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From Steve Loughran <>
Subject Re: spark 2.0.0 - when saving a model to S3 spark creates temporary files. Why?
Date Thu, 25 Aug 2016 15:04:45 GMT

With Hadoop 2.7 or later, set

spark.hadooop.mapreduce.fileoutputcommitter.algorithm.version  2
spark.hadoop.mapreduce.fileoutputcommitter.cleanup.skipped true

This switches to a no -rename version of the file output committer, is faster all round. You
are still at risk of things going wrong on failure though, and with speculation enabled.

you are still at risk o

On 25 Aug 2016, at 13:16, Tal Grynbaum <<>>

Is/was there an option similar to DirectParquetOutputCommitter to write json files to S3 ?

On Thu, Aug 25, 2016 at 2:56 PM, Takeshi Yamamuro <<>>

Seems this just prevents writers from leaving partial data in a destination dir when jobs
In the previous versions of Spark, there was a way to directly write data in a destination
Spark v2.0+ has no way to do that because of the critial issue on S3 (See: SPARK-10063).

// maropu

On Thu, Aug 25, 2016 at 2:40 PM, Tal Grynbaum <<>>

I read somewhere that its because s3 has to know the size of the file upfront
I dont really understand this,  as to why is it ok  not to know it for the temp files and
not ok for the final files.
The delete permission is the minor disadvantage from my side,  the worst thing is that i have
a cluster of 100 machines sitting idle for 15 minutes waiting for copy to end.

Any suggestions how to avoid that?

On Thu, Aug 25, 2016, 08:21 Aseem Bansal <<>>

When Spark saves anything to S3 it creates temporary files. Why? Asking this as this requires
the the access credentails to be given delete permissions along with write permissions.

Takeshi Yamamuro

Tal Grynbaum / CTO & co-founder

m# +972-54-7875797

        mobile retention done right

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