tgravescs commented on a change in pull request #28656:
URL: https://github.com/apache/spark/pull/28656#discussion_r432515370
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File path: core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
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@@ -1107,10 +1107,19 @@ private[spark] class TaskSetManager(
def recomputeLocality(): Unit = {
// A zombie TaskSetManager may reach here while executorLost happens
if (isZombie) return
+ val previousLocalityIndex = currentLocalityIndex
val previousLocalityLevel = myLocalityLevels(currentLocalityIndex)
+ val previousMyLocalityLevels = myLocalityLevels
myLocalityLevels = computeValidLocalityLevels()
localityWaits = myLocalityLevels.map(getLocalityWait)
currentLocalityIndex = getLocalityIndex(previousLocalityLevel)
+ if (currentLocalityIndex > previousLocalityIndex) {
+ // SPARK-31837: If the new level is more local, shift to the new most
local locality
+ // level in terms of better data locality. For example, say the previous
locality
+ // levels are [PROCESS, NODE, ANY] and current level is ANY. After
recompute, the
+ // locality levels are [PROCESS, NODE, RACK, ANY]. Then, we'll shift to
RACK level.
+ currentLocalityIndex =
getLocalityIndex(myLocalityLevels.diff(previousMyLocalityLevels).head)
Review comment:
yes this is one of the cases I was referring to. Ideally you would never
run into this case because a host is on a rack so you would always have it.
Unfortunately Spark defaults the rack to None so you can. I was going to
improve upon it in the jira I filed. We can certainly handle some here if you
want
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