spark on yarn 动态资源分配

张映 发表于 2019-10-24

分类目录: hadoop/spark/scala

标签:,

不同的模式运行spark,动态资源分配的配置是不一样的,本文的模式是spark on  yarn。

动态资源分配的意思是,需要的时候拿资源,不需要的时候,归还资源。

优点提高资源的利用率,还有不需要像静态模式那样,根据cpu数,内存数,指定Executor内存数,去算Executor了。

一,官方配置说明

Configuring the External Shuffle Service

To start the Spark Shuffle Service on each NodeManager in your YARN cluster, follow these instructions:

1,Build Spark with the YARN profile. Skip this step if you are using a pre-packaged distribution.
2,Locate the spark-<version>-yarn-shuffle.jar. This should be under $SPARK_HOME/common/network-yarn/target/scala-<version> if you are building Spark yourself, and under yarn if you are using a distribution.
3,Add this jar to the classpath of all NodeManagers in your cluster.
4,In the yarn-site.xml on each node, add spark_shuffle to yarn.nodemanager.aux-services, then set yarn.nodemanager.aux-services.spark_shuffle.class to org.apache.spark.network.yarn.YarnShuffleService.
5,Increase NodeManager's heap size by setting YARN_HEAPSIZE (1000 by default) in etc/hadoop/yarn-env.sh to avoid garbage collection issues during shuffle.
6,Restart all NodeManagers in your cluster.

上面要点都说了,但是不同的spark版本,配置有所不同

二,配置yarn-site.xml

# vim ${HADOOP_HOME}/etc/hadoop/yarn-site.xml

<property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce_shuffle,spark_shuffle</value>
</property>

<property>
    <name>yarn.nodemanager.aux-services.spark_shuffle.class</name>
    <value>org.apache.spark.network.yarn.YarnShuffleService</value>
</property>

<property>
    <name>spark.shuffle.service.port</name>
    <value>7337</value>
</property>

同步该配置到所有的hadoop节点中

三,配置spark

1,配置spark-defaults.conf

# vim $SPARK_HOME/conf/spark-defaults.conf

spark.master yarn
spark.eventLog.enabled true
spark.eventLog.dir hdfs://bigdata1/spark/logs
spark.driver.memory 2g
spark.executor.memory 2g

spark.shuffle.service.enabled true
spark.shuffle.service.port 7337
spark.dynamicAllocation.enabled true
spark.dynamicAllocation.minExecutors 1
spark.dynamicAllocation.maxExecutors 6
spark.dynamicAllocation.schedulerBacklogTimeout 1s
spark.dynamicAllocation.sustainedSchedulerBacklogTimeout 5s

spark.submit.deployMode client
spark.yarn.jars hdfs://bigdata1/spark/jars/*
spark.serializer org.apache.spark.serializer.KryoSerializer

同步该配置到所有的spark节点中

主要参数说明:

参数名 默认值 描述
spark.dynamicAllocation.executorIdleTimeout 60s executor空闲时间达到规定值,则将该executor移除。
spark.dynamicAllocation.cachedExecutorIdleTimeout infinity 缓存了数据的executor默认不会被移除
spark.dynamicAllocation.maxExecutors infinity 最多使用的executor数,默认为你申请的最大executor数
spark.dynamicAllocation.minExecutors 0 最少保留的executor数
spark.dynamicAllocation.schedulerBacklogTimeout 1s 有task等待运行时间超过该值后开始启动executor
spark.dynamicAllocation.executorIdleTimeout schedulerBacklogTimeout 动态启动executor的间隔
spark.dynamicAllocation.initialExecutors spark.dynamicAllocation.minExecutors 如果所有的executor都移除了,重新请求时启动的初始executor数

2,加载包spark-2.4.0-yarn-shuffle.jar

# ln -s ${SPARK_HOME}/yarn/spark-2.4.0-yarn-shuffle.jar ${HADOOP_HOME}/share/hadoop/yarn/lib

copy或者加软连都可以,这一步很重要,不同的spark版本,shuffle.jar包的位置是不一样的

3,重启hadoop集群

[root@bigserver2 conf]# jps
8880 DataNode
25505 HRegionServer
8979 JournalNode
24198 QuorumPeerMain
9078 NodeManager   //nodemanager
16630 Jps
26473 Kafka
[root@bigserver2 conf]# netstat -tpnl |grep 7337
tcp6 0 0 :::7337 :::* LISTEN 9078/java   //只有nodemanager才有该进程

四,测试动态资源分配

1,命令行启动spark-sql,执行sql

spark 动态资源分配

spark 动态资源分配

spark 执行历史中查看

spark 执行历史中查看

对了一下正式环境,正式环境是静态资源分配,正式环境不会回收

2,在执行一次sql

spark 执行完回收executor

spark 执行完回收executor



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作者:海底苍鹰
地址:http://blog.51yip.com/hadoop/2213.html

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