看不到图的话可以看我CSDN上的博客：
https://blog.csdn.net/u013332124/article/details/83692537
一、Consumer的poll模型
poll执行流程
一般我们通过KafkaConsumer#poll()方法拉取最新的日志。这个方法执行的时候大概经过了以下流程：
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相关源码解析
// KafkaConsumer.java
//timeout如果为0，表示不等待立即返回
public ConsumerRecords<K, V> poll(long timeout) {
acquire();
try {
//timeout不能小于0.
if (timeout < 0)
throw new IllegalArgumentException("Timeout must not be negative");
//subscribed和assigned两种订阅模式必须选择一个
if (this.subscriptions.hasNoSubscriptionOrUserAssignment())
throw new IllegalStateException("Consumer is not subscribed to any topics or assigned any partitions");
// poll for new data until the timeout expires
long start = time.milliseconds();
long remaining = timeout;
do {
//拉取数据
Map<TopicPartition, List<ConsumerRecord<K, V>>> records = pollOnce(remaining);
if (!records.isEmpty()) {
//如果拉取到的数据不为空，则可以提前把下次的拉取请求也发送出去，这样下次执行poll时就不会因为等待数据而阻塞太久
if (fetcher.sendFetches() > 0 || client.pendingRequestCount() > 0)
client.pollNoWakeup();
if (this.interceptors == null)
return new ConsumerRecords<>(records);
else
return this.interceptors.onConsume(new ConsumerRecords<>(records));
}
long elapsed = time.milliseconds() - start;
remaining = timeout - elapsed;
} while (remaining > 0);
return ConsumerRecords.empty();
} finally {
release();
}
}
private Map<TopicPartition, List<ConsumerRecord<K, V>>> pollOnce(long timeout) {
//加入到指定Group组中，只有在subscribed模式下才需要加入到Group
//这个过程包括了获取要消费的partition
coordinator.poll(time.milliseconds());
//判断要消费的partition的对应offset是否已经知道了，如果不知道就要去获取起始的offset
if (!subscriptions.hasAllFetchPositions())
updateFetchPositions(this.subscriptions.missingFetchPositions());
//可能FETCH请求之前提前发送过，我们把之前获取到的数据直接拿回来就好了
Map<TopicPartition, List<ConsumerRecord<K, V>>> records = fetcher.fetchedRecords();
if (!records.isEmpty())
return records;
//发送FETCH请求
fetcher.sendFetches();
long now = time.milliseconds();
long pollTimeout = Math.min(coordinator.timeToNextPoll(now), timeout);
client.poll(pollTimeout, now, new PollCondition() {
@Override
public boolean shouldBlock() {
return !fetcher.hasCompletedFetches();
}
});
//在拉取数据的过程中，可能Group的状态发生了改变，所以需要检查一下Group的状态是否发生了改变
//如果Group状态发生了改变，就需要重新rejoin，以尽快让Group状态处于稳定
if (coordinator.needRejoin())
return Collections.emptyMap();
return fetcher.fetchedRecords();
}
下面我们详细的介绍一下各个流程
二、获取要消费的partition
三种订阅模式
在使用KafkaConsumer时，有三种模式获取要消费的partiton。这些模式定义在SubscriptionType枚举类中
- AUTO_TOPICS模式：通过订阅相关topic，加入到指定Group后，由GroupCoordinator来分配要消费的partition。AUTO_TOPICS模式是topic粒度级别的订阅
- AUTO_PATTERN模式：用户可以指定一个parttern，consumer需要去获取所有topics，然后去匹配parttern，匹配上的那些topic就是要消费的那些topic，之后和AUTO_TOPICS模式加入Group获取要消费的partition。AUTO_PATTERN模式是topic粒度级别的订阅
- USER_ASSIGNED模式：直接执行KafkaConsumer#assign()方法来指定要消费的topic-partition。USER_ASSIGNED模式是parition粒度级别的订阅
AUTO_TOPICS 和 AUTO_PATTERN模式
subscribe有3个重载方法：
public void subscribe(Collection<String> topics);
public void subscribe(Collection<String> topics, ConsumerRebalanceListener callback);
public void subscribe(Pattern pattern, ConsumerRebalanceListener callback);
我们可以指定一个topic列表，或者直接指定要消费topic的pattern。
如果是AUTO_TOPICS模式，Consumer会去broker拉取指定topics的元数据。如果是AUTO_PATTERN，Consumer就会将所有topics的元数据拉取下来，然后去匹配获取真正要消费的topics是哪些。
获取到topics的元数据后，在执行poll方法拉取数据的时候，consumer就会自动帮我们加入Group，然后获取要消费的partition。
Consumer如何加入和离开Group的，可以看我的这篇文章：
USER_ASSIGNED模式
通过assign方法，我们可以直接指定要消费那些topic-partition。
public void assign(Collection<TopicPartition> partitions) {}
由于该模式是用户自己指定要消费哪些topic-partition，因此，当topic的partition数量发生改变时，程序不会做通知，用户需要自行去处理。
该模式下Consumer就不会执行加入Group的那些操作。
三、获取partition当前commitedOffset
获取到要消费哪些partition后，就要知道要从partiton的哪个offset开始消费。也就是要确定commitedOffset
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Consumer在消费时，会在内存中保存各个partition的commitedOffset。
但是在刚刚获取到要消费的parititon是哪些的时候，还并没有partition的commitedOffset数据。这时候就需要向GroupCoordinator发送OFFSET_FETCH请求来获取对应partition的commitedOffset。
不过，只有AUTO_TOPICS 和 AUTO_PATTERN模式才可能可以从GroupCoordinator获取到commitedOffset记录（如果指定的Group是新建的，则也获取不到）。USER_ASSIGNED模式由于不归GroupCoordinator管理，所以GroupCoordinator无法知道其commitedOffset。
如果无法从GroupCoordinator获取消费过的offset，Consumer就会通过auto.offset.reset配置定义的策略设置一个新的offset作为commitedOffset。auto.offset.reset有三种策略：
- latest:默认策略。获取partition中最新的一条offset。也就是LEO
- earliest：获取partition的最早的一条offset。
- none：配置了该策略的话，如果没有从GroupCoordinator获取到commitedOffset，就会抛异常。
Consumer根据策略的获取offset的方式是向broker发送LIST_OFFSETS请求。broker就会根据策略返回指定partition的offset
四、Consumer 消费数据
知道了消费的parition和对应的commitedOffset后，Consumer就开始消费数据了。Consumer会遍历要消费的partition，找出要往哪些broker发送FETCH请求(根据partition的leader所在的broker)。之后遍历这些broker，逐个发送FETCH请求，leader在同一个broker的partition的FETCH请求会一起发送。
broker 处理 FETCH 请求
broker收到fetch请求后会先过滤掉一些没有权限和不合法的partition。之后调用broker上replicaManager组件的fetchMessages方法。
ReplicaManager组件的介绍可以看我的另一篇博客：
下面介绍一下replicaManager#fetchMessages()方法
def fetchMessages(timeout: Long,
replicaId: Int,
fetchMinBytes: Int,
fetchMaxBytes: Int,
hardMaxBytesLimit: Boolean,
fetchInfos: Seq[(TopicPartition, PartitionData)],
quota: ReplicaQuota = UnboundedQuota,
responseCallback: Seq[(TopicPartition, FetchPartitionData)] => Unit) {
//判断是否来自follower
val isFromFollower = replicaId >= 0
//如果replicaId!=-2，就说明是正常的fetch请求，则只能拉取leader partition的数据
val fetchOnlyFromLeader: Boolean = replicaId != Request.DebuggingConsumerId
//判断是否是从broker发出的fetch请求，不是的话就只能读取HW线以下的数据
val fetchOnlyCommitted: Boolean = ! Request.isValidBrokerId(replicaId)
//从本地文件读取log
val logReadResults = readFromLocalLog(
replicaId = replicaId,
fetchOnlyFromLeader = fetchOnlyFromLeader,
readOnlyCommitted = fetchOnlyCommitted,
fetchMaxBytes = fetchMaxBytes,
hardMaxBytesLimit = hardMaxBytesLimit,
readPartitionInfo = fetchInfos,
quota = quota)
//如果是从其他的broker发来的fetch请求，在读取完数据后还要更新一些数据
if(Request.isValidBrokerId(replicaId))
updateFollowerLogReadResults(replicaId, logReadResults)
val logReadResultValues = logReadResults.map { case (_, v) => v }
//获取拉取到的数据总大小是多少
val bytesReadable = logReadResultValues.map(_.info.records.sizeInBytes).sum
val errorReadingData = logReadResultValues.foldLeft(false) ((errorIncurred, readResult) =>
errorIncurred || (readResult.error != Errors.NONE))
//如果满足下面这些条件，则可以立刻返回数据给请求者
if (timeout <= 0 || fetchInfos.isEmpty || bytesReadable >= fetchMinBytes || errorReadingData) {
val fetchPartitionData = logReadResults.map { case (tp, result) =>
tp -> FetchPartitionData(result.error, result.hw, result.info.records)
}
responseCallback(fetchPartitionData)
} else {
//否则就构建一个延迟操作等待条件满足
val fetchPartitionStatus = logReadResults.map { case (topicPartition, result) =>
val fetchInfo = fetchInfos.collectFirst {
case (tp, v) if tp == topicPartition => v
}.getOrElse(sys.error(s"Partition $topicPartition not found in fetchInfos"))
(topicPartition, FetchPartitionStatus(result.info.fetchOffsetMetadata, fetchInfo))
}
val fetchMetadata = FetchMetadata(fetchMinBytes, fetchMaxBytes, hardMaxBytesLimit, fetchOnlyFromLeader,
fetchOnlyCommitted, isFromFollower, replicaId, fetchPartitionStatus)
val delayedFetch = new DelayedFetch(timeout, fetchMetadata, this, quota, responseCallback)
val delayedFetchKeys = fetchPartitionStatus.map { case (tp, _) => new TopicPartitionOperationKey(tp) }
delayedFetchPurgatory.tryCompleteElseWatch(delayedFetch, delayedFetchKeys)
}
}
- 从本地文件中读取数据
- 如果是其他broker发来的fetch请求，还会更新一些数据，比如partition HW的变动，以及ISR的变动等。
- 根据一些条件判断是否要将读取到的数据立即返回。有一些情况需要等待一定时间后再返回，kafka会将构造一个延迟操作。比如fetch请求配置了fetchMinBytes，但是之前拉取到的数据又没达到指定的最小大小，则就需要等待更多的数据来消费。
readFromLocalLog():
def readFromLocalLog(replicaId: Int,
fetchOnlyFromLeader: Boolean,
readOnlyCommitted: Boolean,
fetchMaxBytes: Int,
hardMaxBytesLimit: Boolean,
readPartitionInfo: Seq[(TopicPartition, PartitionData)],
quota: ReplicaQuota): Seq[(TopicPartition, LogReadResult)] = {
def read(tp: TopicPartition, fetchInfo: PartitionData, limitBytes: Int, minOneMessage: Boolean): LogReadResult = {
... }
//计算limitBytes、minOneMessage，然后逐个遍历topicPartition读取数据
var limitBytes = fetchMaxBytes
val result = new mutable.ArrayBuffer[(TopicPartition, LogReadResult)]
//是否至少要获取一条数据。有时可能因为每一条消息都比较大，都大于fetchMaxBytes，导致数据永远无法被消费的问题,这时候就需要设置minOneMessage为true保证一定可以获取到一条数据
var minOneMessage = !hardMaxBytesLimit
readPartitionInfo.foreach { case (tp, fetchInfo) =>
//根据相关限制条件从partition中读取数据
val readResult = read(tp, fetchInfo, limitBytes, minOneMessage)
//从该partition中读取的数据大小
val messageSetSize = readResult.info.records.sizeInBytes
//
if (messageSetSize > 0)
minOneMessage = false
//计算剩下的limitBytes
limitBytes = math.max(0, limitBytes - messageSetSize)
result += (tp -> readResult)
}
result
}
//readFromLocalLog中定义的read方法
def read(tp: TopicPartition, fetchInfo: PartitionData, limitBytes: Int, minOneMessage: Boolean): LogReadResult = {
val offset = fetchInfo.offset
//每个partition也有自己拉取的maxBytes
val partitionFetchSize = fetchInfo.maxBytes
BrokerTopicStats.getBrokerTopicStats(tp.topic).totalFetchRequestRate.mark()
BrokerTopicStats.getBrokerAllTopicsStats().totalFetchRequestRate.mark()
try {
trace(s"Fetching log segment for partition $tp, offset $offset, partition fetch size $partitionFetchSize, " +
s"remaining response limit $limitBytes" +
(if (minOneMessage) s", ignoring response/partition size limits" else ""))
//是否只能从leader读数据
val localReplica = if (fetchOnlyFromLeader)
getLeaderReplicaIfLocal(tp)
else
getReplicaOrException(tp)
//是否只能读取已提交的offset(也就是HW水位线以下的offset)
val maxOffsetOpt = if (readOnlyCommitted)
Some(localReplica.highWatermark.messageOffset)
else
None
val initialLogEndOffset = localReplica.logEndOffset.messageOffset
val initialHighWatermark = localReplica.highWatermark.messageOffset
val fetchTimeMs = time.milliseconds
val logReadInfo = localReplica.log match {
case Some(log) =>
val adjustedFetchSize = math.min(partitionFetchSize, limitBytes)
val fetch = log.read(offset, adjustedFetchSize, maxOffsetOpt, minOneMessage)
if (shouldLeaderThrottle(quota, tp, replicaId))
FetchDataInfo(fetch.fetchOffsetMetadata, MemoryRecords.EMPTY)
else if (!hardMaxBytesLimit && fetch.firstEntryIncomplete)
FetchDataInfo(fetch.fetchOffsetMetadata, MemoryRecords.EMPTY)
else fetch
case None =>
error(s"Leader for partition $tp does not have a local log")
FetchDataInfo(LogOffsetMetadata.UnknownOffsetMetadata, MemoryRecords.EMPTY)
}
LogReadResult(info = logReadInfo,
hw = initialHighWatermark,
leaderLogEndOffset = initialLogEndOffset,
fetchTimeMs = fetchTimeMs,
readSize = partitionFetchSize,
exception = None)
} catch {
case e@ (_: UnknownTopicOrPartitionException |
_: NotLeaderForPartitionException |
_: ReplicaNotAvailableException |
_: OffsetOutOfRangeException) =>
LogReadResult(info = FetchDataInfo(LogOffsetMetadata.UnknownOffsetMetadata, MemoryRecords.EMPTY),
hw = -1L,
leaderLogEndOffset = -1L,
fetchTimeMs = -1L,
readSize = partitionFetchSize,
exception = Some(e))
case e: Throwable =>
BrokerTopicStats.getBrokerTopicStats(tp.topic).failedFetchRequestRate.mark()
BrokerTopicStats.getBrokerAllTopicsStats().failedFetchRequestRate.mark()
error(s"Error processing fetch operation on partition $tp, offset $offset", e)
LogReadResult(info = FetchDataInfo(LogOffsetMetadata.UnknownOffsetMetadata, MemoryRecords.EMPTY),
hw = -1L,
leaderLogEndOffset = -1L,
fetchTimeMs = -1L,
readSize = partitionFetchSize,
exception = Some(e))
}
}
该方法主要是遍历要消费的partition，然后获取Log对象根据限定条件去读取数据。有两点值得注意：
- 读取数据的时候有一个布尔值的参数minOneMessage，表示本次是否至少要读取一条数据。在0.10.2之前的版本，如果有一条消息的大小超过了Consumer的fetchMaxBytes所设置的值，就会导致这条消息以及后面的消息都无法消费的情况，程序就一直卡在那了。因此，0.10.2之后新增了这个参数，用来保证这种情况下还能顺利的消费数据。
- 从上面的代码可以看出，partition是逐个消费的，有个limitBytes的变量表示当前还能消费多少。如果第一个消费的partition拉取的数据大小就达到了limitBytes，就会导致后面的partition都无法消费的问题。因此每个partition也有自己的消费上限值maxBytes，这个值是由Consumer的配置
max.partition.fetch.bytes决定的，初始值是1M。这个配置最好要远小于fetch.max.bytes比较好，就不会导致排在后面的partition无法消费的问题。(partition的排序顺序是根据TopicPartition对象的hash值决定的)
Log.read()方法:
def read(startOffset: Long, maxLength: Int, maxOffset: Option[Long] = None, minOneMessage: Boolean = false): FetchDataInfo = {
trace("Reading %d bytes from offset %d in log %s of length %d bytes".format(maxLength, startOffset, name, size))
val currentNextOffsetMetadata = nextOffsetMetadata
val next = currentNextOffsetMetadata.messageOffset
if(startOffset == next)
return FetchDataInfo(currentNextOffsetMetadata, MemoryRecords.EMPTY)
//根据startOffset获取对应的segment
var entry = segments.floorEntry(startOffset)
// attempt to read beyond the log end offset is an error
if(startOffset > next || entry == null)
throw new OffsetOutOfRangeException("Request for offset %d but we only have log segments in the range %d to %d.".format(startOffset, segments.firstKey, next))
while(entry != null) {
//获取该segment中最大的那个offset所在的位置
val maxPosition = {
if (entry == segments.lastEntry) {
val exposedPos = nextOffsetMetadata.relativePositionInSegment.toLong
//为了防止在这个过程中active segment变动的情况
if (entry != segments.lastEntry)
entry.getValue.size
else
exposedPos
} else {
entry.getValue.size
}
}
// 通过segment的方法读取数据
val fetchInfo = entry.getValue.read(startOffset, maxOffset, maxLength, maxPosition, minOneMessage)
if(fetchInfo == null) {
entry = segments.higherEntry(entry.getKey)
} else {
return fetchInfo
}
}
FetchDataInfo(nextOffsetMetadata, MemoryRecords.EMPTY)
}
//LogSegment.scala
def read(startOffset: Long, maxOffset: Option[Long], maxSize: Int, maxPosition: Long = size,
minOneMessage: Boolean = false): FetchDataInfo = {
if (maxSize < 0)
throw new IllegalArgumentException("Invalid max size for log read (%d)".format(maxSize))
val logSize = log.sizeInBytes // this may change, need to save a consistent copy
val startOffsetAndSize = translateOffset(startOffset)
// if the start position is already off the end of the log, return null
if (startOffsetAndSize == null)
return null
val startPosition = startOffsetAndSize.position.toInt
val offsetMetadata = new LogOffsetMetadata(startOffset, this.baseOffset, startPosition)
val adjustedMaxSize =
if (minOneMessage) math.max(maxSize, startOffsetAndSize.size)
else maxSize
// return a log segment but with zero size in the case below
if (adjustedMaxSize == 0)
return FetchDataInfo(offsetMetadata, MemoryRecords.EMPTY)
// calculate the length of the message set to read based on whether or not they gave us a maxOffset
val length = maxOffset match {
case None =>
min((maxPosition - startPosition).toInt, adjustedMaxSize)
case Some(offset) =>
if (offset < startOffset)
return FetchDataInfo(offsetMetadata, MemoryRecords.EMPTY, firstEntryIncomplete = false)
val mapping = translateOffset(offset, startPosition)
val endPosition =
if (mapping == null)
logSize // the max offset is off the end of the log, use the end of the file
else
mapping.position
min(min(maxPosition, endPosition) - startPosition, adjustedMaxSize).toInt
}
FetchDataInfo(offsetMetadata, log.read(startPosition, length),
firstEntryIncomplete = adjustedMaxSize < startOffsetAndSize.size)
}
-
Log.read()方法会先获取到startOffset对应的那个segment，同时计算出该segment可以消费的最大的那个位置。这个过程并没有使用任何同步原语，因此如果刚好找到的segment是active segment的话，可能导致在计算时active segment刚好出现变动的情况，因此上面的代码中出现了
if (entry == segments.lastEntry)的判断。用这种巧妙的方法替代同步原语，也能提升一定的性能。 -
在LogSegment.read()方法中，主要就是计算出要读的起始位置以及结束的位置，然后执行FileRecords.read()读取信息。FileRecords.read()其实就只是对LogSegment的FileRecords根据startPosition和endPosition做了一个切片。之后FETCH请求在真正返回的时候只会读取startPosition到endPosition之间的数据。
kafka在读取数据的时候，底层其实使用了javaNio的
FileChannel#transferTo()方法。正常情况下，我们读数据都是将数据从输入流读取到内存，然后通过输出流传输过去。这种方式直接在日志文件输入流和socket输出流之间打通了一条通道，性能会提升很多。
延迟fetch操作的相关代码：
//DelayedFetch.scala
/**
* The operation can be completed if:
*
* Case A: This broker is no longer the leader for some partitions it tries to fetch
* Case B: This broker does not know of some partitions it tries to fetch
* Case C: The fetch offset locates not on the last segment of the log
* Case D: The accumulated bytes from all the fetching partitions exceeds the minimum bytes
*
* Upon completion, should return whatever data is available for each valid partition
*/
override def tryComplete() : Boolean = {
var accumulatedSize = 0
var accumulatedThrottledSize = 0
fetchMetadata.fetchPartitionStatus.foreach {
case (topicPartition, fetchStatus) =>
val fetchOffset = fetchStatus.startOffsetMetadata
try {
if (fetchOffset != LogOffsetMetadata.UnknownOffsetMetadata) {
val replica = replicaManager.getLeaderReplicaIfLocal(topicPartition)
val endOffset =
if (fetchMetadata.fetchOnlyCommitted)
replica.highWatermark
else
replica.logEndOffset
// Go directly to the check for Case D if the message offsets are the same. If the log segment
// has just rolled, then the high watermark offset will remain the same but be on the old segment,
// which would incorrectly be seen as an instance of Case C.
if (endOffset.messageOffset != fetchOffset.messageOffset) {
if (endOffset.onOlderSegment(fetchOffset)) {
// Case C, this can happen when the new fetch operation is on a truncated leader
debug("Satisfying fetch %s since it is fetching later segments of partition %s.".format(fetchMetadata, topicPartition))
return forceComplete()
} else if (fetchOffset.onOlderSegment(endOffset)) {
// Case C, this can happen when the fetch operation is falling behind the current segment
// or the partition has just rolled a new segment
debug("Satisfying fetch %s immediately since it is fetching older segments.".format(fetchMetadata))
// We will not force complete the fetch request if a replica should be throttled.
if (!replicaManager.shouldLeaderThrottle(quota, topicPartition, fetchMetadata.replicaId))
return forceComplete()
} else if (fetchOffset.messageOffset < endOffset.messageOffset) {
// we take the partition fetch size as upper bound when accumulating the bytes (skip if a throttled partition)
val bytesAvailable = math.min(endOffset.positionDiff(fetchOffset), fetchStatus.fetchInfo.maxBytes)
if (quota.isThrottled(topicPartition))
accumulatedThrottledSize += bytesAvailable
else
accumulatedSize += bytesAvailable
}
}
}
} catch {
case _: UnknownTopicOrPartitionException => // Case B
debug("Broker no longer know of %s, satisfy %s immediately".format(topicPartition, fetchMetadata))
return forceComplete()
case _: NotLeaderForPartitionException => // Case A
debug("Broker is no longer the leader of %s, satisfy %s immediately".format(topicPartition, fetchMetadata))
return forceComplete()
}
}
// Case D
if (accumulatedSize >= fetchMetadata.fetchMinBytes
|| ((accumulatedSize + accumulatedThrottledSize) >= fetchMetadata.fetchMinBytes && !quota.isQuotaExceeded()))
forceComplete()
else
false
}
//在tryComplete中执行forceComplete()最终会触发onComplete()方法的执行
override def onComplete() {
val logReadResults = replicaManager.readFromLocalLog(
replicaId = fetchMetadata.replicaId,
fetchOnlyFromLeader = fetchMetadata.fetchOnlyLeader,
readOnlyCommitted = fetchMetadata.fetchOnlyCommitted,
fetchMaxBytes = fetchMetadata.fetchMaxBytes,
hardMaxBytesLimit = fetchMetadata.hardMaxBytesLimit,
readPartitionInfo = fetchMetadata.fetchPartitionStatus.map { case (tp, status) => tp -> status.fetchInfo },
quota = quota
)
val fetchPartitionData = logReadResults.map { case (tp, result) =>
tp -> FetchPartitionData(result.error, result.hw, result.info.records)
}
responseCallback(fetchPartitionData)
}
在前面我们有讲到，如果拉取到的数据还没达到了minFetchBytes，kafka会建立一个延迟操作，以尽量保证可以拉取到更多的数据。对于这个延迟操作，有下面4种情况可能提前让该操作完成：
- 要拉取的partition的leader不在本broker上了
- broker获取不到要拉取的partiton的相关信息
- 要拉取的offset不属于最新的那个segment了
- 可以拉取的数据已经达到minFetchBytes了
关于kafka延迟操作的相关介绍可以看我的另外一篇博客：
五、offset提交
消费完数据之后，offset的提交方式非常重要。offset提交早了可能导致消息丢失，offset提交晚了可能导致消息重复消费。在Consumer类中，有5个方法可以提交offset。
//同步提交已经拉取回来的partition的offset
public void commitSync();
//同步提交指定topic-partition的指定offset
public void commitSync(Map<TopicPartition, OffsetAndMetadata> offsets);
//异步提交已经拉取回来的partition的offset
public void commitAsync();
//异步提交已经拉取回来的partition的offset，带上一个回调函数
public void commitAsync(OffsetCommitCallback callback);
//异步提交指定topic-partition的指定offset，带上一个回调函数
public void commitAsync(Map<TopicPartition, OffsetAndMetadata> offsets, OffsetCommitCallback callback);
这里总体分为两种形式，同步提交和异步提交。但是如果保证消息至少消费一次、至多消费一次、刚好只消费一次只能由用户自己来控制。后面我会再写一篇博客专门介绍这个。
同步和异步提交
同步提交和异步提交其实底层都是向broker发送OFFSET_COMMIT请求。不同的是同步的会等待请求返回，异步的不会等待请求返回。
同步提交commit代码：
public boolean commitOffsetsSync(Map<TopicPartition, OffsetAndMetadata> offsets, long timeoutMs) {
invokeCompletedOffsetCommitCallbacks();
if (offsets.isEmpty())
return true;
long now = time.milliseconds();
long startMs = now;
long remainingMs = timeoutMs;
do {
if (coordinatorUnknown()) {
if (!ensureCoordinatorReady(now, remainingMs))
return false;
remainingMs = timeoutMs - (time.milliseconds() - startMs);
}
//发送OFFSET_COMMIT请求
RequestFuture<Void> future = sendOffsetCommitRequest(offsets);
client.poll(future, remainingMs);
if (future.succeeded()) {
if (interceptors != null)
interceptors.onCommit(offsets);
return true;
}
if (!future.isRetriable())
throw future.exception();
time.sleep(retryBackoffMs);
now = time.milliseconds();
remainingMs = timeoutMs - (now - startMs);
} while (remainingMs > 0);
return false;
}
在同步提交的方法中，发送完OFFSET_COMMIT请求后会等待请求返回了方法才返回。
异步提交commit代码：
private void doCommitOffsetsAsync(final Map<TopicPartition, OffsetAndMetadata> offsets, final OffsetCommitCallback callback) {
this.subscriptions.needRefreshCommits();
RequestFuture<Void> future = sendOffsetCommitRequest(offsets);
final OffsetCommitCallback cb = callback == null ? defaultOffsetCommitCallback : callback;
future.addListener(new RequestFutureListener<Void>() {
@Override
public void onSuccess(Void value) {
if (interceptors != null)
interceptors.onCommit(offsets);
completedOffsetCommits.add(new OffsetCommitCompletion(cb, offsets, null));
}
@Override
public void onFailure(RuntimeException e) {
Exception commitException = e;
if (e instanceof RetriableException)
commitException = new RetriableCommitFailedException(e);
completedOffsetCommits.add(new OffsetCommitCompletion(cb, offsets, commitException));
}
});
}
异步请求只是发送一个OFFSET_COMMIT请求，并不会等待请求返回。
自动提交offset
如果partition是由GroupCoordinator分配并管理的，并且Consumer配置enable.auto.commit为true，则在每次poll拉取数据之前，kafka都会自动异步提交已经拉取过的offset。该配置默认是开启的，自动提交的频率和auto.commit.interval.ms有关，该配置的默认值是5000。
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