feat: Port Datafusion Covariance to Comet

EXPRESSIONS.md

@@ -101,3 +101,6 @@ The following Spark expressions are currently available:
     + BitAnd
     + BitOr
     + BitXor
+    + BoolAnd
+    + BoolOr
+    + Covariance

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

@@ -0,0 +1,308 @@
+/*
+ * 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.
+ */
+
+use std::{any::Any, sync::Arc};
+
+use crate::execution::datafusion::expressions::stats::StatsType;
+use arrow::{
+    array::{ArrayRef, Float64Array},
+    compute::cast,
+    datatypes::{DataType, Field},
+};
+use datafusion::logical_expr::Accumulator;
+use datafusion_common::{
+    downcast_value, unwrap_or_internal_err, DataFusionError, Result, ScalarValue,
+};
+use datafusion_physical_expr::{
+    aggregate::utils::down_cast_any_ref, expressions::format_state_name, AggregateExpr,
+    PhysicalExpr,
+};
+
+/// COVAR_SAMP and COVAR_POP aggregate expression
+#[derive(Debug, Clone)]
+pub struct Covariance {
+    name: String,
+    expr1: Arc<dyn PhysicalExpr>,
+    expr2: Arc<dyn PhysicalExpr>,
+    stats_type: StatsType,
+}
+
+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,
+        stats_type: StatsType,
+    ) -> Self {
+        // the result of covariance just support FLOAT64 data type.
+        assert!(matches!(data_type, DataType::Float64));
+        Self {
+            name: name.into(),
+            expr1,
+            expr2,
+            stats_type,
+        }
+    }
+}
+
+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(self.stats_type)?))
+    }
+
+    fn state_fields(&self) -> Result<Vec<Field>> {
+        Ok(vec![
+            Field::new(
+                format_state_name(&self.name, "count"),
+                DataType::Float64,
+                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)
+                    && self.stats_type == x.stats_type
+            })
+            .unwrap_or(false)
+    }
+}
+
+/// An accumulator to compute covariance
+#[derive(Debug)]
+pub struct CovarianceAccumulator {
+    algo_const: f64,
+    mean1: f64,
+    mean2: f64,
+    count: f64,
+    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_f64,
+            stats_type: s_type,
+        })
+    }
+
+    pub fn get_count(&self) -> f64 {
+        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 + 1.0;
+            let delta1 = value1 - self.mean1;
+            let new_mean1 = delta1 / new_count + self.mean1;
+            let delta2 = value2 - self.mean2;
+            let new_mean2 = delta2 / new_count + self.mean2;
+            let new_c = delta1 * (value2 - new_mean2) + self.algo_const;
+
+            self.count += 1.0;
+            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 - 1.0;
+            let delta1 = self.mean1 - value1;
+            let new_mean1 = delta1 / new_count + self.mean1;
+            let delta2 = self.mean2 - value2;
+            let new_mean2 = delta2 / new_count + self.mean2;
+            let new_c = self.algo_const - delta1 * (new_mean2 - value2);
+
+            self.count -= 1.0;
+            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], Float64Array);
+        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 == 0.0 {
+                continue;
+            }
+            let new_count = self.count + c;
+            let new_mean1 = self.mean1 * self.count / new_count + means1.value(i) * c / new_count;
+            let new_mean2 = self.mean2 * self.count / new_count + means2.value(i) * c / new_count;
+            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 * c / new_count;
+
+            self.count = new_count;
+            self.mean1 = new_mean1;
+            self.mean2 = new_mean2;
+            self.algo_const = new_c;
+        }
+        Ok(())
+    }
+
+    fn evaluate(&mut self) -> Result<ScalarValue> {
+        let count = match self.stats_type {
+            StatsType::Population => self.count,
+            StatsType::Sample => {
+                if self.count > 0.0 {
+                    self.count - 1.0
+                } else {
+                    self.count
+                }
+            }
+        };
+
+        if count == 0.0 {
+            Ok(ScalarValue::Float64(None))
+        } else {
+            Ok(ScalarValue::Float64(Some(self.algo_const / count)))
+        }
+    }
+
+    fn size(&self) -> usize {
+        std::mem::size_of_val(self)
+    }
+}

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

@@ -27,6 +27,8 @@ pub use normalize_nan::NormalizeNaNAndZero;
 pub mod avg;
 pub mod avg_decimal;
 pub mod bloom_filter_might_contain;
+pub mod covariance;
+pub mod stats;
 pub mod strings;
 pub mod subquery;
 pub mod sum_decimal;

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

@@ -0,0 +1,27 @@
+/*
+ * 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.
+ */
+
+/// Enum used for differentiating population and sample for statistical functions
+#[derive(PartialEq, Eq, Debug, Clone, Copy)]
+pub enum StatsType {
+    /// Population
+    Population,
+    /// Sample
+    Sample,
+}

core/src/execution/datafusion/planner.rs

@@ -67,8 +67,10 @@ use crate::{
                 bloom_filter_might_contain::BloomFilterMightContain,
                 cast::Cast,
                 checkoverflow::CheckOverflow,
+                covariance::Covariance,
                 if_expr::IfExpr,
                 scalar_funcs::create_comet_physical_fun,
+                stats::StatsType,
                 strings::{Contains, EndsWith, Like, StartsWith, StringSpaceExec, SubstringExec},
                 subquery::Subquery,
                 sum_decimal::SumDecimal,
@@ -1180,6 +1182,30 @@ impl PhysicalPlanner {
                 let datatype = to_arrow_datatype(expr.datatype.as_ref().unwrap());
                 Ok(Arc::new(BitXor::new(child, "bit_xor", datatype)))
             }
+            AggExprStruct::CovSample(expr) => {
+                let child1 = self.create_expr(expr.child1.as_ref().unwrap(), schema.clone())?;
+                let child2 = self.create_expr(expr.child2.as_ref().unwrap(), schema.clone())?;
+                let datatype = to_arrow_datatype(expr.datatype.as_ref().unwrap());
+                Ok(Arc::new(Covariance::new(
+                    child1,
+                    child2,
+                    "covariance",
+                    datatype,
+                    StatsType::Sample,
+                )))
+            }
+            AggExprStruct::CovPopulation(expr) => {
+                let child1 = self.create_expr(expr.child1.as_ref().unwrap(), schema.clone())?;
+                let child2 = self.create_expr(expr.child2.as_ref().unwrap(), schema.clone())?;
+                let datatype = to_arrow_datatype(expr.datatype.as_ref().unwrap());
+                Ok(Arc::new(Covariance::new(
+                    child1,
+                    child2,
+                    "covariance_pop",
+                    datatype,
+                    StatsType::Population,
+                )))
+            }
         }
     }