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From markhamstra <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-1103] [WIP] Automatic garbage collectio...
Date Thu, 13 Mar 2014 21:06:14 GMT
Github user markhamstra commented on a diff in the pull request:

    https://github.com/apache/spark/pull/126#discussion_r10586047
  
    --- Diff: core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala ---
    @@ -17,28 +17,24 @@
     
     package org.apache.spark.scheduler
     
    +import scala.collection.mutable.HashMap
    +
     import java.io._
     import java.util.zip.{GZIPInputStream, GZIPOutputStream}
     
    -import scala.collection.mutable.HashMap
    -
     import org.apache.spark._
     import org.apache.spark.executor.ShuffleWriteMetrics
    -import org.apache.spark.rdd.RDD
    -import org.apache.spark.rdd.RDDCheckpointData
    +import org.apache.spark.rdd.{RDD, RDDCheckpointData}
     import org.apache.spark.storage._
    -import org.apache.spark.util.{MetadataCleaner, MetadataCleanerType, TimeStampedHashMap}
    +import org.apache.spark.util.BoundedHashMap
     
     private[spark] object ShuffleMapTask {
     
       // A simple map between the stage id to the serialized byte array of a task.
       // Served as a cache for task serialization because serialization can be
       // expensive on the master node if it needs to launch thousands of tasks.
    -  val serializedInfoCache = new TimeStampedHashMap[Int, Array[Byte]]
    -
    -  // TODO: This object shouldn't have global variables
    -  val metadataCleaner = new MetadataCleaner(
    -    MetadataCleanerType.SHUFFLE_MAP_TASK, serializedInfoCache.clearOldValues, new SparkConf)
    +  val MAX_CACHE_SIZE = 100
    +  val serializedInfoCache = new BoundedHashMap[Int, Array[Byte]](MAX_CACHE_SIZE, true)
    --- End diff --
    
    "This is because by the time the dependency or RDD goes out of scope, the stage will already
have been removed."
    
    Right, but do be aware that it doesn't work the other way around.  A stage and stageId
can be created and associated with a ShuffleDependency when a job runs, then that stage and
stageId can disappear from the DAGScheduler when the job completes (finished, canceled or
aborted); but metadata, cached data, etc. for the associated ShuffleDependency should stick
around as long as that ShuffleDependency is in scope, since DAGScheduler#newOrUsedStage will
want to make use of prior mapOutputs (now associated with a fresh stageId) when it can instead
of forcing re-evaluation of those results.
    
    Just because one job and stage is done with a shuffleDep, and as long as that shuffleDep
is in scope from some RDD, that doesn't me that another job will not want to make use of that
shuffleDep. 


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