Github user markhamstra commented on a diff in the pull request:
https://github.com/apache/spark/pull/1689#discussion_r15920203
--- Diff: core/src/main/scala/org/apache/spark/Partitioner.scala ---
@@ -113,8 +113,12 @@ class RangePartitioner[K : Ordering : ClassTag, V](
private var ordering = implicitly[Ordering[K]]
// An array of upper bounds for the first (partitions - 1) partitions
- private var rangeBounds: Array[K] = {
- if (partitions <= 1) {
+ @volatile private var valRB: Array[K] = null
--- End diff --
It wouldn't surprise me if this performance figure varied with different
combinations of hardware and Java version; but for at least one such
combination, volatile reads are roughly 2-3x as costly as non-volatile reads as
long as they are uncontended -- much more expensive when there are concurrent
writes to contend with. http://brooker.co.za/blog/2012/09/10/volatile.html
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
