Spark On YARN内存分配

2015.06.09 | Comments

本文主要了解Spark On YARN部署模式下的内存分配情况,因为没有深入研究Spark的源代码,所以只能根据日志去看相关的源代码,从而了解“为什么会这样,为什么会那样”。

说明

按照Spark应用程序中的driver分布方式不同,Spark on YARN有两种模式: yarn-client模式、yarn-cluster模式。

当在YARN上运行Spark作业,每个Spark executor作为一个YARN容器运行。Spark可以使得多个Tasks在同一个容器里面运行。

下图是yarn-cluster模式的作业执行图,图片来源于网络:

关于Spark On YARN相关的配置参数,请参考Spark配置参数。本文主要讨论内存分配情况,所以只需要关注以下几个内心相关的参数:

  • spark.driver.memory:默认值512m
  • spark.executor.memory:默认值512m
  • spark.yarn.am.memory:默认值512m
  • spark.yarn.executor.memoryOverhead:值为executorMemory * 0.07, with minimum of 384
  • spark.yarn.driver.memoryOverhead:值为driverMemory * 0.07, with minimum of 384
  • spark.yarn.am.memoryOverhead:值为AM memory * 0.07, with minimum of 384

注意:

  • --executor-memory/spark.executor.memory 控制 executor 的堆的大小,但是 JVM 本身也会占用一定的堆空间,比如内部的 String 或者直接 byte buffer,spark.yarn.XXX.memoryOverhead属性决定向 YARN 请求的每个 executor 或dirver或am 的额外堆内存大小,默认值为 max(384, 0.07 * spark.executor.memory)
  • 在 executor 执行的时候配置过大的 memory 经常会导致过长的GC延时,64G是推荐的一个 executor 内存大小的上限。
  • HDFS client 在大量并发线程时存在性能问题。大概的估计是每个 executor 中最多5个并行的 task 就可以占满写入带宽。

另外,因为任务是提交到YARN上运行的,所以YARN中有几个关键参数,参考YARN的内存和CPU配置

  • yarn.app.mapreduce.am.resource.mb:AM能够申请的最大内存,默认值为1536MB
  • yarn.nodemanager.resource.memory-mb:nodemanager能够申请的最大内存,默认值为8192MB
  • yarn.scheduler.minimum-allocation-mb:调度时一个container能够申请的最小资源,默认值为1024MB
  • yarn.scheduler.maximum-allocation-mb:调度时一个container能够申请的最大资源,默认值为8192MB

测试

Spark集群测试环境为:

  • master:64G内存,16核cpu
  • worker:128G内存,32核cpu
  • worker:128G内存,32核cpu
  • worker:128G内存,32核cpu
  • worker:128G内存,32核cpu

注意:YARN集群部署在Spark集群之上的,每一个worker节点上同时部署了一个NodeManager,并且YARN集群中的配置如下:

<property>
      <name>yarn.nodemanager.resource.memory-mb</name>
      <value>106496</value> <!-- 104G -->
  </property>
  <property>
      <name>yarn.scheduler.minimum-allocation-mb</name>
      <value>2048</value>
  </property>
  <property>
      <name>yarn.scheduler.maximum-allocation-mb</name>
      <value>106496</value>
  </property>
  <property>
      <name>yarn.app.mapreduce.am.resource.mb</name>
      <value>2048</value>
  </property>

将spark的日志基本调为DEBUG,并将log4j.logger.org.apache.hadoop设置为WARN建设不必要的输出,修改/etc/spark/conf/log4j.properties:

# Set everything to be logged to the console
log4j.rootCategory=DEBUG, console
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

# Settings to quiet third party logs that are too verbose
log4j.logger.org.eclipse.jetty=WARN
log4j.logger.org.apache.hadoop=WARN
log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO

接下来是运行测试程序,以官方自带的SparkPi例子为例,下面主要测试client模式,至于cluster模式请参考下面的过程。运行下面命令:

spark-submit --class org.apache.spark.examples.SparkPi \
    --master yarn-client  \
    --num-executors 4 \
    --driver-memory 2g \
    --executor-memory 3g \
    --executor-cores 4 \
    /usr/lib/spark/lib/spark-examples-1.3.0-cdh5.4.0-hadoop2.6.0-cdh5.4.0.jar \
    100000

观察输出日志(无关的日志被略去):

15/06/08 13:57:01 INFO SparkContext: Running Spark version 1.3.0
15/06/08 13:57:02 INFO SecurityManager: Changing view acls to: root
15/06/08 13:57:02 INFO SecurityManager: Changing modify acls to: root

15/06/08 13:57:03 INFO MemoryStore: MemoryStore started with capacity 1060.3 MB

15/06/08 13:57:04 DEBUG YarnClientSchedulerBackend: ClientArguments called with: --arg bj03-bi-pro-hdpnamenn:51568 --num-executors 4 --num-executors 4 --executor-memory 3g --executor-memory 3g --executor-cores 4 --executor-cores 4 --name Spark Pi
15/06/08 13:57:04 DEBUG YarnClientSchedulerBackend: [actor] handled message (24.52531 ms) ReviveOffers from Actor[akka://sparkDriver/user/CoarseGrainedScheduler#864850679]
15/06/08 13:57:05 INFO Client: Requesting a new application from cluster with 4 NodeManagers
15/06/08 13:57:05 INFO Client: Verifying our application has not requested more than the maximum memory capability of the cluster (106496 MB per container)
15/06/08 13:57:05 INFO Client: Will allocate AM container, with 896 MB memory including 384 MB overhead
15/06/08 13:57:05 INFO Client: Setting up container launch context for our AM

15/06/08 13:57:07 DEBUG Client: ===============================================================================
15/06/08 13:57:07 DEBUG Client: Yarn AM launch context:
15/06/08 13:57:07 DEBUG Client:     user class: N/A
15/06/08 13:57:07 DEBUG Client:     env:
15/06/08 13:57:07 DEBUG Client:         CLASSPATH -> <CPS>/__spark__.jar<CPS>$HADOOP_CONF_DIR<CPS>$HADOOP_COMMON_HOME/*<CPS>$HADOOP_COMMON_HOME/lib/*<CPS>$HADOOP_HDFS_HOME/*<CPS>$HADOOP_HDFS_HOME/lib/*<CPS>$HADOOP_MAPRED_HOME/*<CPS>$HADOOP_MAPRED_HOME/lib/*<CPS>$HADOOP_YARN_HOME/*<CPS>$HADOOP_YARN_HOME/lib/*<CPS>$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/*<CPS>$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/lib/*<CPS>:/usr/lib/spark/lib/spark-assembly.jar::/usr/lib/hadoop/lib/*:/usr/lib/hadoop/*:/usr/lib/hadoop-hdfs/lib/*:/usr/lib/hadoop-hdfs/*:/usr/lib/hadoop-mapreduce/lib/*:/usr/lib/hadoop-mapreduce/*:/usr/lib/hadoop-yarn/lib/*:/usr/lib/hadoop-yarn/*:/usr/lib/hive/lib/*:/usr/lib/flume-ng/lib/*:/usr/lib/paquet/lib/*:/usr/lib/avro/lib/*
15/06/08 13:57:07 DEBUG Client:         SPARK_DIST_CLASSPATH -> :/usr/lib/spark/lib/spark-assembly.jar::/usr/lib/hadoop/lib/*:/usr/lib/hadoop/*:/usr/lib/hadoop-hdfs/lib/*:/usr/lib/hadoop-hdfs/*:/usr/lib/hadoop-mapreduce/lib/*:/usr/lib/hadoop-mapreduce/*:/usr/lib/hadoop-yarn/lib/*:/usr/lib/hadoop-yarn/*:/usr/lib/hive/lib/*:/usr/lib/flume-ng/lib/*:/usr/lib/paquet/lib/*:/usr/lib/avro/lib/*
15/06/08 13:57:07 DEBUG Client:         SPARK_YARN_CACHE_FILES_FILE_SIZES -> 97237208
15/06/08 13:57:07 DEBUG Client:         SPARK_YARN_STAGING_DIR -> .sparkStaging/application_1433742899916_0001
15/06/08 13:57:07 DEBUG Client:         SPARK_YARN_CACHE_FILES_VISIBILITIES -> PRIVATE
15/06/08 13:57:07 DEBUG Client:         SPARK_USER -> root
15/06/08 13:57:07 DEBUG Client:         SPARK_YARN_MODE -> true
15/06/08 13:57:07 DEBUG Client:         SPARK_YARN_CACHE_FILES_TIME_STAMPS -> 1433743027399
15/06/08 13:57:07 DEBUG Client:         SPARK_YARN_CACHE_FILES -> hdfs://mycluster:8020/user/root/.sparkStaging/application_1433742899916_0001/spark-assembly-1.3.0-cdh5.4.0-hadoop2.6.0-cdh5.4.0.jar#__spark__.jar
15/06/08 13:57:07 DEBUG Client:     resources:
15/06/08 13:57:07 DEBUG Client:         __spark__.jar -> resource { scheme: "hdfs" host: "mycluster" port: 8020 file: "/user/root/.sparkStaging/application_1433742899916_0001/spark-assembly-1.3.0-cdh5.4.0-hadoop2.6.0-cdh5.4.0.jar" } size: 97237208 timestamp: 1433743027399 type: FILE visibility: PRIVATE
15/06/08 13:57:07 DEBUG Client:     command:
15/06/08 13:57:07 DEBUG Client:         /bin/java -server -Xmx512m -Djava.io.tmpdir=/tmp '-Dspark.eventLog.enabled=true' '-Dspark.executor.instances=4' '-Dspark.executor.memory=3g' '-Dspark.executor.cores=4' '-Dspark.driver.port=51568' '-Dspark.serializer=org.apache.spark.serializer.KryoSerializer' '-Dspark.driver.appUIAddress=http://bj03-bi-pro-hdpnamenn:4040' '-Dspark.executor.id=<driver>' '-Dspark.kryo.classesToRegister=scala.collection.mutable.BitSet,scala.Tuple2,scala.Tuple1,org.apache.spark.mllib.recommendation.Rating' '-Dspark.driver.maxResultSize=8g' '-Dspark.jars=file:/usr/lib/spark/lib/spark-examples-1.3.0-cdh5.4.0-hadoop2.6.0-cdh5.4.0.jar' '-Dspark.driver.memory=2g' '-Dspark.eventLog.dir=hdfs://mycluster:8020/user/spark/applicationHistory' '-Dspark.app.name=Spark Pi' '-Dspark.fileserver.uri=http://X.X.X.X:49172' '-Dspark.tachyonStore.folderName=spark-81ae0186-8325-40f2-867b-65ee7c922357' -Dspark.yarn.app.container.log.dir=<LOG_DIR> org.apache.spark.deploy.yarn.ExecutorLauncher --arg 'bj03-bi-pro-hdpnamenn:51568' --executor-memory 3072m --executor-cores 4 --num-executors  4 1> <LOG_DIR>/stdout 2> <LOG_DIR>/stderr
15/06/08 13:57:07 DEBUG Client: ===============================================================================

Will allocate AM container, with 896 MB memory including 384 MB overhead日志可以看到,AM占用了896 MB内存,除掉384 MB的overhead内存,实际上只有512 MB,即spark.yarn.am.memory的默认值,另外可以看到YARN集群有4个NodeManager,每个container最多有106496 MB内存。

Yarn AM launch context启动了一个Java进程,设置的JVM内存为512m,见/bin/java -server -Xmx512m

这里为什么会取默认值呢?查看打印上面这行日志的代码,见org.apache.spark.deploy.yarn.Client:

  private def verifyClusterResources(newAppResponse: GetNewApplicationResponse): Unit = {
    val maxMem = newAppResponse.getMaximumResourceCapability().getMemory()
    logInfo("Verifying our application has not requested more than the maximum " +
      s"memory capability of the cluster ($maxMem MB per container)")
    val executorMem = args.executorMemory + executorMemoryOverhead
    if (executorMem > maxMem) {
      throw new IllegalArgumentException(s"Required executor memory (${args.executorMemory}" +
        s"+$executorMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster!")
    }
    val amMem = args.amMemory + amMemoryOverhead
    if (amMem > maxMem) {
      throw new IllegalArgumentException(s"Required AM memory (${args.amMemory}" +
        s"+$amMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster!")
    }
    logInfo("Will allocate AM container, with %d MB memory including %d MB overhead".format(
      amMem,
      amMemoryOverhead))
  }

args.amMemory来自ClientArguments类,这个类中会校验输出参数:

  private def validateArgs(): Unit = {
    if (numExecutors <= 0) {
      throw new IllegalArgumentException(
        "You must specify at least 1 executor!\n" + getUsageMessage())
    }
    if (executorCores < sparkConf.getInt("spark.task.cpus", 1)) {
      throw new SparkException("Executor cores must not be less than " +
        "spark.task.cpus.")
    }
    if (isClusterMode) {
      for (key <- Seq(amMemKey, amMemOverheadKey, amCoresKey)) {
        if (sparkConf.contains(key)) {
          println(s"$key is set but does not apply in cluster mode.")
        }
      }
      amMemory = driverMemory
      amCores = driverCores
    } else {
      for (key <- Seq(driverMemOverheadKey, driverCoresKey)) {
        if (sparkConf.contains(key)) {
          println(s"$key is set but does not apply in client mode.")
        }
      }
      sparkConf.getOption(amMemKey)
        .map(Utils.memoryStringToMb)
        .foreach { mem => amMemory = mem }
      sparkConf.getOption(amCoresKey)
        .map(_.toInt)
        .foreach { cores => amCores = cores }
    }
  }

从上面代码可以看到当 isClusterMode 为true时,则args.amMemory值为driverMemory的值;否则,则从spark.yarn.am.memory中取,如果没有设置该属性,则取默认值512m。isClusterMode 为true的条件是 userClass 不为空,def isClusterMode: Boolean = userClass != null,即输出参数需要有--class参数,而从下面日志可以看到ClientArguments的输出参数中并没有该参数。

15/06/08 13:57:04 DEBUG YarnClientSchedulerBackend: ClientArguments called with: --arg bj03-bi-pro-hdpnamenn:51568 --num-executors 4 --num-executors 4 --executor-memory 3g --executor-memory 3g --executor-cores 4 --executor-cores 4 --name Spark Pi

故,要想设置AM申请的内存值,要么使用cluster模式,要么在client模式中,是有--conf手动设置spark.yarn.am.memory属性,例如:

spark-submit --class org.apache.spark.examples.SparkPi \
    --master yarn-client  \
    --num-executors 4 \
    --driver-memory 2g \
    --executor-memory 3g \
    --executor-cores 4 \
    --conf spark.yarn.am.memory=1024m \
    /usr/lib/spark/lib/spark-examples-1.3.0-cdh5.4.0-hadoop2.6.0-cdh5.4.0.jar \
    100000

打开YARN管理界面,可以看到:

a. Spark Pi 应用启动了5个Container,使用了18G内存、5个CPU core

b. YARN为AM启动了一个Container,占用内存为2048M

c. YARN启动了4个Container运行任务,每一个Container占用内存为4096M

为什么会是2G +4G *4=18G呢?第一个Container只申请了2G内存,是因为我们的程序只为AM申请了512m内存,而yarn.scheduler.minimum-allocation-mb参数决定了最少要申请2G内存。至于其余的Container,我们设置了executor-memory内存为3G,为什么每一个Container占用内存为4096M呢?

为了找出规律,多测试几组数据,分别测试并收集executor-memory为3G、4G、5G、6G时每个executor对应的Container内存申请情况:

  • executor-memory=3g:2G+4G * 4=18G
  • executor-memory=4g:2G+6G * 4=26G
  • executor-memory=5g:2G+6G * 4=26G
  • executor-memory=6g:2G+8G * 4=34G

关于这个问题,我是查看源代码,根据org.apache.spark.deploy.yarn.ApplicationMaster -> YarnRMClient -> YarnAllocator的类查找路径找到YarnAllocator中有这样一段代码:

  // Executor memory in MB.
  protected val executorMemory = args.executorMemory
  // Additional memory overhead.
  protected val memoryOverhead: Int = sparkConf.getInt("spark.yarn.executor.memoryOverhead",
    math.max((MEMORY_OVERHEAD_FACTOR * executorMemory).toInt, MEMORY_OVERHEAD_MIN))
  // Number of cores per executor.
  protected val executorCores = args.executorCores
  // Resource capability requested for each executors
  private val resource = Resource.newInstance(executorMemory + memoryOverhead, executorCores)

因为没有具体的去看YARN的源代码,所以这里猜测Container的大小是根据executorMemory + memoryOverhead计算出来的,大概的规则是每一个Container的大小必须为yarn.scheduler.minimum-allocation-mb值的整数倍,当executor-memory=3g时,executorMemory + memoryOverhead为3G+384M=3456M,需要申请的Container大小为yarn.scheduler.minimum-allocation-mb * 2 =4096m=4G,其他依此类推。

注意:

  • Yarn always rounds up memory requirement to multiples of yarn.scheduler.minimum-allocation-mb, which by default is 1024 or 1GB.
  • Spark adds an overhead to SPARK_EXECUTOR_MEMORY/SPARK_DRIVER_MEMORY before asking Yarn for the amount.

另外,需要注意memoryOverhead的计算方法,当executorMemory的值很大时,memoryOverhead的值相应会变大,这个时候就不是384m了,相应的Container申请的内存值也变大了,例如:当executorMemory设置为90G时,memoryOverhead值为math.max(0.07 * 90G, 384m)=6.3G,其对应的Container申请的内存为98G。

回头看看给AM对应的Container分配2G内存原因,512+384=896,小于2G,故分配2G,你可以在设置spark.yarn.am.memory的值之后再来观察。

打开Spark的管理界面 http://ip:4040 ,可以看到driver和Executor中内存的占用情况:

从上图可以看到Executor占用了1566.7 MB内存,这是怎样计算出来的?参考Spark on Yarn: Where Have All the Memory Gone?这篇文章,totalExecutorMemory的计算方式为:

//yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
  val MEMORY_OVERHEAD_FACTOR = 0.07
  val MEMORY_OVERHEAD_MIN = 384

//yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
  protected val memoryOverhead: Int = sparkConf.getInt("spark.yarn.executor.memoryOverhead",
    math.max((MEMORY_OVERHEAD_FACTOR * executorMemory).toInt, MEMORY_OVERHEAD_MIN))
......
      val totalExecutorMemory = executorMemory + memoryOverhead
      numPendingAllocate.addAndGet(missing)
      logInfo(s"Will allocate $missing executor containers, each with $totalExecutorMemory MB " +
        s"memory including $memoryOverhead MB overhead")

这里我们给executor-memory设置的3G内存,memoryOverhead的值为math.max(0.07 * 3072, 384)=384,其最大可用内存通过下面代码来计算:

//core/src/main/scala/org/apache/spark/storage/BlockManager.scala
/** Return the total amount of storage memory available. */
private def getMaxMemory(conf: SparkConf): Long = {
  val memoryFraction = conf.getDouble("spark.storage.memoryFraction", 0.6)
  val safetyFraction = conf.getDouble("spark.storage.safetyFraction", 0.9)
  (Runtime.getRuntime.maxMemory * memoryFraction * safetyFraction).toLong
}

即,对于executor-memory设置3G时,executor内存占用大约为 3072m * 0.6 * 0.9 = 1658.88m,注意:实际上是应该乘以Runtime.getRuntime.maxMemory的值,该值小于3072m。

上图中driver占用了1060.3 MB,此时driver-memory的值是位2G,故driver中存储内存占用为:2048m * 0.6 * 0.9 =1105.92m,注意:实际上是应该乘以Runtime.getRuntime.maxMemory的值,该值小于2048m。

这时候,查看worker节点CoarseGrainedExecutorBackend进程启动脚本:

$ jps
46841 Worker
21894 CoarseGrainedExecutorBackend
9345
21816 ExecutorLauncher
43369
24300 NodeManager
38012 JournalNode
36929 QuorumPeerMain
22909 Jps

$ ps -ef|grep 21894
nobody   21894 21892 99 17:28 ?        00:04:49 /usr/java/jdk1.7.0_71/bin/java -server -XX:OnOutOfMemoryError=kill %p -Xms3072m -Xmx3072m  -Djava.io.tmpdir=/data/yarn/local/usercache/root/appcache/application_1433742899916_0069/container_1433742899916_0069_01_000003/tmp -Dspark.driver.port=60235 -Dspark.yarn.app.container.log.dir=/data/yarn/logs/application_1433742899916_0069/container_1433742899916_0069_01_000003 org.apache.spark.executor.CoarseGrainedExecutorBackend --driver-url akka.tcp://sparkDriver@bj03-bi-pro-hdpnamenn:60235/user/CoarseGrainedScheduler --executor-id 2 --hostname X.X.X.X --cores 4 --app-id application_1433742899916_0069 --user-class-path file:/data/yarn/local/usercache/root/appcache/application_1433742899916_0069/container_1433742899916_0069_01_000003/__app__.jar

可以看到每个CoarseGrainedExecutorBackend进程分配的内存为3072m,如果我们想查看每个executor的jvm运行情况,可以开启jmx。在/etc/spark/conf/spark-defaults.conf中添加下面一行代码:

spark.executor.extraJavaOptions -Dcom.sun.management.jmxremote.port=1099 -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.authenticate=false

然后,通过jconsole监控jvm堆内存运行情况,这样方便调试内存大小。

总结

由上可知,在client模式下,AM对应的Container内存由spark.yarn.am.memory加上spark.yarn.am.memoryOverhead来确定,executor加上spark.yarn.executor.memoryOverhead的值之后确定对应Container需要申请的内存大小,driver和executor的内存加上spark.yarn.driver.memoryOverheadspark.yarn.executor.memoryOverhead的值之后再乘以0.54确定storage memory内存大小。在YARN中,Container申请的内存大小必须为yarn.scheduler.minimum-allocation-mb的整数倍。

下面这张图展示了Spark on YARN 内存结构,图片来自How-to: Tune Your Apache Spark Jobs (Part 2)

至于cluster模式下的分析,请参考上面的过程。希望这篇文章对你有所帮助!

参考文章


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