It could make sense to add a skipHeader argument to SparkContext.textFile?
On Mon, Jul 21, 2014 at 10:37 PM, Reynold Xin <r...@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 <m...@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 <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/ > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >
