jonathanc-n commented on code in PR #17632:
URL: https://github.com/apache/datafusion/pull/17632#discussion_r2357425738
##########
datafusion/physical-plan/src/joins/hash_join/shared_bounds.rs:
##########
@@ -167,24 +183,112 @@ impl SharedBoundsAccumulator {
};
Self {
inner: Mutex::new(SharedBoundsState {
- bounds: Vec::with_capacity(expected_calls),
+ bounds: Vec::with_capacity(total_partitions),
+ completed_partitions: HashSet::new(),
+ filter_optimized: false,
}),
- barrier: Barrier::new(expected_calls),
+ total_partitions,
dynamic_filter,
on_right,
+ config_options: Arc::new(ConfigOptions::default()),
}
}
- /// Create a filter expression from individual partition bounds using OR
logic.
- ///
- /// This creates a filter where each partition's bounds form a conjunction
(AND)
- /// of column range predicates, and all partitions are combined with OR.
- ///
- /// For example, with 2 partitions and 2 columns:
- /// ((col0 >= p0_min0 AND col0 <= p0_max0 AND col1 >= p0_min1 AND col1 <=
p0_max1)
- /// OR
- /// (col0 >= p1_min0 AND col0 <= p1_max0 AND col1 >= p1_min1 AND col1 <=
p1_max1))
- pub(crate) fn create_filter_from_partition_bounds(
+ /// Create hash expression for the join keys: hash(col1, col2, ...)
+ fn create_hash_expression(&self) -> Result<Arc<dyn PhysicalExpr>> {
+ // Use the same random state as RepartitionExec for consistent
partitioning
+ // This ensures hash(row) % num_partitions produces the same partition
assignment
+ // as the original repartitioning operation
+ let hash_udf = Arc::new(ScalarUDF::from(Hash::new_with_random_state(
+ REPARTITION_RANDOM_STATE,
+ )));
+
+ // Create the hash expression using ScalarFunctionExpr
+ Ok(Arc::new(ScalarFunctionExpr::new(
+ "hash",
+ hash_udf,
+ self.on_right.clone(),
+ Field::new("hash_result", DataType::UInt64, false).into(),
+ Arc::clone(&self.config_options),
+ )))
+ }
+
+ /// Create a bounds predicate for a single partition: (col >= min AND col
<= max) for all columns
+ fn create_partition_bounds_predicate(
+ &self,
+ partition_bounds: &PartitionBounds,
+ ) -> Result<Arc<dyn PhysicalExpr>> {
+ let mut column_predicates = Vec::with_capacity(partition_bounds.len());
+
+ for (col_idx, right_expr) in self.on_right.iter().enumerate() {
+ if let Some(column_bounds) =
partition_bounds.get_column_bounds(col_idx) {
+ // Create predicate: col >= min AND col <= max
+ let min_expr = Arc::new(BinaryExpr::new(
+ Arc::clone(right_expr),
+ Operator::GtEq,
+ lit(column_bounds.min.clone()),
+ )) as Arc<dyn PhysicalExpr>;
+ let max_expr = Arc::new(BinaryExpr::new(
+ Arc::clone(right_expr),
+ Operator::LtEq,
+ lit(column_bounds.max.clone()),
+ )) as Arc<dyn PhysicalExpr>;
+ let range_expr =
+ Arc::new(BinaryExpr::new(min_expr, Operator::And,
max_expr))
+ as Arc<dyn PhysicalExpr>;
+ column_predicates.push(range_expr);
+ }
+ }
+
+ // Combine all column predicates for this partition with AND
+ if column_predicates.is_empty() {
Review Comment:
If we return `lit(true)` here it will nullify the filter once everything is
`or`ed together in the final phase. Maybe we can pass back an Option and skip
the partition filter that returns `None`
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