动相应的副本同步线程。

到这里 LeaderAndIsr 请求的大部分处理已经完成,但是有一个比较特殊的 topic(__consumer_offset),如果这 Partition 的 leader 发生变化,是需要一些额外的处理。

__consumer_offset leader 切换处理

__consumer_offset 这个 Topic 如果发生了 leader 切换,GroupCoordinator 需要进行相应的处理,其处理过程如下:

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def onLeadershipChange(updatedLeaders: Iterable[Partition], updatedFollowers: Iterable[Partition]) {
// for each new leader or follower, call coordinator to handle consumer group migration.
// this callback is invoked under the replica state change lock to ensure proper order of
// leadership changes
//note: __consumer_offset 是 leader 的情况,读取相应 group 的 offset 信息
updatedLeaders.foreach { partition =>
if (partition.topic == Topic.GroupMetadataTopicName)
coordinator.handleGroupImmigration(partition.partitionId)
}
//note: __consumer_offset 是 follower 的情况,如果之前是 leader,那么移除这个 partition 对应的信息
updatedFollowers.foreach { partition =>
if (partition.topic == Topic.GroupMetadataTopicName)
coordinator.handleGroupEmigration(partition.partitionId)
}
}

成为 leader

如果当前节点这个 __consumer_offset 有 Partition 成为 leader,GroupCoordinator 通过 handleGroupImmigration() 方法进行相应的处理。

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//note: 加载这个 Partition 对应的 group offset 信息
def handleGroupImmigration(offsetTopicPartitionId: Int) {
groupManager.loadGroupsForPartition(offsetTopicPartitionId, onGroupLoaded)
}
//note: 异步地加载这个 offset Partition 的信息
def loadGroupsForPartition(offsetsPartition: Int, onGroupLoaded: GroupMetadata => Unit) {
val topicPartition = new TopicPartition(Topic.GroupMetadataTopicName, offsetsPartition)
def doLoadGroupsAndOffsets() {
info(s"Loading offsets and group metadata from $topicPartition")
//note: 添加到 loadingPartitions 集合中
inLock(partitionLock) {
if (loadingPartitions.contains(offsetsPartition)) {
info(s"Offset load from $topicPartition already in progress.")
return
} else {
loadingPartitions.add(offsetsPartition)
}
}
//note: 开始加载,加载成功的话,将该 Partition 从 loadingPartitions 集合中移除,添加到 ownedPartition 集合中
try {
loadGroupsAndOffsets(topicPartition, onGroupLoaded)
} catch {
case t: Throwable => error(s"Error loading offsets from $topicPartition", t)
} finally {
inLock(partitionLock) {
ownedPartitions.add(offsetsPartition)
loadingPartitions.remove(offsetsPartition)
}
}
}
scheduler.schedule(topicPartition.toString, doLoadGroupsAndOffsets)
}

这个方法的做的事情是:

  1. 将正在处理的 Partition 添加到 loadingPartitions 集合中,这个集合内都是当前正在加载的 Partition(特指 __consumer_offset Topic);
  2. 通过 loadGroupsAndOffsets() 加载这个 Partition 的数据,处理完成后,该 Partition 从 loadingPartitions 中清除,并添加到 ownedPartitions 集合中。

loadGroupsAndOffsets() 的实现如下:

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//note: 读取该 group offset Partition 数据
private[coordinator] def loadGroupsAndOffsets(topicPartition: TopicPartition, onGroupLoaded: GroupMetadata => Unit) {
//note: 这个必然有本地副本,现获取 hw(如果本地是 leader 的情况,否则返回-1)
def highWaterMark = replicaManager.getHighWatermark(topicPartition).getOrElse(-1L)
val startMs = time.milliseconds()
replicaManager.getLog(topicPartition) match {
case None =>
warn(s"Attempted to load offsets and group metadata from $topicPartition, but found no log")
case Some(log) =>
var currOffset = log.logStartOffset //note: 这副本最起始的 offset
val buffer = ByteBuffer.allocate(config.loadBufferSize) //note: 默认5MB
// loop breaks if leader changes at any time during the load, since getHighWatermark is -1
//note: group 与 offset 的对应关系
val loadedOffsets = mutable.Map[GroupTopicPartition, OffsetAndMetadata]()
val removedOffsets = mutable.Set[GroupTopicPartition]()
//note: Group 对应的 meta 信息
val loadedGroups = mutable.Map[String, GroupMetadata]()
val removedGroups = mutable.Set[String]()
while (currOffset < highWaterMark && !shuttingDown.get()) { //note: 直到读取到 hw 位置,或服务关闭
buffer.clear()
val fileRecords = log.read(currOffset, config.loadBufferSize, maxOffset = None, minOneMessage = true)
.records.asInstanceOf[FileRecords]
val bufferRead = fileRecords.readInto(buffer, 0)
MemoryRecords.readableRecords(bufferRead).deepEntries.asScala.foreach { entry =>
val record = entry.record
require(record.hasKey, "Group metadata/offset entry key should not be null")
GroupMetadataManager.readMessageKey(record.key) match {
case offsetKey: OffsetKey => //note: GroupTopicPartition,有 group 和 topic-partition
// load offset
//note: 加载 offset 信息
val key = offsetKey.key
if (record.hasNullValue) { //note: value 为空
loadedOffsets.remove(key)
removedOffsets.add(key)
} else { //note: 有 commit offset 信息
val value = GroupMetadataManager.readOffsetMessageValue(record.value)
loadedOffsets.put(key, value)
removedOffsets.remove(key)
}
case groupMetadataKey: GroupMetadataKey =>
// load group metadata
//note: 加载 group metadata 信息
val groupId = groupMetadataKey.key
val groupMetadata = GroupMetadataManager.readGroupMessageValue(groupId, record.value)
if (groupMetadata != null) {
trace(s"Loaded group metadata for group $groupId with generation ${groupMetadata.generationId}")
removedGroups.remove(groupId)
loadedGroups.put(groupId, groupMetadata)
} else { //note: 更新最新的信息
loadedGroups.remove(groupId)
removedGroups.add(groupId)
}
case unknownKey =>
throw new IllegalStateException(s"Unexpected message key $unknownKey while loading offsets and group metadata")
}
currOffset = entry.nextOffset
}
}
val (groupOffsets, emptyGroupOffsets) = loadedOffsets
.groupBy(_._1.group)
.mapValues(_.map { case (groupTopicPartition, offset) => (groupTopicPartition.topicPartition, offset)} )
.partition { case (group, _) => loadedGroups.contains(group) } //note: 把集合根据条件分两个部分
loadedGroups.values.foreach { group =>
val offsets = groupOffsets.getOrElse(group.groupId, Map.empty[TopicPartition, OffsetAndMetadata])
loadGroup(group, offsets) //note: 在缓存中添加 group 和初始化 offset 信息
onGroupLoaded(group) //note: 设置 group 下一次心跳超时时间
}
// load groups which store offsets in kafka, but which have no active members and thus no group
// metadata stored in the log
//note: 加载哪些有 offset 信息但是当前没有活跃的 member 信息的 group
emptyGroupOffsets.foreach { case (groupId, offsets) =>
val group = new GroupMetadata(groupId)
loadGroup(group, offsets)
onGroupLoaded(group)
}
removedGroups.foreach { groupId =>
// if the cache already contains a group which should be removed, raise an error. Note that it
// is possible (however unlikely) for a consumer group to be removed, and then to be used only for
// offset storage (i.e. by "simple" consumers)
if (groupMetadataCache.contains(groupId) && !emptyGroupOffsets.contains(groupId))
throw new IllegalStateException(s"Unexpected unload of active group $groupId while " +
s"loading partition $topicPartition")
}
if (!shuttingDown.get())
info("Finished loading offsets from %s in %d milliseconds."
.format(topicPartition, time.milliseconds() - startMs))
}
}

上面方法的实现虽然比较长,但是处理逻辑还是比较简单的,实现结果如下:

  1. 获取这个 Partition 的 HW 值(如果 leader 不在本地,那么返回-1);
  2. 初始化 loadedOffsets 和 removedOffsets、loadedGroups 和 removedGroups 集合,它们就是 group offset 信息以及 consumer member 信息;
  3. 从这个 Partition 第一条数据开始读取,直到读取到 HW 位置,加载相应的 commit offset、consumer member 信息,因为是顺序读取的,所以会新的值会覆盖前面的值;
  4. 通过 loadGroup() 加载到 GroupCoordinator 的缓存中。

经过上面这些步骤,这个 Partition 的数据就被完整加载缓存中了。

变成 follower

如果 __consumer_offset 有 Partition 变成了 follower(之前是 leader,如果之前不是 leader,不会走到这一步的),GroupCoordinator 通过 handleGroupEmigration() 移除这个 Partition 相应的缓存信息。

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//note: 移除这个 Partition 对应的 group offset 信息
def handleGroupEmigration(offsetTopicPartitionId: Int) {
groupManager.removeGroupsForPartition(offsetTopicPartitionId, onGroupUnloaded)
}

removeGroupsForPartition() 的实现如下:

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//note: 当一个 broker 变成一个 follower 时,清空这个 partition 的相关缓存信息
def removeGroupsForPartition(offsetsPartition: Int,
onGroupUnloaded: GroupMetadata => Unit) {
val topicPartition = new TopicPartition(Topic.GroupMetadataTopicName, offsetsPartition)
scheduler.schedule(topicPartition.toString, removeGroupsAndOffsets)
def removeGroupsAndOffsets() {
var numOffsetsRemoved = 0
var numGroupsRemoved = 0
inLock(partitionLock) {
// we need to guard the group removal in cache in the loading partition lock
// to prevent coordinator's check-and-get-group race condition
ownedPartitions.remove(offsetsPartition)
for (group <- groupMetadataCache.values) {
if (partitionFor(group.groupId) == offsetsPartition) {
onGroupUnloaded(group) //note: 将 group 状态转移成 dead
groupMetadataCache.remove(group.groupId, group) //note: 清空 group 的信息
numGroupsRemoved += 1
numOffsetsRemoved += group.numOffsets
}
}
}
if (numOffsetsRemoved > 0)
info(s"Removed $numOffsetsRemoved cached offsets for $topicPartition on follower transition.")
if (numGroupsRemoved > 0)
info(s"Removed $numGroupsRemoved cached groups for $topicPartition on follower transition.")
}
}
private def onGroupUnloaded(group: GroupMetadata) {
group synchronized {
info(s"Unloading group metadata for ${group.groupId} with generation ${group.generationId}")
val previousState = group.currentState
group.transitionTo(Dead) //note: 状态转移成 dead
previousState match {
case Empty | Dead =>
case PreparingRebalance =>
for (member <- group.allMemberMetadata) { //note: 如果有 member 信息返回异常
if (member.awaitingJoinCallback != null) {
member.awaitingJoinCallback(joinError(member.memberId, Errors.NOT_COORDINATOR_FOR_GROUP.code))
member.awaitingJoinCallback = null
}
}
joinPurgatory.checkAndComplete(GroupKey(group.groupId))
case Stable | AwaitingSync =>
for (member <- group.allMemberMetadata) { //note: 如果有 member 信息,返回异常
if (member.awaitingSyncCallback != null) {
member.awaitingSyncCallback(Array.empty[Byte], Errors.NOT_COORDINATOR_FOR_GROUP.code)
member.awaitingSyncCallback = null
}
heartbeatPurgatory.checkAndComplete(MemberKey(member.groupId, member.memberId))
}
}
}
}

对于在这个 Partition 上的所有 Group,会按下面的步骤执行:

  1. 通过 onGroupUnloaded() 方法先将这个 Group 的状态转换为 dead,如果 Group 处在 PreparingRebalance/Stable/AwaitingSync 状态,并且设置了相应的回调函数,那么就在回调函数中返回带有 NOT_COORDINATOR_FOR_GROUP 异常信息的响应,consumer 在收到这个异常信息会重新加入 group;
  2. 从缓存中移除这个 Group 的信息。

这个遍历执行完成之后,这个 Topic Partition 就从 Leader 变成了 follower 状态。