Refactor HashJoinExec to progressively accumulate dynamic filter bounds

datafusion/physical-plan/Cargo.toml

@@ -53,7 +53,7 @@ datafusion-common = { workspace = true, default-features = true }
 datafusion-common-runtime = { workspace = true, default-features = true }
 datafusion-execution = { workspace = true }
 datafusion-expr = { workspace = true }
-datafusion-functions-aggregate-common = { workspace = true }
+datafusion-functions-aggregate = { workspace = true }
 datafusion-functions-window-common = { workspace = true }
 datafusion-physical-expr = { workspace = true, default-features = true }
 datafusion-physical-expr-common = { workspace = true }

datafusion/physical-plan/src/joins/hash_join/exec.rs

@@ -54,20 +54,21 @@ use crate::{
     PlanProperties, SendableRecordBatchStream, Statistics,
 };
 
-use arrow::array::{Array, ArrayRef, BooleanBufferBuilder};
+use arrow::array::{ArrayRef, BooleanBufferBuilder};
 use arrow::compute::concat_batches;
 use arrow::datatypes::SchemaRef;
 use arrow::record_batch::RecordBatch;
 use arrow::util::bit_util;
+use arrow_schema::DataType;
 use datafusion_common::config::ConfigOptions;
 use datafusion_common::utils::memory::estimate_memory_size;
 use datafusion_common::{
     internal_err, plan_err, project_schema, JoinSide, JoinType, NullEquality, Result,
-    ScalarValue,
 };
 use datafusion_execution::memory_pool::{MemoryConsumer, MemoryReservation};
 use datafusion_execution::TaskContext;
-use datafusion_functions_aggregate_common::min_max::{max_batch, min_batch};
+use datafusion_expr::Accumulator;
+use datafusion_functions_aggregate::min_max::{MaxAccumulator, MinAccumulator};
 use datafusion_physical_expr::equivalence::{
     join_equivalence_properties, ProjectionMapping,
 };
@@ -1188,29 +1189,123 @@ impl ExecutionPlan for HashJoinExec {
     }
 }
 
-/// Compute min/max bounds for each column in the given arrays
-fn compute_bounds(arrays: &[ArrayRef]) -> Result<Vec<ColumnBounds>> {
-    arrays
-        .iter()
-        .map(|array| {
-            if array.is_empty() {
-                // Return NULL values for empty arrays
-                return Ok(ColumnBounds::new(
-                    ScalarValue::try_from(array.data_type())?,
-                    ScalarValue::try_from(array.data_type())?,
-                ));
+/// Accumulator for collecting min/max bounds from build-side data during hash join.
+///
+/// This struct encapsulates the logic for progressively computing column bounds
+/// (minimum and maximum values) for a specific join key expression as batches
+/// are processed during the build phase of a hash join.
+///
+/// The bounds are used for dynamic filter pushdown optimization, where filters
+/// based on the actual data ranges can be pushed down to the probe side to
+/// eliminate unnecessary data early.
+struct CollectLeftAccumulator {
+    /// The physical expression to evaluate for each batch
+    expr: Arc<dyn PhysicalExpr>,
+    /// Accumulator for tracking the minimum value across all batches
+    min: MinAccumulator,
+    /// Accumulator for tracking the maximum value across all batches
+    max: MaxAccumulator,
+}
+
+impl CollectLeftAccumulator {
+    /// Creates a new accumulator for tracking bounds of a join key expression.
+    ///
+    /// # Arguments
+    /// * `expr` - The physical expression to track bounds for
+    /// * `schema` - The schema of the input data
+    ///
+    /// # Returns
+    /// A new `CollectLeftAccumulator` instance configured for the expression's data type
+    fn try_new(expr: Arc<dyn PhysicalExpr>, schema: &SchemaRef) -> Result<Self> {
+        /// Recursively unwraps dictionary types to get the underlying value type.
+        fn dictionary_value_type(data_type: &DataType) -> DataType {
+            match data_type {
+                DataType::Dictionary(_, value_type) => {
+                    dictionary_value_type(value_type.as_ref())
+                }
+                _ => data_type.clone(),
             }
+        }
+
+        let data_type = expr
+            .data_type(schema)
+            // Min/Max can operate on dictionary data but expect to be initialized with the underlying value type
+            .map(|dt| dictionary_value_type(&dt))?;
+        Ok(Self {
+            expr,
+            min: MinAccumulator::try_new(&data_type)?,
+            max: MaxAccumulator::try_new(&data_type)?,
+        })
+    }
 
-            // Use Arrow kernels for efficient min/max computation
-            let min_val = min_batch(array)?;
-            let max_val = max_batch(array)?;
+    /// Updates the accumulators with values from a new batch.
+    ///
+    /// Evaluates the expression on the batch and updates both min and max
+    /// accumulators with the resulting values.
+    ///
+    /// # Arguments
+    /// * `batch` - The record batch to process
+    ///
+    /// # Returns
+    /// Ok(()) if the update succeeds, or an error if expression evaluation fails
+    fn update_batch(&mut self, batch: &RecordBatch) -> Result<()> {
+        let array = self.expr.evaluate(batch)?.into_array(batch.num_rows())?;
+        self.min.update_batch(std::slice::from_ref(&array))?;
+        self.max.update_batch(std::slice::from_ref(&array))?;
+        Ok(())
+    }
 
-            Ok(ColumnBounds::new(min_val, max_val))
+    /// Finalizes the accumulation and returns the computed bounds.
+    ///
+    /// Consumes self to extract the final min and max values from the accumulators.
+    ///
+    /// # Returns
+    /// The `ColumnBounds` containing the minimum and maximum values observed
+    fn evaluate(mut self) -> Result<ColumnBounds> {
+        Ok(ColumnBounds::new(
+            self.min.evaluate()?,
+            self.max.evaluate()?,
+        ))
+    }
+}
+
+/// State for collecting the build-side data during hash join
+struct BuildSideState {
+    batches: Vec<RecordBatch>,
+    num_rows: usize,
+    metrics: BuildProbeJoinMetrics,
+    reservation: MemoryReservation,
+    bounds_accumulators: Option<Vec<CollectLeftAccumulator>>,
+}
+
+impl BuildSideState {
+    /// Create a new BuildSideState with optional accumulators for bounds computation
+    fn try_new(
+        metrics: BuildProbeJoinMetrics,
+        reservation: MemoryReservation,
+        on_left: Vec<Arc<dyn PhysicalExpr>>,
+        schema: &SchemaRef,
+        should_compute_bounds: bool,
+    ) -> Result<Self> {
+        Ok(Self {
+            batches: Vec::new(),
+            num_rows: 0,
+            metrics,
+            reservation,
+            bounds_accumulators: should_compute_bounds
+                .then(|| {
+                    on_left
+                        .iter()
+                        .map(|expr| {
+                            CollectLeftAccumulator::try_new(Arc::clone(expr), schema)
+                        })
+                        .collect::<Result<Vec<_>>>()
+                })
+                .transpose()?,
         })
-        .collect()
+    }
 }
 
-#[expect(clippy::too_many_arguments)]
 /// Collects all batches from the left (build) side stream and creates a hash map for joining.
 ///
 /// This function is responsible for:
@@ -1239,6 +1334,7 @@ fn compute_bounds(arrays: &[ArrayRef]) -> Result<Vec<ColumnBounds>> {
 /// # Returns
 /// `JoinLeftData` containing the hash map, consolidated batch, join key values,
 /// visited indices bitmap, and computed bounds (if requested).
+#[allow(clippy::too_many_arguments)]
 async fn collect_left_input(
     random_state: RandomState,
     left_stream: SendableRecordBatchStream,
@@ -1254,24 +1350,48 @@ async fn collect_left_input(
     // This operation performs 2 steps at once:
     // 1. creates a [JoinHashMap] of all batches from the stream
     // 2. stores the batches in a vector.
-    let initial = (Vec::new(), 0, metrics, reservation);
-    let (batches, num_rows, metrics, mut reservation) = left_stream
-        .try_fold(initial, |mut acc, batch| async {
+    let initial = BuildSideState::try_new(
+        metrics,
+        reservation,
+        on_left.clone(),
+        &schema,
+        should_compute_bounds,
+    )?;
+
+    let state = left_stream
+        .try_fold(initial, |mut state, batch| async move {
+            // Update accumulators if computing bounds
+            if let Some(ref mut accumulators) = state.bounds_accumulators {
+                for accumulator in accumulators {
+                    accumulator.update_batch(&batch)?;
+                }
+            }
+
+            // Decide if we spill or not
             let batch_size = get_record_batch_memory_size(&batch);
             // Reserve memory for incoming batch
-            acc.3.try_grow(batch_size)?;
+            state.reservation.try_grow(batch_size)?;
             // Update metrics
-            acc.2.build_mem_used.add(batch_size);
-            acc.2.build_input_batches.add(1);
-            acc.2.build_input_rows.add(batch.num_rows());
+            state.metrics.build_mem_used.add(batch_size);
+            state.metrics.build_input_batches.add(1);
+            state.metrics.build_input_rows.add(batch.num_rows());
             // Update row count
-            acc.1 += batch.num_rows();
+            state.num_rows += batch.num_rows();
             // Push batch to output
-            acc.0.push(batch);
-            Ok(acc)
+            state.batches.push(batch);
+            Ok(state)
         })
         .await?;
 
+    // Extract fields from state
+    let BuildSideState {
+        batches,
+        num_rows,
+        metrics,
+        mut reservation,
+        bounds_accumulators,
+    } = state;
+
     // Estimation of memory size, required for hashtable, prior to allocation.
     // Final result can be verified using `RawTable.allocation_info()`
     let fixed_size_u32 = size_of::<JoinHashMapU32>();
@@ -1338,10 +1458,15 @@ async fn collect_left_input(
         .collect::<Result<Vec<_>>>()?;
 
     // Compute bounds for dynamic filter if enabled
-    let bounds = if should_compute_bounds && num_rows > 0 {
-        Some(compute_bounds(&left_values)?)
-    } else {
-        None
+    let bounds = match bounds_accumulators {
+        Some(accumulators) if num_rows > 0 => {
+            let bounds = accumulators
+                .into_iter()
+                .map(CollectLeftAccumulator::evaluate)
+                .collect::<Result<Vec<_>>>()?;
+            Some(bounds)
+        }
+        _ => None,
     };
 
     let data = JoinLeftData::new(