kafka LogCleaner 源码
kafka LogCleaner 代码
文件路径：/core/src/main/scala/kafka/log/LogCleaner.scala
/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package kafka.log
import java.io.{File, IOException}
import java.nio._
import java.util.Date
import java.util.concurrent.TimeUnit
import kafka.common._
import kafka.metrics.KafkaMetricsGroup
import kafka.server.{BrokerReconfigurable, KafkaConfig, LogDirFailureChannel}
import kafka.utils._
import org.apache.kafka.common.{KafkaException, TopicPartition}
import org.apache.kafka.common.config.ConfigException
import org.apache.kafka.common.errors.{CorruptRecordException, KafkaStorageException}
import org.apache.kafka.common.record.MemoryRecords.RecordFilter
import org.apache.kafka.common.record.MemoryRecords.RecordFilter.BatchRetention
import org.apache.kafka.common.record._
import org.apache.kafka.common.utils.{BufferSupplier, Time}
import scala.jdk.CollectionConverters._
import scala.collection.mutable.ListBuffer
import scala.collection.{Iterable, Seq, Set, mutable}
import scala.util.control.ControlThrowable
/**
* The cleaner is responsible for removing obsolete records from logs which have the "compact" retention strategy.
* A message with key K and offset O is obsolete if there exists a message with key K and offset O' such that O < O'.
*
* Each log can be thought of being split into two sections of segments: a "clean" section which has previously been cleaned followed by a
* "dirty" section that has not yet been cleaned. The dirty section is further divided into the "cleanable" section followed by an "uncleanable" section.
* The uncleanable section is excluded from cleaning. The active log segment is always uncleanable. If there is a
* compaction lag time set, segments whose largest message timestamp is within the compaction lag time of the cleaning operation are also uncleanable.
*
* The cleaning is carried out by a pool of background threads. Each thread chooses the dirtiest log that has the "compact" retention policy
* and cleans that. The dirtiness of the log is guessed by taking the ratio of bytes in the dirty section of the log to the total bytes in the log.
*
* To clean a log the cleaner first builds a mapping of key=>last_offset for the dirty section of the log. See kafka.log.OffsetMap for details of
* the implementation of the mapping.
*
* Once the key=>last_offset map is built, the log is cleaned by recopying each log segment but omitting any key that appears in the offset map with a
* higher offset than what is found in the segment (i.e. messages with a key that appears in the dirty section of the log).
*
* To avoid segments shrinking to very small sizes with repeated cleanings we implement a rule by which if we will merge successive segments when
* doing a cleaning if their log and index size are less than the maximum log and index size prior to the clean beginning.
*
* Cleaned segments are swapped into the log as they become available.
*
* One nuance that the cleaner must handle is log truncation. If a log is truncated while it is being cleaned the cleaning of that log is aborted.
*
* Messages with null payload are treated as deletes for the purpose of log compaction. This means that they receive special treatment by the cleaner.
* The cleaner will only retain delete records for a period of time to avoid accumulating space indefinitely. This period of time is configurable on a per-topic
* basis and is measured from the time the segment enters the clean portion of the log (at which point any prior message with that key has been removed).
* Delete markers in the clean section of the log that are older than this time will not be retained when log segments are being recopied as part of cleaning.
* This time is tracked by setting the base timestamp of a record batch with delete markers when the batch is recopied in the first cleaning that encounters
* it. The relative timestamps of the records in the batch are also modified when recopied in this cleaning according to the new base timestamp of the batch.
*
* Note that cleaning is more complicated with the idempotent/transactional producer capabilities. The following
* are the key points:
*
* 1. In order to maintain sequence number continuity for active producers, we always retain the last batch
* from each producerId, even if all the records from the batch have been removed. The batch will be removed
* once the producer either writes a new batch or is expired due to inactivity.
* 2. We do not clean beyond the last stable offset. This ensures that all records observed by the cleaner have
* been decided (i.e. committed or aborted). In particular, this allows us to use the transaction index to
* collect the aborted transactions ahead of time.
* 3. Records from aborted transactions are removed by the cleaner immediately without regard to record keys.
* 4. Transaction markers are retained until all record batches from the same transaction have been removed and
* a sufficient amount of time has passed to reasonably ensure that an active consumer wouldn't consume any
* data from the transaction prior to reaching the offset of the marker. This follows the same logic used for
* tombstone deletion.
*
* @param initialConfig Initial configuration parameters for the cleaner. Actual config may be dynamically updated.
* @param logDirs The directories where offset checkpoints reside
* @param logs The pool of logs
* @param time A way to control the passage of time
*/
class LogCleaner(initialConfig: CleanerConfig,
val logDirs: Seq[File],
val logs: Pool[TopicPartition, UnifiedLog],
val logDirFailureChannel: LogDirFailureChannel,
time: Time = Time.SYSTEM) extends Logging with KafkaMetricsGroup with BrokerReconfigurable
{
/* Log cleaner configuration which may be dynamically updated */
@volatile private var config = initialConfig
/* for managing the state of partitions being cleaned. package-private to allow access in tests */
private[log] val cleanerManager = new LogCleanerManager(logDirs, logs, logDirFailureChannel)
/* a throttle used to limit the I/O of all the cleaner threads to a user-specified maximum rate */
private[log] val throttler = new Throttler(desiredRatePerSec = config.maxIoBytesPerSecond,
checkIntervalMs = 300,
throttleDown = true,
"cleaner-io",
"bytes",
time = time)
private[log] val cleaners = mutable.ArrayBuffer[CleanerThread]()
/**
* scala 2.12 does not support maxOption so we handle the empty manually.
* @param f to compute the result
* @return the max value (int value) or 0 if there is no cleaner
*/
private def maxOverCleanerThreads(f: CleanerThread => Double): Int =
cleaners.foldLeft(0.0d)((max: Double, thread: CleanerThread) => math.max(max, f(thread))).toInt
/* a metric to track the maximum utilization of any thread's buffer in the last cleaning */
newGauge("max-buffer-utilization-percent",
() => maxOverCleanerThreads(_.lastStats.bufferUtilization) * 100)
/* a metric to track the recopy rate of each thread's last cleaning */
newGauge("cleaner-recopy-percent", () => {
val stats = cleaners.map(_.lastStats)
val recopyRate = stats.iterator.map(_.bytesWritten).sum.toDouble / math.max(stats.iterator.map(_.bytesRead).sum, 1)
(100 * recopyRate).toInt
})
/* a metric to track the maximum cleaning time for the last cleaning from each thread */
newGauge("max-clean-time-secs",
() => maxOverCleanerThreads(_.lastStats.elapsedSecs))
// a metric to track delay between the time when a log is required to be compacted
// as determined by max compaction lag and the time of last cleaner run.
newGauge("max-compaction-delay-secs",
() => maxOverCleanerThreads(_.lastPreCleanStats.maxCompactionDelayMs.toDouble) / 1000)
newGauge("DeadThreadCount", () => deadThreadCount)
private[log] def deadThreadCount: Int = cleaners.count(_.isThreadFailed)
/**
* Start the background cleaning
*/
def startup(): Unit = {
info("Starting the log cleaner")
(0 until config.numThreads).foreach { i =>
val cleaner = new CleanerThread(i)
cleaners += cleaner
cleaner.start()
}
}
/**
* Stop the background cleaning
*/
def shutdown(): Unit = {
info("Shutting down the log cleaner.")
cleaners.foreach(_.shutdown())
cleaners.clear()
}
override def reconfigurableConfigs: Set[String] = {
LogCleaner.ReconfigurableConfigs
}
override def validateReconfiguration(newConfig: KafkaConfig): Unit = {
val newCleanerConfig = LogCleaner.cleanerConfig(newConfig)
val numThreads = newCleanerConfig.numThreads
val currentThreads = config.numThreads
if (numThreads < 1)
throw new ConfigException(s"Log cleaner threads should be at least 1")
if (numThreads < currentThreads / 2)
throw new ConfigException(s"Log cleaner threads cannot be reduced to less than half the current value $currentThreads")
if (numThreads > currentThreads * 2)
throw new ConfigException(s"Log cleaner threads cannot be increased to more than double the current value $currentThreads")
}
/**
* Reconfigure log clean config. The will:
* 1. update desiredRatePerSec in Throttler with logCleanerIoMaxBytesPerSecond, if necessary
* 2. stop current log cleaners and create new ones.
* That ensures that if any of the cleaners had failed, new cleaners are created to match the new config.
*/
override def reconfigure(oldConfig: KafkaConfig, newConfig: KafkaConfig): Unit = {
config = LogCleaner.cleanerConfig(newConfig)
val maxIoBytesPerSecond = config.maxIoBytesPerSecond;
if (maxIoBytesPerSecond != oldConfig.logCleanerIoMaxBytesPerSecond) {
info(s"Updating logCleanerIoMaxBytesPerSecond: $maxIoBytesPerSecond")
throttler.updateDesiredRatePerSec(maxIoBytesPerSecond)
}
shutdown()
startup()
}
/**
* Abort the cleaning of a particular partition, if it's in progress. This call blocks until the cleaning of
* the partition is aborted.
*/
def abortCleaning(topicPartition: TopicPartition): Unit = {
cleanerManager.abortCleaning(topicPartition)
}
/**
* Update checkpoint file to remove partitions if necessary.
*/
def updateCheckpoints(dataDir: File, partitionToRemove: Option[TopicPartition] = None): Unit = {
cleanerManager.updateCheckpoints(dataDir, partitionToRemove = partitionToRemove)
}
/**
* alter the checkpoint directory for the topicPartition, to remove the data in sourceLogDir, and add the data in destLogDir
*/
def alterCheckpointDir(topicPartition: TopicPartition, sourceLogDir: File, destLogDir: File): Unit = {
cleanerManager.alterCheckpointDir(topicPartition, sourceLogDir, destLogDir)
}
/**
* Stop cleaning logs in the provided directory
*
* @param dir the absolute path of the log dir
*/
def handleLogDirFailure(dir: String): Unit = {
cleanerManager.handleLogDirFailure(dir)
}
/**
* Truncate cleaner offset checkpoint for the given partition if its checkpointed offset is larger than the given offset
*/
def maybeTruncateCheckpoint(dataDir: File, topicPartition: TopicPartition, offset: Long): Unit = {
cleanerManager.maybeTruncateCheckpoint(dataDir, topicPartition, offset)
}
/**
* Abort the cleaning of a particular partition if it's in progress, and pause any future cleaning of this partition.
* This call blocks until the cleaning of the partition is aborted and paused.
*/
def abortAndPauseCleaning(topicPartition: TopicPartition): Unit = {
cleanerManager.abortAndPauseCleaning(topicPartition)
}
/**
* Resume the cleaning of paused partitions.
*/
def resumeCleaning(topicPartitions: Iterable[TopicPartition]): Unit = {
cleanerManager.resumeCleaning(topicPartitions)
}
/**
* For testing, a way to know when work has completed. This method waits until the
* cleaner has processed up to the given offset on the specified topic/partition
*
* @param topicPartition The topic and partition to be cleaned
* @param offset The first dirty offset that the cleaner doesn't have to clean
* @param maxWaitMs The maximum time in ms to wait for cleaner
*
* @return A boolean indicating whether the work has completed before timeout
*/
def awaitCleaned(topicPartition: TopicPartition, offset: Long, maxWaitMs: Long = 60000L): Boolean = {
def isCleaned = cleanerManager.allCleanerCheckpoints.get(topicPartition).fold(false)(_ >= offset)
var remainingWaitMs = maxWaitMs
while (!isCleaned && remainingWaitMs > 0) {
val sleepTime = math.min(100, remainingWaitMs)
Thread.sleep(sleepTime)
remainingWaitMs -= sleepTime
}
isCleaned
}
/**
* To prevent race between retention and compaction,
* retention threads need to make this call to obtain:
* @return A list of log partitions that retention threads can safely work on
*/
def pauseCleaningForNonCompactedPartitions(): Iterable[(TopicPartition, UnifiedLog)] = {
cleanerManager.pauseCleaningForNonCompactedPartitions()
}
// Only for testing
private[kafka] def currentConfig: CleanerConfig = config
// Only for testing
private[log] def cleanerCount: Int = cleaners.size
/**
* The cleaner threads do the actual log cleaning. Each thread processes does its cleaning repeatedly by
* choosing the dirtiest log, cleaning it, and then swapping in the cleaned segments.
*/
private[log] class CleanerThread(threadId: Int)
extends ShutdownableThread(name = s"kafka-log-cleaner-thread-$threadId", isInterruptible = false) {
protected override def loggerName = classOf[LogCleaner].getName
if (config.dedupeBufferSize / config.numThreads > Int.MaxValue)
warn("Cannot use more than 2G of cleaner buffer space per cleaner thread, ignoring excess buffer space...")
val cleaner = new Cleaner(id = threadId,
offsetMap = new SkimpyOffsetMap(memory = math.min(config.dedupeBufferSize / config.numThreads, Int.MaxValue).toInt,
hashAlgorithm = config.hashAlgorithm),
ioBufferSize = config.ioBufferSize / config.numThreads / 2,
maxIoBufferSize = config.maxMessageSize,
dupBufferLoadFactor = config.dedupeBufferLoadFactor,
throttler = throttler,
time = time,
checkDone = checkDone)
@volatile var lastStats: CleanerStats = new CleanerStats()
@volatile var lastPreCleanStats: PreCleanStats = new PreCleanStats()
private def checkDone(topicPartition: TopicPartition): Unit = {
if (!isRunning)
throw new ThreadShutdownException
cleanerManager.checkCleaningAborted(topicPartition)
}
/**
* The main loop for the cleaner thread
* Clean a log if there is a dirty log available, otherwise sleep for a bit
*/
override def doWork(): Unit = {
val cleaned = tryCleanFilthiestLog()
if (!cleaned)
pause(config.backOffMs, TimeUnit.MILLISECONDS)
cleanerManager.maintainUncleanablePartitions()
}
/**
* Cleans a log if there is a dirty log available
* @return whether a log was cleaned
*/
private def tryCleanFilthiestLog(): Boolean = {
try {
cleanFilthiestLog()
} catch {
case e: LogCleaningException =>
warn(s"Unexpected exception thrown when cleaning log ${e.log}. Marking its partition (${e.log.topicPartition}) as uncleanable", e)
cleanerManager.markPartitionUncleanable(e.log.parentDir, e.log.topicPartition)
false
}
}
@throws(classOf[LogCleaningException])
private def cleanFilthiestLog(): Boolean = {
val preCleanStats = new PreCleanStats()
val ltc = cleanerManager.grabFilthiestCompactedLog(time, preCleanStats)
val cleaned = ltc match {
case None =>
false
case Some(cleanable) =>
// there's a log, clean it
this.lastPreCleanStats = preCleanStats
try {
cleanLog(cleanable)
true
} catch {
case e @ (_: ThreadShutdownException | _: ControlThrowable) => throw e
case e: Exception => throw new LogCleaningException(cleanable.log, e.getMessage, e)
}
}
val deletable: Iterable[(TopicPartition, UnifiedLog)] = cleanerManager.deletableLogs()
try {
deletable.foreach { case (_, log) =>
try {
log.deleteOldSegments()
} catch {
case e @ (_: ThreadShutdownException | _: ControlThrowable) => throw e
case e: Exception => throw new LogCleaningException(log, e.getMessage, e)
}
}
} finally {
cleanerManager.doneDeleting(deletable.map(_._1))
}
cleaned
}
private def cleanLog(cleanable: LogToClean): Unit = {
val startOffset = cleanable.firstDirtyOffset
var endOffset = startOffset
try {
val (nextDirtyOffset, cleanerStats) = cleaner.clean(cleanable)
endOffset = nextDirtyOffset
recordStats(cleaner.id, cleanable.log.name, startOffset, endOffset, cleanerStats)
} catch {
case _: LogCleaningAbortedException => // task can be aborted, let it go.
case _: KafkaStorageException => // partition is already offline. let it go.
case e: IOException =>
val logDirectory = cleanable.log.parentDir
val msg = s"Failed to clean up log for ${cleanable.topicPartition} in dir $logDirectory due to IOException"
logDirFailureChannel.maybeAddOfflineLogDir(logDirectory, msg, e)
} finally {
cleanerManager.doneCleaning(cleanable.topicPartition, cleanable.log.parentDirFile, endOffset)
}
}
/**
* Log out statistics on a single run of the cleaner.
*/
def recordStats(id: Int, name: String, from: Long, to: Long, stats: CleanerStats): Unit = {
this.lastStats = stats
def mb(bytes: Double) = bytes / (1024*1024)
val message =
"%n\tLog cleaner thread %d cleaned log %s (dirty section = [%d, %d])%n".format(id, name, from, to) +
"\t%,.1f MB of log processed in %,.1f seconds (%,.1f MB/sec).%n".format(mb(stats.bytesRead.toDouble),
stats.elapsedSecs,
mb(stats.bytesRead.toDouble / stats.elapsedSecs)) +
"\tIndexed %,.1f MB in %.1f seconds (%,.1f Mb/sec, %.1f%% of total time)%n".format(mb(stats.mapBytesRead.toDouble),
stats.elapsedIndexSecs,
mb(stats.mapBytesRead.toDouble) / stats.elapsedIndexSecs,
100 * stats.elapsedIndexSecs / stats.elapsedSecs) +
"\tBuffer utilization: %.1f%%%n".format(100 * stats.bufferUtilization) +
"\tCleaned %,.1f MB in %.1f seconds (%,.1f Mb/sec, %.1f%% of total time)%n".format(mb(stats.bytesRead.toDouble),
stats.elapsedSecs - stats.elapsedIndexSecs,
mb(stats.bytesRead.toDouble) / (stats.elapsedSecs - stats.elapsedIndexSecs), 100 * (stats.elapsedSecs - stats.elapsedIndexSecs) / stats.elapsedSecs) +
"\tStart size: %,.1f MB (%,d messages)%n".format(mb(stats.bytesRead.toDouble), stats.messagesRead) +
"\tEnd size: %,.1f MB (%,d messages)%n".format(mb(stats.bytesWritten.toDouble), stats.messagesWritten) +
"\t%.1f%% size reduction (%.1f%% fewer messages)%n".format(100.0 * (1.0 - stats.bytesWritten.toDouble/stats.bytesRead),
100.0 * (1.0 - stats.messagesWritten.toDouble/stats.messagesRead))
info(message)
if (lastPreCleanStats.delayedPartitions > 0) {
info("\tCleanable partitions: %d, Delayed partitions: %d, max delay: %d".format(lastPreCleanStats.cleanablePartitions, lastPreCleanStats.delayedPartitions, lastPreCleanStats.maxCompactionDelayMs))
}
if (stats.invalidMessagesRead > 0) {
warn("\tFound %d invalid messages during compaction.".format(stats.invalidMessagesRead))
}
}
}
}
object LogCleaner {
val ReconfigurableConfigs = Set(
KafkaConfig.LogCleanerThreadsProp,
KafkaConfig.LogCleanerDedupeBufferSizeProp,
KafkaConfig.LogCleanerDedupeBufferLoadFactorProp,
KafkaConfig.LogCleanerIoBufferSizeProp,
KafkaConfig.MessageMaxBytesProp,
KafkaConfig.LogCleanerIoMaxBytesPerSecondProp,
KafkaConfig.LogCleanerBackoffMsProp
)
def cleanerConfig(config: KafkaConfig): CleanerConfig = {
CleanerConfig(numThreads = config.logCleanerThreads,
dedupeBufferSize = config.logCleanerDedupeBufferSize,
dedupeBufferLoadFactor = config.logCleanerDedupeBufferLoadFactor,
ioBufferSize = config.logCleanerIoBufferSize,
maxMessageSize = config.messageMaxBytes,
maxIoBytesPerSecond = config.logCleanerIoMaxBytesPerSecond,
backOffMs = config.logCleanerBackoffMs,
enableCleaner = config.logCleanerEnable)
}
}
/**
* This class holds the actual logic for cleaning a log
* @param id An identifier used for logging
* @param offsetMap The map used for deduplication
* @param ioBufferSize The size of the buffers to use. Memory usage will be 2x this number as there is a read and write buffer.
* @param maxIoBufferSize The maximum size of a message that can appear in the log
* @param dupBufferLoadFactor The maximum percent full for the deduplication buffer
* @param throttler The throttler instance to use for limiting I/O rate.
* @param time The time instance
* @param checkDone Check if the cleaning for a partition is finished or aborted.
*/
private[log] class Cleaner(val id: Int,
val offsetMap: OffsetMap,
ioBufferSize: Int,
maxIoBufferSize: Int,
dupBufferLoadFactor: Double,
throttler: Throttler,
time: Time,
checkDone: TopicPartition => Unit) extends Logging {
protected override def loggerName = classOf[LogCleaner].getName
this.logIdent = s"Cleaner $id: "
/* buffer used for read i/o */
private var readBuffer = ByteBuffer.allocate(ioBufferSize)
/* buffer used for write i/o */
private var writeBuffer = ByteBuffer.allocate(ioBufferSize)
private val decompressionBufferSupplier = BufferSupplier.create()
require(offsetMap.slots * dupBufferLoadFactor > 1, "offset map is too small to fit in even a single message, so log cleaning will never make progress. You can increase log.cleaner.dedupe.buffer.size or decrease log.cleaner.threads")
/**
* Clean the given log
*
* @param cleanable The log to be cleaned
*
* @return The first offset not cleaned and the statistics for this round of cleaning
*/
private[log] def clean(cleanable: LogToClean): (Long, CleanerStats) = {
doClean(cleanable, time.milliseconds())
}
private[log] def doClean(cleanable: LogToClean, currentTime: Long): (Long, CleanerStats) = {
info("Beginning cleaning of log %s".format(cleanable.log.name))
// figure out the timestamp below which it is safe to remove delete tombstones
// this position is defined to be a configurable time beneath the last modified time of the last clean segment
// this timestamp is only used on the older message formats older than MAGIC_VALUE_V2
val legacyDeleteHorizonMs =
cleanable.log.logSegments(0, cleanable.firstDirtyOffset).lastOption match {
case None => 0L
case Some(seg) => seg.lastModified - cleanable.log.config.deleteRetentionMs
}
val log = cleanable.log
val stats = new CleanerStats()
// build the offset map
info("Building offset map for %s...".format(cleanable.log.name))
val upperBoundOffset = cleanable.firstUncleanableOffset
buildOffsetMap(log, cleanable.firstDirtyOffset, upperBoundOffset, offsetMap, stats)
val endOffset = offsetMap.latestOffset + 1
stats.indexDone()
// determine the timestamp up to which the log will be cleaned
// this is the lower of the last active segment and the compaction lag
val cleanableHorizonMs = log.logSegments(0, cleanable.firstUncleanableOffset).lastOption.map(_.lastModified).getOrElse(0L)
// group the segments and clean the groups
info("Cleaning log %s (cleaning prior to %s, discarding tombstones prior to upper bound deletion horizon %s)...".format(log.name, new Date(cleanableHorizonMs), new Date(legacyDeleteHorizonMs)))
val transactionMetadata = new CleanedTransactionMetadata
val groupedSegments = groupSegmentsBySize(log.logSegments(0, endOffset), log.config.segmentSize,
log.config.maxIndexSize, cleanable.firstUncleanableOffset)
for (group <- groupedSegments)
cleanSegments(log, group, offsetMap, currentTime, stats, transactionMetadata, legacyDeleteHorizonMs)
// record buffer utilization
stats.bufferUtilization = offsetMap.utilization
stats.allDone()
(endOffset, stats)
}
/**
* Clean a group of segments into a single replacement segment
*
* @param log The log being cleaned
* @param segments The group of segments being cleaned
* @param map The offset map to use for cleaning segments
* @param currentTime The current time in milliseconds
* @param stats Collector for cleaning statistics
* @param transactionMetadata State of ongoing transactions which is carried between the cleaning
* of the grouped segments
* @param legacyDeleteHorizonMs The delete horizon used for tombstones whose version is less than 2
*/
private[log] def cleanSegments(log: UnifiedLog,
segments: Seq[LogSegment],
map: OffsetMap,
currentTime: Long,
stats: CleanerStats,
transactionMetadata: CleanedTransactionMetadata,
legacyDeleteHorizonMs: Long): Unit = {
// create a new segment with a suffix appended to the name of the log and indexes
val cleaned = UnifiedLog.createNewCleanedSegment(log.dir, log.config, segments.head.baseOffset)
transactionMetadata.cleanedIndex = Some(cleaned.txnIndex)
try {
// clean segments into the new destination segment
val iter = segments.iterator
var currentSegmentOpt: Option[LogSegment] = Some(iter.next())
val lastOffsetOfActiveProducers = log.lastRecordsOfActiveProducers
while (currentSegmentOpt.isDefined) {
val currentSegment = currentSegmentOpt.get
val nextSegmentOpt = if (iter.hasNext) Some(iter.next()) else None
// Note that it is important to collect aborted transactions from the full log segment
// range since we need to rebuild the full transaction index for the new segment.
val startOffset = currentSegment.baseOffset
val upperBoundOffset = nextSegmentOpt.map(_.baseOffset).getOrElse(currentSegment.readNextOffset)
val abortedTransactions = log.collectAbortedTransactions(startOffset, upperBoundOffset)
transactionMetadata.addAbortedTransactions(abortedTransactions)
val retainLegacyDeletesAndTxnMarkers = currentSegment.lastModified > legacyDeleteHorizonMs
info(s"Cleaning $currentSegment in log ${log.name} into ${cleaned.baseOffset} " +
s"with an upper bound deletion horizon $legacyDeleteHorizonMs computed from " +
s"the segment last modified time of ${currentSegment.lastModified}," +
s"${if(retainLegacyDeletesAndTxnMarkers) "retaining" else "discarding"} deletes.")
try {
cleanInto(log.topicPartition, currentSegment.log, cleaned, map, retainLegacyDeletesAndTxnMarkers, log.config.deleteRetentionMs,
log.config.maxMessageSize, transactionMetadata, lastOffsetOfActiveProducers, stats, currentTime = currentTime)
} catch {
case e: LogSegmentOffsetOverflowException =>
// Split the current segment. It's also safest to abort the current cleaning process, so that we retry from
// scratch once the split is complete.
info(s"Caught segment overflow error during cleaning: ${e.getMessage}")
log.splitOverflowedSegment(currentSegment)
throw new LogCleaningAbortedException()
}
currentSegmentOpt = nextSegmentOpt
}
cleaned.onBecomeInactiveSegment()
// flush new segment to disk before swap
cleaned.flush()
// update the modification date to retain the last modified date of the original files
val modified = segments.last.lastModified
cleaned.lastModified = modified
// swap in new segment
info(s"Swapping in cleaned segment $cleaned for segment(s) $segments in log $log")
log.replaceSegments(List(cleaned), segments)
} catch {
case e: LogCleaningAbortedException =>
try cleaned.deleteIfExists()
catch {
case deleteException: Exception =>
e.addSuppressed(deleteException)
} finally throw e
}
}
/**
* Clean the given source log segment into the destination segment using the key=>offset mapping
* provided
*
* @param topicPartition The topic and partition of the log segment to clean
* @param sourceRecords The dirty log segment
* @param dest The cleaned log segment
* @param map The key=>offset mapping
* @param retainLegacyDeletesAndTxnMarkers Should tombstones (lower than version 2) and markers be retained while cleaning this segment
* @param deleteRetentionMs Defines how long a tombstone should be kept as defined by log configuration
* @param maxLogMessageSize The maximum message size of the corresponding topic
* @param stats Collector for cleaning statistics
* @param currentTime The time at which the clean was initiated
*/
private[log] def cleanInto(topicPartition: TopicPartition,
sourceRecords: FileRecords,
dest: LogSegment,
map: OffsetMap,
retainLegacyDeletesAndTxnMarkers: Boolean,
deleteRetentionMs: Long,
maxLogMessageSize: Int,
transactionMetadata: CleanedTransactionMetadata,
lastRecordsOfActiveProducers: Map[Long, LastRecord],
stats: CleanerStats,
currentTime: Long): Unit = {
val logCleanerFilter: RecordFilter = new RecordFilter(currentTime, deleteRetentionMs) {
var discardBatchRecords: Boolean = _
override def checkBatchRetention(batch: RecordBatch): RecordFilter.BatchRetentionResult = {
// we piggy-back on the tombstone retention logic to delay deletion of transaction markers.
// note that we will never delete a marker until all the records from that transaction are removed.
val canDiscardBatch = shouldDiscardBatch(batch, transactionMetadata)
if (batch.isControlBatch)
discardBatchRecords = canDiscardBatch && batch.deleteHorizonMs().isPresent && batch.deleteHorizonMs().getAsLong <= currentTime
else
discardBatchRecords = canDiscardBatch
def isBatchLastRecordOfProducer: Boolean = {
// We retain the batch in order to preserve the state of active producers. There are three cases:
// 1) The producer is no longer active, which means we can delete all records for that producer.
// 2) The producer is still active and has a last data offset. We retain the batch that contains
// this offset since it also contains the last sequence number for this producer.
// 3) The last entry in the log is a transaction marker. We retain this marker since it has the
// last producer epoch, which is needed to ensure fencing.
lastRecordsOfActiveProducers.get(batch.producerId).exists { lastRecord =>
lastRecord.lastDataOffset match {
case Some(offset) => batch.lastOffset == offset
case None => batch.isControlBatch && batch.producerEpoch == lastRecord.producerEpoch
}
}
}
val batchRetention: BatchRetention =
if (batch.hasProducerId && isBatchLastRecordOfProducer)
BatchRetention.RETAIN_EMPTY
else if (discardBatchRecords)
BatchRetention.DELETE
else
BatchRetention.DELETE_EMPTY
new RecordFilter.BatchRetentionResult(batchRetention, canDiscardBatch && batch.isControlBatch)
}
override def shouldRetainRecord(batch: RecordBatch, record: Record): Boolean = {
if (discardBatchRecords)
// The batch is only retained to preserve producer sequence information; the records can be removed
false
else if (batch.isControlBatch)
true
else
Cleaner.this.shouldRetainRecord(map, retainLegacyDeletesAndTxnMarkers, batch, record, stats, currentTime = currentTime)
}
}
var position = 0
while (position < sourceRecords.sizeInBytes) {
checkDone(topicPartition)
// read a chunk of messages and copy any that are to be retained to the write buffer to be written out
readBuffer.clear()
writeBuffer.clear()
sourceRecords.readInto(readBuffer, position)
val records = MemoryRecords.readableRecords(readBuffer)
throttler.maybeThrottle(records.sizeInBytes)
val result = records.filterTo(topicPartition, logCleanerFilter, writeBuffer, maxLogMessageSize, decompressionBufferSupplier)
stats.readMessages(result.messagesRead, result.bytesRead)
stats.recopyMessages(result.messagesRetained, result.bytesRetained)
position += result.bytesRead
// if any messages are to be retained, write them out
val outputBuffer = result.outputBuffer
if (outputBuffer.position() > 0) {
outputBuffer.flip()
val retained = MemoryRecords.readableRecords(outputBuffer)
// it's OK not to hold the Log's lock in this case, because this segment is only accessed by other threads
// after `Log.replaceSegments` (which acquires the lock) is called
dest.append(largestOffset = result.maxOffset,
largestTimestamp = result.maxTimestamp,
shallowOffsetOfMaxTimestamp = result.shallowOffsetOfMaxTimestamp,
records = retained)
throttler.maybeThrottle(outputBuffer.limit())
}
// if we read bytes but didn't get even one complete batch, our I/O buffer is too small, grow it and try again
// `result.bytesRead` contains bytes from `messagesRead` and any discarded batches.
if (readBuffer.limit() > 0 && result.bytesRead == 0)
growBuffersOrFail(sourceRecords, position, maxLogMessageSize, records)
}
restoreBuffers()
}
/**
* Grow buffers to process next batch of records from `sourceRecords.` Buffers are doubled in size
* up to a maximum of `maxLogMessageSize`. In some scenarios, a record could be bigger than the
* current maximum size configured for the log. For example:
* 1. A compacted topic using compression may contain a message set slightly larger than max.message.bytes
* 2. max.message.bytes of a topic could have been reduced after writing larger messages
* In these cases, grow the buffer to hold the next batch.
*/
private def growBuffersOrFail(sourceRecords: FileRecords,
position: Int,
maxLogMessageSize: Int,
memoryRecords: MemoryRecords): Unit = {
val maxSize = if (readBuffer.capacity >= maxLogMessageSize) {
val nextBatchSize = memoryRecords.firstBatchSize
val logDesc = s"log segment ${sourceRecords.file} at position $position"
if (nextBatchSize == null)
throw new IllegalStateException(s"Could not determine next batch size for $logDesc")
if (nextBatchSize <= 0)
throw new IllegalStateException(s"Invalid batch size $nextBatchSize for $logDesc")
if (nextBatchSize <= readBuffer.capacity)
throw new IllegalStateException(s"Batch size $nextBatchSize < buffer size ${readBuffer.capacity}, but not processed for $logDesc")
val bytesLeft = sourceRecords.channel.size - position
if (nextBatchSize > bytesLeft)
throw new CorruptRecordException(s"Log segment may be corrupt, batch size $nextBatchSize > $bytesLeft bytes left in segment for $logDesc")
nextBatchSize.intValue
} else
maxLogMessageSize
growBuffers(maxSize)
}
private def shouldDiscardBatch(batch: RecordBatch,
transactionMetadata: CleanedTransactionMetadata): Boolean = {
if (batch.isControlBatch)
transactionMetadata.onControlBatchRead(batch)
else
transactionMetadata.onBatchRead(batch)
}
private def shouldRetainRecord(map: kafka.log.OffsetMap,
retainDeletesForLegacyRecords: Boolean,
batch: RecordBatch,
record: Record,
stats: CleanerStats,
currentTime: Long): Boolean = {
val pastLatestOffset = record.offset > map.latestOffset
if (pastLatestOffset)
return true
if (record.hasKey) {
val key = record.key
val foundOffset = map.get(key)
/* First,the message must have the latest offset for the key
* then there are two cases in which we can retain a message:
* 1) The message has value
* 2) The message doesn't has value but it can't be deleted now.
*/
val latestOffsetForKey = record.offset() >= foundOffset
val legacyRecord = batch.magic() < RecordBatch.MAGIC_VALUE_V2
def shouldRetainDeletes = {
if (!legacyRecord)
!batch.deleteHorizonMs().isPresent || currentTime < batch.deleteHorizonMs().getAsLong
else
retainDeletesForLegacyRecords
}
val isRetainedValue = record.hasValue || shouldRetainDeletes
latestOffsetForKey && isRetainedValue
} else {
stats.invalidMessage()
false
}
}
/**
* Double the I/O buffer capacity
*/
def growBuffers(maxLogMessageSize: Int): Unit = {
val maxBufferSize = math.max(maxLogMessageSize, maxIoBufferSize)
if(readBuffer.capacity >= maxBufferSize || writeBuffer.capacity >= maxBufferSize)
throw new IllegalStateException("This log contains a message larger than maximum allowable size of %s.".format(maxBufferSize))
val newSize = math.min(this.readBuffer.capacity * 2, maxBufferSize)
info(s"Growing cleaner I/O buffers from ${readBuffer.capacity} bytes to $newSize bytes.")
this.readBuffer = ByteBuffer.allocate(newSize)
this.writeBuffer = ByteBuffer.allocate(newSize)
}
/**
* Restore the I/O buffer capacity to its original size
*/
def restoreBuffers(): Unit = {
if(this.readBuffer.capacity > this.ioBufferSize)
this.readBuffer = ByteBuffer.allocate(this.ioBufferSize)
if(this.writeBuffer.capacity > this.ioBufferSize)
this.writeBuffer = ByteBuffer.allocate(this.ioBufferSize)
}
/**
* Group the segments in a log into groups totaling less than a given size. the size is enforced separately for the log data and the index data.
* We collect a group of such segments together into a single
* destination segment. This prevents segment sizes from shrinking too much.
*
* @param segments The log segments to group
* @param maxSize the maximum size in bytes for the total of all log data in a group
* @param maxIndexSize the maximum size in bytes for the total of all index data in a group
*
* @return A list of grouped segments
*/
private[log] def groupSegmentsBySize(segments: Iterable[LogSegment], maxSize: Int, maxIndexSize: Int, firstUncleanableOffset: Long): List[Seq[LogSegment]] = {
var grouped = List[List[LogSegment]]()
var segs = segments.toList
while(segs.nonEmpty) {
var group = List(segs.head)
var logSize = segs.head.size.toLong
var indexSize = segs.head.offsetIndex.sizeInBytes.toLong
var timeIndexSize = segs.head.timeIndex.sizeInBytes.toLong
segs = segs.tail
while(segs.nonEmpty &&
logSize + segs.head.size <= maxSize &&
indexSize + segs.head.offsetIndex.sizeInBytes <= maxIndexSize &&
timeIndexSize + segs.head.timeIndex.sizeInBytes <= maxIndexSize &&
//if first segment size is 0, we don't need to do the index offset range check.
//this will avoid empty log left every 2^31 message.
(segs.head.size == 0 ||
lastOffsetForFirstSegment(segs, firstUncleanableOffset) - group.last.baseOffset <= Int.MaxValue)) {
group = segs.head :: group
logSize += segs.head.size
indexSize += segs.head.offsetIndex.sizeInBytes
timeIndexSize += segs.head.timeIndex.sizeInBytes
segs = segs.tail
}
grouped ::= group.reverse
}
grouped.reverse
}
/**
* We want to get the last offset in the first log segment in segs.
* LogSegment.nextOffset() gives the exact last offset in a segment, but can be expensive since it requires
* scanning the segment from the last index entry.
* Therefore, we estimate the last offset of the first log segment by using
* the base offset of the next segment in the list.
* If the next segment doesn't exist, first Uncleanable Offset will be used.
*
* @param segs - remaining segments to group.
* @return The estimated last offset for the first segment in segs
*/
private def lastOffsetForFirstSegment(segs: List[LogSegment], firstUncleanableOffset: Long): Long = {
if (segs.size > 1) {
/* if there is a next segment, use its base offset as the bounding offset to guarantee we know
* the worst case offset */
segs(1).baseOffset - 1
} else {
//for the last segment in the list, use the first uncleanable offset.
firstUncleanableOffset - 1
}
}
/**
* Build a map of key_hash => offset for the keys in the cleanable dirty portion of the log to use in cleaning.
* @param log The log to use
* @param start The offset at which dirty messages begin
* @param end The ending offset for the map that is being built
* @param map The map in which to store the mappings
* @param stats Collector for cleaning statistics
*/
private[log] def buildOffsetMap(log: UnifiedLog,
start: Long,
end: Long,
map: OffsetMap,
stats: CleanerStats): Unit = {
map.clear()
val dirty = log.logSegments(start, end).toBuffer
val nextSegmentStartOffsets = new ListBuffer[Long]
if (dirty.nonEmpty) {
for (nextSegment <- dirty.tail) nextSegmentStartOffsets.append(nextSegment.baseOffset)
nextSegmentStartOffsets.append(end)
}
info("Building offset map for log %s for %d segments in offset range [%d, %d).".format(log.name, dirty.size, start, end))
val transactionMetadata = new CleanedTransactionMetadata
val abortedTransactions = log.collectAbortedTransactions(start, end)
transactionMetadata.addAbortedTransactions(abortedTransactions)
// Add all the cleanable dirty segments. We must take at least map.slots * load_factor,
// but we may be able to fit more (if there is lots of duplication in the dirty section of the log)
var full = false
for ((segment, nextSegmentStartOffset) <- dirty.zip(nextSegmentStartOffsets) if !full) {
checkDone(log.topicPartition)
full = buildOffsetMapForSegment(log.topicPartition, segment, map, start, nextSegmentStartOffset, log.config.maxMessageSize,
transactionMetadata, stats)
if (full)
debug("Offset map is full, %d segments fully mapped, segment with base offset %d is partially mapped".format(dirty.indexOf(segment), segment.baseOffset))
}
info("Offset map for log %s complete.".format(log.name))
}
/**
* Add the messages in the given segment to the offset map
*
* @param segment The segment to index
* @param map The map in which to store the key=>offset mapping
* @param stats Collector for cleaning statistics
*
* @return If the map was filled whilst loading from this segment
*/
private def buildOffsetMapForSegment(topicPartition: TopicPartition,
segment: LogSegment,
map: OffsetMap,
startOffset: Long,
nextSegmentStartOffset: Long,
maxLogMessageSize: Int,
transactionMetadata: CleanedTransactionMetadata,
stats: CleanerStats): Boolean = {
var position = segment.offsetIndex.lookup(startOffset).position
val maxDesiredMapSize = (map.slots * this.dupBufferLoadFactor).toInt
while (position < segment.log.sizeInBytes) {
checkDone(topicPartition)
readBuffer.clear()
try {
segment.log.readInto(readBuffer, position)
} catch {
case e: Exception =>
throw new KafkaException(s"Failed to read from segment $segment of partition $topicPartition " +
"while loading offset map", e)
}
val records = MemoryRecords.readableRecords(readBuffer)
throttler.maybeThrottle(records.sizeInBytes)
val startPosition = position
for (batch <- records.batches.asScala) {
if (batch.isControlBatch) {
transactionMetadata.onControlBatchRead(batch)
stats.indexMessagesRead(1)
} else {
val isAborted = transactionMetadata.onBatchRead(batch)
if (isAborted) {
// If the batch is aborted, do not bother populating the offset map.
// Note that abort markers are supported in v2 and above, which means count is defined.
stats.indexMessagesRead(batch.countOrNull)
} else {
val recordsIterator = batch.streamingIterator(decompressionBufferSupplier)
try {
for (record <- recordsIterator.asScala) {
if (record.hasKey && record.offset >= startOffset) {
if (map.size < maxDesiredMapSize)
map.put(record.key, record.offset)
else
return true
}
stats.indexMessagesRead(1)
}
} finally recordsIterator.close()
}
}
if (batch.lastOffset >= startOffset)
map.updateLatestOffset(batch.lastOffset)
}
val bytesRead = records.validBytes
position += bytesRead
stats.indexBytesRead(bytesRead)
// if we didn't read even one complete message, our read buffer may be too small
if(position == startPosition)
growBuffersOrFail(segment.log, position, maxLogMessageSize, records)
}
// In the case of offsets gap, fast forward to latest expected offset in this segment.
map.updateLatestOffset(nextSegmentStartOffset - 1L)
restoreBuffers()
false
}
}
/**
* A simple struct for collecting pre-clean stats
*/
private class PreCleanStats() {
var maxCompactionDelayMs = 0L
var delayedPartitions = 0
var cleanablePartitions = 0
def updateMaxCompactionDelay(delayMs: Long): Unit = {
maxCompactionDelayMs = Math.max(maxCompactionDelayMs, delayMs)
if (delayMs > 0) {
delayedPartitions += 1
}
}
def recordCleanablePartitions(numOfCleanables: Int): Unit = {
cleanablePartitions = numOfCleanables
}
}
/**
* A simple struct for collecting stats about log cleaning
*/
private class CleanerStats(time: Time = Time.SYSTEM) {
val startTime = time.milliseconds
var mapCompleteTime = -1L
var endTime = -1L
var bytesRead = 0L
var bytesWritten = 0L
var mapBytesRead = 0L
var mapMessagesRead = 0L
var messagesRead = 0L
var invalidMessagesRead = 0L
var messagesWritten = 0L
var bufferUtilization = 0.0d
def readMessages(messagesRead: Int, bytesRead: Int): Unit = {
this.messagesRead += messagesRead
this.bytesRead += bytesRead
}
def invalidMessage(): Unit = {
invalidMessagesRead += 1
}
def recopyMessages(messagesWritten: Int, bytesWritten: Int): Unit = {
this.messagesWritten += messagesWritten
this.bytesWritten += bytesWritten
}
def indexMessagesRead(size: Int): Unit = {
mapMessagesRead += size
}
def indexBytesRead(size: Int): Unit = {
mapBytesRead += size
}
def indexDone(): Unit = {
mapCompleteTime = time.milliseconds
}
def allDone(): Unit = {
endTime = time.milliseconds
}
def elapsedSecs: Double = (endTime - startTime) / 1000.0
def elapsedIndexSecs: Double = (mapCompleteTime - startTime) / 1000.0
}
/**
* Helper class for a log, its topic/partition, the first cleanable position, the first uncleanable dirty position,
* and whether it needs compaction immediately.
*/
private case class LogToClean(topicPartition: TopicPartition,
log: UnifiedLog,
firstDirtyOffset: Long,
uncleanableOffset: Long,
needCompactionNow: Boolean = false) extends Ordered[LogToClean] {
val cleanBytes = log.logSegments(-1, firstDirtyOffset).map(_.size.toLong).sum
val (firstUncleanableOffset, cleanableBytes) = LogCleanerManager.calculateCleanableBytes(log, firstDirtyOffset, uncleanableOffset)
val totalBytes = cleanBytes + cleanableBytes
val cleanableRatio = cleanableBytes / totalBytes.toDouble
override def compare(that: LogToClean): Int = math.signum(this.cleanableRatio - that.cleanableRatio).toInt
}
/**
* This is a helper class to facilitate tracking transaction state while cleaning the log. It maintains a set
* of the ongoing aborted and committed transactions as the cleaner is working its way through the log. This
* class is responsible for deciding when transaction markers can be removed and is therefore also responsible
* for updating the cleaned transaction index accordingly.
*/
private[log] class CleanedTransactionMetadata {
private val ongoingCommittedTxns = mutable.Set.empty[Long]
private val ongoingAbortedTxns = mutable.Map.empty[Long, AbortedTransactionMetadata]
// Minheap of aborted transactions sorted by the transaction first offset
private val abortedTransactions = mutable.PriorityQueue.empty[AbortedTxn](new Ordering[AbortedTxn] {
override def compare(x: AbortedTxn, y: AbortedTxn): Int = x.firstOffset compare y.firstOffset
}.reverse)
// Output cleaned index to write retained aborted transactions
var cleanedIndex: Option[TransactionIndex] = None
def addAbortedTransactions(abortedTransactions: List[AbortedTxn]): Unit = {
this.abortedTransactions ++= abortedTransactions
}
/**
* Update the cleaned transaction state with a control batch that has just been traversed by the cleaner.
* Return true if the control batch can be discarded.
*/
def onControlBatchRead(controlBatch: RecordBatch): Boolean = {
consumeAbortedTxnsUpTo(controlBatch.lastOffset)
val controlRecordIterator = controlBatch.iterator
if (controlRecordIterator.hasNext) {
val controlRecord = controlRecordIterator.next()
val controlType = ControlRecordType.parse(controlRecord.key)
val producerId = controlBatch.producerId
controlType match {
case ControlRecordType.ABORT =>
ongoingAbortedTxns.remove(producerId) match {
// Retain the marker until all batches from the transaction have been removed.
case Some(abortedTxnMetadata) if abortedTxnMetadata.lastObservedBatchOffset.isDefined =>
cleanedIndex.foreach(_.append(abortedTxnMetadata.abortedTxn))
false
case _ => true
}
case ControlRecordType.COMMIT =>
// This marker is eligible for deletion if we didn't traverse any batches from the transaction
!ongoingCommittedTxns.remove(producerId)
case _ => false
}
} else {
// An empty control batch was already cleaned, so it's safe to discard
true
}
}
private def consumeAbortedTxnsUpTo(offset: Long): Unit = {
while (abortedTransactions.headOption.exists(_.firstOffset <= offset)) {
val abortedTxn = abortedTransactions.dequeue()
ongoingAbortedTxns.getOrElseUpdate(abortedTxn.producerId, new AbortedTransactionMetadata(abortedTxn))
}
}
/**
* Update the transactional state for the incoming non-control batch. If the batch is part of
* an aborted transaction, return true to indicate that it is safe to discard.
*/
def onBatchRead(batch: RecordBatch): Boolean = {
consumeAbortedTxnsUpTo(batch.lastOffset)
if (batch.isTransactional) {
ongoingAbortedTxns.get(batch.producerId) match {
case Some(abortedTransactionMetadata) =>
abortedTransactionMetadata.lastObservedBatchOffset = Some(batch.lastOffset)
true
case None =>
ongoingCommittedTxns += batch.producerId
false
}
} else {
false
}
}
}
private class AbortedTransactionMetadata(val abortedTxn: AbortedTxn) {
var lastObservedBatchOffset: Option[Long] = None
override def toString: String = s"(txn: $abortedTxn, lastOffset: $lastObservedBatchOffset)"
}
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