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...@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-unsubscr...@spark.apache.org > >
