/**
 * 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.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 * A message with key K and offset O is obsolete if there exists a message with key K and offset O' such that O 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.
 *
 * 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, Log],
                 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 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. This simply stops current log cleaners and creates 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)
    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, removing topics and partitions that no longer exist
   */
  def updateCheckpoints(dataDir: File): Unit = {
    cleanerManager.updateCheckpoints(dataDir, update=None)
  }

  def alterCheckpointDir(topicPartition: TopicPartition, sourceLogDir: File, destLogDir: File): Unit = {
    cleanerManager.alterCheckpointDir(topicPartition, sourceLogDir, destLogDir)
  }

  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, Log)] = {
    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)
    }

    /**
     * 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 cleaned = cleanerManager.grabFilthiestCompactedLog(time, preCleanStats) 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, Log)] = 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)

  }

  def createNewCleanedSegment(log: Log, baseOffset: Long): LogSegment = {
    LogSegment.deleteIfExists(log.dir, baseOffset, fileSuffix = Log.CleanedFileSuffix)
    LogSegment.open(log.dir, baseOffset, log.config, Time.SYSTEM, fileAlreadyExists = false,
      fileSuffix = Log.CleanedFileSuffix, initFileSize = log.initFileSize, preallocate = log.config.preallocate)
  }

}

/**
 * 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) = {
    // 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
    val deleteHorizonMs =
      cleanable.log.logSegments(0, cleanable.firstDirtyOffset).lastOption match {
        case None => 0L
        case Some(seg) => seg.lastModified - cleanable.log.config.deleteRetentionMs
    }

    doClean(cleanable, deleteHorizonMs)
  }

  private[log] def doClean(cleanable: LogToClean, deleteHorizonMs: Long): (Long, CleanerStats) = {
    info("Beginning cleaning of log %s.".format(cleanable.log.name))

    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 %s)...".format(log.name, new Date(cleanableHorizonMs), new Date(deleteHorizonMs)))
    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, deleteHorizonMs, stats, transactionMetadata)

    // 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 deleteHorizonMs The time to retain delete tombstones
   * @param stats Collector for cleaning statistics
   * @param transactionMetadata State of ongoing transactions which is carried between the cleaning
   *                            of the grouped segments
   */
  private[log] def cleanSegments(log: Log,
                                 segments: Seq[LogSegment],
                                 map: OffsetMap,
                                 deleteHorizonMs: Long,
                                 stats: CleanerStats,
                                 transactionMetadata: CleanedTransactionMetadata): Unit = {
    // create a new segment with a suffix appended to the name of the log and indexes
    val cleaned = LogCleaner.createNewCleanedSegment(log, 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

        val startOffset = currentSegment.baseOffset
        val upperBoundOffset = nextSegmentOpt.map(_.baseOffset).getOrElse(map.latestOffset + 1)
        val abortedTransactions = log.collectAbortedTransactions(startOffset, upperBoundOffset)
        transactionMetadata.addAbortedTransactions(abortedTransactions)

        val retainDeletesAndTxnMarkers = currentSegment.lastModified > deleteHorizonMs
        info(s"Cleaning $currentSegment in log ${log.name} into ${cleaned.baseOffset} " +
          s"with deletion horizon $deleteHorizonMs, " +
          s"${if(retainDeletesAndTxnMarkers) "retaining" else "discarding"} deletes.")

        try {
          cleanInto(log.topicPartition, currentSegment.log, cleaned, map, retainDeletesAndTxnMarkers, log.config.maxMessageSize,
            transactionMetadata, lastOffsetOfActiveProducers, stats)
        } 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 retainDeletesAndTxnMarkers Should tombstones and markers be retained while cleaning this segment
   * @param maxLogMessageSize The maximum message size of the corresponding topic
   * @param stats Collector for cleaning statistics
   */
  private[log] def cleanInto(topicPartition: TopicPartition,
                             sourceRecords: FileRecords,
                             dest: LogSegment,
                             map: OffsetMap,
                             retainDeletesAndTxnMarkers: Boolean,
                             maxLogMessageSize: Int,
                             transactionMetadata: CleanedTransactionMetadata,
                             lastRecordsOfActiveProducers: Map[Long, LastRecord],
                             stats: CleanerStats): Unit = {
    val logCleanerFilter: RecordFilter = new RecordFilter {
      var discardBatchRecords: Boolean = _

      override def checkBatchRetention(batch: RecordBatch): BatchRetention = {
        // 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.
        discardBatchRecords = shouldDiscardBatch(batch, transactionMetadata, retainTxnMarkers = retainDeletesAndTxnMarkers)

        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
            }
          }
        }

        if (batch.hasProducerId && isBatchLastRecordOfProducer)
          BatchRetention.RETAIN_EMPTY
        else if (discardBatchRecords)
          BatchRetention.DELETE
        else
          BatchRetention.DELETE_EMPTY
      }

      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
          Cleaner.this.shouldRetainRecord(map, retainDeletesAndTxnMarkers, batch, record, stats)
      }
    }

    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,
                                 retainTxnMarkers: Boolean): Boolean = {
    if (batch.isControlBatch) {
      val canDiscardControlBatch = transactionMetadata.onControlBatchRead(batch)
      canDiscardControlBatch && !retainTxnMarkers
    } else {
      val canDiscardBatch = transactionMetadata.onBatchRead(batch)
      canDiscardBatch
    }
  }

  private def shouldRetainRecord(map: kafka.log.OffsetMap,
                                 retainDeletes: Boolean,
                                 batch: RecordBatch,
                                 record: Record,
                                 stats: CleanerStats): 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 isRetainedValue = record.hasValue || retainDeletes
      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 &&
            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: Log,
                                  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: Log,
                              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.
            // We may retain a record from an aborted transaction if it is the last entry
            // written by a given producerId.
            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)"
}
