Initial job has not accepted any resources; check your cluster UI to ensure that workers are registe
2015-12-08 19:00
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spark在提交任务时,出现如下错误:
15/03/26 22:29:36 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory15/03/26 22:29:51 WARN TaskSchedulerImpl: Initial job has not accepted any resources;
check your cluster UI to ensure that workers are registered and have sufficient memory15/03/26 22:30:06 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory15/03/26
22:30:21 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory
从警告信息上看,初始化job时没有获取到任何资源;提示检查集群,确保workers可以被注册并有足够的内存资源。
如上问题产生的原因是多方面的,可能原因如下:
1.因为提交任务的节点不能和spark工作节点交互,因为提交完任务后提交任务节点上会起一个进程,展示任务进度,大多端口为4044,工作节点需要反馈进度给该该端口,所以如果主机名或者IP在hosts中配置不正确。所以检查下主机名和ip是否配置正确
2.有可能是内存不足
检查内存
conf.set("spark.executor.memory", "3000m")
Make sure to set
查看8080端口,确保一些workers保持Alive状态,确保 some cores 是可利用的。
15/03/26 22:29:36 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory15/03/26 22:29:51 WARN TaskSchedulerImpl: Initial job has not accepted any resources;
check your cluster UI to ensure that workers are registered and have sufficient memory15/03/26 22:30:06 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory15/03/26
22:30:21 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory
从警告信息上看,初始化job时没有获取到任何资源;提示检查集群,确保workers可以被注册并有足够的内存资源。
如上问题产生的原因是多方面的,可能原因如下:
1.因为提交任务的节点不能和spark工作节点交互,因为提交完任务后提交任务节点上会起一个进程,展示任务进度,大多端口为4044,工作节点需要反馈进度给该该端口,所以如果主机名或者IP在hosts中配置不正确。所以检查下主机名和ip是否配置正确
2.有可能是内存不足
检查内存
conf.set("spark.executor.memory", "3000m")
Make sure to set
SPARK_LOCAL_IPand
SPARK_MASTER_IP.
查看8080端口,确保一些workers保持Alive状态,确保 some cores 是可利用的。
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