那些遇到过的问题

是否有必要提交火花应用罐？

Mar*_*ski 9 java cassandra datastax apache-spark

正如标题中所述,我想知道是否有必要激发提交*.jar？

我正在使用Datastax Enterprise Cassandra一段时间,但现在我也需要使用Spark.我观看了几乎所有来自DS320的视频:使用Apache Spark的DataStax Enterprise Analytics,并且没有任何关于从Java应用程序远程连接到spark的信息.

现在我有3个DSE运行节点.我可以从火花壳连接到Spark.但是在尝试从java代码连接Spark后2天我放弃了.

这是我的Java代码 

SparkConf sparkConf = new SparkConf();
sparkConf.setAppName("AppName");
//sparkConf.set("spark.shuffle.blockTransferService", "nio");
//sparkConf.set("spark.driver.host", "*.*.*.*");
//sparkConf.set("spark.driver.port", "7007");
sparkConf.setMaster("spark://*.*.*.*:7077");
JavaSparkContext sc = new JavaSparkContext(sparkConf);
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连接结果

16/01/18 14:32:43 ERROR TransportResponseHandler: Still have 2 requests outstanding when connection from *.*.*.*/*.*.*.*:7077 is closed
16/01/18 14:32:43 WARN AppClient$ClientEndpoint: Failed to connect to master *.*.*.*:7077
java.io.IOException: Connection from *.*.*.*/*.*.*.*:7077 closed
    at org.apache.spark.network.client.TransportResponseHandler.channelUnregistered(TransportResponseHandler.java:124)
    at org.apache.spark.network.server.TransportChannelHandler.channelUnregistered(TransportChannelHandler.java:94)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelUnregistered(AbstractChannelHandlerContext.java:158)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelUnregistered(AbstractChannelHandlerContext.java:144)
    at io.netty.channel.ChannelInboundHandlerAdapter.channelUnregistered(ChannelInboundHandlerAdapter.java:53)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelUnregistered(AbstractChannelHandlerContext.java:158)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelUnregistered(AbstractChannelHandlerContext.java:144)
    at io.netty.channel.ChannelInboundHandlerAdapter.channelUnregistered(ChannelInboundHandlerAdapter.java:53)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelUnregistered(AbstractChannelHandlerContext.java:158)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelUnregistered(AbstractChannelHandlerContext.java:144)
    at io.netty.channel.ChannelInboundHandlerAdapter.channelUnregistered(ChannelInboundHandlerAdapter.java:53)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelUnregistered(AbstractChannelHandlerContext.java:158)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelUnregistered(AbstractChannelHandlerContext.java:144)
    at io.netty.channel.DefaultChannelPipeline.fireChannelUnregistered(DefaultChannelPipeline.java:739)
    at io.netty.channel.AbstractChannel$AbstractUnsafe$8.run(AbstractChannel.java:659)
    at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:357)
    at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:357)
    at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)
    at java.lang.Thread.run(Thread.java:745)
16/01/18 14:33:03 ERROR TransportResponseHandler: Still have 2 requests outstanding when connection from *.*.*.*/*.*.*.*:7077 is closed
16/01/18 14:33:03 WARN AppClient$ClientEndpoint: Failed to connect to master *.*.*.*:7077
java.io.IOException: Connection from *.*.*.*/*.*.*.*:7077 closed
    at org.apache.spark.network.client.TransportResponseHandler.channelUnregistered(TransportResponseHandler.java:124)
    at org.apache.spark.network.server.TransportChannelHandler.channelUnregistered(TransportChannelHandler.java:94)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelUnregistered(AbstractChannelHandlerContext.java:158)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelUnregistered(AbstractChannelHandlerContext.java:144)
    at io.netty.channel.ChannelInboundHandlerAdapter.channelUnregistered(ChannelInboundHandlerAdapter.java:53)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelUnregistered(AbstractChannelHandlerContext.java:158)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelUnregistered(AbstractChannelHandlerContext.java:144)
    at io.netty.channel.ChannelInboundHandlerAdapter.channelUnregistered(ChannelInboundHandlerAdapter.java:53)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelUnregistered(AbstractChannelHandlerContext.java:158)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelUnregistered(AbstractChannelHandlerContext.java:144)
    at io.netty.channel.ChannelInboundHandlerAdapter.channelUnregistered(ChannelInboundHandlerAdapter.java:53)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelUnregistered(AbstractChannelHandlerContext.java:158)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelUnregistered(AbstractChannelHandlerContext.java:144)
    at io.netty.channel.DefaultChannelPipeline.fireChannelUnregistered(DefaultChannelPipeline.java:739)
    at io.netty.channel.AbstractChannel$AbstractUnsafe$8.run(AbstractChannel.java:659)
    at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:357)
    at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:357)
    at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)
    at java.lang.Thread.run(Thread.java:745)
16/01/18 14:33:23 ERROR SparkDeploySchedulerBackend: Application has been killed. Reason: All masters are unresponsive! Giving up.
16/01/18 14:33:23 WARN SparkDeploySchedulerBackend: Application ID is not initialized yet.
16/01/18 14:33:23 WARN AppClient$ClientEndpoint: Drop UnregisterApplication(null) because has not yet connected to master
16/01/18 14:33:23 ERROR MapOutputTrackerMaster: Error communicating with MapOutputTracker
java.lang.InterruptedException
    at java.util.concurrent.locks.AbstractQueuedSynchronizer.tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1326)
    at scala.concurrent.impl.Promise$DefaultPromise.tryAwait(Promise.scala:208)
    at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218)
    at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
    at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:190)
    at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
    at scala.concurrent.Await$.result(package.scala:190)
    at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
    at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:101)
    at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:77)
    at org.apache.spark.MapOutputTracker.askTracker(MapOutputTracker.scala:110)
    at org.apache.spark.MapOutputTracker.sendTracker(MapOutputTracker.scala:120)
    at org.apache.spark.MapOutputTrackerMaster.stop(MapOutputTracker.scala:462)
    at org.apache.spark.SparkEnv.stop(SparkEnv.scala:93)
    at org.apache.spark.SparkContext$$anonfun$stop$12.apply$mcV$sp(SparkContext.scala:1756)
    at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1229)
    at org.apache.spark.SparkContext.stop(SparkContext.scala:1755)
    at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.dead(SparkDeploySchedulerBackend.scala:127)
    at org.apache.spark.deploy.client.AppClient$ClientEndpoint.markDead(AppClient.scala:264)
    at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2$$anonfun$run$1.apply$mcV$sp(AppClient.scala:134)
    at org.apache.spark.util.Utils$.tryOrExit(Utils.scala:1163)
    at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2.run(AppClient.scala:129)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
    at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:308)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:180)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:294)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
16/01/18 14:33:23 ERROR Utils: Uncaught exception in thread appclient-registration-retry-thread
org.apache.spark.SparkException: Error communicating with MapOutputTracker
    at org.apache.spark.MapOutputTracker.askTracker(MapOutputTracker.scala:114)
    at org.apache.spark.MapOutputTracker.sendTracker(MapOutputTracker.scala:120)
    at org.apache.spark.MapOutputTrackerMaster.stop(MapOutputTracker.scala:462)
    at org.apache.spark.SparkEnv.stop(SparkEnv.scala:93)
    at org.apache.spark.SparkContext$$anonfun$stop$12.apply$mcV$sp(SparkContext.scala:1756)
    at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1229)
    at org.apache.spark.SparkContext.stop(SparkContext.scala:1755)
    at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.dead(SparkDeploySchedulerBackend.scala:127)
    at org.apache.spark.deploy.client.AppClient$ClientEndpoint.markDead(AppClient.scala:264)
    at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2$$anonfun$run$1.apply$mcV$sp(AppClient.scala:134)
    at org.apache.spark.util.Utils$.tryOrExit(Utils.scala:1163)
    at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2.run(AppClient.scala:129)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
    at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:308)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:180)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:294)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.InterruptedException
    at java.util.concurrent.locks.AbstractQueuedSynchronizer.tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1326)
    at scala.concurrent.impl.Promise$DefaultPromise.tryAwait(Promise.scala:208)
    at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218)
    at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
    at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:190)
    at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
    at scala.concurrent.Await$.result(package.scala:190)
    at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
    at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:101)
    at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:77)
    at org.apache.spark.MapOutputTracker.askTracker(MapOutputTracker.scala:110)
    ... 18 more
16/01/18 14:33:23 ERROR SparkUncaughtExceptionHandler: Uncaught exception in thread Thread[appclient-registration-retry-thread,5,main]
org.apache.spark.SparkException: Exiting due to error from cluster scheduler: All masters are unresponsive! Giving up.
    at org.apache.spark.scheduler.TaskSchedulerImpl.error(TaskSchedulerImpl.scala:438)
    at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.dead(SparkDeploySchedulerBackend.scala:124)
    at org.apache.spark.deploy.client.AppClient$ClientEndpoint.markDead(AppClient.scala:264)
    at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2$$anonfun$run$1.apply$mcV$sp(AppClient.scala:134)
    at org.apache.spark.util.Utils$.tryOrExit(Utils.scala:1163)
    at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2.run(AppClient.scala:129)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
    at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:308)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:180)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:294)
    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:745)
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我尝试更改SPARK_MASTER_IP,SPARK_LOCAL_IP和许多其他配置变量,但没有成功.现在我发现了一些关于向Spark提交罐子的文章,我不确定(找不到任何证据)是否是原因？spark-submit和interactive shell是使用spark的唯一方法吗？

关于它的任何文章？如果你能给我一个小费,我将不胜感激.

Rus*_*ssS 2

我强烈建议dse spark-submit与 dse 一起使用。虽然这不是必需的，但它肯定比确保为 DSE 设置的安全性和类路径选项适用于您的集群要容易得多。它还提供了一种更简单的方法（在我看来）来配置 SparkConf 并将 jar 放在执行器类路径上。

在 DSE 中，它还会自动将您的应用程序路由到正确的 Spark 主 URL，从而进一步简化设置。

如果您确实想手动构建 SparkConf，请务必将您的 Spark Master 映射到 DSE 版本的输出dsetool spark-master或等效版本。

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