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https://issues.apache.org/jira/browse/SPARK-7624?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14544978#comment-14544978
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Davies Liu commented on SPARK-7624:
-----------------------------------
In the context of Spark Streaming, there could be some long running tasks
(receivers), after the patch [1], LocalBackend will schedule another
ReviveOffer if no task could be scheduled, becoming infinite loop. And, each
new stage could introduce a new ReviveOffer infinite loop, the scheduler will
become slower and slower.
It's not easy to tell when we should schedule another ReviveOffer or not, so
I'd like to revert this patch, because the original problem is already resolved
by https://github.com/apache/spark/pull/3779
[1] https://github.com/apache/spark/pull/4147
> Task scheduler delay is increasing time over time in spark local mode
> ---------------------------------------------------------------------
>
> Key: SPARK-7624
> URL: https://issues.apache.org/jira/browse/SPARK-7624
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.3.1
> Reporter: Jack Hu
> Assignee: Davies Liu
> Labels: delay, schedule
>
> I am running a simple spark streaming program with spark 1.3.1 in local mode,
> it receives json string from a socket with rate 50 events per second, it can
> run well in first 6 hours (although the minor gc count per minute is
> increasing all the time), after that, i can see that the scheduler delay in
> every task is significant increased from 10 ms to 100 ms, after 10 hours
> running, the task delay is about 800 ms and cpu is also increased from 2% to
> 30%. This causes the steaming job can not finish in one batch interval (5
> seconds). I dumped the java memory after 16 hours and can see there are about
> 200000 {{org.apache.spark.scheduler.local.ReviveOffers}} objects in
> {{akka.actor.LightArrayRevolverScheduler$TaskQueue[]}}. Then i checked the
> code and see only one place may put the {{ReviveOffers}} to akka
> {{LightArrayRevolverScheduler}}: the {{LocalActor::reviveOffers}}
> {code}
> def reviveOffers() {
> val offers = Seq(new WorkerOffer(localExecutorId, localExecutorHostname,
> freeCores))
> val tasks = scheduler.resourceOffers(offers).flatten
> for (task <- tasks) {
> freeCores -= scheduler.CPUS_PER_TASK
> executor.launchTask(executorBackend, taskId = task.taskId,
> attemptNumber = task.attemptNumber,
> task.name, task.serializedTask)
> }
> if (tasks.isEmpty && scheduler.activeTaskSets.nonEmpty) {
> // Try to reviveOffer after 1 second, because scheduler may wait for
> locality timeout
> context.system.scheduler.scheduleOnce(1000 millis, self, ReviveOffers)
> }
> }
> {code}
> I removed the last three lines in this method (the whole {{if}} block, which
> is introduced from https://issues.apache.org/jira/browse/SPARK-4939), it
> worked smooth after 20 hours running, the scheduler delay is about 10 ms all
> the time. So there should have some conditions that the ReviveOffers will be
> duplicate scheduled? I am not sure why this happens, but i feel that this is
> the root cause of this issue.
> My spark settings:
> # Memor: 3G
> # CPU: 8 cores
> # Streaming Batch interval: 5 seconds.
> Here are my streaming code:
> {code}
> val input = ssc.socketTextStream(
> hostname, port, StorageLevel.MEMORY_ONLY_SER).mapPartitions(
> /// parse the json to Order
> Order(_), preservePartitioning = true)
> val mresult = input.map(
> v => (v.customer, UserSpending(v.customer, v.count * v.price,
> v.timestamp.toLong))).cache()
> val tempr = mresult.window(
> Seconds(firstStageWindowSize),
> Seconds(firstStageWindowSize)
> ).transform(
> rdd => rdd.union(rdd).union(rdd).union(rdd)
> )
> tempr.count.print
> tempr.cache().foreachRDD((rdd, t) => {
> for (i <- 1 to 5) {
> val c = rdd.filter(x=>scala.util.Random.nextInt(5) == i).count()
> println("""T: """ + t + """: """ + c)
> }
> })
> {code}
> ========================================================
> Updated at 2015-05-15
> I did print some detail schedule times of the suspect lines in
> {{LocalActor::reviveOffers}}: {color:red}*1685343501*{color} times after 18
> hours running.
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