feat: Port Datafusion Covariance to Comet

core/src/execution/datafusion/expressions/covariance.rs

@@ -0,0 +1,394 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+//! Defines physical expressions that can evaluated at runtime during query execution
+
+use std::any::Any;
+use std::sync::Arc;
+
+use arrow::array::Float64Array;
+use arrow::{
+    array::{ArrayRef, Int64Array},
+    compute::cast,
+    datatypes::DataType,
+    datatypes::Field,
+};
+use datafusion::logical_expr::Accumulator;
+use datafusion_common::{downcast_value, unwrap_or_internal_err, ScalarValue};
+use datafusion_common::{DataFusionError, Result};
+use datafusion_physical_expr::{aggregate::utils::down_cast_any_ref, expressions::format_state_name, AggregateExpr, PhysicalExpr};
+use datafusion_physical_expr::expressions::StatsType;
+
+/// COVAR and COVAR_SAMP aggregate expression
+#[derive(Debug, Clone)]
+pub struct Covariance {
+    name: String,
+    expr1: Arc<dyn PhysicalExpr>,
+    expr2: Arc<dyn PhysicalExpr>,
+}
+
+/// COVAR_POP aggregate expression
+#[derive(Debug)]
+pub struct CovariancePop {
+    name: String,
+    expr1: Arc<dyn PhysicalExpr>,
+    expr2: Arc<dyn PhysicalExpr>,
+}
+
+impl Covariance {
+    /// Create a new COVAR aggregate function
+    pub fn new(
+        expr1: Arc<dyn PhysicalExpr>,
+        expr2: Arc<dyn PhysicalExpr>,
+        name: impl Into<String>,
+        data_type: DataType,
+    ) -> Self {
+        // the result of covariance just support FLOAT64 data type.
+        assert!(matches!(data_type, DataType::Float64));
+        Self {
+            name: name.into(),
+            expr1,
+            expr2,
+        }
+    }
+}
+
+impl AggregateExpr for Covariance {
+    /// Return a reference to Any that can be used for downcasting
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn field(&self) -> Result<Field> {
+        Ok(Field::new(&self.name, DataType::Float64, true))
+    }
+
+    fn create_accumulator(&self) -> Result<Box<dyn Accumulator>> {
+        Ok(Box::new(CovarianceAccumulator::try_new(StatsType::Sample)?))
+    }
+
+    fn state_fields(&self) -> Result<Vec<Field>> {
+        Ok(vec![
+            Field::new(
+                format_state_name(&self.name, "count"),
+                DataType::Int64,
+                true,
+            ),
+            Field::new(
+                format_state_name(&self.name, "mean1"),
+                DataType::Float64,
+                true,
+            ),
+            Field::new(
+                format_state_name(&self.name, "mean2"),
+                DataType::Float64,
+                true,
+            ),
+            Field::new(
+                format_state_name(&self.name, "algo_const"),
+                DataType::Float64,
+                true,
+            ),
+        ])
+    }
+
+    fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
+        vec![self.expr1.clone(), self.expr2.clone()]
+    }
+
+    fn name(&self) -> &str {
+        &self.name
+    }
+}
+
+impl PartialEq<dyn Any> for Covariance {
+    fn eq(&self, other: &dyn Any) -> bool {
+        down_cast_any_ref(other)
+            .downcast_ref::<Self>()
+            .map(|x| {
+                self.name == x.name && self.expr1.eq(&x.expr1) && self.expr2.eq(&x.expr2)
+            })
+            .unwrap_or(false)
+    }
+}
+
+impl CovariancePop {
+    /// Create a new COVAR_POP aggregate function
+    pub fn new(
+        expr1: Arc<dyn PhysicalExpr>,
+        expr2: Arc<dyn PhysicalExpr>,
+        name: impl Into<String>,
+        data_type: DataType,
+    ) -> Self {
+        // the result of covariance just support FLOAT64 data type.
+        assert!(matches!(data_type, DataType::Float64));
+        Self {
+            name: name.into(),
+            expr1,
+            expr2,
+        }
+    }
+}
+
+impl AggregateExpr for CovariancePop {
+    /// Return a reference to Any that can be used for downcasting
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn field(&self) -> Result<Field> {
+        Ok(Field::new(&self.name, DataType::Float64, true))
+    }
+
+    fn create_accumulator(&self) -> Result<Box<dyn Accumulator>> {
+        Ok(Box::new(CovarianceAccumulator::try_new(
+            StatsType::Population,
+        )?))
+    }
+
+    fn state_fields(&self) -> Result<Vec<Field>> {
+        Ok(vec![
+            Field::new(
+                format_state_name(&self.name, "count"),
+                DataType::Int64,
+                true,
+            ),
+            Field::new(
+                format_state_name(&self.name, "mean1"),
+                DataType::Float64,
+                true,
+            ),
+            Field::new(
+                format_state_name(&self.name, "mean2"),
+                DataType::Float64,
+                true,
+            ),
+            Field::new(
+                format_state_name(&self.name, "algo_const"),
+                DataType::Float64,
+                true,
+            ),
+        ])
+    }
+
+    fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
+        vec![self.expr1.clone(), self.expr2.clone()]
+    }
+
+    fn name(&self) -> &str {
+        &self.name
+    }
+}
+
+impl PartialEq<dyn Any> for CovariancePop {
+    fn eq(&self, other: &dyn Any) -> bool {
+        down_cast_any_ref(other)
+            .downcast_ref::<Self>()
+            .map(|x| {
+                self.name == x.name && self.expr1.eq(&x.expr1) && self.expr2.eq(&x.expr2)
+            })
+            .unwrap_or(false)
+    }
+}
+
+/// An accumulator to compute covariance
+#[derive(Debug)]
+pub struct CovarianceAccumulator {
+    algo_const: f64,
+    mean1: f64,
+    mean2: f64,
+    count: i64,
+    stats_type: StatsType,
+}
+
+impl CovarianceAccumulator {
+    /// Creates a new `CovarianceAccumulator`
+    pub fn try_new(s_type: StatsType) -> Result<Self> {
+        Ok(Self {
+            algo_const: 0_f64,
+            mean1: 0_f64,
+            mean2: 0_f64,
+            count: 0_i64,
+            stats_type: s_type,
+        })
+    }
+
+    pub fn get_count(&self) -> i64 {
+        self.count
+    }
+
+    pub fn get_mean1(&self) -> f64 {
+        self.mean1
+    }
+
+    pub fn get_mean2(&self) -> f64 {
+        self.mean2
+    }
+
+    pub fn get_algo_const(&self) -> f64 {
+        self.algo_const
+    }
+}
+
+impl Accumulator for CovarianceAccumulator {
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
+        Ok(vec![
+            ScalarValue::from(self.count),
+            ScalarValue::from(self.mean1),
+            ScalarValue::from(self.mean2),
+            ScalarValue::from(self.algo_const),
+        ])
+    }
+
+    fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+        let values1 = &cast(&values[0], &DataType::Float64)?;
+        let values2 = &cast(&values[1], &DataType::Float64)?;
+
+        let mut arr1 = downcast_value!(values1, Float64Array).iter().flatten();
+        let mut arr2 = downcast_value!(values2, Float64Array).iter().flatten();
+
+        for i in 0..values1.len() {
+            let value1 = if values1.is_valid(i) {
+                arr1.next()
+            } else {
+                None
+            };
+            let value2 = if values2.is_valid(i) {
+                arr2.next()
+            } else {
+                None
+            };
+
+            if value1.is_none() || value2.is_none() {
+                continue;
+            }
+
+            let value1 = unwrap_or_internal_err!(value1);
+            let value2 = unwrap_or_internal_err!(value2);
+            let new_count = self.count + 1;
+            let delta1 = value1 - self.mean1;
+            let new_mean1 = delta1 / new_count as f64 + self.mean1;
+            let delta2 = value2 - self.mean2;
+            let new_mean2 = delta2 / new_count as f64 + self.mean2;
+            let new_c = delta1 * (value2 - new_mean2) + self.algo_const;
+
+            self.count += 1;
+            self.mean1 = new_mean1;
+            self.mean2 = new_mean2;
+            self.algo_const = new_c;
+        }
+
+        Ok(())
+    }
+
+    fn retract_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+        let values1 = &cast(&values[0], &DataType::Float64)?;
+        let values2 = &cast(&values[1], &DataType::Float64)?;
+        let mut arr1 = downcast_value!(values1, Float64Array).iter().flatten();
+        let mut arr2 = downcast_value!(values2, Float64Array).iter().flatten();
+
+        for i in 0..values1.len() {
+            let value1 = if values1.is_valid(i) {
+                arr1.next()
+            } else {
+                None
+            };
+            let value2 = if values2.is_valid(i) {
+                arr2.next()
+            } else {
+                None
+            };
+
+            if value1.is_none() || value2.is_none() {
+                continue;
+            }
+
+            let value1 = unwrap_or_internal_err!(value1);
+            let value2 = unwrap_or_internal_err!(value2);
+
+            let new_count = self.count - 1;
+            let delta1 = self.mean1 - value1;
+            let new_mean1 = delta1 / new_count as f64 + self.mean1;
+            let delta2 = self.mean2 - value2;
+            let new_mean2 = delta2 / new_count as f64 + self.mean2;
+            let new_c = self.algo_const - delta1 * (new_mean2 - value2);
+
+            self.count -= 1;
+            self.mean1 = new_mean1;
+            self.mean2 = new_mean2;
+            self.algo_const = new_c;
+        }
+
+        Ok(())
+    }
+
+    fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
+        let counts = downcast_value!(states[0], Int64Array);
+        let means1 = downcast_value!(states[1], Float64Array);
+        let means2 = downcast_value!(states[2], Float64Array);
+        let cs = downcast_value!(states[3], Float64Array);
+
+        for i in 0..counts.len() {
+            let c = counts.value(i);
+            if c == 0 {
+                continue;
+            }
+            let new_count = self.count + c;
+            let new_count_casted = new_count as f64;
+            let count_casted = self.count as f64;
+            let new_mean1 = self.mean1 * count_casted / new_count_casted
+                + means1.value(i) * c as f64 / new_count_casted;
+            let new_mean2 = self.mean2 * count_casted / new_count_casted
+                + means2.value(i) * c as f64 / new_count_casted;
+            let delta1 = self.mean1 - means1.value(i);
+            let delta2 = self.mean2 - means2.value(i);
+            let new_c = self.algo_const
+                + cs.value(i)
+                + delta1 * delta2 * self.count as f64 * c as f64 / new_count as f64;
+
+            self.count = new_count;
+            self.mean1 = new_mean1;
+            self.mean2 = new_mean2;
+            self.algo_const = new_c;
+        }
+        Ok(())
+    }
+
+    fn evaluate(&mut self) -> Result<ScalarValue> {
+        println!("evaluate evaluate evaluate");
+        let count = match self.stats_type {
+            datafusion_physical_expr::expressions::StatsType::Population => self.count,
+            StatsType::Sample => {
+                if self.count > 0 {
+                    self.count - 1
+                } else {
+                    self.count
+                }
+            }
+        };
+
+        if count == 0 {
+            Ok(ScalarValue::Float64(None))
+        } else {
+            Ok(ScalarValue::Float64(Some(self.algo_const / count as f64)))
+        }
+    }
+
+    fn size(&self) -> usize {
+        std::mem::size_of_val(self)
+    }
+}

core/src/execution/datafusion/expressions/mod.rs

@@ -32,3 +32,4 @@ pub mod subquery;
 pub mod sum_decimal;
 pub mod temporal;
 mod utils;
+pub mod covariance;