如何从docker

时间：2016-08-16 18:01:31

标签： python apache-spark ssh docker paramiko

我在一个用户为docker-user的容器中安装了Spark 2.0.0和Python 3。独立模式似乎正在运行。

我们已在AWS和hadoop上设置了Spark群集。随着VPN的运行，从笔记本电脑我可以ssh到＆＃34;内部IP＆＃34;，如

ssh ubuntu@1.1.1.1

登录。然后

cd /opt/spark/bin
./pyspark

这显示了Spark 2.0.0和Python 2.7.6。天真的parallelize示例有效。

现在在Docker支持的Jupyter Notebook中，执行

from pyspark import SparkConf, SparkContext
conf = SparkConf().setAppName('hello').setMaster('spark://1.1.1.1:7077').setSparkHome('/opt/spark/')
sc = SparkContext(conf=conf)

这显然是通过群集，因为我可以看到应用程序＆＃34;你好＆＃34;在1.1.1.1:8080的Spark仪表板中。让我感到困惑的是，它已远离Docker，而不关心ssh，密码等。

现在尝试一个天真的parallelize示例，

x = ['spark', 'rdd', 'example', 'sample', 'example']
y = sc.parallelize(x)

看起来不错。然后，

y.collect()

它挂在那里。

在仪表板上＆＃34;执行人摘要＆＃34;桌子，我不知道到底要找什么。但是一个状态为exited的工人有stderr这样：

16/08/16 17:37:01 INFO SignalUtils: Registered signal handler for TERM
16/08/16 17:37:01 INFO SignalUtils: Registered signal handler for HUP
16/08/16 17:37:01 INFO SignalUtils: Registered signal handler for INT
16/08/16 17:37:02 INFO SecurityManager: Changing view acls to: ubuntu,docker-user
16/08/16 17:37:02 INFO SecurityManager: Changing modify acls to: ubuntu,docker-user
16/08/16 17:37:02 INFO SecurityManager: Changing view acls groups to: 
16/08/16 17:37:02 INFO SecurityManager: Changing modify acls groups to: 
16/08/16 17:37:02 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(ubuntu, docker-user); groups with view permissions: Set(); users  with modify permissions: Set(ubuntu, docker-user); groups with modify permissions: Set()
Exception in thread "main" java.lang.reflect.UndeclaredThrowableException
    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1671)
    at org.apache.spark.deploy.SparkHadoopUtil.runAsSparkUser(SparkHadoopUtil.scala:70)
    at org.apache.spark.executor.CoarseGrainedExecutorBackend$.run(CoarseGrainedExecutorBackend.scala:166)
    at org.apache.spark.executor.CoarseGrainedExecutorBackend$.main(CoarseGrainedExecutorBackend.scala:262)
    at org.apache.spark.executor.CoarseGrainedExecutorBackend.main(CoarseGrainedExecutorBackend.scala)
Caused by: org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
    at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
    at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
    at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
    at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:36)
    at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:216)
    at scala.util.Try$.apply(Try.scala:192)
    at scala.util.Failure.recover(Try.scala:216)
    at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:326)
    at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:326)
    at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
    at org.spark_project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
    at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:136)
    at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
    at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
    at scala.concurrent.Promise$class.complete(Promise.scala:55)
    at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
    at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:237)
    at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:237)
    at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
    at scala.concurrent.BatchingExecutor$Batch$$anonfun$run$1.processBatch$1(BatchingExecutor.scala:63)
    at scala.concurrent.BatchingExecutor$Batch$$anonfun$run$1.apply$mcV$sp(BatchingExecutor.scala:78)
    at scala.concurrent.BatchingExecutor$Batch$$anonfun$run$1.apply(BatchingExecutor.scala:55)
    at scala.concurrent.BatchingExecutor$Batch$$anonfun$run$1.apply(BatchingExecutor.scala:55)
    at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
    at scala.concurrent.BatchingExecutor$Batch.run(BatchingExecutor.scala:54)
    at scala.concurrent.Future$InternalCallbackExecutor$.unbatchedExecute(Future.scala:601)
    at scala.concurrent.BatchingExecutor$class.execute(BatchingExecutor.scala:106)
    at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:599)
    at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
    at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
    at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
    at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
    at org.apache.spark.rpc.netty.NettyRpcEnv.org$apache$spark$rpc$netty$NettyRpcEnv$$onFailure$1(NettyRpcEnv.scala:205)
    at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:239)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
    at java.util.concurrent.FutureTask.run(FutureTask.java:266)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
    ... 8 more
java.lang.IllegalArgumentException: requirement failed: TransportClient has not yet been set.
    at scala.Predef$.require(Predef.scala:224)
    at org.apache.spark.rpc.netty.RpcOutboxMessage.onTimeout(Outbox.scala:70)
    at org.apache.spark.rpc.netty.NettyRpcEnv$$anonfun$ask$1.applyOrElse(NettyRpcEnv.scala:232)
    at org.apache.spark.rpc.netty.NettyRpcEnv$$anonfun$ask$1.applyOrElse(NettyRpcEnv.scala:231)
    at scala.concurrent.Future$$anonfun$onFailure$1.apply(Future.scala:138)
    at scala.concurrent.Future$$anonfun$onFailure$1.apply(Future.scala:136)
    at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
    at org.spark_project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
    at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:136)
    at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
    at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
    at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)

请注意，Docker用户docker-user可能存在问题，因为服务器计算机需要ubuntu。可能还有其他问题。

Python包paramiko可以在这里提供帮助吗？我知道如何使用paramiko创建一个客户端对象，通过该对象发出命令等，就像我登录到服务器一样。但不知道如何将其与SparkConf和SparkContext结合使用。

各种消息来源停止说SparkConf().setMaster('spark://1.1.1.1:7077')好像它会起作用。我相信对于登录，密码，ssh，auth来说，有些箍是不可避免的。

谢谢！

1 个答案:

答案 0 :(得分：2)

必须从群集中访问

spark驱动程序，请确保您可以ping通运行spark驱动程序的计算机。这是因为执行者必须主动与驱动程序联系。它们无法保持TCP连接有效（否则无法扩展）。

另一种方法是使用客户端模式以外的群集模式。

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