上一篇文章讲了LogSegment和Log的初始化，这篇来讲讲Log的主要操作有哪些。
一般来说Log 的常见操作分为 4 大部分。
- 高水位管理操作
- 日志段管理
- 关键位移值管理
- 读写操作
其中关键位移值管理主要包含Log Start Offset 和 LEO等。
高水位HighWatermark
高水位HighWatermark初始化
高水位是通过LogOffsetMetadata类来定义的：
@volatile private var highWatermarkMetadata: LogOffsetMetadata = LogOffsetMetadata(logStartOffset)
这里传入的初始值是logStartOffset，表明当首次构建高水位时，它会被赋值成 Log Start Offset 值。
我们再来看看LogOffsetMetadata类：
case class LogOffsetMetadata(messageOffset: Long,
segmentBaseOffset: Long = Log.UnknownOffset,
relativePositionInSegment: Int = LogOffsetMetadata.UnknownFilePosition) {
// check if this offset is already on an older segment compared with the given offset
def onOlderSegment(that: LogOffsetMetadata): Boolean = {
if (messageOffsetOnly)
throw new KafkaException(s"$this cannot compare its segment info with $that since it only has message offset info")
this.segmentBaseOffset < that.segmentBaseOffset
}
...
}
LogOffsetMetadata有三个初始值：
messageOffset表示消息位移值；
segmentBaseOffset保存消息位移值所在日志段的起始位移，用来判断两条消息是否处于同一个日志段的；
relativePositionSegment保存消息位移值所在日志段的物理磁盘位置；
上面的onOlderSegment表明，要比较哪个日志段更老，只需要比较segmentBaseOffset的大小就可以了。
高水位HighWatermark设值与更新
private def updateHighWatermarkMetadata(newHighWatermark: LogOffsetMetadata): Unit = {
//高水位的值不可能小于零
if (newHighWatermark.messageOffset < 0)
throw new IllegalArgumentException("High watermark offset should be non-negative")
lock synchronized {// 保护Log对象修改的Monitor锁
highWatermarkMetadata = newHighWatermark// 赋值新的高水位值
//事务相关，暂时忽略
producerStateManager.onHighWatermarkUpdated(newHighWatermark.messageOffset)
//事务相关，暂时忽略
maybeIncrementFirstUnstableOffset()
}
trace(s"Setting high watermark $newHighWatermark")
}
设置高水位的值是很简单的，首先校验高水位的值是否大于零，然后通过直接加锁之后更新高水位的值。
更新更新高水位值的方法有两个：updateHighWatermark 和 maybeIncrementHighWatermark，我们分别分析。
updateHighWatermark
def updateHighWatermark(hw: Long): Long = {
//传入的高水位的值如果小于logStartOffset，设置为logStartOffset
val newHighWatermark = if (hw < logStartOffset)
logStartOffset
// 传入的高水位的值如果大于LEO，那么设置为LEO
else if (hw > logEndOffset)
logEndOffset
else
hw
//将newHighWatermark封装成一个LogOffsetMetadata然后更新高水位的值
updateHighWatermarkMetadata(LogOffsetMetadata(newHighWatermark))
//返回新的高水位的值
newHighWatermark
}
这个方法逻辑也很简洁，因为高水位的值是不可能大于LEO，也不可能小于logStartOffset，所以需要对传入的hw校验然后设置成正确的值，然后调用上面的设置高水位的方法设值。
maybeIncrementHighWatermark
/**
* Update the high watermark to a new value if and only if it is larger than the old value. It is
* an error to update to a value which is larger than the log end offset.
*
* This method is intended to be used by the leader to update the high watermark after follower
* fetch offsets have been updated.
*
* @return the old high watermark, if updated by the new value
*/
// 当新的高水位的值大于旧的高水位的值时才做更新，如果新的高水位的值大于LEO，会报错
// 这个方法是leader在确认Follower已经拉取了日志之后才做更新
def maybeIncrementHighWatermark(newHighWatermark: LogOffsetMetadata): Option[LogOffsetMetadata] = {
//如果新的高水位的值大于LEO，会报错
if (newHighWatermark.messageOffset > logEndOffset)
throw new IllegalArgumentException(s"High watermark $newHighWatermark update exceeds current " +
s"log end offset $logEndOffsetMetadata")
lock.synchronized {
// 获取老的高水位值
val oldHighWatermark = fetchHighWatermarkMetadata
// Ensure that the high watermark increases monotonically. We also update the high watermark when the new
// offset metadata is on a newer segment, which occurs whenever the log is rolled to a new segment.
//只有当新的高水位值大于老的值，因为要维护高水位的单调递增性
//或者当新的高水位值和老的高水位值相等，但是新的高水位在一个新的日志段上面时才做更新
if (oldHighWatermark.messageOffset < newHighWatermark.messageOffset ||
(oldHighWatermark.messageOffset == newHighWatermark.messageOffset && oldHighWatermark.onOlderSegment(newHighWatermark))) {
updateHighWatermarkMetadata(newHighWatermark)
Some(oldHighWatermark)// 返回老的高水位值
} else {
None
}
}
}
这个方法我将这个方法的英文注释贴出来了，这个注释的说明我也写到方法上了，逻辑很清楚，大家看看注释应该能理解。
这两个方法主要的区别是，updateHighWatermark 方法，主要用在 Follower 副本从 Leader 副本获取到消息后更新高水位值。而 maybeIncrementHighWatermark 方法，主要是用来更新 Leader 副本的高水位值。
上面的方法中通过调用fetchHighWatermarkMetadata来获取高水位的值，我们下面看看这个方法：
fetchHighWatermarkMetadata
private def fetchHighWatermarkMetadata: LogOffsetMetadata = {
// 读取时确保日志不能被关闭
checkIfMemoryMappedBufferClosed()
val offsetMetadata = highWatermarkMetadata
if (offsetMetadata.messageOffsetOnly) {//没有获得到完整的高水位元数据
lock.synchronized {
// 通过读日志文件的方式把完整的高水位元数据信息拉出来
val fullOffset = convertToOffsetMetadataOrThrow(highWatermark)
updateHighWatermarkMetadata(fullOffset)
fullOffset
}
} else {
offsetMetadata
}
}
private def convertToOffsetMetadataOrThrow(offset: Long): LogOffsetMetadata = {
//通过给的offset，去日志文件中找到相应的日志信息
val fetchDataInfo = read(offset,
maxLength = 1,
isolation = FetchLogEnd,
minOneMessage = false)
fetchDataInfo.fetchOffsetMetadata
}
然后我们提前看一下日志的read方法，是如何根据索引读取数据的：
日志段操作
日志读取操作
read
def read(startOffset: Long,
maxLength: Int,
isolation: FetchIsolation,
minOneMessage: Boolean): FetchDataInfo = {
maybeHandleIOException(s"Exception while reading from $topicPartition in dir ${dir.getParent}") {
trace(s"Reading $maxLength bytes from offset $startOffset of length $size bytes")
//convertToOffsetMetadataOrThrow传进来是FetchLogEnd，所以这里是false
val includeAbortedTxns = isolation == FetchTxnCommitted
// 由于没有使用锁，所以使用变量缓存当前的nextOffsetMetadata状态
val endOffsetMetadata = nextOffsetMetadata
val endOffset = endOffsetMetadata.messageOffset
// 到日字段中根据索引寻找最近的日志段
var segmentEntry = segments.floorEntry(startOffset)
// return error on attempt to read beyond the log end offset or read below log start offset
// 这里给出了几种异常场景：
// 1. 给的日志索引大于最大值；
// 2. 通过索引找的日志段为空；
// 3. 给的日志索引小于logStartOffset
if (startOffset > endOffset || segmentEntry == null || startOffset < logStartOffset)
throw new OffsetOutOfRangeException(s"Received request for offset $startOffset for partition $topicPartition, " +
s"but we only have log segments in the range $logStartOffset to $endOffset.")
//convertToOffsetMetadataOrThrow传进来是FetchLogEnd，所以最大值是endOffsetMetadata
// 查看一下读取隔离级别设置。
// 普通消费者能够看到[Log Start Offset, LEO)之间的消息
// 事务型消费者只能看到[Log Start Offset, Log Stable Offset]之间的消息。Log Stable Offset(LSO)是比LEO值小的位移值，为Kafka事务使用
// Follower副本消费者能够看到[Log Start Offset，高水位值]之间的消息
val maxOffsetMetadata = isolation match {
case FetchLogEnd => endOffsetMetadata
case FetchHighWatermark => fetchHighWatermarkMetadata
case FetchTxnCommitted => fetchLastStableOffsetMetadata
}
//如果寻找的索引等于maxOffsetMetadata，那么直接返回
if (startOffset == maxOffsetMetadata.messageOffset) {
return emptyFetchDataInfo(maxOffsetMetadata, includeAbortedTxns)
//如果寻找的索引大于maxOffsetMetadata，返回空的消息集合，因为没法读取任何消息
} else if (startOffset > maxOffsetMetadata.messageOffset) {
val startOffsetMetadata = convertToOffsetMetadataOrThrow(startOffset)
return emptyFetchDataInfo(startOffsetMetadata, includeAbortedTxns)
}
// 开始遍历日志段对象，直到读出东西来或者读到日志末尾
while (segmentEntry != null) {
val segment = segmentEntry.getValue
// 找到日志段中最大的日志位移
val maxPosition = {
if (maxOffsetMetadata.segmentBaseOffset == segment.baseOffset) {
maxOffsetMetadata.relativePositionInSegment
} else {
segment.size
}
}
// 根据位移信息从日志段中读取日志信息
val fetchInfo = segment.read(startOffset, maxLength, maxPosition, minOneMessage)
// 如果找不到日志信息，那么去日志段集合中找更大的日志位移的日志段
if (fetchInfo == null) {
segmentEntry = segments.higherEntry(segmentEntry.getKey)
} else {
return if (includeAbortedTxns)
addAbortedTransactions(startOffset, segmentEntry, fetchInfo)
else
fetchInfo
}
}
//找了所有日志段的位移依然找不到，这可能是因为大于指定的日志位移的消息都被删除了，这种情况返回空
FetchDataInfo(nextOffsetMetadata, MemoryRecords.EMPTY)
}
}
read方法，有四个参数，分别是：
- startOffset：读取的日志索引位置。
- maxLength：读取数据量长度。
- isolation：隔离级别，多用于 Kafka 事务。
- minOneMessage：是否至少返回一条消息。设想如果消息很大，超过了 maxLength，正常情况下 read 方法永远不会返回任何消息。但如果设置了该参数为 true，read 方法就保证至少能够返回一条消息。
代码中使用了segments，来根据位移查找日志段：
private val segments: ConcurrentNavigableMap[java.lang.Long, LogSegment] = new ConcurrentSkipListMap[java.lang.Long, LogSegment]
我们下面看看read方法具体做了哪些事：
- 由于没有使用锁，所以使用变量缓存当前的nextOffsetMetadata状态，作为最大索引LEO；
- 去日志段集合里寻找小于或等于指定索引的日志段；
- 校验异常情况：
- startOffset是不是超过了LEO；
- 是不是日志段集合里没有索引小于startOffset；
- startOffset小于Log Start Offset；
- 接下来获取一下隔离级别；
- 如果寻找的索引等于LEO，那么返回空；
- 如果寻找的索引大于LEO，返回空的消息集合，因为没法读取任何消息；
- 开始遍历日志段对象，直到读出东西来或者读到日志末尾；
- 首先找到日志段中最大的位置；
- 根据位移信息从日志段中读取日志信息（这个read方法我们上一篇已经讲解过了）；
- 如果找不到日志信息，那么读取日志段集合中下一个日志段；
- 找了所有日志段的位移依然找不到，这可能是因为大于指定的日志位移的消息都被删除了，这种情况返回空；
我们在上面的read操作中可以看到，使用了segments来查找日志。我们主要看看删除操作
删除日志
删除日志的入口是：deleteOldSegments
// 如果topic deletion开关是打开的，那么会删去过期的日志段以及超过设置保留日志大小的日志
// 无论是否开启删除规则，都会删除在log start offset之前的日志段
def deleteOldSegments(): Int = {
if (config.delete) {
deleteRetentionMsBreachedSegments() + deleteRetentionSizeBreachedSegments() + deleteLogStartOffsetBreachedSegments()
} else {
deleteLogStartOffsetBreachedSegments()
}
}
deleteOldSegments方法会判断是否开启删除规则，如果开启，那么会分别调用：
deleteRetentionMsBreachedSegments删除segment的时间戳超过了设置时间的日志段；
deleteRetentionSizeBreachedSegments删除日志段空间超过设置空间大小的日志段；
deleteLogStartOffsetBreachedSegments删除日志段的baseOffset小于logStartOffset的日志段；
我这里列举一下这三个方法主要是怎么实现的：
private def deleteRetentionMsBreachedSegments(): Int = {
if (config.retentionMs < 0) return 0
val startMs = time.milliseconds
//调用deleteOldSegments方法，并传入匿名函数，判断当前的segment的时间戳是否超过了设置时间
deleteOldSegments((segment, _) => startMs - segment.largestTimestamp > config.retentionMs,
reason = s"retention time ${config.retentionMs}ms breach")
}
private def deleteRetentionSizeBreachedSegments(): Int = {
if (config.retentionSize < 0 || size < config.retentionSize) return 0
var diff = size - config.retentionSize
//判断日志段空间是否超过设置空间大小
//shouldDelete函数会将传入的日志段去减diff，直到小于等于零
def shouldDelete(segment: LogSegment, nextSegmentOpt: Option[LogSegment]) = {
if (diff - segment.size >= 0) {
diff -= segment.size
true
} else {
false
}
}
deleteOldSegments(shouldDelete, reason = s"retention size in bytes ${config.retentionSize} breach")
}
private def deleteLogStartOffsetBreachedSegments(): Int = {
//shouldDelete函数主要判断日志段的baseOffset是否小于logStartOffset
def shouldDelete(segment: LogSegment, nextSegmentOpt: Option[LogSegment]) =
nextSegmentOpt.exists(_.baseOffset <= logStartOffset)
deleteOldSegments(shouldDelete, reason = s"log start offset $logStartOffset breach")
}
这种写代码的方式非常的灵活，通过不同方法设置不同的函数来实现代码复用的目的，最后都是通过调用deleteOldSegments来实现删除日志段的目的。
下面我们来看一下deleteOldSegments的操作：
deleteOldSegments
这个deleteOldSegments方法和上面的入口方法传入的参数是不一致的，这个方法传入了一个predicate函数，用于判断哪些日志段是可以被删除的，reason用来说明被删除的原因。
private def deleteOldSegments(predicate: (LogSegment, Option[LogSegment]) => Boolean, reason: String): Int = {
//删除任何匹配到predicate规则的日志段
lock synchronized {
val deletable = deletableSegments(predicate)
if (deletable.nonEmpty)
info(s"Found deletable segments with base offsets [${deletable.map(_.baseOffset).mkString(",")}] due to $reason")
deleteSegments(deletable)
}
}
这个方法调用了两个主要的方法，一个是deletableSegments，用于获取可以被删除的日志段的集合；deleteSegments用于删除日志段。
deletableSegments
private def deletableSegments(predicate: (LogSegment, Option[LogSegment]) => Boolean): Iterable[LogSegment] = {
//如果日志段是空的，那么直接返回
if (segments.isEmpty) {
Seq.empty
} else {
val deletable = ArrayBuffer.empty[LogSegment]
var segmentEntry = segments.firstEntry
//如果日志段集合不为空，找到第一个日志段
while (segmentEntry != null) {
val segment = segmentEntry.getValue
//获取下一个日志段
val nextSegmentEntry = segments.higherEntry(segmentEntry.getKey)
val (nextSegment, upperBoundOffset, isLastSegmentAndEmpty) = if (nextSegmentEntry != null)
(nextSegmentEntry.getValue, nextSegmentEntry.getValue.baseOffset, false)
else
(null, logEndOffset, segment.size == 0)
//如果下一个日志段的位移没有大于或等于HW，并且日志段是匹配predicate函数的，下一个日志段也不是空的
//那么将这个日志段放入可删除集合中，然后遍历下一个日志段
if (highWatermark >= upperBoundOffset && predicate(segment, Option(nextSegment)) && !isLastSegmentAndEmpty) {
deletable += segment
segmentEntry = nextSegmentEntry
} else {
segmentEntry = null
}
}
deletable
}
}
这个方法逻辑十分清晰，主要做了如下几件事：
-
判断日志段集合是否为空，为空那么直接返回空集合；
-
如果日志段集合不为空，那么从日志段集合的第一个日志段开始遍历；
-
判断当前被遍历日志段是否能够被删除
- 日志段的下一个日志段的位移有没有大于或等于HW；
- 日志段是否能够通过predicate函数校验；
- 日志段是否是最后一个日志段；
-
将符合条件的日志段都加入到deletable集合中，并返回。
接下来调用deleteSegments函数：
private def deleteSegments(deletable: Iterable[LogSegment]): Int = {
maybeHandleIOException(s"Error while deleting segments for $topicPartition in dir ${dir.getParent}") {
val numToDelete = deletable.size
if (numToDelete > 0) {
// we must always have at least one segment, so if we are going to delete all the segments, create a new one first
// 我们至少保证要存在一个日志段，如果要删除所有的日志；
//所以调用roll方法创建一个全新的日志段对象，并且关闭当前写入的日志段对象；
if (segments.size == numToDelete)
roll()
lock synchronized {
// 确保Log对象没有被关闭
checkIfMemoryMappedBufferClosed()
// remove the segments for lookups
// 删除给定的日志段对象以及底层的物理文件
removeAndDeleteSegments(deletable, asyncDelete = true)
// 尝试更新日志的Log Start Offset值
maybeIncrementLogStartOffset(segments.firstEntry.getValue.baseOffset)
}
}
numToDelete
}
}
写日志
写日志的方法主要有两个：
appendAsLeader
def appendAsLeader(records: MemoryRecords, leaderEpoch: Int, isFromClient: Boolean = true,
interBrokerProtocolVersion: ApiVersion = ApiVersion.latestVersion): LogAppendInfo = {
append(records, isFromClient, interBrokerProtocolVersion, assignOffsets = true, leaderEpoch)
}
appendAsFollower
def appendAsFollower(records: MemoryRecords): LogAppendInfo = {
append(records, isFromClient = false, interBrokerProtocolVersion = ApiVersion.latestVersion, assignOffsets = false, leaderEpoch = -1)
}
appendAsLeader 是用于写 Leader 副本的，appendAsFollower 是用于 Follower 副本同步的。它们的底层都调用了 append 方法
append
private def append(records: MemoryRecords, isFromClient: Boolean, interBrokerProtocolVersion: ApiVersion, assignOffsets: Boolean, leaderEpoch: Int): LogAppendInfo = {
maybeHandleIOException(s"Error while appending records to $topicPartition in dir ${dir.getParent}") {
// 第1步：分析和验证待写入消息集合，并返回校验结果
val appendInfo = analyzeAndValidateRecords(records, isFromClient = isFromClient)
// return if we have no valid messages or if this is a duplicate of the last appended entry
// 如果压根就不需要写入任何消息，直接返回即可
if (appendInfo.shallowCount == 0)
return appendInfo
// trim any invalid bytes or partial messages before appending it to the on-disk log
// 第2步：消息格式规整，即删除无效格式消息或无效字节
var validRecords = trimInvalidBytes(records, appendInfo)
// they are valid, insert them in the log
lock synchronized {
// 确保Log对象未关闭
checkIfMemoryMappedBufferClosed()
//需要分配位移值
if (assignOffsets) {
// assign offsets to the message set
// 第3步：使用当前LEO值作为待写入消息集合中第一条消息的位移值，nextOffsetMetadata为LEO值
val offset = new LongRef(nextOffsetMetadata.messageOffset)
appendInfo.firstOffset = Some(offset.value)
val now = time.milliseconds
val validateAndOffsetAssignResult = try {
LogValidator.validateMessagesAndAssignOffsets(validRecords,
topicPartition,
offset,
time,
now,
appendInfo.sourceCodec,
appendInfo.targetCodec,
config.compact,
config.messageFormatVersion.recordVersion.value,
config.messageTimestampType,
config.messageTimestampDifferenceMaxMs,
leaderEpoch,
isFromClient,
interBrokerProtocolVersion,
brokerTopicStats)
} catch {
case e: IOException =>
throw new KafkaException(s"Error validating messages while appending to log $name", e)
}
// 更新校验结果对象类LogAppendInfo
validRecords = validateAndOffsetAssignResult.validatedRecords
appendInfo.maxTimestamp = validateAndOffsetAssignResult.maxTimestamp
appendInfo.offsetOfMaxTimestamp = validateAndOffsetAssignResult.shallowOffsetOfMaxTimestamp
appendInfo.lastOffset = offset.value - 1
appendInfo.recordConversionStats = validateAndOffsetAssignResult.recordConversionStats
if (config.messageTimestampType == TimestampType.LOG_APPEND_TIME)
appendInfo.logAppendTime = now
// re-validate message sizes if there's a possibility that they have changed (due to re-compression or message
// format conversion)
// 第4步：验证消息，确保消息大小不超限
if (validateAndOffsetAssignResult.messageSizeMaybeChanged) {
for (batch <- validRecords.batches.asScala) {
if (batch.sizeInBytes > config.maxMessageSize) {
// we record the original message set size instead of the trimmed size
// to be consistent with pre-compression bytesRejectedRate recording
brokerTopicStats.topicStats(topicPartition.topic).bytesRejectedRate.mark(records.sizeInBytes)
brokerTopicStats.allTopicsStats.bytesRejectedRate.mark(records.sizeInBytes)
throw new RecordTooLargeException(s"Message batch size is ${batch.sizeInBytes} bytes in append to" +
s"partition $topicPartition which exceeds the maximum configured size of ${config.maxMessageSize}.")
}
}
}
// 直接使用给定的位移值，无需自己分配位移值
} else {
// we are taking the offsets we are given
if (!appendInfo.offsetsMonotonic)// 确保消息位移值的单调递增性
throw new OffsetsOutOfOrderException(s"Out of order offsets found in append to $topicPartition: " +
records.records.asScala.map(_.offset))
if (appendInfo.firstOrLastOffsetOfFirstBatch < nextOffsetMetadata.messageOffset) {
// we may still be able to recover if the log is empty
// one example: fetching from log start offset on the leader which is not batch aligned,
// which may happen as a result of AdminClient#deleteRecords()
val firstOffset = appendInfo.firstOffset match {
case Some(offset) => offset
case None => records.batches.asScala.head.baseOffset()
}
val firstOrLast = if (appendInfo.firstOffset.isDefined) "First offset" else "Last offset of the first batch"
throw new UnexpectedAppendOffsetException(
s"Unexpected offset in append to $topicPartition. $firstOrLast " +
s"${appendInfo.firstOrLastOffsetOfFirstBatch} is less than the next offset ${nextOffsetMetadata.messageOffset}. " +
s"First 10 offsets in append: ${records.records.asScala.take(10).map(_.offset)}, last offset in" +
s" append: ${appendInfo.lastOffset}. Log start offset = $logStartOffset",
firstOffset, appendInfo.lastOffset)
}
}
// update the epoch cache with the epoch stamped onto the message by the leader
// 第5步：更新Leader Epoch缓存
validRecords.batches.asScala.foreach { batch =>
if (batch.magic >= RecordBatch.MAGIC_VALUE_V2) {
maybeAssignEpochStartOffset(batch.partitionLeaderEpoch, batch.baseOffset)
} else {
// In partial upgrade scenarios, we may get a temporary regression to the message format. In
// order to ensure the safety of leader election, we clear the epoch cache so that we revert
// to truncation by high watermark after the next leader election.
leaderEpochCache.filter(_.nonEmpty).foreach { cache =>
warn(s"Clearing leader epoch cache after unexpected append with message format v${batch.magic}")
cache.clearAndFlush()
}
}
}
// check messages set size may be exceed config.segmentSize
// 第6步：确保消息大小不超限
if (validRecords.sizeInBytes > config.segmentSize) {
throw new RecordBatchTooLargeException(s"Message batch size is ${validRecords.sizeInBytes} bytes in append " +
s"to partition $topicPartition, which exceeds the maximum configured segment size of ${config.segmentSize}.")
}
// maybe roll the log if this segment is full
// 第7步：执行日志切分。当前日志段剩余容量可能无法容纳新消息集合，因此有必要创建一个新的日志段来保存待写入的所有消息
//下面情况将会执行日志切分：
//logSegment 已经满了
//日志段中的第一个消息的maxTime已经过期
//index索引满了
val segment = maybeRoll(validRecords.sizeInBytes, appendInfo)
val logOffsetMetadata = LogOffsetMetadata(
messageOffset = appendInfo.firstOrLastOffsetOfFirstBatch,
segmentBaseOffset = segment.baseOffset,
relativePositionInSegment = segment.size)
// now that we have valid records, offsets assigned, and timestamps updated, we need to
// validate the idempotent/transactional state of the producers and collect some metadata
// 第8步：验证事务状态
val (updatedProducers, completedTxns, maybeDuplicate) = analyzeAndValidateProducerState(
logOffsetMetadata, validRecords, isFromClient)
maybeDuplicate.foreach { duplicate =>
appendInfo.firstOffset = Some(duplicate.firstOffset)
appendInfo.lastOffset = duplicate.lastOffset
appendInfo.logAppendTime = duplicate.timestamp
appendInfo.logStartOffset = logStartOffset
return appendInfo
}
// 第9步：执行真正的消息写入操作，主要调用日志段对象的append方法实现
segment.append(largestOffset = appendInfo.lastOffset,
largestTimestamp = appendInfo.maxTimestamp,
shallowOffsetOfMaxTimestamp = appendInfo.offsetOfMaxTimestamp,
records = validRecords)
// Increment the log end offset. We do this immediately after the append because a
// write to the transaction index below may fail and we want to ensure that the offsets
// of future appends still grow monotonically. The resulting transaction index inconsistency
// will be cleaned up after the log directory is recovered. Note that the end offset of the
// ProducerStateManager will not be updated and the last stable offset will not advance
// if the append to the transaction index fails.
// 第10步：更新LEO对象，其中，LEO值是消息集合中最后一条消息位移值+1
// 前面说过，LEO值永远指向下一条不存在的消息
updateLogEndOffset(appendInfo.lastOffset + 1)
// update the producer state
// 第11步：更新事务状态
for (producerAppendInfo <- updatedProducers.values) {
producerStateManager.update(producerAppendInfo)
}
// update the transaction index with the true last stable offset. The last offset visible
// to consumers using READ_COMMITTED will be limited by this value and the high watermark.
for (completedTxn <- completedTxns) {
val lastStableOffset = producerStateManager.lastStableOffset(completedTxn)
segment.updateTxnIndex(completedTxn, lastStableOffset)
producerStateManager.completeTxn(completedTxn)
}
// always update the last producer id map offset so that the snapshot reflects the current offset
// even if there isn't any idempotent data being written
producerStateManager.updateMapEndOffset(appendInfo.lastOffset + 1)
// update the first unstable offset (which is used to compute LSO)
maybeIncrementFirstUnstableOffset()
trace(s"Appended message set with last offset: ${appendInfo.lastOffset}, " +
s"first offset: ${appendInfo.firstOffset}, " +
s"next offset: ${nextOffsetMetadata.messageOffset}, " +
s"and messages: $validRecords")
// 是否需要手动落盘。一般情况下我们不需要设置Broker端参数log.flush.interval.messages
// 落盘操作交由操作系统来完成。但某些情况下，可以设置该参数来确保高可靠性
if (unflushedMessages >= config.flushInterval)
flush()
// 第12步：返回写入结果
appendInfo
}
}
}
上面代码的主要步骤如下：
我们下面看看analyzeAndValidateRecords是如何进行消息校验的：
analyzeAndValidateRecords
private def analyzeAndValidateRecords(records: MemoryRecords, isFromClient: Boolean): LogAppendInfo = {
var shallowMessageCount = 0
var validBytesCount = 0
var firstOffset: Option[Long] = None
var lastOffset = -1L
var sourceCodec: CompressionCodec = NoCompressionCodec
var monotonic = true
var maxTimestamp = RecordBatch.NO_TIMESTAMP
var offsetOfMaxTimestamp = -1L
var readFirstMessage = false
var lastOffsetOfFirstBatch = -1L
for (batch <- records.batches.asScala) {
// we only validate V2 and higher to avoid potential compatibility issues with older clients
// 消息格式Version 2的消息批次，起始位移值必须从0开始
if (batch.magic >= RecordBatch.MAGIC_VALUE_V2 && isFromClient && batch.baseOffset != 0)
throw new InvalidRecordException(s"The baseOffset of the record batch in the append to $topicPartition should " +
s"be 0, but it is ${batch.baseOffset}")
// update the first offset if on the first message. For magic versions older than 2, we use the last offset
// to avoid the need to decompress the data (the last offset can be obtained directly from the wrapper message).
// For magic version 2, we can get the first offset directly from the batch header.
// When appending to the leader, we will update LogAppendInfo.baseOffset with the correct value. In the follower
// case, validation will be more lenient.
// Also indicate whether we have the accurate first offset or not
if (!readFirstMessage) {
if (batch.magic >= RecordBatch.MAGIC_VALUE_V2)
firstOffset = Some(batch.baseOffset) // 更新firstOffset字段
lastOffsetOfFirstBatch = batch.lastOffset // 更新lastOffsetOfFirstBatch字段
readFirstMessage = true
}
// check that offsets are monotonically increasing
// 一旦出现当前lastOffset不小于下一个batch的lastOffset，说明上一个batch中有消息的位移值大于后面batch的消息
// 这违反了位移值单调递增性
if (lastOffset >= batch.lastOffset)
monotonic = false
// update the last offset seen
// 使用当前batch最后一条消息的位移值去更新lastOffset
lastOffset = batch.lastOffset
// Check if the message sizes are valid.
val batchSize = batch.sizeInBytes
// 检查消息批次总字节数大小是否超限，即是否大于Broker端参数max.message.bytes值
if (batchSize > config.maxMessageSize) {
brokerTopicStats.topicStats(topicPartition.topic).bytesRejectedRate.mark(records.sizeInBytes)
brokerTopicStats.allTopicsStats.bytesRejectedRate.mark(records.sizeInBytes)
throw new RecordTooLargeException(s"The record batch size in the append to $topicPartition is $batchSize bytes " +
s"which exceeds the maximum configured value of ${config.maxMessageSize}.")
}
// check the validity of the message by checking CRC
// 执行消息批次校验，包括格式是否正确以及CRC校验
if (!batch.isValid) {
brokerTopicStats.allTopicsStats.invalidMessageCrcRecordsPerSec.mark()
throw new CorruptRecordException(s"Record is corrupt (stored crc = ${batch.checksum()}) in topic partition $topicPartition.")
}
// 更新maxTimestamp字段和offsetOfMaxTimestamp
if (batch.maxTimestamp > maxTimestamp) {
maxTimestamp = batch.maxTimestamp
offsetOfMaxTimestamp = lastOffset
}
// 累加消息批次计数器以及有效字节数，更新shallowMessageCount字段
shallowMessageCount += 1
validBytesCount += batchSize
// 从消息批次中获取压缩器类型
val messageCodec = CompressionCodec.getCompressionCodec(batch.compressionType.id)
if (messageCodec != NoCompressionCodec)
sourceCodec = messageCodec
}
// Apply broker-side compression if any
// 获取Broker端设置的压缩器类型，即Broker端参数compression.type值。
// 该参数默认值是producer，表示sourceCodec用的什么压缩器，targetCodec就用什么
val targetCodec = BrokerCompressionCodec.getTargetCompressionCodec(config.compressionType, sourceCodec)
// 最后生成LogAppendInfo对象并返回
LogAppendInfo(firstOffset, lastOffset, maxTimestamp, offsetOfMaxTimestamp, RecordBatch.NO_TIMESTAMP, logStartOffset,
RecordConversionStats.EMPTY, sourceCodec, targetCodec, shallowMessageCount, validBytesCount, monotonic, lastOffsetOfFirstBatch)
}