如标题所述，我想知道是否有必要 spark-submit *.jar？
我使用 Datastax Enterprise Cassandra 有一段时间了，但现在我也需要使用 Spark。 DS320: DataStax Enterprise Analytics with Apache Spark的几乎所有视频我都看了并且没有关于从 Java 应用程序远程连接到 Spark 的内容。
现在我有 3 个正在运行的 DSE 节点。我可以从 spark shell 连接到 Spark。但在尝试从 Java 代码连接到 Spark 2 天后，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);
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)
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将 jars 提交给 Spark 的文章，我不确定(找不到任何证据)是否是原因？ spark-submit 和交互式 shell 是使用 spark 的唯一方法吗？
有相关文章吗？如果您能给我小费，我将不胜感激。
最佳答案
我强烈推荐将 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)来配置您的 SparkConf 并将 jar 放在执行程序类路径上。
在 DSE 中，它还会自动将您的应用程序路由到正确的 Spark 主 url，进一步简化设置。
如果你真的想手动构建你的 SparkConf，请确保将你的 spark master 映射到 dsetool spark-master 的输出或它在你的 DSE 版本中的等价物。
关于java - spark应用jar是否需要提交？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/34876451/
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