Hello Romi,

Do you mean that in my particular case I’m causing computation on dataFrame or 
it is regular behavior of DataFrame.toJavaRDD ? 
If it’s regular behavior, do you know which approach could be used to perform 
make/reduce on dataFrame without causing it to load all data to driver program ?

> On Nov 4, 2015, at 12:34 PM, Romi Kuntsman <r...@totango.com> wrote:
> 
> I noticed that toJavaRDD causes a computation on the DataFrame, so is it 
> considered an action, even though logically it's a transformation?
> 
> On Nov 4, 2015 6:51 PM, "Aliaksei Tsyvunchyk" <atsyvunc...@exadel.com 
> <mailto:atsyvunc...@exadel.com>> wrote:
> Hello folks,
> 
> Recently I have noticed unexpectedly big network traffic between Driver 
> Program and Worker node.
> During debugging I have figured out that it is caused by following block of 
> code 
> 
> —————— Java ——— ————— 
> DataFrame etpvRecords = context.sql(" SOME SQL query here");
> Mapper m = new Mapper(localValue, ProgramId::toProgId);
> return etpvRecords
>                 .toJavaRDD()
>                 .map(m::mapHutPutViewingRow)
>                 .reduce(Reducer::reduce);
> —————— Java ———————— 
> 
> I’m using debug breakpoint and OS X nettop to monitor traffic between 
> processes. So before approaching line toJavaRDD() I have 500Kb of traffic and 
> after executing this line I have 2.2 Mb of traffic. But when I check size of 
> result of reduce function it is 10 Kb.
> So .toJavaRDD() seems causing worker process return dataset to driver process 
> and seems further map/reduce occurs on Driver. 
> 
> This is definitely not expected by me, so I have 2 questions.
> 1.  Is it really expected behavior that DataFrame.toJavaRDD cause whole 
> dataset return to driver or I’m doing something wrong?
> 2.  What is expected way to perform transformation with DataFrame using 
> custom Java map\reduce functions in case if standard SQL features are not fit 
> all my needs?
> 
> Env: Spark 1.5.1 in standalone mode, (1 master, 1 worker sharing same 
> machine). Java 1.8.0_60.
> 
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