As far as I know, there is an implementation of tensor type in C++/Python already. Should we just finalize the spec and add implementation to Java?
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On the Spark side, it's probably more complicated as Vector and Matrix are not "first class" types in Spark SQL. Spark ML implements them as UDT (user-defined types) so it's not clear how to make Spark/Arrow converter work with them. I wonder if Bryan and Holden have some more thoughts on that? Li On Mon, Apr 9, 2018 at 5:22 PM, Leif Walsh <leif.wa...@gmail.com> wrote: > Hi all, > > I’ve been doing some work lately with Spark’s ML interfaces, which include > sparse and dense Vector and Matrix types, backed on the Scala side by > Breeze. Using these interfaces, you can construct DataFrames whose column > types are vectors and matrices, and though the API isn’t terribly rich, it > is possible to run Python UDFs over such a DataFrame and get numpy ndarrays > out of each row. However, if you’re using Spark’s Arrow serialization > between the executor and Python workers, you get this > UnsupportedOperationException: > https://github.com/apache/spark/blob/252468a744b95082400ba9e8b2e3b3 > d9d50ab7fa/sql/core/src/main/scala/org/apache/spark/sql/ > execution/arrow/ArrowWriter.scala#L71 > > I think it would be useful for Arrow to support something like a column of > tensors, and I’d like to see if anyone else here is interested in such a > thing. If so, I’d like to propose adding it to the spec and getting it > implemented in at least Java and C++/Python. > > Some initial mildly-scattered thoughts: > > 1. You can certainly represent these today as List<Double> and > List<List<Double>>, but then need to do some copying to get them back into > numpy ndarrays. > > 2. In some cases it might be useful to know that a column contains 3x3x4 > tensors, for example, and not just that there are three dimensions as you’d > get with List<List<List<Double>>>. This could constrain what operations > are meaningful (for example, in Spark you could imagine type checking that > verifies dimension alignment for matrix multiplication). > > 3. You could approximate that with a FixedSizeList and metadata about the > tensor shape. > > 4. But I kind of feel like this is generally useful enough that it’s worth > having one implementation of it (well, one for each runtime) in Arrow. > > 5. Or, maybe everyone here thinks Spark should just do this with metadata? > > Curious to hear what you all think. > > Thanks, > Leif > > -- > -- > Cheers, > Leif >
