在上篇文章中讲述了 Fetch 请求是如何处理的,其中包括来自副本同步的 Fetch 请求和 Consumer 的 Fetch 请求,副本同步是 Kafka 多副本机制(可靠性)实现的基础,它也是通过向 leader replica 发送 Fetch 请求来实现数据同步的。本篇文章我们就来看一下 Kafka 副本同步这块的内容,对于每个 broker 来说,它上面的 replica 对象,除了 leader 就是 follower,只要这台 broker 有 follower replica,broker 就会启动副本同步流程从 leader 同步数据,副本同步机制的实现是 Kafka Server 端非常重要的内容,在这篇文章中,主要会从以下几块来讲解:

  1. Kafka 在什么情况下会启动副本同步线程?
  2. Kafka 副本同步线程启动流程及付副本同步流程的处理逻辑;
  3. Kafka 副本同步需要解决的问题以及 Kafka 是如何解决这些问题的?
  4. Kafka 在什么情况下会关闭一个副本同步线程。

小插曲:本来想先介绍一下与 LeaderAndIsr 请求相关的,因为副本同步线程的启动与这部分是息息相关的,但是发现涉及到了很多 controller 端的内容,而 controller 这部分还没开始涉及,所以本篇文章涉及到 LeaderAndIsr 请求的部分先简单讲述一下其处理逻辑,在 controller 这块再详细介绍。

整体流程

Kafka Server 端的副本同步,是由 replica fetcher 线程来负责的,而它又是由 ReplicaManager 来控制的。关于 ReplicaManger,不知道大家还记不记得在 Kafka 源码解析之 Server 端如何处理 Produce 请求(十二) 有一个简单的表格,如下所示。ReplicaManager 通过对 Partition 对象的管理,来控制着 Partition 对应的 Replica 实例,而 Replica 实例又是通过 Log 对象实例来管理着其底层的存储内容。

管理对象 组成部分
日志管理器(LogManager) 日志(Log) 日志分段(LogSegment)
副本管理器(ReplicaManager) 分区(Partition) 副本(Replica)

关于 ReplicaManager 的内容准备专门写一篇文章来介绍,刚好也作为对 Kafka 存储层内容的一个总结。

下面回到这篇文章的主题 —— 副本同步机制,在 ReplicaManager 中有一个实例变量 replicaFetcherManager,它负责管理所有副本同步线程,副本同步线程的启动和关闭都是由这个实例来操作的,关于副本同步相关处理逻辑,下面这张图可以作为一个整体流程,包括了 replica fetcher 线程的启动、工作流程、关闭三个部分,如下图所示:

副本同步机制

后面的讲述会围绕着这张图开始,这里看不懂或不理解也没有关系,后面会一一讲解。

replica fetcher 线程何时启动

Broker 会在什么情况下启动副本同步线程呢?简单想一下这部分的逻辑:首先 broker 分配的任何一个 partition 都是以 Replica 对象实例的形式存在,而 Replica 在 Kafka 上是有两个角色: leader 和 follower,只要这个 Replica 是 follower,它便会向 leader 进行数据同步。

反应在 ReplicaManager 上就是如果 Broker 的本地副本被选举为 follower,那么它将会启动副本同步线程,其具体实现如下所示:

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//note: 对于给定的这些副本,将本地副本设置为 follower
//note: 1. 从 leader partition 集合移除这些 partition;
//note: 2. 将这些 partition 标记为 follower,之后这些 partition 就不会再接收 produce 的请求了;
//note: 3. 停止对这些 partition 的副本同步,这样这些副本就不会再有(来自副本请求线程)的数据进行追加了;
//note: 4. 对这些 partition 的 offset 进行 checkpoint,如果日志需要截断就进行截断操作;
//note: 5. 清空 purgatory 中的 produce 和 fetch 请求;
//note: 6. 如果 broker 没有掉线,向这些 partition 的新 leader 启动副本同步线程;
//note: 上面这些操作的顺序性,保证了这些副本在 offset checkpoint 之前将不会接收新的数据,这样的话,在 checkpoint 之前这些数据都可以保证刷到磁盘
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: 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
// Only change partition state when the leader is available
case Some(_) => //note: 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: 删除对这些 partition 的副本同步线程
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: 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: 完成那些延迟请求的处理
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: 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
}

简单来说,makeFollowers() 的处理过程如下:

  1. 先从本地记录 leader partition 的集合中将这些 partition 移除,因为这些 partition 已经被选举为了 follower;
  2. 将这些 partition 的本地副本设置为 follower,后面就不会接收关于这个 partition 的 Produce 请求了,如果依然有 client 在向这台 broker 发送数据,那么它将会返回相应的错误;
  3. 先停止关于这些 partition 的副本同步线程(如果本地副本之前是 follower 现在还是 follower,先关闭的原因是:这个 partition 的 leader 可能发生了变化),这样的话可以保证这些 partition 的本地副本将不会再有新的数据追加;
  4. 对这些 partition 本地副本日志文件进行截断操作并进行 checkpoint 操作;
  5. 完成那些延迟处理的 Produce 和 Fetch 请求;
  6. 如果本地的 broker 没有掉线,那么向这些 partition 新选举出来的 leader 启动副本同步线程。

关于第6步,并不一定会为每一个 partition 都启动一个 fetcher 线程,对于一个目的 broker,只会启动 num.replica.fetchers 个线程,具体这个 topic-partition 会分配到哪个 fetcher 线程上,是根据 topic 名和 partition id 进行计算得到,实现所示:

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//note: 获取分配到这个 topic-partition 的 fetcher 线程 id
private def getFetcherId(topic: String, partitionId: Int) : Int = {
Utils.abs(31 * topic.hashCode() + partitionId) % numFetchers
}

replica fetcher 线程参数设置

关于副本同步线程有一些参数配置,具体如下表所示:

参数 说明 默认值
num.replica.fetchers 从一个 broker 同步数据的 fetcher 线程数,增加这个值时也会增加该 broker 的 Io 并行度(也就是说:从一台 broker 同步数据,最多能开这么大的线程数) 1
replica.fetch.wait.max.ms 对于 follower replica 而言,每个 Fetch 请求的最大等待时间,这个值应该比 replica.lag.time.max.ms 要小,否则对于那些吞吐量特别低的 topic 可能会导致 isr 频繁抖动 500
replica.high.watermark.checkpoint.interval.ms hw 刷到磁盘频率 500
replica.lag.time.max.ms 如果一个 follower 在这个时间内没有发送任何 fetch 请求或者在这个时间内没有追上 leader 当前的 log end offset,那么将会从 isr 中移除 10000
replica.fetch.min.bytes 每次 fetch 请求最少拉取的数据量,如果不满足这个条件,那么要等待 replicaMaxWaitTimeMs 1
replica.fetch.backoff.ms 拉取时,如果遇到错误,下次拉取等待的时间 1000
replica.fetch.max.bytes 在对每个 partition 拉取时,最大的拉取数量,这并不是一个绝对值,如果拉取的第一条 msg 的大小超过了这个值,只要不超过这个 topic 设置(defined via message.max.bytes (broker config) or max.message.bytes (topic config))的单条大小限制,依然会返回。 1048576
replica.fetch.response.max.bytes 对于一个 fetch 请求,返回的最大数据量(可能会涉及多个 partition),这并不是一个绝对值,如果拉取的第一条 msg 的大小超过了这个值,只要不超过这个 topic 设置(defined via message.max.bytes (broker config) or max.message.bytes (topic config))的单条大小限制,依然会返回。 10MB

replica fetcher 线程启动

如上面的图所示,在 ReplicaManager 调用 makeFollowers() 启动 replica fetcher 线程后,它实际上是通过 ReplicaFetcherManager 实例进行相关 topic-partition 同步线程的启动和关闭,其启动过程分为下面两步:

  1. ReplicaFetcherManager 调用 addFetcherForPartitions() 添加对这些 topic-partition 的数据同步流程;
  2. ReplicaFetcherManager 调用 createFetcherThread() 初始化相应的 ReplicaFetcherThread 线程。

addFetcherForPartitions

addFetcherForPartitions() 的具体实现如下所示:

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//note: 为一个 topic-partition 添加 replica-fetch 线程
def addFetcherForPartitions(partitionAndOffsets: Map[TopicPartition, BrokerAndInitialOffset]) {
mapLock synchronized {
//note: 为这些 topic-partition 分配相应的 fetch 线程 id
val partitionsPerFetcher = partitionAndOffsets.groupBy { case(topicPartition, brokerAndInitialOffset) =>
BrokerAndFetcherId(brokerAndInitialOffset.broker, getFetcherId(topicPartition.topic, topicPartition.partition))}
for ((brokerAndFetcherId, partitionAndOffsets) <- partitionsPerFetcher) {
//note: 为 BrokerAndFetcherId 构造 fetcherThread 线程
var fetcherThread: AbstractFetcherThread = null
fetcherThreadMap.get(brokerAndFetcherId) match {
case Some(f) => fetcherThread = f
case None =>
//note: 创建 fetcher 线程
fetcherThread = createFetcherThread(brokerAndFetcherId.fetcherId, brokerAndFetcherId.broker)
fetcherThreadMap.put(brokerAndFetcherId, fetcherThread)
fetcherThread.start
}
//note: 添加 topic-partition 列表
fetcherThreadMap(brokerAndFetcherId).addPartitions(partitionAndOffsets.map { case (tp, brokerAndInitOffset) =>
tp -> brokerAndInitOffset.initOffset
})
}
}
info("Added fetcher for partitions %s".format(partitionAndOffsets.map { case (topicPartition, brokerAndInitialOffset) =>
"[" + topicPartition + ", initOffset " + brokerAndInitialOffset.initOffset + " to broker " + brokerAndInitialOffset.broker + "] "}))
}

这个方法其实是做了下面这几件事:

  1. 先计算这个 topic-partition 对应的 fetcher id;
  2. 根据 leader 和 fetcher id 获取对应的 replica fetcher 线程,如果没有找到,就调用 createFetcherThread() 创建一个新的 fetcher 线程;
  3. 如果是新启动的 replica fetcher 线程,那么就启动这个线程;
  4. 将 topic-partition 记录到 fetcherThreadMap 中,这个变量记录每个 replica fetcher 线程要同步的 topic-partition 列表。

createFetcherThread

ReplicaFetcherManager 创建 replica Fetcher 线程的实现如下:

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//note: 创建 replica-fetch 线程
override def createFetcherThread(fetcherId: Int, sourceBroker: BrokerEndPoint): AbstractFetcherThread = {
val threadName = threadNamePrefix match {
case None =>
"ReplicaFetcherThread-%d-%d".format(fetcherId, sourceBroker.id)
case Some(p) =>
"%s:ReplicaFetcherThread-%d-%d".format(p, fetcherId, sourceBroker.id)
}
new ReplicaFetcherThread(threadName, fetcherId, sourceBroker, brokerConfig,
replicaMgr, metrics, time, quotaManager) //note: replica-fetch 线程
}

replica fetcher 线程处理过程

replica fetcher 线程在启动之后就开始进行正常数据同步流程了,在文章最开始流程图中的第二部分(线程处理过程)已经给出了大概的处理过程,这节会详细介绍一下,这个过程都是在 ReplicaFetcherThread 线程中实现的。

doWoker

ReplicaFetcherThread 的 doWork() 方法是一直在这个线程中的 run() 中调用的,实现方法如下:

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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 ")
}
override def doWork() {
//note: 构造 fetch request
val fetchRequest = inLock(partitionMapLock) {
val fetchRequest = buildFetchRequest(partitionStates.partitionStates.asScala.map { state =>
state.topicPartition -> state.value
})
if (fetchRequest.isEmpty) { //note: 如果没有活跃的 partition,在下次调用之前,sleep fetchBackOffMs 时间
trace("There are no active partitions. Back off for %d ms before sending a fetch request".format(fetchBackOffMs))
partitionMapCond.await(fetchBackOffMs, TimeUnit.MILLISECONDS)
}
fetchRequest
}
if (!fetchRequest.isEmpty)
processFetchRequest(fetchRequest) //note: 发送 fetch 请求,处理 fetch 的结果
}

doWork() 方法中主要做了两件事:

  1. 构造相应的 Fetch 请求(buildFetchRequest());
  2. 通过 processFetchRequest() 方法发送 Fetch 请求,并对其结果进行相应的处理。

buildFetchRequest

通过 buildFetchRequest() 方法构造相应的 Fetcher 请求时,会设置 replicaId,该值会代表了这个 Fetch 请求是来自副本同步,而不是来自 consumer。

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//note: 构造 Fetch 请求
protected def buildFetchRequest(partitionMap: Seq[(TopicPartition, PartitionFetchState)]): FetchRequest = {
val requestMap = new util.LinkedHashMap[TopicPartition, JFetchRequest.PartitionData]
partitionMap.foreach { case (topicPartition, partitionFetchState) =>
// We will not include a replica in the fetch request if it should be throttled.
if (partitionFetchState.isActive && !shouldFollowerThrottle(quota, topicPartition))
requestMap.put(topicPartition, new JFetchRequest.PartitionData(partitionFetchState.offset, fetchSize))
}
//note: 关键在于 setReplicaId 方法,设置了 replicaId, 对于 consumer, 该值为 CONSUMER_REPLICA_ID(-1)
val requestBuilder = new JFetchRequest.Builder(maxWait, minBytes, requestMap).
setReplicaId(replicaId).setMaxBytes(maxBytes)
requestBuilder.setVersion(fetchRequestVersion)
new FetchRequest(requestBuilder)
}

processFetchRequest

processFetchRequest() 这个方法的作用是发送 Fetch 请求,并对返回的结果进行处理,最终写入到本地副本的 Log 实例中,其具体实现:

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private def processFetchRequest(fetchRequest: REQ) {
val partitionsWithError = mutable.Set[TopicPartition]()
def updatePartitionsWithError(partition: TopicPartition): Unit = {
partitionsWithError += partition
partitionStates.moveToEnd(partition)
}
var responseData: Seq[(TopicPartition, PD)] = Seq.empty
try {
trace("Issuing to broker %d of fetch request %s".format(sourceBroker.id, fetchRequest))
responseData = fetch(fetchRequest) //note: 发送 fetch 请求,获取 fetch 结果
} catch {
case t: Throwable =>
if (isRunning.get) {
warn(s"Error in fetch $fetchRequest", t)
inLock(partitionMapLock) { //note: fetch 时发生错误,sleep 一会
partitionStates.partitionSet.asScala.foreach(updatePartitionsWithError)
// there is an error occurred while fetching partitions, sleep a while
// note that `ReplicaFetcherThread.handlePartitionsWithError` will also introduce the same delay for every
// partition with error effectively doubling the delay. It would be good to improve this.
partitionMapCond.await(fetchBackOffMs, TimeUnit.MILLISECONDS)
}
}
}
fetcherStats.requestRate.mark()
if (responseData.nonEmpty) { //note: fetch 结果不为空
// process fetched data
inLock(partitionMapLock) {
responseData.foreach { case (topicPartition, partitionData) =>
val topic = topicPartition.topic
val partitionId = topicPartition.partition
Option(partitionStates.stateValue(topicPartition)).foreach(currentPartitionFetchState =>
// we append to the log if the current offset is defined and it is the same as the offset requested during fetch
//note: 如果 fetch 的 offset 与返回结果的 offset 相同,并且返回没有异常,那么就将拉取的数据追加到对应的 partition 上
if (fetchRequest.offset(topicPartition) == currentPartitionFetchState.offset) {
Errors.forCode(partitionData.errorCode) match {
case Errors.NONE =>
try {
val records = partitionData.toRecords
val newOffset = records.shallowEntries.asScala.lastOption.map(_.nextOffset).getOrElse(
currentPartitionFetchState.offset)
fetcherLagStats.getAndMaybePut(topic, partitionId).lag = Math.max(0L, partitionData.highWatermark - newOffset)
// Once we hand off the partition data to the subclass, we can't mess with it any more in this thread
//note: 将 fetch 的数据追加到日志文件中
processPartitionData(topicPartition, currentPartitionFetchState.offset, partitionData)
val validBytes = records.validBytes
if (validBytes > 0) {
// Update partitionStates only if there is no exception during processPartitionData
//note: 更新 fetch 的 offset 位置
partitionStates.updateAndMoveToEnd(topicPartition, new PartitionFetchState(newOffset))
fetcherStats.byteRate.mark(validBytes) //note: 更新 metrics
}
} catch {
case ime: CorruptRecordException =>
// we log the error and continue. This ensures two things
// 1. If there is a corrupt message in a topic partition, it does not bring the fetcher thread down and cause other topic partition to also lag
// 2. If the message is corrupt due to a transient state in the log (truncation, partial writes can cause this), we simply continue and
// should get fixed in the subsequent fetches
//note: CRC 验证失败时,打印日志,并继续进行(这个线程还会有其他的 tp 拉取,防止影响其他副本同步)
logger.error("Found invalid messages during fetch for partition [" + topic + "," + partitionId + "] offset " + currentPartitionFetchState.offset + " error " + ime.getMessage)
updatePartitionsWithError(topicPartition);
case e: Throwable =>
//note: 这里还会抛出异常,是 RUNTimeException
throw new KafkaException("error processing data for partition [%s,%d] offset %d"
.format(topic, partitionId, currentPartitionFetchState.offset), e)
}
case Errors.OFFSET_OUT_OF_RANGE => //note: Out-of-range 的情况处理
try {
val newOffset = handleOffsetOutOfRange(topicPartition)
partitionStates.updateAndMoveToEnd(topicPartition, new PartitionFetchState(newOffset))
error("Current offset %d for partition [%s,%d] out of range; reset offset to %d"
.format(currentPartitionFetchState.offset, topic, partitionId, newOffset))
} catch { //note: 处理 out-of-range 是抛出的异常
case e: Throwable =>
error("Error getting offset for partition [%s,%d] to broker %d".format(topic, partitionId, sourceBroker.id), e)
updatePartitionsWithError(topicPartition)
}
case _ => //note: 其他的异常情况
if (isRunning.get) {
error("Error for partition [%s,%d] to broker %d:%s".format(topic, partitionId, sourceBroker.id,
partitionData.exception.get))
updatePartitionsWithError(topicPartition)
}
}
})
}
}
}
//note: 处理拉取遇到的错误读的 tp
if (partitionsWithError.nonEmpty) {
debug("handling partitions with error for %s".format(partitionsWithError))
handlePartitionsWithErrors(partitionsWithError)
}
}

其处理过程简单总结一下:

  1. 通过 fetch() 方法,发送 Fetch 请求,获取相应的 response(如果遇到异常,那么在下次发送 Fetch 请求之前,会 sleep 一段时间再发);
  2. 如果返回的结果 不为空,并且 Fetch 请求的 offset 信息与返回结果的 offset 信息对得上,那么就会调用 processPartitionData() 方法将拉取到的数据追加本地副本的日志文件中,如果返回结果有错误信息,那么就对相应错误进行相应的处理;
  3. 对在 Fetch 过程中遇到异常或返回错误的 topic-partition,会进行 delay 操作,下次 Fetch 请求的发生至少要间隔 replica.fetch.backoff.ms 时间。

fetch

fetch() 方法作用是发送 Fetch 请求,并返回相应的结果,其具体的实现,如下:

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//note: 发送 fetch 请求,获取拉取结果
protected def fetch(fetchRequest: FetchRequest): Seq[(TopicPartition, PartitionData)] = {
val clientResponse = sendRequest(fetchRequest.underlying)
val fetchResponse = clientResponse.responseBody.asInstanceOf[FetchResponse]
fetchResponse.responseData.asScala.toSeq.map { case (key, value) =>
key -> new PartitionData(value)
}
}
//note: 发送请求
private def sendRequest(requestBuilder: AbstractRequest.Builder[_ <: AbstractRequest]): ClientResponse = {
import kafka.utils.NetworkClientBlockingOps._
try {
if (!networkClient.blockingReady(sourceNode, socketTimeout)(time))
throw new SocketTimeoutException(s"Failed to connect within $socketTimeout ms")
else {
val clientRequest = networkClient.newClientRequest(sourceBroker.id.toString, requestBuilder,
time.milliseconds(), true)
networkClient.blockingSendAndReceive(clientRequest)(time) //note: 阻塞直到获取返回结果
}
}
catch {
case e: Throwable =>
networkClient.close(sourceBroker.id.toString)
throw e
}
}

processPartitionData

这个方法的作用是,处理 Fetch 请求的具体数据内容,简单来说就是:检查一下数据大小是否超过限制、将数据追加到本地副本的日志文件中、更新本地副本的 hw 值。

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// process fetched data
//note: 处理 fetch 的数据,将 fetch 的数据追加的日志文件中
def processPartitionData(topicPartition: TopicPartition, fetchOffset: Long, partitionData: PartitionData) {
try {
val replica = replicaMgr.getReplica(topicPartition).get
val records = partitionData.toRecords
//note: 检查 records
maybeWarnIfOversizedRecords(records, topicPartition)
if (fetchOffset != replica.logEndOffset.messageOffset)
throw new RuntimeException("Offset mismatch for partition %s: fetched offset = %d, log end offset = %d.".format(topicPartition, fetchOffset, replica.logEndOffset.messageOffset))
if (logger.isTraceEnabled)
trace("Follower %d has replica log end offset %d for partition %s. Received %d messages and leader hw %d"
.format(replica.brokerId, replica.logEndOffset.messageOffset, topicPartition, records.sizeInBytes, partitionData.highWatermark))
replica.log.get.append(records, assignOffsets = false) //note: 将 fetch 的数据追加到 log 中
if (logger.isTraceEnabled)
trace("Follower %d has replica log end offset %d after appending %d bytes of messages for partition %s"
.format(replica.brokerId, replica.logEndOffset.messageOffset, records.sizeInBytes, topicPartition))
//note: 更新 replica 的 hw(logEndOffset 在追加数据后也会立马进行修改)
val followerHighWatermark = replica.logEndOffset.messageOffset.min(partitionData.highWatermark)
// for the follower replica, we do not need to keep
// its segment base offset the physical position,
// these values will be computed upon making the leader
//note: 这个值主要是用在 leader replica 上的
replica.highWatermark = new LogOffsetMetadata(followerHighWatermark)
if (logger.isTraceEnabled)
trace(s"Follower ${replica.brokerId} set replica high watermark for partition $topicPartition to $followerHighWatermark")
if (quota.isThrottled(topicPartition))
quota.record(records.sizeInBytes)
} catch {
case e: KafkaStorageException =>
fatal(s"Disk error while replicating data for $topicPartition", e)
Runtime.getRuntime.halt(1)
}
}

副本同步异常情况的处理

在副本同步的过程中,会遇到哪些异常情况呢?

大家一定会想到关于 offset 的问题,在 Kafka 中,关于 offset 的处理,无论是 producer 端、consumer 端还是其他地方,offset 似乎都是一个形影不离的问题。在副本同步时,关于 offset,会遇到什么问题呢?下面举两个异常的场景:

  1. 假如当前本地(id:1)的副本现在是 leader,其 LEO 假设为1000,而另一个在 isr 中的副本(id:2)其 LEO 为800,此时出现网络抖动,id 为1 的机器掉线后又上线了,但是此时副本的 leader 实际上已经变成了 2,而2的 LEO 为800,这时候1启动副本同步线程去2上拉取数据,希望从 offset=1000 的地方开始拉取,但是2上最大的 offset 才是800,这种情况该如何处理呢?
  2. 假设一个 replica (id:1)其 LEO 是10,它已经掉线好几天,这个 partition leader 的 offset 范围是 [100, 800],那么 1 重启启动时,它希望从 offset=10 的地方开始拉取数据时,这时候发生了 OutOfRange,不过跟上面不同的是这里是小于了 leader offset 的范围,这种情况又该怎么处理?

以上两种情况都是 offset OutOfRange 的情况,只不过:一是 Fetch Offset 超过了 leader 的 LEO,二是 Fetch Offset 小于 leader 最小的 offset,在介绍 Kafka 解决方案之前,我们先来自己思考一下这两种情况应该怎么处理?

  1. 如果 fetch offset 超过 leader 的 offset,这时候副本应该是回溯到 leader 的 LEO 位置(超过这个值的数据删除),然后再去进行副本同步,当然这种解决方案其实是无法保证 leader 与 follower 数据的完全一致,再次发生 leader 切换时,可能会导致数据的可见性不一致,但既然用户允许了脏选举的发生,其实我们是可以认为用户是可以接收这种情况发生的;
  2. 这种就比较容易处理,首先清空本地的数据,因为本地的数据都已经过期了,然后从 leader 的最小 offset 位置开始拉取数据。

上面是我们比较容易想出的解决方案,而在 Kafka 中,其解决方案也很类似,不过遇到情况比上面我们列出的两种情况多了一些复杂,其解决方案如下:

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/**
* Unclean leader election: A follower goes down, in the meanwhile the leader keeps appending messages. The follower comes back up
* and before it has completely caught up with the leader's logs, all replicas in the ISR go down. The follower is now uncleanly
* elected as the new leader, and it starts appending messages from the client. The old leader comes back up, becomes a follower
* and it may discover that the current leader's end offset is behind its own end offset.
*
* In such a case, truncate the current follower's log to the current leader's end offset and continue fetching.
*
* There is a potential for a mismatch between the logs of the two replicas here. We don't fix this mismatch as of now.
*/
//note: 脏选举的发生
//note: 获取最新的 offset
val leaderEndOffset: Long = earliestOrLatestOffset(topicPartition, ListOffsetRequest.LATEST_TIMESTAMP,
brokerConfig.brokerId)
if (leaderEndOffset < replica.logEndOffset.messageOffset) { //note: leaderEndOffset 小于 副本 LEO 的情况
// Prior to truncating the follower's log, ensure that doing so is not disallowed by the configuration for unclean leader election.
// This situation could only happen if the unclean election configuration for a topic changes while a replica is down. Otherwise,
// we should never encounter this situation since a non-ISR leader cannot be elected if disallowed by the broker configuration.
//note: 这种情况只是发生在 unclear election 的情况下
if (!LogConfig.fromProps(brokerConfig.originals, AdminUtils.fetchEntityConfig(replicaMgr.zkUtils,
ConfigType.Topic, topicPartition.topic)).uncleanLeaderElectionEnable) { //note: 不允许 unclear elect 时,直接退出进程
// Log a fatal error and shutdown the broker to ensure that data loss does not unexpectedly occur.
fatal("Exiting because log truncation is not allowed for partition %s,".format(topicPartition) +
" Current leader %d's latest offset %d is less than replica %d's latest offset %d"
.format(sourceBroker.id, leaderEndOffset, brokerConfig.brokerId, replica.logEndOffset.messageOffset))
System.exit(1)
}
//note: warn 日志信息
warn("Replica %d for partition %s reset its fetch offset from %d to current leader %d's latest offset %d"
.format(brokerConfig.brokerId, topicPartition, replica.logEndOffset.messageOffset, sourceBroker.id, leaderEndOffset))
//note: 进行截断操作,将offset 大于等于targetOffset 的数据和索引删除
replicaMgr.logManager.truncateTo(Map(topicPartition -> leaderEndOffset))
leaderEndOffset
} else { //note: leader 的 LEO 大于 follower 的 LEO 的情况下,还发生了 OutOfRange
//note: 1. follower 下线了很久,其 LEO 已经小于了 leader 的 StartOffset;
//note: 2. 脏选举发生时, 如果 old leader 的 HW 大于 new leader 的 LEO,此时 old leader 回溯到 HW,并且这个位置开始拉取数据发生了 Out of range
//note: 当这个方法调用时,随着 produce 持续产生数据,可能出现 leader LEO 大于 Follower LEO 的情况(不做任何处理,重试即可解决,但
//note: 无法保证数据的一致性)。
/**
* If the leader's log end offset is greater than the follower's log end offset, there are two possibilities:
* 1. The follower could have been down for a long time and when it starts up, its end offset could be smaller than the leader's
* start offset because the leader has deleted old logs (log.logEndOffset < leaderStartOffset).
* 2. When unclean leader election occurs, it is possible that the old leader's high watermark is greater than
* the new leader's log end offset. So when the old leader truncates its offset to its high watermark and starts
* to fetch from the new leader, an OffsetOutOfRangeException will be thrown. After that some more messages are
* produced to the new leader. While the old leader is trying to handle the OffsetOutOfRangeException and query
* the log end offset of the new leader, the new leader's log end offset becomes higher than the follower's log end offset.
*
* In the first case, the follower's current log end offset is smaller than the leader's log start offset. So the
* follower should truncate all its logs, roll out a new segment and start to fetch from the current leader's log
* start offset.
* In the second case, the follower should just keep the current log segments and retry the fetch. In the second
* case, there will be some inconsistency of data between old and new leader. We are not solving it here.
* If users want to have strong consistency guarantees, appropriate configurations needs to be set for both
* brokers and producers.
*
* Putting the two cases together, the follower should fetch from the higher one of its replica log end offset
* and the current leader's log start offset.
*
*/
val leaderStartOffset: Long = earliestOrLatestOffset(topicPartition, ListOffsetRequest.EARLIEST_TIMESTAMP,
brokerConfig.brokerId)
warn("Replica %d for partition %s reset its fetch offset from %d to current leader %d's start offset %d"
.format(brokerConfig.brokerId, topicPartition, replica.logEndOffset.messageOffset, sourceBroker.id, leaderStartOffset))
val offsetToFetch = Math.max(leaderStartOffset, replica.logEndOffset.messageOffset)
// Only truncate log when current leader's log start offset is greater than follower's log end offset.
if (leaderStartOffset > replica.logEndOffset.messageOffset) //note: 如果 leader 的 startOffset 大于副本的最大 offset
//note: 将这个 log 的数据全部清空,并且从 leaderStartOffset 开始拉取数据
replicaMgr.logManager.truncateFullyAndStartAt(topicPartition, leaderStartOffset)
offsetToFetch
}
}

针对第一种情况,在 Kafka 中,实际上还会发生这样一种情况,1 在收到 OutOfRange 错误时,这时去 leader 上获取的 LEO 值与最小的 offset 值,这时候却发现 leader 的 LEO 已经从 800 变成了 1100(这个 topic-partition 的数据量增长得比较快),再按照上面的解决方案就不太合理,Kafka 这边的解决方案是:遇到这种情况,进行重试就可以了,下次同步时就会正常了,但是依然会有上面说的那个问题。

replica fetcher 线程的关闭

最后我们再来介绍一下 replica fetcher 线程在什么情况下会关闭,同样,看一下最开始那张图的第三部分,图中已经比较清晰地列出了 replica fetcher 线程关闭的条件,在三种情况下会关闭对这个 topic-partition 的拉取操作(becomeLeaderOrFollower() 这个方法会在对 LeaderAndIsr 请求处理的文章中讲解,这里先忽略):

  1. stopReplica():broker 收到了 controller 发来的 StopReplica 请求,这时会开始关闭对指定 topic-partition 的同步线程;
  2. makeLeaders:这些 partition 的本地副本被选举成了 leader,这时候就会先停止对这些 topic-partition 副本同步线程;
  3. makeFollowers():前面已经介绍过,这里实际上停止副本同步,然后再开启副本同步线程,因为这些 topic-partition 的 leader 可能发生了切换。

这里直接说线程关闭,其实不是很准确,因为每个 replica fetcher 线程操作的是多个 topic-partition,而在关闭的粒度是 partition 级别,只有这个线程分配的 partition 全部关闭后,这个线程才会真正被关闭。

关闭副本同步

看下 ReplicaManager 中触发 replica fetcher 线程关闭的三个方法。

stopReplica

StopReplica 的请求实际上是 Controller 发送过来的,这个在 controller 部分会讲述,它触发的条件有多种,比如:broker 下线、partition replica 迁移等等,ReplicaManager 这里的实现如下:

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//note: 获取 tp 的 leader replica
def getLeaderReplicaIfLocal(topicPartition: TopicPartition): Replica = {
val partitionOpt = getPartition(topicPartition) //note: 获取对应的 Partiion 对象
partitionOpt match {
case None =>
throw new UnknownTopicOrPartitionException(s"Partition $topicPartition doesn't exist on $localBrokerId")
case Some(partition) =>
partition.leaderReplicaIfLocal match {
case Some(leaderReplica) => leaderReplica //note: 返回 leader 对应的副本
case None =>
throw new NotLeaderForPartitionException(s"Leader not local for partition $topicPartition on broker $localBrokerId")
}
}
}

makeLeaders

makeLeaders() 方法的调用是在 broker 上这个 partition 的副本被设置为 leader 时触发的,其实现如下:

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/*
* Make the current broker to become leader for a given set of partitions by:
*
* 1. Stop fetchers for these partitions
* 2. Update the partition metadata in cache
* 3. Add these partitions to the leader partitions set
*
* If an unexpected error is thrown in this function, it will be propagated to KafkaApis where
* the error message will be set on each partition since we do not know which partition caused it. Otherwise,
* return the set of partitions that are made leader due to this method
*
* TODO: the above may need to be fixed later
*/
//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: 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: 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
}

简单来说,这个方法的过程逻辑如下:

  1. 先停止对这些 partition 的副本同步流程,因为这些 partition 的本地副本已经被选举成为了 leader;
  2. 将这些 partition 的本地副本设置为 leader,并且开始更新相应 meta 信息(主要是记录其他 follower 副本的相关信息);
  3. 将这些 partition 添加到本地记录的 leader partition 集合中。

makeFollowers

这个在前面已经讲述过了,参考前面的讲述。

removeFetcherForPartitions

调用 ReplicaFetcherManager 的 removeFetcherForPartitions() 删除对这些 topic-partition 的副本同步设置,这里在实现时,会遍历所有的 replica fetcher 线程,都执行 removePartitions() 方法来移除对应的 topic-partition 集合。

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//note: 删除一个 partition 的 replica-fetch 线程
def removeFetcherForPartitions(partitions: Set[TopicPartition]) {
mapLock synchronized {
for (fetcher <- fetcherThreadMap.values) //note: 遍历所有的 fetchThread 去移除这个 topic-partition 集合
fetcher.removePartitions(partitions)
}
info("Removed fetcher for partitions %s".format(partitions.mkString(",")))
}

removePartitions

这个方法的作用是:ReplicaFetcherThread 将这些 topic-partition 从自己要拉取的 partition 列表中移除。

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def removePartitions(topicPartitions: Set[TopicPartition]) {
partitionMapLock.lockInterruptibly()
try {
topicPartitions.foreach { topicPartition =>
partitionStates.remove(topicPartition)
fetcherLagStats.unregister(topicPartition.topic, topicPartition.partition)
}
} finally partitionMapLock.unlock()
}

ReplicaFetcherThread 的关闭

前面介绍那么多,似乎还是没有真正去关闭,那么 ReplicaFetcherThread 真正关闭是哪里操作的呢?

实际上 ReplicaManager 每次处理完 LeaderAndIsr 请求后,都会调用 ReplicaFetcherManager 的 shutdownIdleFetcherThreads() 方法,如果 fetcher 线程要拉取的 topic-partition 集合为空,那么就会关闭掉对应的 fetcher 线程。

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//note: 关闭没有拉取 topic-partition 任务的拉取线程
def shutdownIdleFetcherThreads() {
mapLock synchronized {
val keysToBeRemoved = new mutable.HashSet[BrokerAndFetcherId]
for ((key, fetcher) <- fetcherThreadMap) {
if (fetcher.partitionCount <= 0) { //note: 如果该线程拉取的 partition 数小于 0
fetcher.shutdown()
keysToBeRemoved += key
}
}
fetcherThreadMap --= keysToBeRemoved
}
}

关于 Replica Fetcher 线程这部分的内容终于讲解完了,希望能对大家有所帮助,有问题欢迎通过留言、微博或邮件进行交流。