【spark】spark计算Pi

最新推荐文章于 2024-04-16 03:32:32 发布

原创最新推荐文章于 2024-04-16 03:32:32 发布 · 7.3k 阅读

3 ·

CC 4.0 BY-SA版权

文章标签：

#spark

大数据专栏收录该内容

30 篇文章

订阅专栏

本文介绍了如何使用Spark计算Pi，包括命令格式和参数设置。在遇到`SparkException: Could not find CoarseGrainedScheduler`错误时，通过添加`--total-executor-cores`参数解决了问题。此外，还探讨了在cluster模式下运行Spark应用，通过WebUI查看结果的方法。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

cd $SPARK_HOME/bin
./spark-submit --master spark://node111:7077 --class org.apache.spark.examples.SparkPi ../examples/jars/spark-examples_2.11-2.1.1.jar 100

命令格式：./$SPARK_HOME/bin/spark-submit --master spark://[master hostname]:7077 --class[包名.类名] [jar包位置]

hostname:node111

不同spark版本，jar包位置不一样

--class CLASS_NAME          Your application's main class (for Java / Scala apps).
--name NAME                 A name of your application.
--jars JARS                 Comma-separated list of local jars to include on the driv

1000代表启动1000个线程，根据自己core的数量设置最佳线程数量。

运行结果：Pi is roughly 3.1419091141909115

报错：

org.apache.spark.SparkException: Could not find CoarseGrainedScheduler.
   at org.apache.spark.rpc.netty.Dispatcher.postMessage(Dispatcher.scala:154)
   at org.apache.spark.rpc.netty.Dispatcher.postOneWayMessage(Dispatcher.scala:134)
   at org.apache.spark.rpc.netty.NettyRpcEnv.send(NettyRpcEnv.scala:186)
   at org.apache.spark.rpc.netty.NettyRpcEndpointRef.send(NettyRpcEnv.scala:512)
   at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.reviveOffers(CoarseGrainedSchedulerBackend.scala:406)
   at org.apache.spark.scheduler.TaskSchedulerImpl.executorLost(TaskSchedulerImpl.scala:518)
   at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint.org$apache$spark$scheduler$cluster$CoarseGrainedSchedulerBackend$DriverEndpoint$$removeExecutor(CoarseGrainedSchedulerBackend.scala:293)
   at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint$$anonfun$onDisconnected$1.apply(CoarseGrainedSchedulerBackend.scala:228)
   at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint$$anonfun$onDisconnected$1.apply(CoarseGrainedSchedulerBackend.scala:228)
   at scala.Option.foreach(Option.scala:257)
   at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint.onDisconnected(CoarseGrainedSchedulerBackend.scala:228)
   at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:143)
   at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:205)
   at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:101)
   at org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:213)
   at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
   at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
   at java.lang.Thread.run(Thread.java:748)

【解决上面报错】

修改：新加参数 --total-executor-cores

./spark-submit --master spark://node111:7077 --total-executor-cores 3 --class org.apache.spark.examples.SparkPi ../examples/jars/spark-examples_2.11-2.1.1.jar 100

--total-executor-cores NUM Total cores for all executors.

集群内当前的application提供了core的数量

运行结果：

Pi is roughly 3.1417603141760315

不报错。

【cluster模式】

新增参数 --deploy-mode cluster

运行结果：