[jira] [Resolved] (SPARK-27112) Spark Scheduler encounters two independent Deadlocks when trying to kill executors either due to dynamic allocation or blacklisting

["Imran Rashid (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-27112?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Imran Rashid resolved SPARK-27112.
----------------------------------
       Resolution: Fixed
         Assignee: Parth Gandhi
    Fix Version/s: 3.0.0

Fixed by https://github.com/apache/spark/pull/24072

> 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: 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*



--
This message was sent by Atlassian JIRA
(v7.6.3#76005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org