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 <e...@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 <e...@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 <and...@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 <e...@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/ > > > > > > > > > > > > > > > > > > > > >
