fix(accumulators): preserve state in evaluate() for window frame queries

datafusion/expr-common/src/accumulator.rs

@@ -58,17 +58,30 @@ pub trait Accumulator: Send + Sync + Debug {
     /// running sum.
     fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()>;
 
-    /// Returns the final aggregate value, consuming the internal state.
+    /// Returns the final aggregate value.
     ///
     /// For example, the `SUM` accumulator maintains a running sum,
     /// and `evaluate` will produce that running sum as its output.
     ///
-    /// This function should not be called twice, otherwise it will
-    /// result in potentially non-deterministic behavior.
-    ///
     /// This function gets `&mut self` to allow for the accumulator to build
     /// arrow-compatible internal state that can be returned without copying
-    /// when possible (for example distinct strings)
+    /// when possible (for example distinct strings).
+    ///
+    /// ## Correctness
+    ///
+    /// This function must not consume the internal state, as it is also used in window
+    /// aggregate functions where it can be executed multiple times depending on the
+    /// current window frame. Consuming the internal state can cause the next invocation
+    /// to have incorrect results.
+    ///
+    /// - Even if this accumulator doesn't implement [`retract_batch`] it may still be used
+    ///   in window aggregate functions where the window frame is
+    ///   `ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW`
+    ///
+    /// It is fine to modify the state (e.g. re-order elements within internal state vec) so long
+    /// as this doesn't cause an incorrect computation on the next call of evaluate.
+    ///
+    /// [`retract_batch`]: Self::retract_batch
     fn evaluate(&mut self) -> Result<ScalarValue>;
 
     /// Returns the allocated size required for this accumulator, in

datafusion/functions-aggregate/src/percentile_cont.rs

@@ -15,6 +15,7 @@
 // specific language governing permissions and limitations
 // under the License.
 
+use std::collections::HashMap;
 use std::fmt::Debug;
 use std::mem::{size_of, size_of_val};
 use std::sync::Arc;
@@ -52,7 +53,7 @@ use datafusion_expr::{
 };
 use datafusion_functions_aggregate_common::aggregate::groups_accumulator::accumulate::accumulate;
 use datafusion_functions_aggregate_common::aggregate::groups_accumulator::nulls::filtered_null_mask;
-use datafusion_functions_aggregate_common::utils::GenericDistinctBuffer;
+use datafusion_functions_aggregate_common::utils::{GenericDistinctBuffer, Hashable};
 use datafusion_macros::user_doc;
 
 use crate::utils::validate_percentile_expr;
@@ -427,14 +428,48 @@ impl<T: ArrowNumericType + Debug> Accumulator for PercentileContAccumulator<T> {
     }
 
     fn evaluate(&mut self) -> Result<ScalarValue> {
-        let d = std::mem::take(&mut self.all_values);
-        let value = calculate_percentile::<T>(d, self.percentile);
+        let value = calculate_percentile::<T>(&mut self.all_values, self.percentile);
         ScalarValue::new_primitive::<T>(value, &T::DATA_TYPE)
     }
 
     fn size(&self) -> usize {
         size_of_val(self) + self.all_values.capacity() * size_of::<T::Native>()
     }
+
+    fn retract_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+        let mut to_remove: HashMap<ScalarValue, usize> = HashMap::new();
+        for i in 0..values[0].len() {
+            let v = ScalarValue::try_from_array(&values[0], i)?;
+            if !v.is_null() {
+                *to_remove.entry(v).or_default() += 1;
+            }
+        }
+
+        let mut i = 0;
+        while i < self.all_values.len() {
+            let k =
+                ScalarValue::new_primitive::<T>(Some(self.all_values[i]), &T::DATA_TYPE)?;
+            if let Some(count) = to_remove.get_mut(&k)
+                && *count > 0
+            {
+                self.all_values.swap_remove(i);
+                *count -= 1;
+                if *count == 0 {
+                    to_remove.remove(&k);
+                    if to_remove.is_empty() {
+                        break;
+                    }
+                }
+            } else {
+                i += 1;
+            }
+        }
+        Ok(())
+    }
+
+    fn supports_retract_batch(&self) -> bool {
+        true
+    }
 }
 
 /// The percentile_cont groups accumulator accumulates the raw input values
@@ -549,13 +584,13 @@ impl<T: ArrowNumericType + Send> GroupsAccumulator
 
     fn evaluate(&mut self, emit_to: EmitTo) -> Result<ArrayRef> {
         // Emit values
-        let emit_group_values = emit_to.take_needed(&mut self.group_values);
+        let mut emit_group_values = emit_to.take_needed(&mut self.group_values);
 
         // Calculate percentile for each group
         let mut evaluate_result_builder =
             PrimitiveBuilder::<T>::with_capacity(emit_group_values.len());
-        for values in emit_group_values {
-            let value = calculate_percentile::<T>(values, self.percentile);
+        for values in &mut emit_group_values {
+            let value = calculate_percentile::<T>(values.as_mut_slice(), self.percentile);
             evaluate_result_builder.append_option(value);
         }
 
@@ -652,17 +687,31 @@ impl<T: ArrowNumericType + Debug> Accumulator for DistinctPercentileContAccumula
     }
 
     fn evaluate(&mut self) -> Result<ScalarValue> {
-        let d = std::mem::take(&mut self.distinct_values.values)
-            .into_iter()
-            .map(|v| v.0)
-            .collect::<Vec<_>>();
-        let value = calculate_percentile::<T>(d, self.percentile);
+        let mut values: Vec<T::Native> =
+            self.distinct_values.values.iter().map(|v| v.0).collect();
+        let value = calculate_percentile::<T>(&mut values, self.percentile);
         ScalarValue::new_primitive::<T>(value, &T::DATA_TYPE)
     }
 
     fn size(&self) -> usize {
         size_of_val(self) + self.distinct_values.size()
     }
+
+    fn retract_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+        if values.is_empty() {
+            return Ok(());
+        }
+
+        let arr = values[0].as_primitive::<T>();
+        for value in arr.iter().flatten() {
+            self.distinct_values.values.remove(&Hashable(value));
+        }
+        Ok(())
+    }
+
+    fn supports_retract_batch(&self) -> bool {
+        true
+    }
 }
 
 /// Calculate the percentile value for a given set of values.
@@ -672,8 +721,12 @@ impl<T: ArrowNumericType + Debug> Accumulator for DistinctPercentileContAccumula
 /// For percentile p and n values:
 /// - If p * (n-1) is an integer, return the value at that position
 /// - Otherwise, interpolate between the two closest values
+///
+/// Note: This function takes a mutable slice and sorts it in place, but does not
+/// consume the data. This is important for window frame queries where evaluate()
+/// may be called multiple times on the same accumulator state.
 fn calculate_percentile<T: ArrowNumericType>(
-    mut values: Vec<T::Native>,
+    values: &mut [T::Native],
     percentile: f64,
 ) -> Option<T::Native> {
     let cmp = |x: &T::Native, y: &T::Native| x.compare(*y);

datafusion/functions-aggregate/src/string_agg.rs

@@ -384,14 +384,13 @@ impl Accumulator for SimpleStringAggAccumulator {
     }
 
     fn evaluate(&mut self) -> Result<ScalarValue> {
-        let result = if self.has_value {
-            ScalarValue::LargeUtf8(Some(std::mem::take(&mut self.accumulated_string)))
+        if self.has_value {
+            Ok(ScalarValue::LargeUtf8(Some(
+                self.accumulated_string.clone(),
+            )))
         } else {
-            ScalarValue::LargeUtf8(None)
-        };
-
-        self.has_value = false;
-        Ok(result)
+            Ok(ScalarValue::LargeUtf8(None))
+        }
     }
 
     fn size(&self) -> usize {

datafusion/sqllogictest/test_files/aggregate.slt

@@ -1130,6 +1130,102 @@ ORDER BY tags, timestamp;
 4 tag2 90 75 80 95
 5 tag2 100 80 80 100
 
+###########
+# Issue #19612: Test that percentile_cont produces correct results
+# in window frame queries. Previously percentile_cont consumed its internal state
+# during evaluate(), causing incorrect results when called multiple times.
+###########
+
+# Test percentile_cont sliding window (same as median)
+query ITRR
+SELECT
+    timestamp,
+    tags,
+    value,
+    percentile_cont(value, 0.5) OVER (
+        PARTITION BY tags
+        ORDER BY timestamp
+        ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING
+    ) AS value_percentile_50
+FROM median_window_test
+ORDER BY tags, timestamp;
+----
+1 tag1 10 15
+2 tag1 20 20
+3 tag1 30 30
+4 tag1 40 40
+5 tag1 50 45
+1 tag2 60 65
+2 tag2 70 70
+3 tag2 80 80
+4 tag2 90 90
+5 tag2 100 95
+
+# Test percentile_cont non-sliding window
+query ITRRRR
+SELECT
+    timestamp,
+    tags,
+    value,
+    percentile_cont(value, 0.5) OVER (
+        PARTITION BY tags
+        ORDER BY timestamp
+        ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
+    ) AS value_percentile_unbounded_preceding,
+    percentile_cont(value, 0.5) OVER (
+        PARTITION BY tags
+        ORDER BY timestamp
+        ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
+    ) AS value_percentile_unbounded_both,
+    percentile_cont(value, 0.5) OVER (
+        PARTITION BY tags
+        ORDER BY timestamp
+        ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING
+    ) AS value_percentile_unbounded_following
+FROM median_window_test
+ORDER BY tags, timestamp;
+----
+1 tag1 10 10 30 30
+2 tag1 20 15 30 35
+3 tag1 30 20 30 40
+4 tag1 40 25 30 45
+5 tag1 50 30 30 50
+1 tag2 60 60 80 80
+2 tag2 70 65 80 85
+3 tag2 80 70 80 90
+4 tag2 90 75 80 95
+5 tag2 100 80 80 100
+
+# Test percentile_cont with different percentile values
+query ITRRR
+SELECT
+    timestamp,
+    tags,
+    value,
+    percentile_cont(value, 0.25) OVER (
+        PARTITION BY tags
+        ORDER BY timestamp
+        ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
+    ) AS p25,
+    percentile_cont(value, 0.75) OVER (
+        PARTITION BY tags
+        ORDER BY timestamp
+        ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
+    ) AS p75
+FROM median_window_test
+ORDER BY tags, timestamp;
+----
+1 tag1 10 10 10
+2 tag1 20 12.5 17.5
+3 tag1 30 15 25
+4 tag1 40 17.5 32.5
+5 tag1 50 20 40
+1 tag2 60 60 60
+2 tag2 70 62.5 67.5
+3 tag2 80 65 75
+4 tag2 90 67.5 82.5
+5 tag2 100 70 90
+
 statement ok
 DROP TABLE median_window_test;
 
@@ -8250,3 +8346,44 @@ query R
 select percentile_cont(null, 0.5);
 ----
 NULL
+
+# Test string_agg window frame behavior (fix for issue #19612)
+statement ok
+CREATE TABLE string_agg_window_test (
+    id INT,
+    grp VARCHAR,
+    val VARCHAR
+);
+
+statement ok
+INSERT INTO string_agg_window_test (id, grp, val) VALUES
+(1, 'A', 'a'),
+(2, 'A', 'b'),
+(3, 'A', 'c'),
+(1, 'B', 'x'),
+(2, 'B', 'y'),
+(3, 'B', 'z');
+
+# Test string_agg with ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
+# The function should maintain state correctly across multiple evaluate() calls
+query ITT
+SELECT
+    id,
+    grp,
+    string_agg(val, ',') OVER (
+        PARTITION BY grp
+        ORDER BY id
+        ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
+    ) AS cumulative_string
+FROM string_agg_window_test
+ORDER BY grp, id;
+----
+1 A a
+2 A a,b
+3 A a,b,c
+1 B x
+2 B x,y
+3 B x,y,z
+
+statement ok
+DROP TABLE string_agg_window_test;