These intermediate file what sort of files are there. Are there csv type
files.
I agree that DF is more efficient than an RDD as it follows tabular format
(I assume that is what you mean by "columnar" format). So if you read these
files in a bath process you may not worry too much about execution time?
A textFile saving is simply a one to one mapping from your DF to HDFS. I
think it is pretty efficient.
For myself, I would do something like below
myDF.rdd.repartition(1).cache.saveAsTextFile("mypath/output")
HTH
Dr Mich Talebzadeh
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On 14 September 2016 at 12:46, sanat kumar Patnaik <patnaik.sa...@gmail.com>
wrote:
> Hi All,
>
>
> - I am writing a batch application using Spark SQL and Dataframes.
> This application has a bunch of file joins and there are intermediate
> points where I need to drop a file for downstream applications to consume.
> - The problem is all these downstream applications are still on
> legacy, so they still require us to drop them a text file.As you all must
> be knowing Dataframe stores the data in columnar format internally.
>
> Only way I found out how to do this and which looks awfully slow is this:
>
> myDF=sc.textFile("inputpath").toDF()
> myDF.rdd.repartition(1).saveAsTextFile("mypath/output")
>
> Is there any better way to do this?
>
> *P.S: *The other workaround would be to use RDDs for all my operations.
> But I am wary of using them as the documentation says Dataframes are way
> faster because of the Catalyst engine running behind the scene.
>
> Please suggest if any of you might have tried something similar.
>
