[英]How to connect to remote Spark cluster from python in docker
我在一個帶有用戶docker-user
的容器中安裝了Spark 2.0.0和Python 3。 獨立模式似乎正在運行。
我們在AWS和hadoop上設置了Spark集群。 隨着VPN運行,從筆記本電腦我可以ssh到“內部IP”,如
ssh ubuntu@1.1.1.1
這會登錄。然后
cd /opt/spark/bin
./pyspark
這顯示了Spark 2.0.0和Python 2.7.6。 一個天真的parallelize
示例有效。
現在在Docker支持的Jupyter筆記本中,做
from pyspark import SparkConf, SparkContext
conf = SparkConf().setAppName('hello').setMaster('spark://1.1.1.1:7077').setSparkHome('/opt/spark/')
sc = SparkContext(conf=conf)
這顯然是通過集群進行的,因為我可以在1.1.1.1:8080的Spark儀表板中看到應用程序“hello”。 讓我感到困惑的是,它已經遠離Docker,而不關心ssh,密碼等。
現在嘗試一個天真的parallelize
示例,
x = ['spark', 'rdd', 'example', 'sample', 'example']
y = sc.parallelize(x)
看起來不錯。 然后,
y.collect()
它掛在那里。
在儀表板“執行者摘要”表中,我不確切地知道要查找什么。 但是一個國家exited
工人有這樣的stderr
:
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)
注意Docker用戶docker-user
可能是個問題,因為服務器機器需要ubuntu
。 可能還有其他問題。
Python包paramiko
幫助嗎? 我知道如何使用paramiko
創建一個客戶端對象,通過它發出命令等,就像我登錄到服務器一樣。 但是不知道如何將它與SparkConf
和SparkContext
結合起來。
各種消息來源停止說SparkConf().setMaster('spark://1.1.1.1:7077')
好像它會起作用。 我相信在登錄,密碼,ssh,auth方面有些箍是不可避免的。
謝謝!
火花驅動器必須可以從群集訪問,確保您可以ping您正在運行火花驅動器的機器。 這是因為執行者必須積極聯系司機。 它們不會使TCP連接保持活動狀態(否則不可擴展)。
其他方法是使用客戶端模式以外的群集模式。
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