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https://issues.apache.org/jira/browse/SPARK-27112?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16797321#comment-16797321
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Imran Rashid commented on SPARK-27112:
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[~Dhruve Ashar] -- rc8 is already defined, there is nothing I (or anybody else)
can do to change that. I simply updated the jira to reflect that. However,
you might request that rc8 does not become 2.4.1, and instead we roll an rc9
with this this fix. You should respond to the VOTE thread for rc8 on the dev
list with your concerns, that's the right forum for this (thanks for the
reminder btw, I will mention it there as well).
> Spark Scheduler encounters two independent Deadlocks when trying to kill
> executors either due to dynamic allocation or blacklisting
> ------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-27112
> URL: https://issues.apache.org/jira/browse/SPARK-27112
> Project: Spark
> Issue Type: Bug
> Components: Scheduler, Spark Core
> Affects Versions: 2.4.0, 3.0.0
> Reporter: Parth Gandhi
> Assignee: Parth Gandhi
> Priority: Major
> Fix For: 2.3.4, 2.4.2, 3.0.0
>
> Attachments: Screen Shot 2019-02-26 at 4.10.26 PM.png, Screen Shot
> 2019-02-26 at 4.10.48 PM.png, Screen Shot 2019-02-26 at 4.11.11 PM.png,
> Screen Shot 2019-02-26 at 4.11.26 PM.png
>
>
> Recently, a few spark users in the organization have reported that their jobs
> were getting stuck. On further analysis, it was found out that there exist
> two independent deadlocks and either of them occur under different
> circumstances. The screenshots for these two deadlocks are attached here.
> We were able to reproduce the deadlocks with the following piece of code:
>
> {code:java}
> import org.apache.hadoop.conf.Configuration
> import org.apache.hadoop.fs.{FileSystem, Path}
> import org.apache.spark._
> import org.apache.spark.TaskContext
> // Simple example of Word Count in Scala
> object ScalaWordCount {
> def main(args: Array[String]) {
> if (args.length < 2) {
> System.err.println("Usage: ScalaWordCount <inputFilesURI> <outputFilesUri>")
> System.exit(1)
> }
> val conf = new SparkConf().setAppName("Scala Word Count")
> val sc = new SparkContext(conf)
> // get the input file uri
> val inputFilesUri = args(0)
> // get the output file uri
> val outputFilesUri = args(1)
> while (true) {
> val textFile = sc.textFile(inputFilesUri)
> val counts = textFile.flatMap(line => line.split(" "))
> .map(word => {if (TaskContext.get.partitionId == 5 &&
> TaskContext.get.attemptNumber == 0) throw new Exception("Fail for
> blacklisting") else (word, 1)})
> .reduceByKey(_ + _)
> counts.saveAsTextFile(outputFilesUri)
> val conf: Configuration = new Configuration()
> val path: Path = new Path(outputFilesUri)
> val hdfs: FileSystem = FileSystem.get(conf)
> hdfs.delete(path, true)
> }
> sc.stop()
> }
> }
> {code}
>
> Additionally, to ensure that the deadlock surfaces up soon enough, I also
> added a small delay in the Spark code here:
> [https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/BlacklistTracker.scala#L256]
>
> {code:java}
> executorIdToFailureList.remove(exec)
> updateNextExpiryTime()
> Thread.sleep(2000)
> killBlacklistedExecutor(exec)
> {code}
>
> Also make sure that the following configs are set when launching the above
> spark job:
> *spark.blacklist.enabled=true*
> *spark.blacklist.killBlacklistedExecutors=true*
> *spark.blacklist.application.maxFailedTasksPerExecutor=1*
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