Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/8764#discussion_r39485102
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala
---
@@ -51,18 +50,15 @@ case class SampleNode(
override def open(): Unit = {
child.open()
- val (sampler, _seed) = if (withReplacement) {
- val random = new Random(seed)
+ val sampler =
+ if (withReplacement) {
// Disable gap sampling since the gap sampling method buffers two
rows internally,
// requiring us to copy the row, which is more expensive than the
random number generator.
- (new PoissonSampler[InternalRow](upperBound - lowerBound,
useGapSamplingIfPossible = false),
- // Use the seed for partition 0 like PartitionwiseSampledRDD to
generate the same result
- // of DataFrame
- random.nextLong())
--- End diff --
@zsxwing I had to remove this to make testing deterministic. Looking at
this further I still don't see the point of introducing another layer of
randomness here. What change in behavior does this entail?
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