Spark源码阅读笔记之MetadataCleaner

MetadataCleaner运行定时任务周期性的清理元数据（metadata），有6种类型的元数据：MAP_OUTPUT_TRACKER，executor跟踪各个map任务输出的存储位置的数据，根据spark.cleaner.ttl.MAP_OUTPUT_TRACKER设置清理时间，默认值为-1，表示不清理；SPARK_CONTEXT，SparkContext中记录缓存到内存中的RDD的数据结构，根据spark.cleaner.ttl.SPARK_CONTEXT设置清理时间，默认值为-1，表示不清理；；HTTP_BROADCAST，采用http方式广播broadcast的元数据，根据spark.cleaner.ttl.HTTP_BROADCAST设置清理时间，默认值为-1，表示不清理；；BLOCK_MANAGER，BlockManager中非Broadcast类型的Block数据，根据spark.cleaner.ttl.BLOCK_MANAGER设置清理时间，默认值为-1，表示不清理；；SHUFFLE_BLOCK_MANAGER，shuffle输出的数据，根据spark.cleaner.ttl.SHUFFLE_BLOCK_MANAGER设置清理时间，默认值为-1，表示不清理；；BROADCAST_VARS，Torrent方式广播broadcast的元数据，底层依赖于BlockManager，根据spark.cleaner.ttl.BROADCAST_VARS设置清理时间，默认值为-1，表示不清理。

Runs a timer task to periodically clean up metadata (e.g. old files or hashtable entries)

MetadataCleaner用MetadataCleanerType枚举类型来记录需要清理的6种元数据：

object MetadataCleanerType extends Enumeration {  val MAP_OUTPUT_TRACKER, SPARK_CONTEXT, HTTP_BROADCAST, BLOCK_MANAGER,  SHUFFLE_BLOCK_MANAGER, BROADCAST_VARS = Value  type MetadataCleanerType = Value  def systemProperty(which: MetadataCleanerType.MetadataCleanerType) =      "spark.cleaner.ttl." + which.toString}

MetadataCleaner属性

• cleanerType：MetadataCleanerType
  清理的元数据类型

• name：String = cleanerType.toString

• delaySeconds：Int
  表示数据多少秒过期，值为conf.get(“spark.cleaner.ttl.” + cleanerType.toString, conf.getInt(“spark.cleaner.ttl”, -1).toString).toInt

• periodSeconds：Int = math.max(10, delaySeconds / 10)
  清理周期，即以periodSeconds的间隔周期性的调用清理函数来判断数据是否过期

• cleanupFunc：(Long) => Unit
  清理函数，MetadataCleaner以periodSeconds为间隔周期性的调用该函数，并把System.currentTimeMillis() - (delaySeconds * 1000)传给该函数，因此该函数需要实现的逻辑是判断数据存储的时间戳是否小于传入的参数，若小于则表明过期，需清理；否则没有过期。

• timer：Timer = new Timer(name + ” cleanup timer”, true)
  定时调度器

• task：TimerTask
  清理任务

task = new TimerTask {    override def run() {      try {        cleanupFunc(System.currentTimeMillis() - (delaySeconds * 1000))        logInfo("Ran metadata cleaner for " + name)      } catch {        case e: Exception => logError("Error running cleanup task for " + name, e)      }    }  }

MetadataCleaner代码：

class MetadataCleaner(    cleanerType: MetadataCleanerType.MetadataCleanerType,    cleanupFunc: (Long) => Unit,    conf: SparkConf)  extends Logging{  val name = cleanerType.toString  private val delaySeconds = MetadataCleaner.getDelaySeconds(conf, cleanerType)  private val periodSeconds = math.max(10, delaySeconds / 10)  private val timer = new Timer(name + " cleanup timer", true)  private val task = new TimerTask {    override def run() {      try {        cleanupFunc(System.currentTimeMillis() - (delaySeconds * 1000))        logInfo("Ran metadata cleaner for " + name)      } catch {        case e: Exception => logError("Error running cleanup task for " + name, e)      }    }  }  if (delaySeconds > 0) {    logDebug(      "Starting metadata cleaner for " + name + " with delay of " + delaySeconds + " seconds " +      "and period of " + periodSeconds + " secs")    timer.schedule(task, delaySeconds * 1000, periodSeconds * 1000)  }  def cancel() {    timer.cancel()  }}