Spark提交Jar任务警告: Initial job has not accepted any resources;
时间:2022-07-22
本文章向大家介绍Spark提交Jar任务警告: Initial job has not accepted any resources;,主要内容包括其使用实例、应用技巧、基本知识点总结和需要注意事项,具有一定的参考价值,需要的朋友可以参考一下。
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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