cloud-fan commented on code in PR #57073:
URL: https://github.com/apache/spark/pull/57073#discussion_r3566805347
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CoalesceHintUtils.scala:
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@@ -40,6 +40,20 @@ object CoalesceHintUtils {
}
}
+ def getAdvisorySizeOfPartitions(hint: UnresolvedHint): (Option[Long],
Seq[Expression]) = {
Review Comment:
The advisory size isn't checked for positivity here, so
`REBALANCE_BY_SIZE(0)` is accepted (`0` parses as an `IntegerLiteral`), and
`df.hint("REBALANCE_BY_SIZE", -1)` passes a negative `Literal` straight
through. A size of `0` reaches `OptimizeSkewInRebalancePartitions`, where it's
used as both the skew threshold and the split target size, so every non-empty
partition is treated as skewed and split down to one-partition-per-map-block,
silently.
Every peer that handles this value rejects non-positive: `REBALANCE`'s num
path via `require(numPartitions > 0)`, the write producer via `if
(partitionSize > 0) Some else None` (`DistributionAndOrderingUtils`), and the
session config via `checkValue(_ > 0)`. I'd match the write producer and coerce
a non-positive value to the session default, and add a test for
`REBALANCE_BY_SIZE(0)`.
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