That's not quite what I'm looking for.  Let me provide an example.  I have a rowmatrix A that is nxm and I have two local matrices b and c.  b is mx1 and c is nx1.  In my spark job I wish to perform the following two computations




I don't think this is possible without being able to transpose a rowmatrix.  Am I correct?



From: Reza Zadeh <>
Sent: Monday, January 12, 2015 1:58 PM
To: Alex Minnaar
Subject: Re: RowMatrix multiplication
As you mentioned, you can perform A * b, where A is a rowmatrix and b is a local matrix.

From your email, I figure you want to compute b * A^T. To do this, you can compute C = A b^T, whose result is the transpose of what you were looking for, i.e. C^T = b * A^T. To undo the transpose, you would have transpose C manually yourself. Be careful though, because the result might not have each Row fit in memory on a single machine, which is what RowMatrix requires. This danger is why we didn't provide a transpose operation in RowMatrix natively.

To address this and more, there is an effort to provide more comprehensive linear algebra through block matrices, which will likely make it to 1.3:


On Mon, Jan 12, 2015 at 6:33 AM, Alex Minnaar <> wrote:

I have a rowMatrix on which I want to perform two multiplications.  The first is a right multiplication with a local matrix which is fine.  But after that I also wish to right multiply the transpose of my rowMatrix with a different local matrix.  I understand that there is no functionality to transpose a rowMatrix at this time but I was wondering if anyone could suggest a any kind of work-around for this.  I had thought that I might be able to initially create two rowMatrices - a normal version and a transposed version - and use either when appropriate.  Can anyone think of another alternative?