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From Sandy Ryza <sandy.r...@cloudera.com>
Subject Re: RFC: Supporting the Scala drop Method for Spark RDDs
Date Tue, 22 Jul 2014 05:50:24 GMT
It could make sense to add a skipHeader argument to SparkContext.textFile?


On Mon, Jul 21, 2014 at 10:37 PM, Reynold Xin <rxin@databricks.com> wrote:

> If the purpose is for dropping csv headers, perhaps we don't really need a
> common drop and only one that drops the first line in a file? I'd really
> try hard to avoid a common drop/dropWhile because they can be expensive to
> do.
>
> Note that I think we will be adding this functionality (ignoring headers)
> to the CsvRDD functionality in Spark SQL.
>  https://github.com/apache/spark/pull/1351
>
>
> On Mon, Jul 21, 2014 at 1:45 PM, Mark Hamstra <mark@clearstorydata.com>
> wrote:
>
> > You can find some of the prior, related discussion here:
> > https://issues.apache.org/jira/browse/SPARK-1021
> >
> >
> > On Mon, Jul 21, 2014 at 1:25 PM, Erik Erlandson <eje@redhat.com> wrote:
> >
> > >
> > >
> > > ----- 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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