ok so it looks like ew scalar needs to do the all-rows test in case of DrmLike[Int], i.e. lazy props nrow == count (and nrow in this case gives max(key)+1). if test doesn't hold, then it needs to pre-process by cogrouping with parallelizing 0 until nrow and insert stuff where is missing. Some care needs to be taken with co-grouping shuffle, as usual, based on expected number of added rows.
ok this is probably an easy fix. On Mon, Jul 21, 2014 at 3:50 PM, Anand Avati <[email protected]> wrote: > On Mon, Jul 21, 2014 at 3:46 PM, Pat Ferrel <[email protected]> wrote: > > > And the conversion to Matrix instantiates the new rows, why not the > > conversion to Dense? > > > I addressed this point too in my previous emails. The problem with plus(n) > is not about sparse vs dense. It is in-core Matrix vs DRM. > > Thanks > > > > > On Jul 21, 2014, at 3:41 PM, Anand Avati <[email protected]> wrote: > > > > On Mon, Jul 21, 2014 at 3:35 PM, Pat Ferrel <[email protected]> > wrote: > > > > > If you do drm.plus(1) this converts to a dense matrix, which is what > the > > > result must be anyway, and does add the scalar to all rows, even > missing > > > ones. > > > > > > > > Pat, I mentioned this in my previous email already. drm.plus(1) > completely > > misses the point. It converts DRM into an in-core matrix and applies > plus() > > method on Matrix. The result is a Matrix, not DRM. > > > > drm.plus(1) is EXACTLY the same as: > > > > Matrix m = drm.collect() > > m.plus(1) > > > > The implicit def drm2InCore() syntactic sugar is probably turning out to > be > > dangerous in this case, in terms of hinting the wrong meaning. > > > > Thanks > > > > > > > > > > > > > On Jul 21, 2014, at 3:23 PM, Dmitriy Lyubimov <[email protected]> > wrote: > > > > > > perhaps just compare row count with max(key)? that's exactly what lazy > > > nrow() currently does in this case. > > > > > > > > > On Mon, Jul 21, 2014 at 3:21 PM, Dmitriy Lyubimov <[email protected]> > > > wrote: > > > > > >> > > >> ok. so it should be easy to fix at least everything but elementwise > > > scalar > > >> i guess. > > >> > > >> Since the notion of "missing rows" is only defined for int-keyed > > > datasets, > > >> then ew scalar technically should work for non-int keyed datasets > > > already. > > >> > > >> as for int-keyed datasets, i am not sure what is the best strategy. > > >> Obviously, one can define sort of normalization/validation of > int-keyed > > >> dataset routine, but it would be fairly expensive to run "just > because". > > >> Perhaps there's a cheap test (as cheap as row count job) to run to > test > > > for > > >> int keys consistency when matrix is first created. > > >> > > >> > > >> > > >> On Mon, Jul 21, 2014 at 3:12 PM, Anand Avati <[email protected]> > wrote: > > >> > > >>> > > >>> > > >>> > > >>> On Mon, Jul 21, 2014 at 3:08 PM, Dmitriy Lyubimov <[email protected] > > > > >>> wrote: > > >>> > > >>>> > > >>>> > > >>>> > > >>>> On Mon, Jul 21, 2014 at 3:06 PM, Anand Avati <[email protected]> > > > wrote: > > >>>> > > >>>>> Dmitriy, comments inline - > > >>>>> > > >>>>> On Jul 21, 2014, at 1:12 PM, Dmitriy Lyubimov <[email protected]> > > >>>>> wrote: > > >>>>> > > >>>>>> And no, i suppose it is ok to have "missing" rows even in case of > > >>>>>> int-keyed matrices. > > >>>>>> > > >>>>>> there's one thing that you probably should be aware in this > context > > >>>>>> though: many algorithms don't survive empty (row-less) partitions, > > in > > >>>>>> whatever way they may come to be. Other than that, I don't feel > > > every row > > >>>>>> must be present -- even if there's implied order of the rows. > > >>>>>> > > >>>>> > > >>>>> I'm not sure if that is necessarily true. There are three operators > > >>>>> which break pretty badly with with missing rows. > > >>>>> > > >>>>> AewScalar - operation like A + 1 is just not applied on the missing > > >>>>> row, so the final matrix will have 0's in place of 1s. > > >>>>> > > >>>> > > >>>> Indeed. i have no recourse at this point. > > >>>> > > >>>> > > >>>>> > > >>>>> AewB, CbindAB - function after cogroup() throws exception if a row > > was > > >>>>> present on only one matrix. So I guess it is OK to have missing > rows > > > as > > >>>>> long as both A and B have the exact same missing row set. Somewhat > > >>>>> quirky/nuanced requirement. > > >>>>> > > >>>> > > >>>> Agree. i actually was not aware that's a cogroup() semantics in > spark. > > > I > > >>>> though it would have an outer join semantics (as in Pig, i believe). > > > Alas, > > >>>> no recourse at this point either. > > >>>> > > >>> > > >>> The exception is actually during reduceLeft after cogroup(). > Cogroup() > > >>> itself is probably an outer-join. > > >>> > > >>> > > >>> > > >> > > > > > > > > > > >
