Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/5848#discussion_r29537987
--- Diff: core/src/main/scala/org/apache/spark/Partitioner.scala ---
@@ -118,7 +118,8 @@ class RangePartitioner[K : Ordering : ClassTag, V](
Array.empty
} else {
// This is the sample size we need to have roughly balanced output
partitions, capped at 1M.
- val sampleSize = math.min(20.0 * partitions, 1e6)
+ val maxSamples =
rdd.sparkContext.getConf.getDouble("spark.partitioner.max_samples", 1e6)
--- End diff --
`max_samples` -> `maxSamples`. You are trying to turn *down* the max right?
Just wondering out loud -- was 1e6 arbitrary and probably too big as a max to
begin with? The max would only matter at 50000 partitions, which is huge. Would
a max of... 1e5, 1e4 be more sensible for 99% of cases? I realize there's no
single value that always works but wondering if we can avoid another flag.
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