【Kafka源码】日志处理

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发布时间：2019-06-14

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目前来说，kafka的日志中记录的内容比较多，具体的存储内容见，写的比较好。可以看到，存储的内容还是比较多的，当存储文件比较大的时候，我们应该如何处理这些日志？下面我们通过kafka启动过程的源码，分析下kafka的日志处理过程。

一、入口方法

在kafkaServer.scala中的start方法中，有一个这样的调用：

/* start log manager */logManager = createLogManager(zkUtils.zkClient, brokerState)logManager.startup()

二、定时任务总方法

这块就是启动了日志相关的定时任务，具体都有哪些内容？我们跟进去看一下：

def startup() {    /* Schedule the cleanup task to delete old logs */    if(scheduler != null) {      info("Starting log cleanup with a period of %d ms.".format(retentionCheckMs))      scheduler.schedule("kafka-log-retention",                          cleanupLogs,                          delay = InitialTaskDelayMs,                          period = retentionCheckMs,                          TimeUnit.MILLISECONDS)      info("Starting log flusher with a default period of %d ms.".format(flushCheckMs))      scheduler.schedule("kafka-log-flusher",                          flushDirtyLogs,                          delay = InitialTaskDelayMs,                          period = flushCheckMs,                          TimeUnit.MILLISECONDS)      scheduler.schedule("kafka-recovery-point-checkpoint",                         checkpointRecoveryPointOffsets,                         delay = InitialTaskDelayMs,                         period = flushCheckpointMs,                         TimeUnit.MILLISECONDS)    }    if(cleanerConfig.enableCleaner)      cleaner.startup()  }

可以看到，这块主要使用了一个定时任务线程池，来处理任务的定时执行。具体包括两块，一部分是清理日志，另一部分是将日志写入文件。

2.1 清理日志

首先是cleanupLogs，这块涉及到配置，log.retention.check.interval.ms，也就是多长时间执行一次日志清理。我们看下具体的方法：

/**   * Delete any eligible logs. Return the number of segments deleted.   */  def cleanupLogs() {    debug("Beginning log cleanup...")    var total = 0    val startMs = time.milliseconds    for(log <- allLogs; if !log.config.compact) {      debug("Garbage collecting '" + log.name + "'")      total += cleanupExpiredSegments(log) + cleanupSegmentsToMaintainSize(log)    }    debug("Log cleanup completed. " + total + " files deleted in " +                  (time.milliseconds - startMs) / 1000 + " seconds")  }

这块还涉及到另一个配置：cleanup.policy，也就是清理的策略，目前有几种，一种是compact，也就是日志压缩，不会清理掉日志文件；还有一种就是delete，也就是删除。这块主要有两个方法，我们分别看下：

2.1.1 清理过期日志

/**   * Runs through the log removing segments older than a certain age   */  private def cleanupExpiredSegments(log: Log): Int = {    if (log.config.retentionMs < 0)      return 0    val startMs = time.milliseconds    log.deleteOldSegments(startMs - _.lastModified > log.config.retentionMs)  }

这块又涉及到一个配置：retention.ms，这个参数表示日志保存的时间。如果小于0，表示永不失效，也就没有了删除这一说。

当然，如果文件的修改时间跟当前时间差，大于设置的日志保存时间，就要执行删除动作了。具体的删除方法为：

/**   * Delete any log segments matching the given predicate function,   * starting with the oldest segment and moving forward until a segment doesn't match.   * @param predicate A function that takes in a single log segment and returns true iff it is deletable   * @return The number of segments deleted   */  def deleteOldSegments(predicate: LogSegment => Boolean): Int = {    lock synchronized {      //find any segments that match the user-supplied predicate UNLESS it is the final segment      //and it is empty (since we would just end up re-creating it)      val lastEntry = segments.lastEntry      val deletable =        if (lastEntry == null) Seq.empty        else logSegments.takeWhile(s => predicate(s) && (s.baseOffset != lastEntry.getValue.baseOffset || s.size > 0))      val numToDelete = deletable.size      if (numToDelete > 0) {        // we must always have at least one segment, so if we are going to delete all the segments, create a new one first        if (segments.size == numToDelete)          roll()        // remove the segments for lookups        deletable.foreach(deleteSegment(_))      }      numToDelete    }  }

这块的逻辑是：根据传入的predicate来判断哪些日志符合被删除的要求，放入到deletable中，最后遍历deletable，进行删除操作。

private def deleteSegment(segment: LogSegment) {    info("Scheduling log segment %d for log %s for deletion.".format(segment.baseOffset, name))    lock synchronized {      segments.remove(segment.baseOffset)      asyncDeleteSegment(segment)    }  }      private def asyncDeleteSegment(segment: LogSegment) {    segment.changeFileSuffixes("", Log.DeletedFileSuffix)    def deleteSeg() {      info("Deleting segment %d from log %s.".format(segment.baseOffset, name))      segment.delete()    }    scheduler.schedule("delete-file", deleteSeg, delay = config.fileDeleteDelayMs)  }

这块是一个异步删除文件的过程，包含一个配置：file.delete.delay.ms。表示每隔多久删除一次日志文件。删除的过程是先把日志的后缀改为.delete，然后定时删除。

2.1.2 清理过大日志

/**   *  Runs through the log removing segments until the size of the log   *  is at least logRetentionSize bytes in size   */  private def cleanupSegmentsToMaintainSize(log: Log): Int = {    if(log.config.retentionSize < 0 || log.size < log.config.retentionSize)      return 0    var diff = log.size - log.config.retentionSize    def shouldDelete(segment: LogSegment) = {      if(diff - segment.size >= 0) {        diff -= segment.size        true      } else {        false      }    }    log.deleteOldSegments(shouldDelete)  }

这块代码比较清晰，如果日志大小大于retention.bytes，那么就会被标记为待删除，然后调用的方法是一样的，也是deleteOldSegments。就不赘述了。

2.2 日志刷到硬盘

这块有两个定时任务。

scheduler.schedule("kafka-log-flusher",                          flushDirtyLogs,                          delay = InitialTaskDelayMs,                          period = flushCheckMs,                          TimeUnit.MILLISECONDS)scheduler.schedule("kafka-recovery-point-checkpoint",                         checkpointRecoveryPointOffsets,                         delay = InitialTaskDelayMs,                         period = flushCheckpointMs,                         TimeUnit.MILLISECONDS)

涉及到两个配置：

log.flush.scheduler.interval.ms：检查是否需要固化到硬盘的时间间隔

log.flush.offset.checkpoint.interval.ms：控制上次固化硬盘的时间点，以便于数据恢复一般不需要去修改

我们分别看下两个任务做了啥。

2.2.1 flushDirtyLogs

/**   * Flush any log which has exceeded its flush interval and has unwritten messages.   */  private def flushDirtyLogs() = {    debug("Checking for dirty logs to flush...")    for ((topicAndPartition, log) <- logs) {      try {        val timeSinceLastFlush = time.milliseconds - log.lastFlushTime        debug("Checking if flush is needed on " + topicAndPartition.topic + " flush interval  " + log.config.flushMs +              " last flushed " + log.lastFlushTime + " time since last flush: " + timeSinceLastFlush)        if(timeSinceLastFlush >= log.config.flushMs)          log.flush      } catch {        case e: Throwable =>          error("Error flushing topic " + topicAndPartition.topic, e)      }    }  }

这个方法的目的是把日志刷新到硬盘中，保证数据不丢。

这块设计到一个配置：flush.ms。当日志的刷新时间与当前时间差，大于配置的值时，就会执行flush操作。

/**   * Flush all log segments   */  def flush(): Unit = flush(this.logEndOffset)  /**   * Flush log segments for all offsets up to offset-1   * @param offset The offset to flush up to (non-inclusive); the new recovery point   */  def flush(offset: Long) : Unit = {    if (offset <= this.recoveryPoint)      return    debug("Flushing log '" + name + " up to offset " + offset + ", last flushed: " + lastFlushTime + " current time: " +          time.milliseconds + " unflushed = " + unflushedMessages)    for(segment <- logSegments(this.recoveryPoint, offset))      segment.flush()    lock synchronized {      if(offset > this.recoveryPoint) {        this.recoveryPoint = offset        lastflushedTime.set(time.milliseconds)      }    }  }      /**   * Flush this log segment to disk   */  @threadsafe  def flush() {    LogFlushStats.logFlushTimer.time {      log.flush()      index.flush()    }  }

找到当前segment的最后一个offset，即logEndOffset，然后调用flush方法，刷新到日志文件中。首先判断，当前offset是否小于recoveryPoint，也就是第一个需要刷新到硬盘的offset，如果小于的话，直接返回，否则继续flush操作。

将日志中从recoveryPoint到offset的所有日志，刷新到日志文件中，调用segment.flush()方法上。刷新log文件和index文件。

2.2.2 checkpointRecoveryPointOffsets

/**   * Write out the current recovery point for all logs to a text file in the log directory    * to avoid recovering the whole log on startup.   */  def checkpointRecoveryPointOffsets() {    this.logDirs.foreach(checkpointLogsInDir)  }      /**   * Make a checkpoint for all logs in provided directory.   */  private def checkpointLogsInDir(dir: File): Unit = {    val recoveryPoints = this.logsByDir.get(dir.toString)    if (recoveryPoints.isDefined) {      this.recoveryPointCheckpoints(dir).write(recoveryPoints.get.mapValues(_.recoveryPoint))    }  }

这块主要是用于写一些恢复点的数据到文件中去，文件名是recovery-point-offset-checkpoint，里面的内容是：