聊聊kafka client chunkQueue 與 MaxLag值

序

前面一篇文章討論了ConsumerFetcherManager的MaxLag與ConsumerOffsetChecker的lag值的區別。可是關於MaxLag的值尚未講的太透徹，這裏再深刻一下，如何讓ConsumerFetcherManager的MaxLag有值。

AbstractFetcherThread#processFetchRequest

kafka_2.10-0.8.2.2-sources.jar!/kafka/server/AbstractFetcherThread.scala

override def doWork() {
    inLock(partitionMapLock) {
      if (partitionMap.isEmpty)
        partitionMapCond.await(200L, TimeUnit.MILLISECONDS)
      partitionMap.foreach {
        case((topicAndPartition, offset)) =>
          fetchRequestBuilder.addFetch(topicAndPartition.topic, topicAndPartition.partition,
                           offset, fetchSize)
      }
    }

    val fetchRequest = fetchRequestBuilder.build()
    if (!fetchRequest.requestInfo.isEmpty)
      processFetchRequest(fetchRequest)
  }

值得注意，這裏構建了fetchRequest
這裏的partitionMap,key是TopicAndPartition，value就是本地最大的offset
每次拉取的時候，以本地已經拉取的最大值，還有拉取大小構造fetchRequest

FetchRequest

kafka_2.10-0.8.2.2-sources.jar!/kafka/api/FetchRequest.scala

def addFetch(topic: String, partition: Int, offset: Long, fetchSize: Int) = {
    requestMap.put(TopicAndPartition(topic, partition), PartitionFetchInfo(offset, fetchSize))
    this
  }

能夠看到這裏的offset與fetchSize決定了這個fetcher從broker拉取數據的開始位置和拉取數據的條數。

ConsumerFetcherThread

kafka_2.10-0.8.2.2-sources.jar!/kafka/consumer/ConsumerFetcherThread.scala

class ConsumerFetcherThread(name: String,
                            val config: ConsumerConfig,
                            sourceBroker: Broker,
                            partitionMap: Map[TopicAndPartition, PartitionTopicInfo],
                            val consumerFetcherManager: ConsumerFetcherManager)
        extends AbstractFetcherThread(name = name, 
                                      clientId = config.clientId,
                                      sourceBroker = sourceBroker,
                                      socketTimeout = config.socketTimeoutMs,
                                      socketBufferSize = config.socketReceiveBufferBytes,
                                      fetchSize = config.fetchMessageMaxBytes,
                                      fetcherBrokerId = Request.OrdinaryConsumerId,
                                      maxWait = config.fetchWaitMaxMs,
                                      minBytes = config.fetchMinBytes,
                                      isInterruptible = true) {
        //...
}

這裏使用的fetchSize來自config.fetchMessageMaxBytes

kafka_2.10-0.8.2.2-sources.jar!/kafka/consumer/ConsumerConfig.scala

class ConsumerConfig private (val props: VerifiableProperties) extends ZKConfig(props) {
    //...
    /** the number of byes of messages to attempt to fetch */
      val fetchMessageMaxBytes = props.getInt("fetch.message.max.bytes", FetchSize)
}
object ConsumerConfig extends Config {
  val RefreshMetadataBackoffMs = 200
  val SocketTimeout = 30 * 1000
  val SocketBufferSize = 64*1024
  val FetchSize = 1024 * 1024
  val MaxFetchSize = 10*FetchSize
  val NumConsumerFetchers = 1
  val DefaultFetcherBackoffMs = 1000
  val AutoCommit = true
  val AutoCommitInterval = 60 * 1000
  val MaxQueuedChunks = 2
  val MaxRebalanceRetries = 4
  val AutoOffsetReset = OffsetRequest.LargestTimeString
  val ConsumerTimeoutMs = -1
  val MinFetchBytes = 1
  val MaxFetchWaitMs = 100
  val MirrorTopicsWhitelist = ""
  val MirrorTopicsBlacklist = ""
  val MirrorConsumerNumThreads = 1
  val OffsetsChannelBackoffMs = 1000
  val OffsetsChannelSocketTimeoutMs = 10000
  val OffsetsCommitMaxRetries = 5
  val OffsetsStorage = "zookeeper"

  val MirrorTopicsWhitelistProp = "mirror.topics.whitelist"
  val MirrorTopicsBlacklistProp = "mirror.topics.blacklist"
  val ExcludeInternalTopics = true
  val DefaultPartitionAssignmentStrategy = "range" /* select between "range", and "roundrobin" */
  val MirrorConsumerNumThreadsProp = "mirror.consumer.numthreads"
  val DefaultClientId = ""
  //...
}

這個fetchSize默認是1024 * 1024，也就是1048576，即每次fetch的時候拉取1048576這麼多條。

AbstractFetcherThread#processFetchRequest

private def processFetchRequest(fetchRequest: FetchRequest) {
    val partitionsWithError = new mutable.HashSet[TopicAndPartition]
    var response: FetchResponse = null
    try {
      trace("Issuing to broker %d of fetch request %s".format(sourceBroker.id, fetchRequest))
      response = simpleConsumer.fetch(fetchRequest)
    } catch {
      case t: Throwable =>
        if (isRunning.get) {
          warn("Error in fetch %s. Possible cause: %s".format(fetchRequest, t.toString))
          partitionMapLock synchronized {
            partitionsWithError ++= partitionMap.keys
          }
        }
    }
    fetcherStats.requestRate.mark()

    if (response != null) {
      // process fetched data
      inLock(partitionMapLock) {
        response.data.foreach {
          case(topicAndPartition, partitionData) =>
            val (topic, partitionId) = topicAndPartition.asTuple
            val currentOffset = partitionMap.get(topicAndPartition)
            // we append to the log if the current offset is defined and it is the same as the offset requested during fetch
            if (currentOffset.isDefined && fetchRequest.requestInfo(topicAndPartition).offset == currentOffset.get) {
              partitionData.error match {
                case ErrorMapping.NoError =>
                  try {
                    val messages = partitionData.messages.asInstanceOf[ByteBufferMessageSet]
                    val validBytes = messages.validBytes
                    //這裏請求以後，若是返回數據爲空，那麼newOffset就是取本地最大的offset
                    val newOffset = messages.shallowIterator.toSeq.lastOption match {
                      case Some(m: MessageAndOffset) => m.nextOffset
                      case None => currentOffset.get
                    }
                    partitionMap.put(topicAndPartition, newOffset)
                    fetcherLagStats.getFetcherLagStats(topic, partitionId).lag = partitionData.hw - newOffset
                    fetcherStats.byteRate.mark(validBytes)

                    //下面這個方法將拉回來的數據放進隊列
                    // Once we hand off the partition data to the subclass, we can't mess with it any more in this thread
                    processPartitionData(topicAndPartition, currentOffset.get, partitionData)
                  } catch {
                    case ime: InvalidMessageException =>
                      // 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
                      logger.error("Found invalid messages during fetch for partition [" + topic + "," + partitionId + "] offset " + currentOffset.get + " error " + ime.getMessage)
                    case e: Throwable =>
                      throw new KafkaException("error processing data for partition [%s,%d] offset %d"
                                               .format(topic, partitionId, currentOffset.get), e)
                  }
                case ErrorMapping.OffsetOutOfRangeCode =>
                  try {
                    val newOffset = handleOffsetOutOfRange(topicAndPartition)
                    partitionMap.put(topicAndPartition, newOffset)
                    error("Current offset %d for partition [%s,%d] out of range; reset offset to %d"
                      .format(currentOffset.get, topic, partitionId, newOffset))
                  } catch {
                    case e: Throwable =>
                      error("Error getting offset for partition [%s,%d] to broker %d".format(topic, partitionId, sourceBroker.id), e)
                      partitionsWithError += topicAndPartition
                  }
                case _ =>
                  if (isRunning.get) {
                    error("Error for partition [%s,%d] to broker %d:%s".format(topic, partitionId, sourceBroker.id,
                      ErrorMapping.exceptionFor(partitionData.error).getClass))
                    partitionsWithError += topicAndPartition
                  }
              }
            }
        }
      }
    }

    if(partitionsWithError.size > 0) {
      debug("handling partitions with error for %s".format(partitionsWithError))
      handlePartitionsWithErrors(partitionsWithError)
    }
  }

ConsumerFetcherThread#processPartitionData

kafka_2.10-0.8.2.2-sources.jar!/kafka/consumer/ConsumerFetcherThread.scala

// process fetched data
  def processPartitionData(topicAndPartition: TopicAndPartition, fetchOffset: Long, partitionData: FetchResponsePartitionData) {
    val pti = partitionMap(topicAndPartition)
    if (pti.getFetchOffset != fetchOffset)
      throw new RuntimeException("Offset doesn't match for partition [%s,%d] pti offset: %d fetch offset: %d"
                                .format(topicAndPartition.topic, topicAndPartition.partition, pti.getFetchOffset, fetchOffset))
    pti.enqueue(partitionData.messages.asInstanceOf[ByteBufferMessageSet])
  }

PartitionTopicInfo#enqueue

kafka_2.10-0.8.2.2-sources.jar!/kafka/consumer/PartitionTopicInfo.scala

/**
   * Enqueue a message set for processing.
   */
  def enqueue(messages: ByteBufferMessageSet) {
    val size = messages.validBytes
    if(size > 0) {
      val next = messages.shallowIterator.toSeq.last.nextOffset
      trace("Updating fetch offset = " + fetchedOffset.get + " to " + next)
      chunkQueue.put(new FetchedDataChunk(messages, this, fetchedOffset.get))
      fetchedOffset.set(next)
      debug("updated fetch offset of (%s) to %d".format(this, next))
      consumerTopicStats.getConsumerTopicStats(topic).byteRate.mark(size)
      consumerTopicStats.getConsumerAllTopicStats().byteRate.mark(size)
    } else if(messages.sizeInBytes > 0) {
      chunkQueue.put(new FetchedDataChunk(messages, this, fetchedOffset.get))
    }
  }

若是數據爲空，則不放進隊列

chunkQueue大小

kafka_2.10-0.8.2.2-sources.jar!/kafka/consumer/ZookeeperConsumerConnector.scala

def consume[K, V](topicCountMap: scala.collection.Map[String,Int], keyDecoder: Decoder[K], valueDecoder: Decoder[V])
      : Map[String,List[KafkaStream[K,V]]] = {
    debug("entering consume ")
    if (topicCountMap == null)
      throw new RuntimeException("topicCountMap is null")

    val topicCount = TopicCount.constructTopicCount(consumerIdString, topicCountMap)

    val topicThreadIds = topicCount.getConsumerThreadIdsPerTopic

    // make a list of (queue,stream) pairs, one pair for each threadId
    val queuesAndStreams = topicThreadIds.values.map(threadIdSet =>
      threadIdSet.map(_ => {
        val queue =  new LinkedBlockingQueue[FetchedDataChunk](config.queuedMaxMessages)
        val stream = new KafkaStream[K,V](
          queue, config.consumerTimeoutMs, keyDecoder, valueDecoder, config.clientId)
        (queue, stream)
      })
    ).flatten.toList

    val dirs = new ZKGroupDirs(config.groupId)
    registerConsumerInZK(dirs, consumerIdString, topicCount)
    reinitializeConsumer(topicCount, queuesAndStreams)

    loadBalancerListener.kafkaMessageAndMetadataStreams.asInstanceOf[Map[String, List[KafkaStream[K,V]]]]
  }

queue在這裏建立了，大小爲config.queuedMaxMessages

/** max number of message chunks buffered for consumption, each chunk can be up to fetch.message.max.bytes*/
  val queuedMaxMessages = props.getInt("queued.max.message.chunks", MaxQueuedChunks)
  val MaxQueuedChunks = 2

默認隊列最大隻能有2個FetchedDataChunk
而每一個FetchedDataChunk裏頭最大的消息數目就是fetchSize大小也就是1024*1024
也就是說每一個消費線程的chunkQueue裏頭默認最大的消息數目爲2 10241024

當超過這個數目的時候，enquue就會阻塞，這樣就造成了對整個fetch的拉取速度的控制。

ConsumerFetcherManager的MaxLag

要使得這個有值的話，那就是修改fetch.message.max.bytes的值，改小一點。好比

props.put("fetch.message.max.bytes","10");
        props.put("queued.max.message.chunks","1");

那麼每次只拉10條消息，假設目前的lag以下

Group  Topic                Pid Offset          logSize         Lag             Owner
mgroup mtopic              0   353             8727            8374            demo-1514550322182-6d67873d-0
mgroup mtopic              1   258             8702            8444            demo-1514550322182-6d67873d-1
mgroup mtopic              2   307             8615            8308            demo-1514550322182-6d67873d-2

拉取一次以後

val newOffset = messages.shallowIterator.toSeq.lastOption match {
                      case Some(m: MessageAndOffset) => m.nextOffset
                      case None => currentOffset.get
                    }
                    partitionMap.put(topicAndPartition, newOffset)
                    fetcherLagStats.getFetcherLagStats(topic, partitionId).lag = partitionData.hw - newOffset

這裏的nextOffset = offset + 1，也就是拉取回來的最大offset+1 = 259，hw的話是8702，那麼lag值就是8702-259=8443
這裏爲了復現，讓消費線程拉取一條以後拋異常退出

小結

生產環境注意根據消息大小以及環境內存等對以下參數進行配置，不然很容易引起OOM

• queued.max.message.chunks，默認是2，控制chunkQueue的容量
• fetch.message.max.bytes，默認是1024*1024，控制每一個chunk的消息最大數目

另外關於ConsumerFetcherManager的MaxLag，只有在上面兩個參數合理設置的狀況下，才能對監控有點點幫助(chunkQueue越小越能從MaxLag反應消費者消費滯後的狀況；不然只能反應client fetcher thread的消息拉取的滯後狀況；不過設置過小的話就得頻繁拉取，影響消費者消費，能夠根據狀況適中調整)。從實際場景來看，仍是通常比較少改動參數的話，那麼仍是得以ConsumerOffsetChecker的lag值作消費者消費滯後的監控才準確。

doc

• ConsumerFetcherManager MaxLag
• apache kafka系列之jmx監控指標參數
• Kafka源碼分析 Consumer(2) Fetcher