Github user viirya commented on the issue:
https://github.com/apache/spark/pull/17459
> I considered having toBlockMatrix check if the rows of IndexedRowMatrix
were dense or sparse, but there is no guarantee of consistency. Like, an
IndexedRowMatrix could be a mix of Dense and Sparse Vectors. In that case, it
would not be clear what type of BlockMatrix to create. A decent approximation
of this would be to just decide the matrix type based on the first vector we
look at in the iterator we get from groupByKey, creating a mix of Dense and
Sparse matrices in a BlockMatrix, but I still think it's best to be explicit.
Also, we currently have the description of toBlockMatrix promising to make a
BlockMatrix backed by instances of SparseMatrix, so we have made promises to
users about the composition of the BlockMatrix before.
I don't mean we don't care about it. I meant there is no guarantee that
`BlockMatrix` is purely consisted of `DenseMatrix` or `SparseMatrix`. It could
be a mix of them.
Thus, we can have a `toBlockMatrix` which creates a `BlockMatrix` which is
a mix of `DenseMatrix` and `SparseMatrix`. A block in a `BlockMatrix` can be a
`DenseMatrix` and `SparseMatrix`, depending on the ratio of values in the
block. Yes, it is like `a decent approximation` you talked.
For a `IndexedRowMatrix` completely consisted of `DenseVector`, this
`toBlockMatrix` definitely returns a `BlockMatrix` backed by `DenseMatrix`. For
other cases, `DenseMatrix` might not be best choice for all blocks in the
`BlockMatrix`, as many blocks will be sparse.
About the promise that `toBlockMatrix` makes a `BlockMatrix` backed by
instances of `SparseMatrix`, as I said it is not explicitly bound to the API
level. I think it is not a big problem.
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