zhengruifeng commented on a change in pull request #25178: [SPARK-28421][ML]
SparseVector.apply performance optimization
URL: https://github.com/apache/spark/pull/25178#discussion_r305176094
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File path: mllib-local/src/main/scala/org/apache/spark/ml/linalg/Vectors.scala
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@@ -603,6 +603,19 @@ class SparseVector @Since("2.0.0") (
private[spark] override def asBreeze: BV[Double] = new BSV[Double](indices,
values, size)
+ override def apply(i: Int): Double = {
+ if (i < 0 || i >= size) {
+ throw new IndexOutOfBoundsException(s"Index $i out of bounds [0, $size)")
+ }
+
+ if (indices.isEmpty || i < indices(0) || i > indices(indices.length - 1)) {
Review comment:
you can see that if the `nnz` grows, the speed up decrese. That is because
with a big `nnz`, the searching complexity `log(nnz)` dominate the whole
process. However, when `nnz` is a small number (most frequently), the
conversion is relatively the main part.
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