It could be that by using the rdd it converts the data from the internal format
to Java objects (-> much more memory is needed), which may lead to spill over
to disk. This conversion takes a lot of time. Then, you need to transfer these
Java objects via network to one single node (repartition ...), which takes on a
1 gbit network for 3 gb (since it may transfer Java objects this might be even
more for 3 gb) under optimal conditions ca 25 seconds (if no other transfers
happening at the same time, jumbo frames activated etc). On the destination
node we may have again spill over to disk. Then you store them to a single disk
(potentially multiple if you have and use HDFS) which takes also time (assuming
that no other process uses this disk).
Btw spark-csv can be used with different dataframes.
As said, other options are compression, avoid repartitioning (to avoid network
transfer), avoid spilling to disk (provide memory in yarn etc), increase
network bandwidth ...
> On 14 Sep 2016, at 14:22, sanat kumar Patnaik <patnaik.sa...@gmail.com> wrote:
>
> These are not csv files, utf8 files with a specific delimiter.
> I tried this out with a file(3 GB):
>
> myDF.write.json("output/myJson")
> Time taken- 60 secs approximately.
>
> myDF.rdd.repartition(1).saveAsTextFile("output/text")
> Time taken 160 secs
>
> That is where I am concerned, the time to write a text file compared to json
> grows exponentially.
>
>> On Wednesday, September 14, 2016, Mich Talebzadeh
>> <mich.talebza...@gmail.com> wrote:
>> 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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>>
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>>
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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.
>>
>
>
> --
> Regards,
> Sanat Patnaik
> Cell->804-882-6424
