Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/10839#discussion_r56062757
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
---
@@ -268,8 +268,26 @@ class BlockMatrix @Since("1.3.0") (
def toIndexedRowMatrix(): IndexedRowMatrix = {
require(numCols() < Int.MaxValue, "The number of columns must be
within the integer range. " +
s"numCols: ${numCols()}")
- // TODO: This implementation may be optimized
- toCoordinateMatrix().toIndexedRowMatrix()
+
+ val rows = blocks.map(block => (block._1._1, (block._1._2, block._2)))
+ .groupByKey()
--- End diff --
This assumes that a partition can hold an entire block row, which is not
always valid. I would suggest the following:
* for each block, break the matrix block into rows and then emit (rowIdx,
row). You can map the matrix block to a breeze matrix, and then call rows.
* call groupByKey and then concat breeze vectors
This could be a more scalable implementation.
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