[jira] [Comment Edited] (SPARK-20760) Memory Leak of RDD blocks

["Binzi Cao (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-20760?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16012161#comment-16012161 ] 

Binzi Cao edited comment on SPARK-20760 at 5/16/17 11:19 AM:
-------------------------------------------------------------

[~srowen] Thank you for the update. 
Some more informations
1. The issue did not happen in spark 1.6.

2. The rdd cache and unpersist are in the same thread, only the computations are in other threads. The future/await is just to simulate a production application. The unpersist call is  after all the computations done. So there is a bug in unpersist?

3. Each cached and unpersist are for different rdds with different ids, there are not multithread running to cache or unpersist  same rdd.

Is it because each computation will cause an rdd cache seperately and the rdd will be cached with multiple copies? That mean rdd is not thread-safe any more ?




was (Author: caobinzi):
[~srowen] Thank you for the update. 
Some more informations
1. The issue did not happen in spark 1.6.

2. The rdd cache and unpersist are in the same thread, only the computations are in other threads. The future/await is just to simulate a production application. The unpersist call is  after all the computations done, so there are no multiple cache/unpersist running in multiple threads

3. Each cached and unpersist are for different rdds with different ids, there are not multithread running to cache or unpersist  same rdd.



> 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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