kosiew commented on code in PR #24587:
URL: https://github.com/apache/datafusion/pull/24587#discussion_r3868366601
##########
datafusion/physical-plan/src/joins/hash_join/stream.rs:
##########
@@ -559,11 +559,13 @@ impl HashJoinStream {
.bounds
.clone()
.unwrap_or_else(|| PartitionBounds::new(vec![]));
- // Arrow tracks null counts per array, so this costs no data scan.
+ // Use the logical null count: a dictionary key whose entry points at a
+ // NULL dictionary value is a NULL key even though the key bitmap has
no
+ // physical nulls (`null_count() == 0` but `logical_null_count() > 0`).
let keys_have_null = left_data
.values()
.iter()
- .any(|array| array.null_count() > 0);
+ .any(|array| array.logical_null_count() > 0);
Review Comment:
Could we add the same dictionary-value logical-NULL regression coverage for
`PartitionMode::Partitioned`, using multiple partitions? `keys_have_null` is
also aggregated by `build_partitioned_filter`, while the new test currently
exercises only `CollectLeft`. Covering the partitioned case would help catch
regressions in how the logical NULL state is aggregated or routed across
partitions.
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