Change Accumulator::evaluate and Accumulator::state to take &mut self

datafusion-examples/examples/advanced_udaf.rs

@@ -125,7 +125,7 @@ impl Accumulator for GeometricMean {
     // This function serializes our state to `ScalarValue`, which DataFusion uses
     // to pass this state between execution stages.
     // Note that this can be arbitrary data.
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         Ok(vec![
             ScalarValue::from(self.prod),
             ScalarValue::from(self.n),
@@ -134,7 +134,7 @@ impl Accumulator for GeometricMean {
 
     // DataFusion expects this function to return the final value of this aggregator.
     // in this case, this is the formula of the geometric mean
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         let value = self.prod.powf(1.0 / self.n as f64);
         Ok(ScalarValue::from(value))
     }

datafusion-examples/examples/simple_udaf.rs

@@ -72,7 +72,7 @@ impl Accumulator for GeometricMean {
     // This function serializes our state to `ScalarValue`, which DataFusion uses
     // to pass this state between execution stages.
     // Note that this can be arbitrary data.
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         Ok(vec![
             ScalarValue::from(self.prod),
             ScalarValue::from(self.n),
@@ -81,7 +81,7 @@ impl Accumulator for GeometricMean {
 
     // DataFusion expects this function to return the final value of this aggregator.
     // in this case, this is the formula of the geometric mean
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         let value = self.prod.powf(1.0 / self.n as f64);
         Ok(ScalarValue::from(value))
     }

datafusion/core/src/datasource/statistics.rs

@@ -194,11 +194,11 @@ pub(crate) fn get_col_stats(
 ) -> Vec<ColumnStatistics> {
     (0..schema.fields().len())
         .map(|i| {
-            let max_value = match &max_values[i] {
+            let max_value = match max_values.get_mut(i).unwrap() {
                 Some(max_value) => max_value.evaluate().ok(),
                 None => None,
             };
-            let min_value = match &min_values[i] {
+            let min_value = match min_values.get_mut(i).unwrap() {
                 Some(min_value) => min_value.evaluate().ok(),
                 None => None,
             };

datafusion/core/tests/user_defined/user_defined_aggregates.rs

@@ -456,7 +456,7 @@ impl TimeSum {
 }
 
 impl Accumulator for TimeSum {
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         Ok(vec![self.evaluate()?])
     }
 
@@ -478,7 +478,7 @@ impl Accumulator for TimeSum {
         self.update_batch(states)
     }
 
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         println!("Evaluating to {}", self.sum);
         Ok(ScalarValue::TimestampNanosecond(Some(self.sum), None))
     }
@@ -603,14 +603,14 @@ impl FirstSelector {
 }
 
 impl Accumulator for FirstSelector {
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         let state = self.to_state().into_iter().collect::<Vec<_>>();
 
         Ok(state)
     }
 
     /// produce the output structure
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         Ok(self.to_scalar())
     }
 
@@ -689,15 +689,15 @@ impl Accumulator for TestGroupsAccumulator {
         Ok(())
     }
 
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         Ok(ScalarValue::from(self.result))
     }
 
     fn size(&self) -> usize {
         std::mem::size_of::<u64>()
     }
 
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         Ok(vec![ScalarValue::from(self.result)])
     }
 

datafusion/core/tests/user_defined/user_defined_scalar_functions.rs

@@ -254,7 +254,7 @@ async fn udaf_as_window_func() -> Result<()> {
     struct MyAccumulator;
 
     impl Accumulator for MyAccumulator {
-        fn state(&self) -> Result<Vec<ScalarValue>> {
+        fn state(&mut self) -> Result<Vec<ScalarValue>> {
             unimplemented!()
         }
 
@@ -266,7 +266,7 @@ async fn udaf_as_window_func() -> Result<()> {
             unimplemented!()
         }
 
-        fn evaluate(&self) -> Result<ScalarValue> {
+        fn evaluate(&mut self) -> Result<ScalarValue> {
             unimplemented!()
         }
 

datafusion/expr/src/accumulator.rs

@@ -56,11 +56,18 @@ pub trait Accumulator: Send + Sync + Debug {
     /// running sum.
     fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()>;
 
-    /// Returns the final aggregate value.
+    /// Returns the final aggregate value, consuming the internal state.
     ///
     /// For example, the `SUM` accumulator maintains a running sum,
     /// and `evaluate` will produce that running sum as its output.
-    fn evaluate(&self) -> Result<ScalarValue>;
+    ///
+    /// After this call, the accumulator's internal state should be
+    /// equivalent to when it was first created.
+    ///
+    /// 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)
+    fn evaluate(&mut self) -> Result<ScalarValue>;
 
     /// Returns the allocated size required for this accumulator, in
     /// bytes, including `Self`.
@@ -72,7 +79,15 @@ pub trait Accumulator: Send + Sync + Debug {
     /// the `capacity` should be used not the `len`.
     fn size(&self) -> usize;
 
-    /// Returns the intermediate state of the accumulator.
+    /// Returns the intermediate state of the accumulator, consuming the
+    /// intermediate state.
+    ///
+    /// After this call, the accumulator's internal state should be
+    /// equivalent to when it was first created.
+    ///
+    /// 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).
     ///
     /// Intermediate state is used for "multi-phase" grouping in
     /// DataFusion, where an aggregate is computed in parallel with
@@ -129,7 +144,7 @@ pub trait Accumulator: Send + Sync + Debug {
     /// Note that [`ScalarValue::List`] can be used to pass multiple
     /// values if the number of intermediate values is not known at
     /// planning time (e.g. for `MEDIAN`)
-    fn state(&self) -> Result<Vec<ScalarValue>>;
+    fn state(&mut self) -> Result<Vec<ScalarValue>>;
 
     /// Updates the accumulator's state from an `Array` containing one
     /// or more intermediate values.

datafusion/physical-expr/src/aggregate/approx_distinct.rs

@@ -244,12 +244,12 @@ macro_rules! default_accumulator_impl {
             Ok(())
         }
 
-        fn state(&self) -> Result<Vec<ScalarValue>> {
+        fn state(&mut self) -> Result<Vec<ScalarValue>> {
             let value = ScalarValue::from(&self.hll);
             Ok(vec![value])
         }
 
-        fn evaluate(&self) -> Result<ScalarValue> {
+        fn evaluate(&mut self) -> Result<ScalarValue> {
             Ok(ScalarValue::UInt64(Some(self.hll.count() as u64)))
         }
 

datafusion/physical-expr/src/aggregate/approx_percentile_cont.rs

@@ -377,7 +377,7 @@ impl ApproxPercentileAccumulator {
 }
 
 impl Accumulator for ApproxPercentileAccumulator {
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         Ok(self.digest.to_scalar_state().into_iter().collect())
     }
 
@@ -389,7 +389,7 @@ impl Accumulator for ApproxPercentileAccumulator {
         Ok(())
     }
 
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         if self.digest.count() == 0.0 {
             return exec_err!("aggregate function needs at least one non-null element");
         }

datafusion/physical-expr/src/aggregate/approx_percentile_cont_with_weight.rs

@@ -129,7 +129,7 @@ impl ApproxPercentileWithWeightAccumulator {
 }
 
 impl Accumulator for ApproxPercentileWithWeightAccumulator {
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         self.approx_percentile_cont_accumulator.state()
     }
 
@@ -155,7 +155,7 @@ impl Accumulator for ApproxPercentileWithWeightAccumulator {
         Ok(())
     }
 
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         self.approx_percentile_cont_accumulator.evaluate()
     }
 

datafusion/physical-expr/src/aggregate/array_agg.rs

@@ -153,11 +153,11 @@ impl Accumulator for ArrayAggAccumulator {
         Ok(())
     }
 
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         Ok(vec![self.evaluate()?])
     }
 
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         // Transform Vec<ListArr> to ListArr
 
         let element_arrays: Vec<&dyn Array> =

datafusion/physical-expr/src/aggregate/array_agg_distinct.rs

@@ -129,7 +129,7 @@ impl DistinctArrayAggAccumulator {
 }
 
 impl Accumulator for DistinctArrayAggAccumulator {
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         Ok(vec![self.evaluate()?])
     }
 
@@ -163,7 +163,7 @@ impl Accumulator for DistinctArrayAggAccumulator {
         Ok(())
     }
 
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         let values: Vec<ScalarValue> = self.values.iter().cloned().collect();
         let arr = ScalarValue::new_list(&values, &self.datatype);
         Ok(ScalarValue::List(arr))

datafusion/physical-expr/src/aggregate/array_agg_ordered.rs

@@ -279,13 +279,13 @@ impl Accumulator for OrderSensitiveArrayAggAccumulator {
         Ok(())
     }
 
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         let mut result = vec![self.evaluate()?];
         result.push(self.evaluate_orderings()?);
         Ok(result)
     }
 
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         let values = self.values.clone();
         let array = if self.reverse {
             ScalarValue::new_list_from_iter(values.into_iter().rev(), &self.datatypes[0])

datafusion/physical-expr/src/aggregate/average.rs

@@ -238,7 +238,7 @@ pub struct AvgAccumulator {
 }
 
 impl Accumulator for AvgAccumulator {
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         Ok(vec![
             ScalarValue::from(self.count),
             ScalarValue::Float64(self.sum),
@@ -276,7 +276,7 @@ impl Accumulator for AvgAccumulator {
         Ok(())
     }
 
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         Ok(ScalarValue::Float64(
             self.sum.map(|f| f / self.count as f64),
         ))
@@ -314,7 +314,7 @@ impl<T: DecimalType + ArrowNumericType> Debug for DecimalAvgAccumulator<T> {
 }
 
 impl<T: DecimalType + ArrowNumericType> Accumulator for DecimalAvgAccumulator<T> {
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         Ok(vec![
             ScalarValue::from(self.count),
             ScalarValue::new_primitive::<T>(
@@ -356,7 +356,7 @@ impl<T: DecimalType + ArrowNumericType> Accumulator for DecimalAvgAccumulator<T>
         Ok(())
     }
 
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         let v = self
             .sum
             .map(|v| {

datafusion/physical-expr/src/aggregate/bit_and_or_xor.rs

@@ -190,11 +190,11 @@ where
         self.update_batch(states)
     }
 
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         Ok(vec![self.evaluate()?])
     }
 
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         ScalarValue::new_primitive::<T>(self.value, &T::DATA_TYPE)
     }
 
@@ -339,7 +339,7 @@ impl<T: ArrowNumericType> Accumulator for BitOrAccumulator<T>
 where
     T::Native: std::ops::BitOr<Output = T::Native>,
 {
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         Ok(vec![self.evaluate()?])
     }
 
@@ -355,7 +355,7 @@ where
         self.update_batch(states)
     }
 
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         ScalarValue::new_primitive::<T>(self.value, &T::DATA_TYPE)
     }
 
@@ -500,7 +500,7 @@ impl<T: ArrowNumericType> Accumulator for BitXorAccumulator<T>
 where
     T::Native: std::ops::BitXor<Output = T::Native>,
 {
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         Ok(vec![self.evaluate()?])
     }
 
@@ -516,7 +516,7 @@ where
         self.update_batch(states)
     }
 
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         ScalarValue::new_primitive::<T>(self.value, &T::DATA_TYPE)
     }
 
@@ -634,7 +634,7 @@ impl<T: ArrowNumericType> Accumulator for DistinctBitXorAccumulator<T>
 where
     T::Native: std::ops::BitXor<Output = T::Native> + std::hash::Hash + Eq,
 {
-    fn state(&self) -> Result<Vec<ScalarValue>> {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
         // 1. Stores aggregate state in `ScalarValue::List`
         // 2. Constructs `ScalarValue::List` state from distinct numeric stored in hash set
         let state_out = {
@@ -679,7 +679,7 @@ where
         Ok(())
     }
 
-    fn evaluate(&self) -> Result<ScalarValue> {
+    fn evaluate(&mut self) -> Result<ScalarValue> {
         let mut acc = T::Native::usize_as(0);
         for distinct_value in self.values.iter() {
             acc = acc ^ *distinct_value;