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From Thomas Demoor <>
Subject Re: performance of saveAsTextFile moving files from _temporary
Date Wed, 28 Jan 2015 09:46:49 GMT
TLDR Extend FileOutPutCommitter to eliminate the temporary_storage. There
are some implementations to be found online, typically called
DirectOutputCommitter, f.i. this spark pull request
Tell Spark to use your shiny new committer when writing to an object store
and all will be well.

Aaron is on the right track but this renaming is bottlenecked by the object
storage system itself, irrespective of being executed in the driver or the
executor. Object stores (s3, google, azure, amplidata :P...) do not have a
native rename (it is implemented as a server side copy operation, thus its
duration is proportional to the object size). By default, Hadoop (and thus
also Spark) uses rename from temporary to final output path to enable both
retrying and speculative execution  because HDFS is a single writer system,
so multiple "job attempts" cannot write to the final output concurrently.
Object stores do allow multiple concurrent-writes to the same output, which
is exactly what makes a native rename nigh impossible. The solution is to
enable this concurrent writing instead of renaming to final output by using
a custom OutputCommitter which does not use a temporary location.

Thomas Demoor
skype: demoor.thomas
mobile: +32 497883833

On Wed, Jan 28, 2015 at 3:54 AM, Josh Walton <> wrote:

> I'm not sure how to confirm how the moving is happening, however, one of
> the jobs just completed that I was talking about with 9k files of 4mb each.
> Spark UI showed the job being complete after ~2 hours. The last four hours
> of the job was just moving the files from _temporary to their final
> destination. The tasks for the write were definitely shown as complete, no
> logging is happening on the master or workers. The last line of my java
> code logs, but the job sits there as the moving of files happens.
> On Tue, Jan 27, 2015 at 7:24 PM, Aaron Davidson <>
> wrote:
>> This renaming from _temporary to the final location is actually done by
>> executors, in parallel, for saveAsTextFile. It should be performed by each
>> task individually before it returns.
>> I have seen an issue similar to what you mention dealing with Hive code
>> which did the renaming serially on the driver, which is very slow for S3
>> (and possibly Google Storage as well), as it actually copies the data
>> rather than doing a metadata-only operation during rename. However, this
>> should not be an issue in this case.
>> Could you confirm how the moving is happening -- i.e., on the executors
>> or the driver?
>> On Tue, Jan 27, 2015 at 4:31 PM, jwalton <> wrote:
>>> We are running spark in Google Compute Engine using their One-Click
>>> Deploy.
>>> By doing so, we get their Google Cloud Storage connector for hadoop for
>>> free
>>> meaning we can specify gs:// paths for input and output.
>>> We have jobs that take a couple of hours, end up with ~9k partitions
>>> which
>>> means 9k output files. After the job is "complete" it then moves the
>>> output
>>> files from our $output_path/_temporary to $output_path. That process can
>>> take longer than the job itself depending on the circumstances. The job I
>>> mentioned previously outputs ~4mb files, and so far has copied 1/3 of the
>>> files in 1.5 hours from _temporary to the final destination.
>>> Is there a solution to this besides reducing the number of partitions?
>>> Anyone else run into similar issues elsewhere? I don't remember this
>>> being
>>> an issue with Map Reduce jobs and hadoop, however, I probably wasn't
>>> tracking the transfer of the output files like I am with Spark.
>>> --
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