我在AWS上有一个独立的Spark群集设置。 我有pyspark程序在我的本地机器上按预期运行。当我使用spark-submit将作业提交给AWS上的主节点时，一切似乎都很好，直到我必须从S3提取一些数据。
这是我提交的作业的主GUI上的日志
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
16/08/11 16:23:04 INFO CoarseGrainedExecutorBackend: Started daemon with process name: 8911@ip-10-32-3-4
16/08/11 16:23:04 INFO SignalUtils: Registered signal handler for TERM
16/08/11 16:23:04 INFO SignalUtils: Registered signal handler for HUP
16/08/11 16:23:04 INFO SignalUtils: Registered signal handler for INT
16/08/11 16:23:04 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/08/11 16:23:04 INFO SecurityManager: Changing view acls to: spark,kasra
16/08/11 16:23:04 INFO SecurityManager: Changing modify acls to: spark,kasra
16/08/11 16:23:04 INFO SecurityManager: Changing view acls groups to:
16/08/11 16:23:04 INFO SecurityManager: Changing modify acls groups to:
16/08/11 16:23:04 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(spark, kasra); groups with view permissions: Set(); users with modify permissions: Set(spark, kasra); groups with modify permissions: Set()
Exception in thread "main" java.lang.reflect.UndeclaredThrowableException
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1672)
at org.apache.spark.deploy.SparkHadoopUtil.runAsSparkUser(SparkHadoopUtil.scala:70)
at org.apache.spark.executor.CoarseGrainedExecutorBackend$.run(CoarseGrainedExecutorBackend.scala:174)
at org.apache.spark.executor.CoarseGrainedExecutorBackend$.main(CoarseGrainedExecutorBackend.scala:270)
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)
根据上面的日志，在我看来，Master以S3的方式进行过滤
这背后的代码片段是
sc._jsc.hadoopConfiguration().set("fs.s3n.awsAccessKeyId", '...')
sc._jsc.hadoopConfiguration().set("fs.s3n.awsSecretAccessKey", '...')
config_dict = {"fs.s3n.awsAccessKeyId":"...",
"fs.s3n.awsSecretAccessKey":"..."}
rdd = sc.hadoopFile('s3n://kasra.districtm.ca/SEGMENTS/SMALL_CSV/aa',
'org.apache.hadoop.mapred.TextInputFormat',
'org.apache.hadoop.io.Text',
'org.apache.hadoop.io.LongWritable',
conf=config_dict)
注意：使用spark 2