Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/18652#discussion_r127918499
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
---
@@ -1912,6 +1913,26 @@ class Analyzer(
nondeterToAttr.get(e).map(_.toAttribute).getOrElse(e)
}.copy(child = newChild)
+ case j: Join if j.condition.isDefined &&
!j.condition.get.deterministic =>
+ j match {
+ // We can push down non-deterministic joining keys.
--- End diff --
Most the RDBMS systems allow non-deterministic join conditions. To support
it correclty in Spark, we need to check how the other systems behave. After we
deciding the rule, we can't break it. Thus, it has to be very careful to design
the initial version.
In the current stage, I do not think we have a bandwidth to make it
perfect. If you want to continue the PR, could you just check how Hive works?
Adding an extra flag for Hive users. It can simplify their migration task. By
default, turn it off.
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