maropu commented on a change in pull request #29871:
URL: https://github.com/apache/spark/pull/29871#discussion_r496321413
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CostBasedJoinReorder.scala
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@@ -206,13 +206,15 @@ object JoinReorderDP extends PredicateHelper with Logging
{
filters: Option[JoinGraphInfo]): JoinPlanMap = {
val nextLevel = new JoinPlanMap
- var k = 0
val lev = existingLevels.length - 1
+ var k = lev
// Build plans for the next level from plans at level k (one side of the
join) and level
// lev - k (the other side of the join).
- // For the lower level k, we only need to search from 0 to lev - k,
because when building
+ // For the higher level k, we only need to search from lev to lev - k,
because when building
// a join from A and B, both A J B and B J A are handled.
- while (k <= lev - k) {
+ // Start searching from highest level to make sure that optimally ordered
input doesn't get
+ // reordered into another plan with the same cost.
Review comment:
Thanks for your detailed explanation. IMO a fix should be simple and
intuitive in terms of maintainability. Another idea that I came up with after I
read the description above was to simply sort input candidate plans by some
values (e.g., `semanticHash`) at the beginning of `search`. It seems there are
multiple options to solve this (like the four factors you described above). Any
reason to choose the current approach out from them? You chose the simplest one?
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