[
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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