[
https://issues.apache.org/jira/browse/SPARK-20760?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Binzi Cao updated SPARK-20760:
------------------------------
Description:
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}
was:
Memory leak for RDD blocks for a long time running rdd process.
I have a long term running application, which is doing computations of RDDs.
and I found the RDD blocks are keep increasing in the spark ui page. The rdd
blocks and memory usage does 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.
The below is the minimized 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}
> 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
> Priority: Critical
> 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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