Github user sryza commented on a diff in the pull request:
https://github.com/apache/spark/pull/5148#discussion_r27126188
--- Diff: core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala
---
@@ -191,25 +191,22 @@ class DoubleRDDFunctions(self: RDD[Double]) extends
Logging with Serializable {
}
}
// Determine the bucket function in constant time. Requires that
buckets are evenly spaced
- def fastBucketFunction(min: Double, increment: Double, count: Int)(e:
Double): Option[Int] = {
+ def fastBucketFunction(min: Double, max: Double, count: Int)(e:
Double): Option[Int] = {
// If our input is not a number unless the increment is also NaN
then we fail fast
- if (e.isNaN()) {
- return None
- }
- val bucketNumber = (e - min)/(increment)
- // We do this rather than buckets.lengthCompare(bucketNumber)
- // because Array[Double] fails to override it (for now).
- if (bucketNumber > count || bucketNumber < 0) {
+ if (e.isNaN || e < min || e > max) {
None
} else {
- Some(bucketNumber.toInt.min(count - 1))
+ val bucketNumber = (((e - min) / (max - min)) * count).toInt
--- End diff --
max - min should stay constant, so I think we could make this decently
faster by precomputing (count / (max - min)) and multiplying by it. Maybe the
compiler makes this kind of optimization, but I certainly wouldn't count on it.
Would that give us the same problem as before?
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