我看到的问题是我在主节点上慢慢耗尽Java堆。下面是我创建的一个简单的例子,它只重复了200次。使用下面的设置,主机在大约1小时内耗尽内存,并出现以下错误:
16/12/15 17:55:46 INFO YarnSchedulerBackend$YarnDriverEndpoint: Launching task 97578 on executor id: 9 hostname: ip-xxx-xxx-xx-xx
#
# java.lang.OutOfMemoryError: Java heap space
# -XX:OnOutOfMemoryError="kill -9 %p"
# Executing /bin/sh -c "kill -9 20160"...
守则:
import org.apache.spark.sql.functions._
import org.apache.spark._
object MemTest {
case class X(colval: Long, colname: Long, ID: Long)
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("MemTest")
val spark = new SparkContext(conf)
val sc = org.apache.spark.sql.SQLContext.getOrCreate(spark)
import sc.implicits._;
for( a <- 1 to 200)
{
var df = spark.parallelize((1 to 5000000).map(x => X(x.toLong, x.toLong % 10, x.toLong / 10 ))).toDF()
df = df.groupBy("ID").pivot("colname").agg(max("colval"))
df.count
}
spark.stop()
}
}
我使用m4.xlarge(4个节点+ 1个主节点)在AWS emr-5.1.0上运行。这是我的火花设置
{
"Classification": "spark-defaults",
"Properties": {
"spark.dynamicAllocation.enabled": "false",
"spark.executor.instances": "16",
"spark.executor.memory": "2560m",
"spark.driver.memory": "768m",
"spark.executor.cores": "1"
}
},
{
"Classification": "spark",
"Properties": {
"maximizeResourceAllocation": "false"
}
},
我用sbt编译
name := "Simple Project"
version := "1.0"
scalaVersion := "2.11.7"
libraryDependencies ++= Seq(
"org.apache.spark" %% "spark-core" % "2.0.2" % "provided",
"org.apache.spark" %% "spark-sql" % "2.0.2")
然后使用
运行它spark-submit --class MemTest target/scala-2.11/simple-project_2.11-1.0.jar
使用jmap -histo
查看内存时,我看到java.lang.Long
和scala.Tuple2
一直在增长。
答案 0 :(得分:0)
您确定群集上安装的spark版本是2.0.2吗?
或者,如果群集中有多个Spark安装,您确定要调用正确的(2.0.2)spark-submit吗?
(我很遗憾无法发表评论,以便我将此作为答案发布的原因)