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From Sebastián Ramírez (JIRA) <j...@apache.org>
Subject [jira] [Resolved] (SPARK-4748) PySpark can't read data in HDFS in YARN mode
Date Fri, 05 Dec 2014 13:55:12 GMT

     [ https://issues.apache.org/jira/browse/SPARK-4748?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Sebastián Ramírez resolved SPARK-4748.
--------------------------------------
    Resolution: Invalid

I don't know what was happening, but once I restarted the cluster and all the HDP services
it worked OK.

> PySpark can't read data in HDFS in YARN mode
> --------------------------------------------
>
>                 Key: SPARK-4748
>                 URL: https://issues.apache.org/jira/browse/SPARK-4748
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark, YARN
>    Affects Versions: 1.1.1
>         Environment: Spark 1.1.1 precompiled for Hadoop 2.4
> Hortonworks HDP 2.1
> CentOS 6.6
> (Anaconda 2.1.0 64-bit) Python 2.7.8
> Numpy 1.9.0
>            Reporter: Sebastián Ramírez
>
> Using *PySpark*, I'm being unable to read and process data in *HDFS* in *YARN* cluster
mode.    
> But I can read data from HDFS in local mode.
> I have a 6 nodes cluster with Hortonworks HDP 2.1.    
> The operating system is CentOS 6.6.
> I have installed Anaconda Python (which includes numpy) on every node for the user yarn.
> ----
> h5. This works (*PySpark* local reading from HDFS):
> When I start the console with:
> {code}
> IPYTHON=1 /home/hdfs/spark-1.1.1-bin-hadoop2.4/bin/pyspark --master local
> {code}
> Then I do (that file is in HDFS):
> {code}
> testdata = sc.textFile('/user/hdfs/testdata.csv')
> {code}
> And then:
> {code}
> testdata.first()
> {code}
> I get my data back:
> {code}
> u'asdf,qwer,1,M'
> {code}
> And if I do:
> {code}
> testdata.count()
> {code}
> It also works, I get:
> {code}
> 500
> {code}
> ----
> h5. This also works (*Scala* in YARN cluster reading from HDFS):
> When I start the console with:
> {code}
> /home/hdfs/spark-1.1.1-bin-hadoop2.4/bin/spark-shell --master yarn-client --num-executors
6 --executor-cores 2 --executor-memory 2G --driver-memory 2G 
> {code}
> Then I do (that file is in HDFS):
> {code}
> val testdata = sc.textFile("/user/hdfs/testdata.csv")
> {code}
> And then:
> {code}
> testdata.first()
> {code}
> I get my data back:
> {code}
> res1: String = asdf,qwer,1,M
> {code}
> And if I do:
> {code}
> testdata.count()
> {code}
> It also works, I get:
> {code}
> res2: Long = 500
> {code}
> ----
> h5. This doesn't work (*PySpark* in YARN cluster reading from HDFS):
> When I start the console with:
> {code}
> IPYTHON=1 /home/hdfs/spark-1.1.1-bin-hadoop2.4/bin/pyspark --master yarn-client --num-executors
6 --executor-cores 2 --executor-memory 2G --driver-memory 2G
> {code}
> Then I do (that file is in HDFS):
> {code}
> testdata = sc.textFile('/user/hdfs/testdata.csv')
> {code}
> And then:
> {code}
> testdata.first()
> {code}
> And I get some *INFO* logs, and then a *WARN*:
> {code}
> 14/12/04 15:26:40 WARN scheduler.TaskSetManager: Lost task 0.0 in stage 0.0 (TID 0, node05):
org.apache.spark.api.python.PythonException: Traceback (most recent call last):
>   File "/hadoop/yarn/local/usercache/hdfs/filecache/44/spark-assembly-1.1.1-hadoop2.4.0.jar/pyspark/worker.py",
line 79, in main
>     serializer.dump_stream(func(split_index, iterator), outfile)
>   File "/hadoop/yarn/local/usercache/hdfs/filecache/44/spark-assembly-1.1.1-hadoop2.4.0.jar/pyspark/serializers.py",
line 196, in dump_stream
>     self.serializer.dump_stream(self._batched(iterator), stream)
>   File "/hadoop/yarn/local/usercache/hdfs/filecache/44/spark-assembly-1.1.1-hadoop2.4.0.jar/pyspark/serializers.py",
line 127, in dump_stream
>     for obj in iterator:
>   File "/hadoop/yarn/local/usercache/hdfs/filecache/44/spark-assembly-1.1.1-hadoop2.4.0.jar/pyspark/serializers.py",
line 185, in _batched
>     for item in iterator:
>   File "/home/hdfs/spark-1.1.1-bin-hadoop2.4/python/pyspark/rdd.py", line 1146, in takeUpToNumLeft
> ImportError: No module named next
>         org.apache.spark.api.python.PythonRDD$$anon$1.read(PythonRDD.scala:124)
>         org.apache.spark.api.python.PythonRDD$$anon$1.<init>(PythonRDD.scala:154)
>         org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:87)
>         org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>         org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>         org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62)
>         org.apache.spark.scheduler.Task.run(Task.scala:54)
>         org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178)
>         java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>         java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>         java.lang.Thread.run(Thread.java:744)
> 14/12/04 15:26:40 INFO scheduler.TaskSetManager: Starting task 0.1 in stage 0.0 (TID
1, node05, NODE_LOCAL, 1254 bytes)
> 14/12/04 15:26:40 INFO scheduler.TaskSetManager: Lost task 0.1 in stage 0.0 (TID 1) on
executor node05: org.apache.spark.api.python.PythonException (Traceback (most recent call
last):
>   File "/hadoop/yarn/local/usercache/hdfs/filecache/44/spark-assembly-1.1.1-hadoop2.4.0.jar/pyspark/worker.py",
line 79, in main
>     serializer.dump_stream(func(split_index, iterator), outfile)
>   File "/hadoop/yarn/local/usercache/hdfs/filecache/44/spark-assembly-1.1.1-hadoop2.4.0.jar/pyspark/serializers.py",
line 196, in dump_stream
>     self.serializer.dump_stream(self._batched(iterator), stream)
>   File "/hadoop/yarn/local/usercache/hdfs/filecache/44/spark-assembly-1.1.1-hadoop2.4.0.jar/pyspark/serializers.py",
line 127, in dump_stream
>     for obj in iterator:
>   File "/hadoop/yarn/local/usercache/hdfs/filecache/44/spark-assembly-1.1.1-hadoop2.4.0.jar/pyspark/serializers.py",
line 185, in _batched
>     for item in iterator:
>   File "/home/hdfs/spark-1.1.1-bin-hadoop2.4/python/pyspark/rdd.py", line 1146, in takeUpToNumLeft
> ImportError: No module named next
> ) [duplicate 1]
> {code}
> I get some other *WARN* like that one and some *INFO*, and then an *ERROR*:
> {code}
> 14/12/04 15:26:45 ERROR scheduler.TaskSetManager: Task 0 in stage 0.0 failed 4 times;
aborting job
> 14/12/04 15:26:45 INFO cluster.YarnClientClusterScheduler: Removed TaskSet 0.0, whose
tasks have all completed, from pool 
> {code}
> And then some *INFO* , and finally a *Py4JJavaError*:
> {code}
> ---------------------------------------------------------------------------
> Py4JJavaError                             Traceback (most recent call last)
> <ipython-input-2-39fd6123a6cd> in <module>()
> ----> 1 testdata.first()
> /home/hdfs/spark-1.1.1-bin-hadoop2.4/python/pyspark/rdd.pyc in first(self)
>    1164         2
>    1165         """
> -> 1166         return self.take(1)[0]
>    1167 
>    1168     def saveAsNewAPIHadoopDataset(self, conf, keyConverter=None, valueConverter=None):
> /home/hdfs/spark-1.1.1-bin-hadoop2.4/python/pyspark/rdd.pyc in take(self, num)
>    1150             p = range(
>    1151                 partsScanned, min(partsScanned + numPartsToTry, totalParts))
> -> 1152             res = self.context.runJob(self, takeUpToNumLeft, p, True)
>    1153 
>    1154             items += res
> /home/hdfs/spark-1.1.1-bin-hadoop2.4/python/pyspark/context.pyc in runJob(self, rdd,
partitionFunc, partitions, allowLocal)
>     768         # SparkContext#runJob.
>     769         mappedRDD = rdd.mapPartitions(partitionFunc)
> --> 770         it = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, javaPartitions,
allowLocal)
>     771         return list(mappedRDD._collect_iterator_through_file(it))
>     772 
> /home/hdfs/spark-1.1.1-bin-hadoop2.4/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py
in __call__(self, *args)
>     536         answer = self.gateway_client.send_command(command)
>     537         return_value = get_return_value(answer, self.gateway_client,
> --> 538                 self.target_id, self.name)
>     539 
>     540         for temp_arg in temp_args:
> /home/hdfs/spark-1.1.1-bin-hadoop2.4/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py
in get_return_value(answer, gateway_client, target_id, name)
>     298                 raise Py4JJavaError(
>     299                     'An error occurred while calling {0}{1}{2}.\n'.
> --> 300                     format(target_id, '.', name), value)
>     301             else:
>     302                 raise Py4JError(
> Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage
0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 3, node05): org.apache.spark.api.python.PythonException:
Traceback (most recent call last):
>   File "/hadoop/yarn/local/usercache/hdfs/filecache/44/spark-assembly-1.1.1-hadoop2.4.0.jar/pyspark/worker.py",
line 79, in main
>     serializer.dump_stream(func(split_index, iterator), outfile)
>   File "/hadoop/yarn/local/usercache/hdfs/filecache/44/spark-assembly-1.1.1-hadoop2.4.0.jar/pyspark/serializers.py",
line 196, in dump_stream
>     self.serializer.dump_stream(self._batched(iterator), stream)
>   File "/hadoop/yarn/local/usercache/hdfs/filecache/44/spark-assembly-1.1.1-hadoop2.4.0.jar/pyspark/serializers.py",
line 127, in dump_stream
>     for obj in iterator:
>   File "/hadoop/yarn/local/usercache/hdfs/filecache/44/spark-assembly-1.1.1-hadoop2.4.0.jar/pyspark/serializers.py",
line 185, in _batched
>     for item in iterator:
>   File "/home/hdfs/spark-1.1.1-bin-hadoop2.4/python/pyspark/rdd.py", line 1146, in takeUpToNumLeft
> ImportError: No module named next
>         org.apache.spark.api.python.PythonRDD$$anon$1.read(PythonRDD.scala:124)
>         org.apache.spark.api.python.PythonRDD$$anon$1.<init>(PythonRDD.scala:154)
>         org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:87)
>         org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>         org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>         org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62)
>         org.apache.spark.scheduler.Task.run(Task.scala:54)
>         org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178)
>         java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>         java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>         java.lang.Thread.run(Thread.java:744)
> Driver stacktrace:
> 	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1185)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1174)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1173)
> 	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> 	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> 	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1173)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:688)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:688)
> 	at scala.Option.foreach(Option.scala:236)
> 	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:688)
> 	at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1391)
> 	at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
> 	at akka.actor.ActorCell.invoke(ActorCell.scala:456)
> 	at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
> 	at akka.dispatch.Mailbox.run(Mailbox.scala:219)
> 	at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
> 	at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
> 	at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
> 	at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
> 	at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
> {code}
> And if I do:
> {code}
> testdata.count()
> {code}
> I get a *WARN*:
> {code}
> 14/12/04 15:34:09 WARN scheduler.TaskSetManager: Lost task 1.0 in stage 1.0 (TID 5, node05):
org.apache.spark.SparkException: Python worker exited unexpectedly (crashed)
>         org.apache.spark.api.python.PythonRDD$$anon$1.read(PythonRDD.scala:150)
>         org.apache.spark.api.python.PythonRDD$$anon$1.<init>(PythonRDD.scala:154)
>         org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:87)
>         org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>         org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>         org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62)
>         org.apache.spark.scheduler.Task.run(Task.scala:54)
>         org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178)
>         java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>         java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>         java.lang.Thread.run(Thread.java:744)
> {code}
> Then some *INFO* and finally an *ERROR*:
> {code}
> 14/12/04 15:34:15 ERROR scheduler.TaskSetManager: Task 0 in stage 1.0 failed 4 times;
aborting job
> 14/12/04 15:34:15 INFO cluster.YarnClientClusterScheduler: Cancelling stage 1
> 14/12/04 15:34:15 INFO cluster.YarnClientClusterScheduler: Stage 1 was cancelled
> 14/12/04 15:34:15 INFO scheduler.DAGScheduler: Failed to run count at <ipython-input-3-74bd1c2768a3>:1
> ---------------------------------------------------------------------------
> Py4JJavaError                             Traceback (most recent call last)
> <ipython-input-3-74bd1c2768a3> in <module>()
> ----> 1 testdata.count()
> /home/hdfs/spark-1.1.1-bin-hadoop2.4/python/pyspark/rdd.pyc in count(self)
>     844         3
>     845         """
> --> 846         return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
>     847 
>     848     def stats(self):
> /home/hdfs/spark-1.1.1-bin-hadoop2.4/python/pyspark/rdd.pyc in sum(self)
>     835         6.0
>     836         """
> --> 837         return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
>     838 
>     839     def count(self):
> /home/hdfs/spark-1.1.1-bin-hadoop2.4/python/pyspark/rdd.pyc in reduce(self, f)
>     756             if acc is not None:
>     757                 yield acc
> --> 758         vals = self.mapPartitions(func).collect()
>     759         return reduce(f, vals)
>     760 
> /home/hdfs/spark-1.1.1-bin-hadoop2.4/python/pyspark/rdd.pyc in collect(self)
>     720         """
>     721         with _JavaStackTrace(self.context) as st:
> --> 722             bytesInJava = self._jrdd.collect().iterator()
>     723         return list(self._collect_iterator_through_file(bytesInJava))
>     724 
> /home/hdfs/spark-1.1.1-bin-hadoop2.4/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py
in __call__(self, *args)
>     536         answer = self.gateway_client.send_command(command)
>     537         return_value = get_return_value(answer, self.gateway_client,
> --> 538                 self.target_id, self.name)
>     539 
>     540         for temp_arg in temp_args:
> /home/hdfs/spark-1.1.1-bin-hadoop2.4/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py
in get_return_value(answer, gateway_client, target_id, name)
>     298                 raise Py4JJavaError(
>     299                     'An error occurred while calling {0}{1}{2}.\n'.
> --> 300                     format(target_id, '.', name), value)
>     301             else:
>     302                 raise Py4JError(
> Py4JJavaError: An error occurred while calling o40.collect.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage
1.0 failed 4 times, most recent failure: Lost task 0.3 in stage 1.0 (TID 9, node04): org.apache.spark.SparkException:
Python worker exited unexpectedly (crashed)
>         org.apache.spark.api.python.PythonRDD$$anon$1.read(PythonRDD.scala:150)
>         org.apache.spark.api.python.PythonRDD$$anon$1.<init>(PythonRDD.scala:154)
>         org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:87)
>         org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>         org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>         org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62)
>         org.apache.spark.scheduler.Task.run(Task.scala:54)
>         org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178)
>         java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>         java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>         java.lang.Thread.run(Thread.java:744)
> Driver stacktrace:
> 	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1185)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1174)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1173)
> 	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> 	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> 	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1173)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:688)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:688)
> 	at scala.Option.foreach(Option.scala:236)
> 	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:688)
> 	at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1391)
> 	at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
> 	at akka.actor.ActorCell.invoke(ActorCell.scala:456)
> 	at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
> 	at akka.dispatch.Mailbox.run(Mailbox.scala:219)
> 	at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
> 	at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
> 	at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
> 	at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
> 	at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
> {code}
> ----
> Maybe I'm doing something wrong, I would appreciate any feedback.



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