Github user mridulm commented on a diff in the pull request:
https://github.com/apache/spark/pull/214#discussion_r10949633
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala ---
@@ -219,18 +229,40 @@ private[spark] class TaskSchedulerImpl(
taskSet.parent.name, taskSet.name, taskSet.runningTasks))
}
+ val ser = SparkEnv.get.closureSerializer.newInstance()
// Take each TaskSet in our scheduling order, and then offer it each
node in increasing order
// of locality levels so that it gets a chance to launch local tasks
on all of them.
var launchedTask = false
+ val serializingTaskNum = new AtomicLong(0)
for (taskSet <- sortedTaskSets; maxLocality <- TaskLocality.values) {
do {
launchedTask = false
for (i <- 0 until shuffledOffers.size) {
val execId = shuffledOffers(i).executorId
val host = shuffledOffers(i).host
- for (task <- taskSet.resourceOffer(execId, host,
availableCpus(i), maxLocality)) {
- tasks(i) += task
- val tid = task.taskId
+ for (taskDesc <- taskSet.resourceOffer(execId, host,
availableCpus(i), maxLocality)) {
+ serializingTaskNum.getAndIncrement()
+ serializeWorkerPool.execute(new Runnable {
+ override def run() {
+ // Serialize and return the task
+ val startTime = System.currentTimeMillis()
+ // We rely on the DAGScheduler to catch non-serializable
closures and RDDs, so in here
+ // we assume the task can be serialized without exceptions.
+ val serializedTask = Task.serializeWithDependencies(
+ taskDesc.taskObject, sc.addedFiles, sc.addedJars, ser)
+ val timeTaken = System.currentTimeMillis() - startTime
+ logInfo("Serialized task %s as %d bytes in %d ms".format(
+ taskDesc.taskName, serializedTask.limit, timeTaken))
+ val task = new TaskDescription(taskDesc.taskId,
taskDesc.executorId,
+ taskDesc.taskName, taskDesc.index, serializedTask)
+ tasks.synchronized {
--- End diff --
hmm, since it is running in a different thread, no need to set spark env,
etc ?
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