[GitHub] [arrow-datafusion] izveigor commented on a diff in pull request #6276: feat: support bitwise and boolean aggregate functions

["izveigor (via GitHub)" <gi...@apache.org>]

izveigor commented on code in PR #6276:
URL: https://github.com/apache/arrow-datafusion/pull/6276#discussion_r1192468116


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datafusion/physical-expr/src/aggregate/bool_and_or.rs:
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@@ -0,0 +1,643 @@
+// 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::convert::TryFrom;
+use std::sync::Arc;
+
+use crate::{AggregateExpr, PhysicalExpr};
+use arrow::datatypes::DataType;
+use arrow::{
+    array::{ArrayRef, BooleanArray},
+    datatypes::Field,
+};
+use datafusion_common::{downcast_value, DataFusionError, Result, ScalarValue};
+use datafusion_expr::Accumulator;
+
+use crate::aggregate::row_accumulator::{
+    is_row_accumulator_support_dtype, RowAccumulator,
+};
+use crate::aggregate::utils::down_cast_any_ref;
+use crate::expressions::format_state_name;
+use arrow::array::Array;
+use datafusion_row::accessor::RowAccessor;
+use std::ops::BitAnd as BitAndImplementation;
+use std::ops::BitOr as BitOrImplementation;
+
+fn bool_and(array: &BooleanArray) -> Option<bool> {
+    if array.null_count() == array.len() {
+        return None;
+    }
+    Some(array.false_count() == 0)
+}
+
+fn bool_or(array: &BooleanArray) -> Option<bool> {
+    if array.null_count() == array.len() {
+        return None;
+    }
+    Some(array.true_count() != 0)
+}
+
+// Bool and/Bool or aggregation can take Dictionary encode input but always produces unpacked
+// (aka non Dictionary) output. We need to adjust the output data type to reflect this.
+// The reason bool and/bool or aggregate produces unpacked output because there is only one
+// bool and/bool or value per group; there is no needs to keep them Dictionary encode
+fn bool_and_or_aggregate_data_type(input_type: DataType) -> DataType {
+    if let DataType::Dictionary(_, value_type) = input_type {
+        *value_type
+    } else {
+        input_type
+    }
+}
+
+// returns the new value after bool_and/bool_or with the new values, taking nullability into account
+macro_rules! typed_bool_and_or_batch {
+    ($VALUES:expr, $ARRAYTYPE:ident, $SCALAR:ident, $OP:ident) => {{
+        let array = downcast_value!($VALUES, $ARRAYTYPE);
+        let delta = $OP(array);
+        Ok(ScalarValue::$SCALAR(delta))
+    }};
+}
+
+// bool_and/bool_or the array and returns a ScalarValue of its corresponding type.
+macro_rules! bool_and_or_batch {
+    ($VALUES:expr, $OP:ident) => {{
+        match $VALUES.data_type() {
+            DataType::Boolean => {
+                typed_bool_and_or_batch!($VALUES, BooleanArray, Boolean, $OP)
+            }
+            e => {
+                return Err(DataFusionError::Internal(format!(
+                    "Bool and/Bool or is not expected to receive the type {e:?}"
+                )));
+            }
+        }
+    }};
+}
+
+/// dynamically-typed bool_and(array) -> ScalarValue
+fn bool_and_batch(values: &ArrayRef) -> Result<ScalarValue> {
+    bool_and_or_batch!(values, bool_and)
+}
+
+/// dynamically-typed bool_or(array) -> ScalarValue
+fn bool_or_batch(values: &ArrayRef) -> Result<ScalarValue> {
+    bool_and_or_batch!(values, bool_or)
+}
+
+// bool_and/bool_or of two scalar values.
+macro_rules! typed_bool_and_or {
+    ($VALUE:expr, $DELTA:expr, $SCALAR:ident, $OP:ident) => {{
+        ScalarValue::$SCALAR(match ($VALUE, $DELTA) {
+            (None, None) => None,
+            (Some(a), None) => Some(*a),
+            (None, Some(b)) => Some(*b),
+            (Some(a), Some(b)) => Some((*a).$OP(*b)),
+        })
+    }};
+}
+
+// bool_and/bool_or of two scalar values.
+macro_rules! typed_bool_and_or_v2 {
+    ($INDEX:ident, $ACC:ident, $SCALAR:expr, $TYPE:ident, $OP:ident) => {{
+        paste::item! {
+            match $SCALAR {
+                None => {}
+                Some(v) => $ACC.[<$OP _ $TYPE>]($INDEX, *v as $TYPE)
+            }
+        }
+    }};
+}
+
+// bool_and/bool_or of two scalar values of the same type
+macro_rules! bool_and_or {
+    ($VALUE:expr, $DELTA:expr, $OP:ident) => {{
+        Ok(match ($VALUE, $DELTA) {
+            (ScalarValue::Boolean(lhs), ScalarValue::Boolean(rhs)) => {
+                typed_bool_and_or!(lhs, rhs, Boolean, $OP)
+            }
+            e => {
+                return Err(DataFusionError::Internal(format!(
+                    "BOOL AND/BOOL OR is not expected to receive scalars of incompatible types {:?}",
+                    e
+                )))
+            }
+        })
+    }};
+}
+
+macro_rules! bool_and_or_v2 {
+    ($INDEX:ident, $ACC:ident, $SCALAR:expr, $OP:ident) => {{
+        Ok(match $SCALAR {
+            ScalarValue::Boolean(rhs) => {
+                typed_bool_and_or_v2!($INDEX, $ACC, rhs, bool, $OP)
+            }
+            ScalarValue::Null => {
+                // do nothing
+            }
+            e => {
+                return Err(DataFusionError::Internal(format!(
+                    "BOOL AND/BOOL OR is not expected to receive scalars of incompatible types {:?}",
+                    e
+                )))
+            }
+        })
+    }};
+}
+
+/// the bool_and of two scalar values
+pub fn booland(lhs: &ScalarValue, rhs: &ScalarValue) -> Result<ScalarValue> {
+    bool_and_or!(lhs, rhs, bitand)
+}
+
+pub fn bool_and_row(
+    index: usize,
+    accessor: &mut RowAccessor,
+    s: &ScalarValue,
+) -> Result<()> {
+    bool_and_or_v2!(index, accessor, s, bitand)
+}
+
+/// the bool_or of two scalar values
+pub fn boolor(lhs: &ScalarValue, rhs: &ScalarValue) -> Result<ScalarValue> {
+    bool_and_or!(lhs, rhs, bitor)
+}
+
+pub fn bool_or_row(
+    index: usize,
+    accessor: &mut RowAccessor,
+    s: &ScalarValue,
+) -> Result<()> {
+    bool_and_or_v2!(index, accessor, s, bitor)
+}
+
+/// BOOL_AND aggregate expression
+#[derive(Debug, Clone)]
+pub struct BoolAnd {
+    name: String,
+    pub data_type: DataType,
+    expr: Arc<dyn PhysicalExpr>,
+    nullable: bool,
+}
+
+impl BoolAnd {
+    /// Create a new BOOL_AND aggregate function
+    pub fn new(
+        expr: Arc<dyn PhysicalExpr>,
+        name: impl Into<String>,
+        data_type: DataType,
+    ) -> Self {
+        Self {
+            name: name.into(),
+            expr,
+            data_type: bool_and_or_aggregate_data_type(data_type),
+            nullable: true,
+        }
+    }
+}
+
+impl AggregateExpr for BoolAnd {
+    /// 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,
+            self.data_type.clone(),
+            self.nullable,
+        ))
+    }
+
+    fn create_accumulator(&self) -> Result<Box<dyn Accumulator>> {
+        Ok(Box::new(BoolAndAccumulator::try_new(&self.data_type)?))
+    }
+
+    fn state_fields(&self) -> Result<Vec<Field>> {
+        Ok(vec![Field::new(
+            format_state_name(&self.name, "bool_and"),
+            self.data_type.clone(),
+            self.nullable,
+        )])
+    }
+
+    fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
+        vec![self.expr.clone()]
+    }
+
+    fn name(&self) -> &str {
+        &self.name
+    }
+
+    fn row_accumulator_supported(&self) -> bool {
+        is_row_accumulator_support_dtype(&self.data_type)
+    }
+
+    fn supports_bounded_execution(&self) -> bool {
+        true
+    }
+
+    fn create_row_accumulator(
+        &self,
+        start_index: usize,
+    ) -> Result<Box<dyn RowAccumulator>> {
+        Ok(Box::new(BoolAndRowAccumulator::new(
+            start_index,
+            self.data_type.clone(),
+        )))
+    }
+
+    fn reverse_expr(&self) -> Option<Arc<dyn AggregateExpr>> {
+        Some(Arc::new(self.clone()))
+    }
+
+    fn create_sliding_accumulator(&self) -> Result<Box<dyn Accumulator>> {
+        Ok(Box::new(BoolAndAccumulator::try_new(&self.data_type)?))
+    }
+}
+
+impl PartialEq<dyn Any> for BoolAnd {
+    fn eq(&self, other: &dyn Any) -> bool {
+        down_cast_any_ref(other)
+            .downcast_ref::<Self>()
+            .map(|x| {
+                self.name == x.name
+                    && self.data_type == x.data_type
+                    && self.nullable == x.nullable
+                    && self.expr.eq(&x.expr)
+            })
+            .unwrap_or(false)
+    }
+}
+
+#[derive(Debug)]
+struct BoolAndAccumulator {
+    bool_and: ScalarValue,
+}
+
+impl BoolAndAccumulator {
+    /// new bool_and accumulator
+    pub fn try_new(data_type: &DataType) -> Result<Self> {
+        Ok(Self {
+            bool_and: ScalarValue::try_from(data_type)?,
+        })
+    }
+}
+
+impl Accumulator for BoolAndAccumulator {
+    fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+        let values = &values[0];
+        let delta = &bool_and_batch(values)?;
+        self.bool_and = booland(&self.bool_and, delta)?;
+        Ok(())
+    }
+
+    fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
+        self.update_batch(states)
+    }
+
+    fn state(&self) -> Result<Vec<ScalarValue>> {
+        Ok(vec![self.bool_and.clone()])
+    }
+
+    fn evaluate(&self) -> Result<ScalarValue> {
+        Ok(self.bool_and.clone())
+    }
+
+    fn size(&self) -> usize {
+        std::mem::size_of_val(self) - std::mem::size_of_val(&self.bool_and)
+            + self.bool_and.size()
+    }
+}
+
+#[derive(Debug)]
+struct BoolAndRowAccumulator {
+    index: usize,
+    datatype: DataType,
+}
+
+impl BoolAndRowAccumulator {
+    pub fn new(index: usize, datatype: DataType) -> Self {
+        Self { index, datatype }
+    }
+}
+
+impl RowAccumulator for BoolAndRowAccumulator {
+    fn update_batch(
+        &mut self,
+        values: &[ArrayRef],
+        accessor: &mut RowAccessor,
+    ) -> Result<()> {
+        let values = &values[0];
+        let delta = &bool_and_batch(values)?;
+        bool_and_row(self.index, accessor, delta)
+    }
+
+    fn update_scalar_values(
+        &mut self,
+        values: &[ScalarValue],
+        accessor: &mut RowAccessor,
+    ) -> Result<()> {
+        let value = &values[0];
+        bool_and_row(self.index, accessor, value)
+    }
+
+    fn update_scalar(
+        &mut self,
+        value: &ScalarValue,
+        accessor: &mut RowAccessor,
+    ) -> Result<()> {
+        bool_and_row(self.index, accessor, value)
+    }
+
+    fn merge_batch(
+        &mut self,
+        states: &[ArrayRef],
+        accessor: &mut RowAccessor,
+    ) -> Result<()> {
+        self.update_batch(states, accessor)
+    }
+
+    fn evaluate(&self, accessor: &RowAccessor) -> Result<ScalarValue> {
+        Ok(accessor.get_as_scalar(&self.datatype, self.index))
+    }
+
+    #[inline(always)]
+    fn state_index(&self) -> usize {
+        self.index
+    }
+}
+
+/// BOOL_OR aggregate expression
+#[derive(Debug, Clone)]
+pub struct BoolOr {
+    name: String,
+    pub data_type: DataType,
+    expr: Arc<dyn PhysicalExpr>,
+    nullable: bool,
+}
+
+impl BoolOr {
+    /// Create a new BOOL_OR aggregate function
+    pub fn new(
+        expr: Arc<dyn PhysicalExpr>,
+        name: impl Into<String>,
+        data_type: DataType,
+    ) -> Self {
+        Self {
+            name: name.into(),
+            expr,
+            data_type: bool_and_or_aggregate_data_type(data_type),
+            nullable: true,
+        }
+    }
+}
+
+impl AggregateExpr for BoolOr {
+    /// 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,
+            self.data_type.clone(),
+            self.nullable,
+        ))
+    }
+
+    fn create_accumulator(&self) -> Result<Box<dyn Accumulator>> {
+        Ok(Box::new(BoolOrAccumulator::try_new(&self.data_type)?))
+    }
+
+    fn state_fields(&self) -> Result<Vec<Field>> {
+        Ok(vec![Field::new(
+            format_state_name(&self.name, "bool_or"),
+            self.data_type.clone(),
+            self.nullable,
+        )])
+    }
+
+    fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
+        vec![self.expr.clone()]
+    }
+
+    fn name(&self) -> &str {
+        &self.name
+    }
+
+    fn row_accumulator_supported(&self) -> bool {
+        is_row_accumulator_support_dtype(&self.data_type)
+    }
+
+    fn supports_bounded_execution(&self) -> bool {
+        true

Review Comment:
   I also want to mention that this situation concerns relatively DISTINCT cases (only BIT_XOR(DISTINCT) makes sense, the rest of DISTINCT cases will produce the same results as without them)



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