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From "David Lewis (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-20760) Memory Leak of RDD blocks
Date Tue, 06 Jun 2017 18:09:18 GMT

    [ https://issues.apache.org/jira/browse/SPARK-20760?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16039375#comment-16039375
] 

David Lewis commented on SPARK-20760:
-------------------------------------

I believe this is fixed by this issue: https://issues.apache.org/jira/browse/SPARK-18991

> Memory Leak of RDD blocks 
> --------------------------
>
>                 Key: SPARK-20760
>                 URL: https://issues.apache.org/jira/browse/SPARK-20760
>             Project: Spark
>          Issue Type: Bug
>          Components: Block Manager
>    Affects Versions: 2.1.0
>         Environment: Spark 2.1.0
>            Reporter: Binzi Cao
>         Attachments: RDD Blocks .png
>
>
> Memory leak for RDD blocks for a long time running rdd process.
> We  have a long term running application, which is doing computations of RDDs. and we
found the RDD blocks are keep increasing in the spark ui page. The rdd blocks and memory usage
do not mach the cached rdds and memory. It looks like spark keeps old rdd in memory and never
released it or never got a chance to release it. The job will eventually die of out of memory.

> In addition, I'm not seeing this issue in spark 1.6. We are seeing the same issue in
Yarn Cluster mode both in kafka streaming and batch applications. The issue in streaming is
similar, however, it seems the rdd blocks grows a bit slower than batch jobs. 
> The below is the sample code and it is reproducible by justing running it in local mode.

> Scala file:
> {code}
> import scala.concurrent.duration.Duration
> import scala.util.{Try, Failure, Success}
> import org.apache.spark.SparkConf
> import org.apache.spark.SparkContext
> import org.apache.spark.rdd.RDD
> import scala.concurrent._
> import ExecutionContext.Implicits.global
> case class Person(id: String, name: String)
> object RDDApp {
>   def run(sc: SparkContext) = {
>     while (true) {
>       val r = scala.util.Random
>       val data = (1 to r.nextInt(100)).toList.map { a =>
>         Person(a.toString, a.toString)
>       }
>       val rdd = sc.parallelize(data)
>       rdd.cache
>       println("running")
>       val a = (1 to 100).toList.map { x =>
>         Future(rdd.filter(_.id == x.toString).collect)
>       }
>       a.foreach { f =>
>         println(Await.ready(f, Duration.Inf).value.get)
>       }
>       rdd.unpersist()
>     }
>   }
>   def main(args: Array[String]): Unit = {
>    val conf = new SparkConf().setAppName("test")
>     val sc   = new SparkContext(conf)
>     run(sc)
>   }
> }
> {code}
> build sbt file:
> {code}
> name := "RDDTest"
> version := "0.1.1"
> scalaVersion := "2.11.5"
> libraryDependencies ++= Seq (
>     "org.scalaz" %% "scalaz-core" % "7.2.0",
>     "org.scalaz" %% "scalaz-concurrent" % "7.2.0",
>     "org.apache.spark" % "spark-core_2.11" % "2.1.0" % "provided",
>     "org.apache.spark" % "spark-hive_2.11" % "2.1.0" % "provided"
>   )
> addCompilerPlugin("org.spire-math" %% "kind-projector" % "0.7.1")
> mainClass in assembly := Some("RDDApp")
> test in assembly := {}
> {code}
> To reproduce it: 
> Just 
> {code}
> spark-2.1.0-bin-hadoop2.7/bin/spark-submit   --driver-memory 4G \
> --executor-memory 4G \
> --executor-cores 1 \
> --num-executors 1 \
> --class "RDDApp" --master local[4] RDDTest-assembly-0.1.1.jar
> {code}



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