Convert VariancePopulation to UDAF

datafusion/expr/src/aggregate_function.rs

@@ -47,8 +47,6 @@ pub enum AggregateFunction {
     ArrayAgg,
     /// N'th value in a group according to some ordering
     NthValue,
-    /// Variance (Population)
-    VariancePop,
     /// Correlation
     Correlation,
     /// Slope from linear regression
@@ -102,7 +100,6 @@ impl AggregateFunction {
             ApproxDistinct => "APPROX_DISTINCT",
             ArrayAgg => "ARRAY_AGG",
             NthValue => "NTH_VALUE",
-            VariancePop => "VAR_POP",
             Correlation => "CORR",
             RegrSlope => "REGR_SLOPE",
             RegrIntercept => "REGR_INTERCEPT",
@@ -153,7 +150,6 @@ impl FromStr for AggregateFunction {
             "string_agg" => AggregateFunction::StringAgg,
             // statistical
             "corr" => AggregateFunction::Correlation,
-            "var_pop" => AggregateFunction::VariancePop,
             "regr_slope" => AggregateFunction::RegrSlope,
             "regr_intercept" => AggregateFunction::RegrIntercept,
             "regr_count" => AggregateFunction::RegrCount,
@@ -216,9 +212,6 @@ impl AggregateFunction {
             AggregateFunction::BoolAnd | AggregateFunction::BoolOr => {
                 Ok(DataType::Boolean)
             }
-            AggregateFunction::VariancePop => {
-                variance_return_type(&coerced_data_types[0])
-            }
             AggregateFunction::Correlation => {
                 correlation_return_type(&coerced_data_types[0])
             }
@@ -291,9 +284,7 @@ impl AggregateFunction {
             AggregateFunction::BoolAnd | AggregateFunction::BoolOr => {
                 Signature::uniform(1, vec![DataType::Boolean], Volatility::Immutable)
             }
-            AggregateFunction::Avg
-            | AggregateFunction::VariancePop
-            | AggregateFunction::ApproxMedian => {
+            AggregateFunction::Avg | AggregateFunction::ApproxMedian => {
                 Signature::uniform(1, NUMERICS.to_vec(), Volatility::Immutable)
             }
             AggregateFunction::NthValue => Signature::any(2, Volatility::Immutable),

datafusion/expr/src/type_coercion/aggregates.rs

@@ -151,16 +151,6 @@ pub fn coerce_types(
             }
             Ok(input_types.to_vec())
         }
-        AggregateFunction::VariancePop => {
-            if !is_variance_support_arg_type(&input_types[0]) {
-                return plan_err!(
-                    "The function {:?} does not support inputs of type {:?}.",
-                    agg_fun,
-                    input_types[0]
-                );
-            }
-            Ok(vec![Float64, Float64])
-        }
         AggregateFunction::Correlation => {
             if !is_correlation_support_arg_type(&input_types[0]) {
                 return plan_err!(

datafusion/functions-aggregate/src/lib.rs

@@ -78,6 +78,7 @@ pub mod expr_fn {
     pub use super::stddev::stddev;
     pub use super::stddev::stddev_pop;
     pub use super::sum::sum;
+    pub use super::variance::var_pop;
     pub use super::variance::var_sample;
 }
 
@@ -91,6 +92,7 @@ pub fn all_default_aggregate_functions() -> Vec<Arc<AggregateUDF>> {
         covariance::covar_pop_udaf(),
         median::median_udaf(),
         variance::var_samp_udaf(),
+        variance::var_pop_udaf(),
         stddev::stddev_udaf(),
         stddev::stddev_pop_udaf(),
     ]

datafusion/functions-aggregate/src/variance.rs

@@ -15,7 +15,8 @@
 // specific language governing permissions and limitations
 // under the License.
 
-//! [`VarianceSample`]: covariance sample aggregations.
+//! [`VarianceSample`]: variance sample aggregations.
+//! [`VariancePopulation`]: variance population aggregations.
 
 use std::fmt::Debug;
 
@@ -43,6 +44,14 @@ make_udaf_expr_and_func!(
     var_samp_udaf
 );
 
+make_udaf_expr_and_func!(
+    VariancePopulation,
+    var_pop,
+    expression,
+    "Computes the population variance.",
+    var_pop_udaf
+);
+
 pub struct VarianceSample {
     signature: Signature,
     aliases: Vec<String>,
@@ -115,6 +124,80 @@ impl AggregateUDFImpl for VarianceSample {
     }
 }
 
+pub struct VariancePopulation {
+    signature: Signature,
+    aliases: Vec<String>,
+}
+
+impl Debug for VariancePopulation {
+    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
+        f.debug_struct("VariancePopulation")
+            .field("name", &self.name())
+            .field("signature", &self.signature)
+            .finish()
+    }
+}
+
+impl Default for VariancePopulation {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl VariancePopulation {
+    pub fn new() -> Self {
+        Self {
+            aliases: vec![String::from("var_population")],
+            signature: Signature::numeric(1, Volatility::Immutable),
+        }
+    }
+}
+
+impl AggregateUDFImpl for VariancePopulation {
+    fn as_any(&self) -> &dyn std::any::Any {
+        self
+    }
+
+    fn name(&self) -> &str {
+        "var_pop"
+    }
+
+    fn signature(&self) -> &Signature {
+        &self.signature
+    }
+
+    fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> {
+        if !arg_types[0].is_numeric() {
+            return plan_err!("Variance requires numeric input types");
+        }
+
+        Ok(DataType::Float64)
+    }
+
+    fn state_fields(&self, args: StateFieldsArgs) -> Result<Vec<Field>> {
+        let name = args.name;
+        Ok(vec![
+            Field::new(format_state_name(name, "count"), DataType::UInt64, true),
+            Field::new(format_state_name(name, "mean"), DataType::Float64, true),
+            Field::new(format_state_name(name, "m2"), DataType::Float64, true),
+        ])
+    }
+
+    fn accumulator(&self, acc_args: AccumulatorArgs) -> Result<Box<dyn Accumulator>> {
+        if acc_args.is_distinct {
+            return not_impl_err!("VAR_POP(DISTINCT) aggregations are not available");
+        }
+
+        Ok(Box::new(VarianceAccumulator::try_new(
+            StatsType::Population,
+        )?))
+    }
+
+    fn aliases(&self) -> &[String] {
+        &self.aliases
+    }
+}
+
 /// An accumulator to compute variance
 /// The algrithm used is an online implementation and numerically stable. It is based on this paper:
 /// Welford, B. P. (1962). "Note on a method for calculating corrected sums of squares and products".

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

@@ -157,12 +157,6 @@ pub fn create_aggregate_expr(
         (AggregateFunction::Avg, true) => {
             return not_impl_err!("AVG(DISTINCT) aggregations are not available");
         }
-        (AggregateFunction::VariancePop, false) => Arc::new(
-            expressions::VariancePop::new(input_phy_exprs[0].clone(), name, data_type),
-        ),
-        (AggregateFunction::VariancePop, true) => {
-            return not_impl_err!("VAR_POP(DISTINCT) aggregations are not available");
-        }
         (AggregateFunction::Correlation, false) => {
             Arc::new(expressions::Correlation::new(
                 input_phy_exprs[0].clone(),
@@ -340,7 +334,6 @@ pub fn create_aggregate_expr(
 #[cfg(test)]
 mod tests {
     use arrow::datatypes::{DataType, Field};
-    use expressions::VariancePop;
 
     use super::*;
     use crate::expressions::{
@@ -693,44 +686,6 @@ mod tests {
         Ok(())
     }
 
-    #[test]
-    fn test_var_pop_expr() -> Result<()> {
-        let funcs = vec![AggregateFunction::VariancePop];
-        let data_types = vec![
-            DataType::UInt32,
-            DataType::UInt64,
-            DataType::Int32,
-            DataType::Int64,
-            DataType::Float32,
-            DataType::Float64,
-        ];
-        for fun in funcs {
-            for data_type in &data_types {
-                let input_schema =
-                    Schema::new(vec![Field::new("c1", data_type.clone(), true)]);
-                let input_phy_exprs: Vec<Arc<dyn PhysicalExpr>> = vec![Arc::new(
-                    expressions::Column::new_with_schema("c1", &input_schema).unwrap(),
-                )];
-                let result_agg_phy_exprs = create_physical_agg_expr_for_test(
-                    &fun,
-                    false,
-                    &input_phy_exprs[0..1],
-                    &input_schema,
-                    "c1",
-                )?;
-                if fun == AggregateFunction::VariancePop {
-                    assert!(result_agg_phy_exprs.as_any().is::<VariancePop>());
-                    assert_eq!("c1", result_agg_phy_exprs.name());
-                    assert_eq!(
-                        Field::new("c1", DataType::Float64, true),
-                        result_agg_phy_exprs.field().unwrap()
-                    )
-                }
-            }
-        }
-        Ok(())
-    }
-
     #[test]
     fn test_median_expr() -> Result<()> {
         let funcs = vec![AggregateFunction::ApproxMedian];