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From Erik Erlandson <...@redhat.com>
Subject Re: RFC: Supporting the Scala drop Method for Spark RDDs
Date Mon, 21 Jul 2014 20:25:37 GMT


----- Original Message -----
> Rather than embrace non-lazy transformations and add more of them, I'd
> rather we 1) try to fully characterize the needs that are driving their
> creation/usage; and 2) design and implement new Spark abstractions that
> will allow us to meet those needs and eliminate existing non-lazy
> transformation.  


In the case of drop, obtaining the index of the boundary partition can be viewed as the action
forcing compute -- one that happens to be invoked inside of a transform.  The concept of a
"lazy action", that is only triggered if the result rdd has compute invoked on it, might be
sufficient to restore laziness to the drop transform.   For that matter, I might find some
way to make use of Scala lazy values directly and achieve the same goal for drop.



> They really mess up things like creation of asynchronous
> FutureActions, job cancellation and accounting of job resource usage, etc.,
> so I'd rather we seek a way out of the existing hole rather than make it
> deeper.
> 
> 
> On Mon, Jul 21, 2014 at 10:24 AM, Erik Erlandson <eje@redhat.com> wrote:
> 
> >
> >
> > ----- Original Message -----
> > > Sure, drop() would be useful, but breaking the "transformations are lazy;
> > > only actions launch jobs" model is abhorrent -- which is not to say that
> > we
> > > haven't already broken that model for useful operations (cf.
> > > RangePartitioner, which is used for sorted RDDs), but rather that each
> > such
> > > exception to the model is a significant source of pain that can be hard
> > to
> > > work with or work around.
> >
> > A thought that comes to my mind here is that there are in fact already two
> > categories of transform: ones that are truly lazy, and ones that are not.
> >  A possible option is to embrace that, and commit to documenting the two
> > categories as such, with an obvious bias towards favoring lazy transforms
> > (to paraphrase Churchill, we're down to haggling over the price).
> >
> >
> > >
> > > I really wouldn't like to see another such model-breaking transformation
> > > added to the API.  On the other hand, being able to write transformations
> > > with dependencies on these kind of "internal" jobs is sometimes very
> > > useful, so a significant reworking of Spark's Dependency model that would
> > > allow for lazily running such internal jobs and making the results
> > > available to subsequent stages may be something worth pursuing.
> >
> >
> > This seems like a very interesting angle.   I don't have much feel for
> > what a solution would look like, but it sounds as if it would involve
> > caching all operations embodied by RDD transform method code for
> > provisional execution.  I believe that these levels of invocation are
> > currently executed in the master, not executor nodes.
> >
> >
> > >
> > >
> > > On Mon, Jul 21, 2014 at 8:27 AM, Andrew Ash <andrew@andrewash.com>
> > wrote:
> > >
> > > > Personally I'd find the method useful -- I've often had a .csv file
> > with a
> > > > header row that I want to drop so filter it out, which touches all
> > > > partitions anyway.  I don't have any comments on the implementation
> > quite
> > > > yet though.
> > > >
> > > >
> > > > On Mon, Jul 21, 2014 at 8:24 AM, Erik Erlandson <eje@redhat.com>
> > wrote:
> > > >
> > > > > A few weeks ago I submitted a PR for supporting rdd.drop(n), under
> > > > > SPARK-2315:
> > > > > https://issues.apache.org/jira/browse/SPARK-2315
> > > > >
> > > > > Supporting the drop method would make some operations convenient,
> > however
> > > > > it forces computation of >= 1 partition of the parent RDD, and
so it
> > > > would
> > > > > behave like a "partial action" that returns an RDD as the result.
> > > > >
> > > > > I wrote up a discussion of these trade-offs here:
> > > > >
> > > > >
> > > >
> > http://erikerlandson.github.io/blog/2014/07/20/some-implications-of-supporting-the-scala-drop-method-for-spark-rdds/
> > > > >
> > > >
> > >
> >
> 

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