Spark提交Jar任务警告: Initial job has not accepted any resources;

时间静止不是简史

发布于 2020-07-27 14:20:15

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发布于 2020-07-27 14:20:15

文章被收录于专栏：Java探索之路 Java探索之路Java探索之路

错误信息描述

在Spark提交任务时, 会一直出现下面警告

会一直出现警告信息

WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources

# 提交任务的命令
 ./spark-submit --master spark://node1:7077 --class  ah.szxy.scalaspark.sql.DataSetAndDataFrame.CreateDataFrameFromHive /root/test/MySpark-1.0-SNAPSHOT-jar-with-dependencies.jar

300秒之后还会导致超时问题

Exception in thread "main" java.util.concurrent.TimeoutException: Futures timed out after [300 seconds]
	at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
	at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
	at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:201)
	at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec.doExecuteBroadcast(BroadcastExchangeExec.scala:136)
	at org.apache.spark.sql.execution.InputAdapter.doExecuteBroadcast(WholeStageCodegenExec.scala:367)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeBroadcast$1.apply(SparkPlan.scala:144)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeBroadcast$1.apply(SparkPlan.scala:140)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
	at org.apache.spark.sql.execution.SparkPlan.executeBroadcast(SparkPlan.scala:140)
	at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.prepareBroadcast(BroadcastHashJoinExec.scala:135)
	at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.codegenInner(BroadcastHashJoinExec.scala:232)
	at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.doConsume(BroadcastHashJoinExec.scala:102)
	at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:181)
	at org.apache.spark.sql.execution.FilterExec.consume(basicPhysicalOperators.scala:85)
	at org.apache.spark.sql.execution.FilterExec.doConsume(basicPhysicalOperators.scala:206)
	at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:181)
	at org.apache.spark.sql.execution.InputAdapter.consume(WholeStageCodegenExec.scala:354)
	at org.apache.spark.sql.execution.InputAdapter.doProduce(WholeStageCodegenExec.scala:383)
	at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:88)
	at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:83)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
	at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:83)
	at org.apache.spark.sql.execution.InputAdapter.produce(WholeStageCodegenExec.scala:354)
	at org.apache.spark.sql.execution.FilterExec.doProduce(basicPhysicalOperators.scala:125)
	at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:88)
	at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:83)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
	at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:83)
	at org.apache.spark.sql.execution.FilterExec.produce(basicPhysicalOperators.scala:85)
	at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.doProduce(BroadcastHashJoinExec.scala:97)
	at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:88)
	at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:83)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
	at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:83)
	at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.produce(BroadcastHashJoinExec.scala:39)
	at org.apache.spark.sql.execution.ProjectExec.doProduce(basicPhysicalOperators.scala:45)
	at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:88)
	at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:83)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
	at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:83)
	at org.apache.spark.sql.execution.ProjectExec.produce(basicPhysicalOperators.scala:35)
	at org.apache.spark.sql.execution.BaseLimitExec$class.doProduce(limit.scala:70)
	at org.apache.spark.sql.execution.LocalLimitExec.doProduce(limit.scala:97)
	at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:88)
	at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:83)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
	at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:83)
	at org.apache.spark.sql.execution.LocalLimitExec.produce(limit.scala:97)
	at org.apache.spark.sql.execution.WholeStageCodegenExec.doCodeGen(WholeStageCodegenExec.scala:524)
	at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:576)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
	at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
	at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:247)
	at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:337)
	at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
	at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3273)
	at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484)
	at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484)
	at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3254)
	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
	at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3253)
	at org.apache.spark.sql.Dataset.head(Dataset.scala:2484)
	at org.apache.spark.sql.Dataset.take(Dataset.scala:2698)
	at org.apache.spark.sql.Dataset.showString(Dataset.scala:254)
	at org.apache.spark.sql.Dataset.show(Dataset.scala:723)
	at org.apache.spark.sql.Dataset.show(Dataset.scala:682)
	at com.bjsxt.scalaspark.sql.DataSetAndDataFrame.CreateDataFrameFromHive$.main(CreateDataFrameFromHive.scala:24)
	at com.bjsxt.scalaspark.sql.DataSetAndDataFrame.CreateDataFrameFromHive.main(CreateDataFrameFromHive.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
	at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:894)
	at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:198)
	at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:228)
	at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:137)
	at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

错误解决思路

根据提示我们查看Spark Master的WebUI

可以看到当前正在运行的程序有两个, 第一个是我们提交的Spark任务, 但是这个任务没有分到相应的核数, 且任务状态是等待状态 ( 原因是申请不到资源 ); 而资源都被第二个任务(Spark-Shell ,处于Running状态 )所占因此我们可以通过WebUI 将该任务 Kill即可
由图我们可以看到阻塞的任务有重新运行了

注意: 我们还可以在提交任务时指定执行核的参数以及内存参数也能解决该问题, 总思路就是能够让当前应用程序能够申请并使用资源

总结

提交任务的时候也会用到Spark shell, 因此应该将别的窗口的Spark Shell关闭
Spark Shell 进入,Spark的bin目录下 ./spark-shell --master spark://node1:7077
Spark Master WebUI端口为 8080
Spark用到的端口\ 50070 Hdfs WebUI 7077 Spark Master通信端口 8080 Spark Master WebUI 8081 Spark Worker WebUI 18080 Spark HistoryServer WebUI 4040 Spark Driver WebUI 端口任务调度 9083 hive源数据服务端口(node3)

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原始发表：2019-12-20 ，如有侵权请联系 cloudcommunity@tencent.com 删除

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