UBarney commented on code in PR #16210:
URL: https://github.com/apache/datafusion/pull/16210#discussion_r2142968386
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datafusion/physical-plan/src/joins/nested_loop_join.rs:
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@@ -178,6 +187,18 @@ pub struct NestedLoopJoinExec {
metrics: ExecutionPlanMetricsSet,
/// Cache holding plan properties like equivalences, output partitioning
etc.
cache: PlanProperties,
+ /// Null matching behavior: If `null_equals_null` is true, rows that have
+ /// `null`s in both left and right equijoin columns will be matched.
+ /// Otherwise, rows that have `null`s in the join columns will not be
+ /// matched and thus will not appear in the output.
+ null_equals_null: bool,
+ /// Set of equijoin columns from the relations: `(left_col, right_col)`
+ ///
+ /// This is optional as a nested loop join can be passed a 'on' clause
+ /// in the case that a Hash Join cost is more expensive than a
+ /// nested loop join or when a user would like to pick nested loop
+ /// join by hint
+ on: Option<Vec<(PhysicalExprRef, PhysicalExprRef)>>,
Review Comment:
> to make sure that we can do the null_equals_null check
We can also do this by merging on condition into filter.
For example: filter: `t1.a < t2.a, on: t1.c = t2.c` -> `filter: t1.a < t2.a
and (t1.c = t2.c or (is_null(t1.c) and (is_null(t2.c))))`
> this is how it is implemented for the other joins as well
Hash Joins and Sort-Merge Joins are fundamentally reliant on equality
conditions. Their high-speed mechanisms (hashing, merging) are impossible
without them.
But for nlj separating equality conditions doesn't improve performance
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