Я пытаюсь запустить процессор Spark на Spring XD для потоковой передачи.
Модуль процессора spark в Spring XD работает, когда spark указывает на локальный. Процессор не запускается, когда мы указываем искру на автономную искру (работающую на той же машине) или на клиент пряжи. Можно ли запустить искровой процессор на автономной искре или на пряже внутри пружины XD, или здесь единственным вариантом является локальная искра?
Модуль процессора определяется как:
class WordCount extends Processor[String, (String, Int)] {
def process(input: ReceiverInputDStream[String]): DStream[(String, Int)] = {
val words = input.flatMap(_.split(" "))
val pairs = words.map(word => (word, 1))
val wordCounts = pairs.reduceByKey(_ + _)
wordCounts
}
@SparkConfig
def properties : Properties = {
val props = new Properties()
// Any specific Spark configuration properties would go here.
// These properties always get the highest precedence
//props.setProperty("spark.master", "spark://a.b.c.d:7077")
**props.setProperty("spark.master", "spark://abcd.hadoop.ambari:7077**")
props
}
}
Процессор нормально работает, когда конфиг задан как локальный. Есть ли что-то, чего мне не хватает в объявлениях.
Спасибо !
РЕДАКТИРОВАТЬ: ЖУРНАЛ ОШИБОК
//commands executed on xd-shell
===================================================================
spark/sbin/start-all.sh
module upload --file /opt/igc_services/SparkDev/XdWordCount/build/libs/spark-streaming-wordcount-scala-processor-0.1.0.jar --name scala-word-count --type processor
stream create spark-streaming-word-count --definition "http | processor:scala-word-count | log" --deploy
// Error Log
====================================================================
2015-09-16T14:28:48+0530 1.2.0.RELEASE INFO DeploymentsPathChildrenCache-0 container.DeploymentListener - Deploying module 'log' for stream 'spark-streaming-word-count'
2015-09-16T14:28:48+0530 1.2.0.RELEASE INFO DeploymentsPathChildrenCache-0 container.DeploymentListener - Deploying module [ModuleDescriptor@6dbc4f81 moduleName = 'log', moduleLabel = 'log', group = 'spark-streaming-word-count', sourceChannelName = [null], sinkChannelName = [null], index = 2, type = sink, parameters = map[[empty]], children = list[[empty]]]
2015-09-16T14:28:48+0530 1.2.0.RELEASE INFO DeploymentsPathChildrenCache-0 container.DeploymentListener - Path cache event: path=/deployments/modules/allocated/4ff3ba84-e6ca-47dd-894f-aa92bdbb3e06/spark-streaming-word-count.processor.processor.1, type=CHILD_ADDED
2015-09-16T14:28:48+0530 1.2.0.RELEASE INFO DeploymentsPathChildrenCache-0 container.DeploymentListener - Deploying module 'processor' for stream 'spark-streaming-word-count'
2015-09-16T14:28:48+0530 1.2.0.RELEASE INFO DeploymentsPathChildrenCache-0 container.DeploymentListener - Deploying module [ModuleDescriptor@5e16dafb moduleName = 'scala-word-count', moduleLabel = 'processor', group = 'spark-streaming-word-count', sourceChannelName = [null], sinkChannelName = [null], index = 1, type = processor, parameters = map[[empty]], children = list[[empty]]]
2015-09-16T14:28:49+0530 1.2.0.RELEASE WARN DeploymentsPathChildrenCache-0 util.NativeCodeLoader - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2015-09-16T14:28:49+0530 1.2.0.RELEASE WARN sparkDriver-akka.actor.default-dispatcher-3 remote.ReliableDeliverySupervisor - Association with remote system [akka.tcp://[email protected]:7077] has failed, address is now gated for [5000] ms. Reason is: [Disassociated].
2015-09-16T14:29:09+0530 1.2.0.RELEASE WARN sparkDriver-akka.actor.default-dispatcher-4 remote.ReliableDeliverySupervisor - Association with remote system [akka.tcp://[email protected]:7077] has failed, address is now gated for [5000] ms. Reason is: [Disassociated].
2015-09-16T14:29:18+0530 1.2.0.RELEASE INFO DeploymentsPathChildrenCache-0 container.DeploymentListener - Path cache event: path=/deployments/modules/allocated/8d07cdba-557e-458a-9225-b90e5a5778ce/spark-streaming-word-count.source.http.1, type=CHILD_ADDED
2015-09-16T14:29:18+0530 1.2.0.RELEASE INFO DeploymentsPathChildrenCache-0 container.DeploymentListener - Deploying module 'http' for stream 'spark-streaming-word-count'
2015-09-16T14:29:18+0530 1.2.0.RELEASE INFO DeploymentsPathChildrenCache-0 container.DeploymentListener - Deploying module [ModuleDescriptor@610e43b0 moduleName = 'http', moduleLabel = 'http', group = 'spark-streaming-word-count', sourceChannelName = [null], sinkChannelName = [null], index = 0, type = source, parameters = map[[empty]], children = list[[empty]]]
2015-09-16T14:29:19+0530 1.2.0.RELEASE INFO DeploymentSupervisor-0 zk.ZKStreamDeploymentHandler - Deployment status for stream 'spark-streaming-word-count': DeploymentStatus{state=failed,error(s)=Deployment of module 'ModuleDeploymentKey{stream='spark-streaming-word-count', type=processor, label='processor'}' to container '4ff3ba84-e6ca-47dd-894f-aa92bdbb3e06' timed out after 30000 ms}
2015-09-16T14:29:29+0530 1.2.0.RELEASE WARN sparkDriver-akka.actor.default-dispatcher-4 remote.ReliableDeliverySupervisor - Association with remote system [akka.tcp://[email protected]:7077] has failed, address is now gated for [5000] ms. Reason is: [Disassociated].
2015-09-16T14:29:49+0530 1.2.0.RELEASE ERROR sparkDriver-akka.actor.default-dispatcher-3 cluster.SparkDeploySchedulerBackend - Application has been killed. Reason: All masters are unresponsive! Giving up.
2015-09-16T14:29:49+0530 1.2.0.RELEASE WARN DeploymentsPathChildrenCache-0 cluster.SparkDeploySchedulerBackend - Application ID is not initialized yet.
2015-09-16T14:29:49+0530 1.2.0.RELEASE ERROR sparkDriver-akka.actor.default-dispatcher-3 scheduler.TaskSchedulerImpl - Exiting due to error from cluster scheduler: All masters are unresponsive! Giving up.
2015-09-16T14:29:50+0530 1.2.0.RELEASE INFO DeploymentSupervisor-0 zk.ContainerListener - Path cache event: path=/containers/4ff3ba84-e6ca-47dd-894f-aa92bdbb3e06, type=CHILD_REMOVED
2015-09-16T14:29:50+0530 1.2.0.RELEASE INFO DeploymentSupervisor-0 zk.ContainerListener - Container departed: Container{name='4ff3ba84-e6ca-47dd-894f-aa92bdbb3e06', attributes={groups=, host=abcd.hadoop.ambari, id=4ff3ba84-e6ca-47dd-894f-aa92bdbb3e06, managementPort=54998, ip=a.b.c.d, pid=4597}}
spark.master
с помощью @SparkConfig, и он отлично работает в кластере Spark. Есть ли у вас трассировка стека или любая другая отладочная информация при работе в кластерном режиме? - person Ilayaperumal Gopinathan   schedule 25.09.2015