Can you post more information about the number of files, their size and the 
executor logs.

A gzipped file is not splittable i.e. Only one executor can gunzip it (the 
unzipped data can then be processed in parallel). 
Wholetextfile was designed to be executed only on one executor (e.g. For 
processing xmls which are difficult to process in parallel).

Then, if you have small files (< HDFS blocksize) they are also only processed 
on one executor by default.

You may repartition though for parallel processing in even those cases.

> On 11 Feb 2017, at 21:40, Paul Tremblay <paulhtremb...@gmail.com> wrote:
> 
> I've been working on this problem for several days (I am doing more to 
> increase my knowledge of Spark). The code you linked to hangs because after 
> reading in the file, I have to gunzip it. 
> Another way that seems to be working is reading each file in using 
> sc.textFile, and then writing it the HDFS, and then using wholeTextFiles for 
> the HDFS result. 
> But the bigger issue is that both methods are not executed in parallel. When 
> I open my yarn manager, it shows that only one node is being used. 
> 
> Henry
> 
>> On 02/06/2017 03:39 PM, Jon Gregg wrote:
>> Strange that it's working for some directories but not others.  Looks like 
>> wholeTextFiles maybe doesn't work with S3?  
>> https://issues.apache.org/jira/browse/SPARK-4414 .  
>> 
>> If it's possible to load the data into EMR and run Spark from there that may 
>> be a workaround.  This blogspot shows a python workaround that might work as 
>> well: http://michaelryanbell.com/processing-whole-files-spark-s3.html
>> 
>> Jon
>> 
>> 
>> On Mon, Feb 6, 2017 at 6:38 PM, Paul Tremblay <paulhtremb...@gmail.com> 
>> wrote:
>>> I've actually been able to trace the problem to the files being read in. If 
>>> I change to a different directory, then I don't get the error. Is one of 
>>> the executors running out of memory?
>>> 
>>> 
>>> 
>>> 
>>> 
>>>> On 02/06/2017 02:35 PM, Paul Tremblay wrote:
>>>> When I try to create an rdd using wholeTextFiles, I get an 
>>>> incomprehensible error. But when I use the same path with sc.textFile, I 
>>>> get no error.
>>>> 
>>>> I am using pyspark with spark 2.1.
>>>> 
>>>> in_path = 
>>>> 's3://commoncrawl/crawl-data/CC-MAIN-2016-50/segments/1480698542939.6/warc/
>>>> 
>>>> rdd = sc.wholeTextFiles(in_path)
>>>> 
>>>> rdd.take(1)
>>>> 
>>>> 
>>>> /usr/lib/spark/python/pyspark/rdd.py in take(self, num)
>>>>    1341
>>>>    1342             p = range(partsScanned, min(partsScanned + 
>>>> numPartsToTry, totalParts))
>>>> -> 1343             res = self.context.runJob(self, takeUpToNumLeft, p)
>>>>    1344
>>>>    1345             items += res
>>>> 
>>>> /usr/lib/spark/python/pyspark/context.py in runJob(self, rdd, 
>>>> partitionFunc, partitions, allowLocal)
>>>>     963         # SparkContext#runJob.
>>>>     964         mappedRDD = rdd.mapPartitions(partitionFunc)
>>>> --> 965         port = self._jvm.PythonRDD.runJob(self._jsc.sc(), 
>>>> mappedRDD._jrdd, partitions)
>>>>     966         return list(_load_from_socket(port, 
>>>> mappedRDD._jrdd_deserializer))
>>>>     967
>>>> 
>>>> /usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py in 
>>>> __call__(self, *args)
>>>>    1131         answer = self.gateway_client.send_command(command)
>>>>    1132         return_value = get_return_value(
>>>> -> 1133             answer, self.gateway_client, self.target_id, self.name)
>>>>    1134
>>>>    1135         for temp_arg in temp_args:
>>>> 
>>>> /usr/lib/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
>>>>      61     def deco(*a, **kw):
>>>>      62         try:
>>>> ---> 63             return f(*a, **kw)
>>>>      64         except py4j.protocol.Py4JJavaError as e:
>>>>      65             s = e.java_exception.toString()
>>>> 
>>>> /usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py in 
>>>> get_return_value(answer, gateway_client, target_id, name)
>>>>     317                 raise Py4JJavaError(
>>>>     318                     "An error occurred while calling {0}{1}{2}.\n".
>>>> --> 319                     format(target_id, ".", name), value)
>>>>     320             else:
>>>>     321                 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 1.0 failed 4 times, most recent failure: Lost task 0.3 in stage 
>>>> 1.0 (TID 7, ip-172-31-45-114.us-west-2.compute.internal, executor 8): 
>>>> ExecutorLostFailure (executor 8 exited caused by one of the running tasks) 
>>>> Reason: Container marked as failed: container_1486415078210_0005_01_000016 
>>>> on host: ip-172-31-45-114.us-west-2.compute.internal. Exit status: 52. 
>>>> Diagnostics: Exception from container-launch.
>>>> Container id: container_1486415078210_0005_01_000016
>>>> Exit code: 52
>>>> Stack trace: ExitCodeException exitCode=52:
>>>>     at org.apache.hadoop.util.Shell.runCommand(Shell.java:582)
>>>>     at org.apache.hadoop.util.Shell.run(Shell.java:479)
>>>>     at 
>>>> org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:773)
>>>>     at 
>>>> org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:212)
>>>>     at 
>>>> org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:302)
>>>>     at 
>>>> org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)
>>>>     at java.util.concurrent.FutureTask.run(FutureTask.java:266)
>>>>     at 
>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>>>>     at 
>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>>>>     at java.lang.Thread.run(Thread.java:745)
>>>> 
>>>> rdd = sc.textFile(in_path)
>>>> 
>>>> In [8]: rdd.take(1)
>>>> Out[8]: [u'WARC/1.0']
>>>> 
>>> 
>>> 
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>>> 
>> 
> 

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