spark-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Akhilanand <akhilanand...@gmail.com>
Subject Re: Difference between dataset and dataframe
Date Tue, 19 Feb 2019 04:59:10 GMT
Thanks for the reply. But can you please tell why dataframes are performant than datasets?
Any specifics would be helpful.

Also, could you comment on the tungsten code gen part of my question?


> On Feb 18, 2019, at 10:47 PM, Koert Kuipers <koert@tresata.com> wrote:
> 
> in the api DataFrame is just Dataset[Row]. so this makes you think Dataset is the generic
api. interestingly enough under the hood everything is really Dataset[Row], so DataFrame is
really the "native" language for spark sql, not Dataset.
> 
> i find DataFrame to be significantly more performant. in general if you use Dataset you
miss out on some optimizations. also Encoders are not very pleasant to work with.
> 
>> On Mon, Feb 18, 2019 at 9:09 PM Akhilanand <akhilanand.bv@gmail.com> wrote:
>> 
>> Hello, 
>> 
>> I have been recently exploring about dataset and dataframes. I would really appreciate
if someone could answer these questions:
>> 
>> 1) Is there any difference in terms performance when we use datasets over dataframes?
Is it significant to choose 1 over other. I do realise there would be some overhead due case
classes but how significant is that? Are there any other implications. 
>> 
>> 2) Is the Tungsten code generation done only for datasets or is there any internal
process to generate bytecode for dataframes as well? Since its related to jvm , I think its
just for datasets but I couldn’t find anything that tells it specifically. If its just for
datasets , does that mean we miss out on the project tungsten optimisation for dataframes?
>> 
>> 
>> 
>> Regards,
>> Akhilanand BV
>> 
>> ---------------------------------------------------------------------
>> To unsubscribe e-mail: user-unsubscribe@spark.apache.org
>> 

Mime
View raw message