binary.rs

// 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.

mod adapter;
mod kernels;
mod kernels_arrow;

use std::convert::TryInto;
use std::{any::Any, sync::Arc};

use arrow::array::*;
use arrow::compute::kernels::arithmetic::{
    add, add_scalar, divide_opt, divide_scalar, modulus, modulus_scalar, multiply,
    multiply_scalar, subtract, subtract_scalar,
};
use arrow::compute::kernels::boolean::{and_kleene, not, or_kleene};
use arrow::compute::kernels::comparison::{
    eq_dyn_binary_scalar, gt_dyn_binary_scalar, gt_eq_dyn_binary_scalar,
    lt_dyn_binary_scalar, lt_eq_dyn_binary_scalar, neq_dyn_binary_scalar,
};
use arrow::compute::kernels::comparison::{
    eq_dyn_bool_scalar, gt_dyn_bool_scalar, gt_eq_dyn_bool_scalar, lt_dyn_bool_scalar,
    lt_eq_dyn_bool_scalar, neq_dyn_bool_scalar,
};
use arrow::compute::kernels::comparison::{
    eq_dyn_scalar, gt_dyn_scalar, gt_eq_dyn_scalar, lt_dyn_scalar, lt_eq_dyn_scalar,
    neq_dyn_scalar,
};
use arrow::compute::kernels::comparison::{
    eq_dyn_utf8_scalar, gt_dyn_utf8_scalar, gt_eq_dyn_utf8_scalar, lt_dyn_utf8_scalar,
    lt_eq_dyn_utf8_scalar, neq_dyn_utf8_scalar,
};
use arrow::compute::kernels::comparison::{
    eq_scalar, gt_eq_scalar, gt_scalar, lt_eq_scalar, lt_scalar, neq_scalar,
};
use arrow::compute::kernels::comparison::{like_utf8, nlike_utf8, regexp_is_match_utf8};
use arrow::compute::kernels::comparison::{
    like_utf8_scalar, nlike_utf8_scalar, regexp_is_match_utf8_scalar,
};

use adapter::{eq_dyn, gt_dyn, gt_eq_dyn, lt_dyn, lt_eq_dyn, neq_dyn};
use arrow::compute::kernels::concat_elements::concat_elements_utf8;
use kernels::{
    bitwise_and, bitwise_and_scalar, bitwise_or, bitwise_or_scalar, bitwise_shift_left,
    bitwise_shift_left_scalar, bitwise_shift_right, bitwise_shift_right_scalar,
    bitwise_xor, bitwise_xor_scalar,
};
use kernels_arrow::{
    add_decimal, add_decimal_scalar, divide_decimal_scalar, divide_opt_decimal,
    is_distinct_from, is_distinct_from_bool, is_distinct_from_decimal,
    is_distinct_from_null, is_distinct_from_utf8, is_not_distinct_from,
    is_not_distinct_from_bool, is_not_distinct_from_decimal, is_not_distinct_from_null,
    is_not_distinct_from_utf8, modulus_decimal, modulus_decimal_scalar, multiply_decimal,
    multiply_decimal_scalar, subtract_decimal, subtract_decimal_scalar,
};

use arrow::datatypes::{DataType, Schema, TimeUnit};
use arrow::record_batch::RecordBatch;

use crate::physical_expr::down_cast_any_ref;
use crate::{AnalysisContext, ExprBoundaries, PhysicalExpr};
use datafusion_common::cast::{as_boolean_array, as_decimal128_array};
use datafusion_common::ScalarValue;
use datafusion_common::{DataFusionError, Result};
use datafusion_expr::type_coercion::binary::binary_operator_data_type;
use datafusion_expr::{ColumnarValue, Operator};

/// Binary expression
#[derive(Debug)]
pub struct BinaryExpr {
    left: Arc<dyn PhysicalExpr>,
    op: Operator,
    right: Arc<dyn PhysicalExpr>,
}

impl BinaryExpr {
    /// Create new binary expression
    pub fn new(
        left: Arc<dyn PhysicalExpr>,
        op: Operator,
        right: Arc<dyn PhysicalExpr>,
    ) -> Self {
        Self { left, op, right }
    }

    /// Get the left side of the binary expression
    pub fn left(&self) -> &Arc<dyn PhysicalExpr> {
        &self.left
    }

    /// Get the right side of the binary expression
    pub fn right(&self) -> &Arc<dyn PhysicalExpr> {
        &self.right
    }

    /// Get the operator for this binary expression
    pub fn op(&self) -> &Operator {
        &self.op
    }
}

impl std::fmt::Display for BinaryExpr {
    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
        write!(f, "{} {} {}", self.left, self.op, self.right)
    }
}

macro_rules! compute_decimal_op_dyn_scalar {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $OP_TYPE:expr) => {{
        let ll = as_decimal128_array($LEFT).unwrap();
        if let ScalarValue::Decimal128(Some(v_i128), _, _) = $RIGHT {
            // Ok(Arc::new(paste::expr! {[<$OP _decimal_scalar>]}(
            Ok(Arc::new(paste::expr! {[<$OP _dyn_scalar>]}(ll, v_i128)?))
        } else {
            // when the $RIGHT is a NULL, generate a NULL array of $OP_TYPE type
            Ok(Arc::new(new_null_array($OP_TYPE, $LEFT.len())))
        }
    }};
}

macro_rules! compute_decimal_op_scalar {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $DT:ident) => {{
        let ll = as_decimal128_array($LEFT).unwrap();
        if let ScalarValue::Decimal128(Some(_), _, _) = $RIGHT {
            Ok(Arc::new(paste::expr! {[<$OP _decimal_scalar>]}(
                ll,
                $RIGHT.try_into()?,
            )?))
        } else {
            // when the $RIGHT is a NULL, generate a NULL array of LEFT's datatype
            Ok(Arc::new(new_null_array($LEFT.data_type(), $LEFT.len())))
        }
    }};
}

macro_rules! compute_decimal_op {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $DT:ident) => {{
        let ll = $LEFT.as_any().downcast_ref::<$DT>().unwrap();
        let rr = $RIGHT.as_any().downcast_ref::<$DT>().unwrap();
        Ok(Arc::new(paste::expr! {[<$OP _decimal>]}(ll, rr)?))
    }};
}

macro_rules! compute_null_op {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $DT:ident) => {{
        let ll = $LEFT
            .as_any()
            .downcast_ref::<$DT>()
            .expect("compute_op failed to downcast array");
        let rr = $RIGHT
            .as_any()
            .downcast_ref::<$DT>()
            .expect("compute_op failed to downcast array");
        Ok(Arc::new(paste::expr! {[<$OP _null>]}(&ll, &rr)?))
    }};
}

/// Invoke a compute kernel on a pair of binary data arrays
macro_rules! compute_utf8_op {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $DT:ident) => {{
        let ll = $LEFT
            .as_any()
            .downcast_ref::<$DT>()
            .expect("compute_op failed to downcast array");
        let rr = $RIGHT
            .as_any()
            .downcast_ref::<$DT>()
            .expect("compute_op failed to downcast array");
        Ok(Arc::new(paste::expr! {[<$OP _utf8>]}(&ll, &rr)?))
    }};
}

/// Invoke a compute kernel on a data array and a scalar value
macro_rules! compute_utf8_op_scalar {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $DT:ident, $OP_TYPE:expr) => {{
        let ll = $LEFT
            .as_any()
            .downcast_ref::<$DT>()
            .expect("compute_op failed to downcast array");
        if let ScalarValue::Utf8(Some(string_value)) = $RIGHT {
            Ok(Arc::new(paste::expr! {[<$OP _utf8_scalar>]}(
                &ll,
                &string_value,
            )?))
        } else if $RIGHT.is_null() {
            Ok(Arc::new(new_null_array($OP_TYPE, $LEFT.len())))
        } else {
            Err(DataFusionError::Internal(format!(
                "compute_utf8_op_scalar for '{}' failed to cast literal value {}",
                stringify!($OP),
                $RIGHT
            )))
        }
    }};
}

/// Invoke a compute kernel on a data array and a scalar value
macro_rules! compute_utf8_op_dyn_scalar {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $OP_TYPE:expr) => {{
        if let Some(string_value) = $RIGHT {
            Ok(Arc::new(paste::expr! {[<$OP _dyn_utf8_scalar>]}(
                $LEFT,
                &string_value,
            )?))
        } else {
            // when the $RIGHT is a NULL, generate a NULL array of $OP_TYPE
            Ok(Arc::new(new_null_array($OP_TYPE, $LEFT.len())))
        }
    }};
}

/// Invoke a compute kernel on a data array and a scalar value
macro_rules! compute_binary_op_dyn_scalar {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $OP_TYPE:expr) => {{
        if let Some(binary_value) = $RIGHT {
            Ok(Arc::new(paste::expr! {[<$OP _dyn_binary_scalar>]}(
                $LEFT,
                &binary_value,
            )?))
        } else {
            // when the $RIGHT is a NULL, generate a NULL array of $OP_TYPE
            Ok(Arc::new(new_null_array($OP_TYPE, $LEFT.len())))
        }
    }};
}

/// Invoke a compute kernel on a boolean data array and a scalar value
macro_rules! compute_bool_op_dyn_scalar {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $OP_TYPE:expr) => {{
        // generate the scalar function name, such as lt_dyn_bool_scalar, from the $OP parameter
        // (which could have a value of lt) and the suffix _scalar
        if let Some(b) = $RIGHT {
            Ok(Arc::new(paste::expr! {[<$OP _dyn_bool_scalar>]}(
                $LEFT,
                b,
            )?))
        } else {
            // when the $RIGHT is a NULL, generate a NULL array of $OP_TYPE
            Ok(Arc::new(new_null_array($OP_TYPE, $LEFT.len())))
        }
    }};
}

/// Invoke a bool compute kernel on array(s)
macro_rules! compute_bool_op {
    // invoke binary operator
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $DT:ident) => {{
        let ll = $LEFT
            .as_any()
            .downcast_ref::<$DT>()
            .expect("compute_op failed to downcast left side array");
        let rr = $RIGHT
            .as_any()
            .downcast_ref::<$DT>()
            .expect("compute_op failed to downcast right side array");
        Ok(Arc::new(paste::expr! {[<$OP _bool>]}(&ll, &rr)?))
    }};
    // invoke unary operator
    ($OPERAND:expr, $OP:ident, $DT:ident) => {{
        let operand = $OPERAND
            .as_any()
            .downcast_ref::<$DT>()
            .expect("compute_op failed to downcast operant array");
        Ok(Arc::new(paste::expr! {[<$OP _bool>]}(&operand)?))
    }};
}

/// Invoke a compute kernel on a data array and a scalar value
/// LEFT is array, RIGHT is scalar value
macro_rules! compute_op_scalar {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $DT:ident) => {{
        if $RIGHT.is_null() {
            Ok(Arc::new(new_null_array($LEFT.data_type(), $LEFT.len())))
        } else {
            let ll = $LEFT
                .as_any()
                .downcast_ref::<$DT>()
                .expect("compute_op failed to downcast array");
            Ok(Arc::new(paste::expr! {[<$OP _scalar>]}(
                &ll,
                $RIGHT.try_into()?,
            )?))
        }
    }};
}

/// Invoke a dyn compute kernel on a data array and a scalar value
/// LEFT is Primitive or Dictionary array of numeric values, RIGHT is scalar value
/// OP_TYPE is the return type of scalar function
macro_rules! compute_op_dyn_scalar {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $OP_TYPE:expr) => {{
        // generate the scalar function name, such as lt_dyn_scalar, from the $OP parameter
        // (which could have a value of lt_dyn) and the suffix _scalar
        if let Some(value) = $RIGHT {
            Ok(Arc::new(paste::expr! {[<$OP _dyn_scalar>]}(
                $LEFT,
                value,
            )?))
        } else {
            // when the $RIGHT is a NULL, generate a NULL array of $OP_TYPE
            Ok(Arc::new(new_null_array($OP_TYPE, $LEFT.len())))
        }
    }};
}

/// Invoke a compute kernel on array(s)
macro_rules! compute_op {
    // invoke binary operator
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $DT:ident) => {{
        let ll = $LEFT
            .as_any()
            .downcast_ref::<$DT>()
            .expect("compute_op failed to downcast array");
        let rr = $RIGHT
            .as_any()
            .downcast_ref::<$DT>()
            .expect("compute_op failed to downcast array");
        Ok(Arc::new($OP(&ll, &rr)?))
    }};
    // invoke unary operator
    ($OPERAND:expr, $OP:ident, $DT:ident) => {{
        let operand = $OPERAND
            .as_any()
            .downcast_ref::<$DT>()
            .expect("compute_op failed to downcast array");
        Ok(Arc::new($OP(&operand)?))
    }};
}

macro_rules! binary_string_array_op_scalar {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $OP_TYPE:expr) => {{
        let result: Result<Arc<dyn Array>> = match $LEFT.data_type() {
            DataType::Utf8 => compute_utf8_op_scalar!($LEFT, $RIGHT, $OP, StringArray, $OP_TYPE),
            other => Err(DataFusionError::Internal(format!(
                "Data type {:?} not supported for scalar operation '{}' on string array",
                other, stringify!($OP)
            ))),
        };
        Some(result)
    }};
}

macro_rules! binary_string_array_op {
    ($LEFT:expr, $RIGHT:expr, $OP:ident) => {{
        match $LEFT.data_type() {
            DataType::Utf8 => compute_utf8_op!($LEFT, $RIGHT, $OP, StringArray),
            other => Err(DataFusionError::Internal(format!(
                "Data type {:?} not supported for binary operation '{}' on string arrays",
                other, stringify!($OP)
            ))),
        }
    }};
}

/// Invoke a compute kernel on a pair of arrays
/// The binary_primitive_array_op macro only evaluates for primitive types
/// like integers and floats.
macro_rules! binary_primitive_array_op {
    ($LEFT:expr, $RIGHT:expr, $OP:ident) => {{
        match $LEFT.data_type() {
            DataType::Decimal128(_,_) => compute_decimal_op!($LEFT, $RIGHT, $OP, Decimal128Array),
            DataType::Int8 => compute_op!($LEFT, $RIGHT, $OP, Int8Array),
            DataType::Int16 => compute_op!($LEFT, $RIGHT, $OP, Int16Array),
            DataType::Int32 => compute_op!($LEFT, $RIGHT, $OP, Int32Array),
            DataType::Int64 => compute_op!($LEFT, $RIGHT, $OP, Int64Array),
            DataType::UInt8 => compute_op!($LEFT, $RIGHT, $OP, UInt8Array),
            DataType::UInt16 => compute_op!($LEFT, $RIGHT, $OP, UInt16Array),
            DataType::UInt32 => compute_op!($LEFT, $RIGHT, $OP, UInt32Array),
            DataType::UInt64 => compute_op!($LEFT, $RIGHT, $OP, UInt64Array),
            DataType::Float32 => compute_op!($LEFT, $RIGHT, $OP, Float32Array),
            DataType::Float64 => compute_op!($LEFT, $RIGHT, $OP, Float64Array),
            other => Err(DataFusionError::Internal(format!(
                "Data type {:?} not supported for binary operation '{}' on primitive arrays",
                other, stringify!($OP)
            ))),
        }
    }};
}

/// Invoke a compute kernel on an array and a scalar
/// The binary_primitive_array_op_scalar macro only evaluates for primitive
/// types like integers and floats.
macro_rules! binary_primitive_array_op_scalar {
    ($LEFT:expr, $RIGHT:expr, $OP:ident) => {{
        let result: Result<Arc<dyn Array>> = match $LEFT.data_type() {
            DataType::Decimal128(_,_) => compute_decimal_op_scalar!($LEFT, $RIGHT, $OP, Decimal128Array),
            DataType::Int8 => compute_op_scalar!($LEFT, $RIGHT, $OP, Int8Array),
            DataType::Int16 => compute_op_scalar!($LEFT, $RIGHT, $OP, Int16Array),
            DataType::Int32 => compute_op_scalar!($LEFT, $RIGHT, $OP, Int32Array),
            DataType::Int64 => compute_op_scalar!($LEFT, $RIGHT, $OP, Int64Array),
            DataType::UInt8 => compute_op_scalar!($LEFT, $RIGHT, $OP, UInt8Array),
            DataType::UInt16 => compute_op_scalar!($LEFT, $RIGHT, $OP, UInt16Array),
            DataType::UInt32 => compute_op_scalar!($LEFT, $RIGHT, $OP, UInt32Array),
            DataType::UInt64 => compute_op_scalar!($LEFT, $RIGHT, $OP, UInt64Array),
            DataType::Float32 => compute_op_scalar!($LEFT, $RIGHT, $OP, Float32Array),
            DataType::Float64 => compute_op_scalar!($LEFT, $RIGHT, $OP, Float64Array),
            other => Err(DataFusionError::Internal(format!(
                "Data type {:?} not supported for scalar operation '{}' on primitive array",
                other, stringify!($OP)
            ))),
        };
        Some(result)
    }};
}

/// The binary_array_op macro includes types that extend beyond the primitive,
/// such as Utf8 strings.
#[macro_export]
macro_rules! binary_array_op {
    ($LEFT:expr, $RIGHT:expr, $OP:ident) => {{
        match $LEFT.data_type() {
            DataType::Null => compute_null_op!($LEFT, $RIGHT, $OP, NullArray),
            DataType::Decimal128(_,_) => compute_decimal_op!($LEFT, $RIGHT, $OP, Decimal128Array),
            DataType::Int8 => compute_op!($LEFT, $RIGHT, $OP, Int8Array),
            DataType::Int16 => compute_op!($LEFT, $RIGHT, $OP, Int16Array),
            DataType::Int32 => compute_op!($LEFT, $RIGHT, $OP, Int32Array),
            DataType::Int64 => compute_op!($LEFT, $RIGHT, $OP, Int64Array),
            DataType::UInt8 => compute_op!($LEFT, $RIGHT, $OP, UInt8Array),
            DataType::UInt16 => compute_op!($LEFT, $RIGHT, $OP, UInt16Array),
            DataType::UInt32 => compute_op!($LEFT, $RIGHT, $OP, UInt32Array),
            DataType::UInt64 => compute_op!($LEFT, $RIGHT, $OP, UInt64Array),
            DataType::Float32 => compute_op!($LEFT, $RIGHT, $OP, Float32Array),
            DataType::Float64 => compute_op!($LEFT, $RIGHT, $OP, Float64Array),
            DataType::Utf8 => compute_utf8_op!($LEFT, $RIGHT, $OP, StringArray),
            DataType::Timestamp(TimeUnit::Nanosecond, _) => {
                compute_op!($LEFT, $RIGHT, $OP, TimestampNanosecondArray)
            }
            DataType::Timestamp(TimeUnit::Microsecond, _) => {
                compute_op!($LEFT, $RIGHT, $OP, TimestampMicrosecondArray)
            }
            DataType::Timestamp(TimeUnit::Millisecond, _) => {
                compute_op!($LEFT, $RIGHT, $OP, TimestampMillisecondArray)
            }
            DataType::Timestamp(TimeUnit::Second, _) => {
                compute_op!($LEFT, $RIGHT, $OP, TimestampSecondArray)
            }
            DataType::Date32 => {
                compute_op!($LEFT, $RIGHT, $OP, Date32Array)
            }
            DataType::Date64 => {
                compute_op!($LEFT, $RIGHT, $OP, Date64Array)
            }
            DataType::Time32(TimeUnit::Second) => {
                compute_op!($LEFT, $RIGHT, $OP, Time32SecondArray)
            }
            DataType::Time32(TimeUnit::Millisecond) => {
                compute_op!($LEFT, $RIGHT, $OP, Time32MillisecondArray)
            }
            DataType::Time64(TimeUnit::Microsecond) => {
                compute_op!($LEFT, $RIGHT, $OP, Time64MicrosecondArray)
            }
            DataType::Time64(TimeUnit::Nanosecond) => {
                compute_op!($LEFT, $RIGHT, $OP, Time64NanosecondArray)
            }
            DataType::Boolean => compute_bool_op!($LEFT, $RIGHT, $OP, BooleanArray),
            other => Err(DataFusionError::Internal(format!(
                "Data type {:?} not supported for binary operation '{}' on dyn arrays",
                other, stringify!($OP)
            ))),
        }
    }};
}

/// Invoke a boolean kernel on a pair of arrays
macro_rules! boolean_op {
    ($LEFT:expr, $RIGHT:expr, $OP:ident) => {{
        let ll = as_boolean_array($LEFT).expect("boolean_op failed to downcast array");
        let rr = as_boolean_array($RIGHT).expect("boolean_op failed to downcast array");
        Ok(Arc::new($OP(&ll, &rr)?))
    }};
}

macro_rules! binary_string_array_flag_op {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $NOT:expr, $FLAG:expr) => {{
        match $LEFT.data_type() {
            DataType::Utf8 => {
                compute_utf8_flag_op!($LEFT, $RIGHT, $OP, StringArray, $NOT, $FLAG)
            }
            DataType::LargeUtf8 => {
                compute_utf8_flag_op!($LEFT, $RIGHT, $OP, LargeStringArray, $NOT, $FLAG)
            }
            other => Err(DataFusionError::Internal(format!(
                "Data type {:?} not supported for binary_string_array_flag_op operation '{}' on string array",
                other, stringify!($OP)
            ))),
        }
    }};
}

/// Invoke a compute kernel on a pair of binary data arrays with flags
macro_rules! compute_utf8_flag_op {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $ARRAYTYPE:ident, $NOT:expr, $FLAG:expr) => {{
        let ll = $LEFT
            .as_any()
            .downcast_ref::<$ARRAYTYPE>()
            .expect("compute_utf8_flag_op failed to downcast array");
        let rr = $RIGHT
            .as_any()
            .downcast_ref::<$ARRAYTYPE>()
            .expect("compute_utf8_flag_op failed to downcast array");

        let flag = if $FLAG {
            Some($ARRAYTYPE::from(vec!["i"; ll.len()]))
        } else {
            None
        };
        let mut array = paste::expr! {[<$OP _utf8>]}(&ll, &rr, flag.as_ref())?;
        if $NOT {
            array = not(&array).unwrap();
        }
        Ok(Arc::new(array))
    }};
}

macro_rules! binary_string_array_flag_op_scalar {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $NOT:expr, $FLAG:expr) => {{
        let result: Result<Arc<dyn Array>> = match $LEFT.data_type() {
            DataType::Utf8 => {
                compute_utf8_flag_op_scalar!($LEFT, $RIGHT, $OP, StringArray, $NOT, $FLAG)
            }
            DataType::LargeUtf8 => {
                compute_utf8_flag_op_scalar!($LEFT, $RIGHT, $OP, LargeStringArray, $NOT, $FLAG)
            }
            other => Err(DataFusionError::Internal(format!(
                "Data type {:?} not supported for binary_string_array_flag_op_scalar operation '{}' on string array",
                other, stringify!($OP)
            ))),
        };
        Some(result)
    }};
}

/// Invoke a compute kernel on a data array and a scalar value with flag
macro_rules! compute_utf8_flag_op_scalar {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $ARRAYTYPE:ident, $NOT:expr, $FLAG:expr) => {{
        let ll = $LEFT
            .as_any()
            .downcast_ref::<$ARRAYTYPE>()
            .expect("compute_utf8_flag_op_scalar failed to downcast array");

        if let ScalarValue::Utf8(Some(string_value)) = $RIGHT {
            let flag = if $FLAG { Some("i") } else { None };
            let mut array =
                paste::expr! {[<$OP _utf8_scalar>]}(&ll, &string_value, flag)?;
            if $NOT {
                array = not(&array).unwrap();
            }
            Ok(Arc::new(array))
        } else {
            Err(DataFusionError::Internal(format!(
                "compute_utf8_flag_op_scalar failed to cast literal value {} for operation '{}'",
                $RIGHT, stringify!($OP)
            )))
        }
    }};
}

impl PhysicalExpr for BinaryExpr {
    /// Return a reference to Any that can be used for downcasting
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn data_type(&self, input_schema: &Schema) -> Result<DataType> {
        binary_operator_data_type(
            &self.left.data_type(input_schema)?,
            &self.op,
            &self.right.data_type(input_schema)?,
        )
    }

    fn nullable(&self, input_schema: &Schema) -> Result<bool> {
        Ok(self.left.nullable(input_schema)? || self.right.nullable(input_schema)?)
    }

    fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue> {
        let left_value = self.left.evaluate(batch)?;
        let right_value = self.right.evaluate(batch)?;
        let left_data_type = left_value.data_type();
        let right_data_type = right_value.data_type();

        match (&left_value, &left_data_type, &right_value, &right_data_type) {
            // Types are equal => valid
            (_, l, _, r) if l == r => {}
            // Allow comparing a dictionary value with its corresponding scalar value
            (
                ColumnarValue::Array(_),
                DataType::Dictionary(_, dict_t),
                ColumnarValue::Scalar(_),
                scalar_t,
            )
            | (
                ColumnarValue::Scalar(_),
                scalar_t,
                ColumnarValue::Array(_),
                DataType::Dictionary(_, dict_t),
            ) if dict_t.as_ref() == scalar_t => {}
            _ => {
                return Err(DataFusionError::Internal(format!(
                    "Cannot evaluate binary expression {:?} with types {:?} and {:?}",
                    self.op, left_data_type, right_data_type
                )));
            }
        }

        // Attempt to use special kernels if one input is scalar and the other is an array
        let scalar_result = match (&left_value, &right_value) {
            (ColumnarValue::Array(array), ColumnarValue::Scalar(scalar)) => {
                // if left is array and right is literal - use scalar operations
                self.evaluate_array_scalar(array, scalar)?
            }
            (ColumnarValue::Scalar(scalar), ColumnarValue::Array(array)) => {
                // if right is literal and left is array - reverse operator and parameters
                self.evaluate_scalar_array(scalar, array)?
            }
            (_, _) => None, // default to array implementation
        };

        if let Some(result) = scalar_result {
            return result.map(|a| ColumnarValue::Array(a));
        }

        // if both arrays or both literals - extract arrays and continue execution
        let (left, right) = (
            left_value.into_array(batch.num_rows()),
            right_value.into_array(batch.num_rows()),
        );
        self.evaluate_with_resolved_args(left, &left_data_type, right, &right_data_type)
            .map(|a| ColumnarValue::Array(a))
    }

    fn children(&self) -> Vec<Arc<dyn PhysicalExpr>> {
        vec![self.left.clone(), self.right.clone()]
    }

    fn with_new_children(
        self: Arc<Self>,
        children: Vec<Arc<dyn PhysicalExpr>>,
    ) -> Result<Arc<dyn PhysicalExpr>> {
        Ok(Arc::new(BinaryExpr::new(
            children[0].clone(),
            self.op,
            children[1].clone(),
        )))
    }

    /// Return the boundaries of this binary expression's result.
    fn boundaries(&self, context: &AnalysisContext) -> Option<ExprBoundaries> {
        match &self.op {
            Operator::Eq
            | Operator::Gt
            | Operator::Lt
            | Operator::LtEq
            | Operator::GtEq => {
                // We currently only support comparison when we know at least one of the sides are
                // a known value (a scalar). This includes predicates like a > 20 or 5 > a.
                let left_boundaries = self.left.boundaries(context)?;
                let right_boundaries = self.right.boundaries(context)?;
                let (op, left, right) = match right_boundaries.reduce() {
                    Some(right_value) => {
                        // We know the right side is a scalar, so we can use the operator as is
                        (self.op, self.left.clone(), right_value)
                    }
                    None => {
                        // If not, we have to swap the operator and left/right (since this means
                        // left has to be a scalar).
                        (
                            self.op.swap()?,
                            self.right.clone(),
                            left_boundaries.reduce()?,
                        )
                    }
                };
                analyze_expr_scalar_comparison(&op, context, &left, right)
            }
            _ => None,
        }
    }
}

impl PartialEq<dyn Any> for BinaryExpr {
    fn eq(&self, other: &dyn Any) -> bool {
        down_cast_any_ref(other)
            .downcast_ref::<Self>()
            .map(|x| self.left.eq(&x.left) && self.op == x.op && self.right.eq(&x.right))
            .unwrap_or(false)
    }
}

// Analyze the comparison between an expression (on the left) and a scalar value
// (on the right). The new boundaries will indicate whether it is always true, always
// false, or unknown (with a probablistic selectivity value attached).
fn analyze_expr_scalar_comparison(
    op: &Operator,
    context: &AnalysisContext,
    left: &Arc<dyn PhysicalExpr>,
    right: ScalarValue,
) -> Option<ExprBoundaries> {
    let left_bounds = left.boundaries(context)?;
    let left_min = left_bounds.min_value;
    let left_max = left_bounds.max_value;

    // Direct selectivity is applicable when we can determine that this comparison will
    // always be true or false (e.g. `x > 10` where the `x`'s min value is 11 or `a < 5`
    // where the `a`'s max value is 4).
    let (always_selects, never_selects) = match op {
        Operator::Lt => (right > left_max, right <= left_min),
        Operator::LtEq => (right >= left_max, right < left_min),
        Operator::Gt => (right < left_min, right >= left_max),
        Operator::GtEq => (right <= left_min, right > left_max),
        Operator::Eq => (
            // Since min/max can be artificial (e.g. the min or max value of a column
            // might be under/over the real value), we can't assume if the right equals
            // to any left.min / left.max values it is always going to be selected. But
            // we can assume that if the range(left) doesn't overlap with right, it is
            // never going to be selected.
            false,
            right < left_min || right > left_max,
        ),
        _ => unreachable!(),
    };

    // Both can not be true at the same time.
    assert!(!(always_selects && never_selects));

    let selectivity = match (always_selects, never_selects) {
        (true, _) => Some(1.0),
        (_, true) => Some(0.0),
        (false, false) => {
            // If there is a partial overlap, then we can estimate the selectivity
            // by computing the ratio of the existing overlap to the total range. Since we
            // currently don't have access to a value distribution histogram, the part below
            // assumes a uniform distribution by default.

            // Our [min, max] is inclusive, so we need to add 1 to the difference.
            let total_range = left_max.distance(&left_min)? + 1;
            let overlap_between_boundaries = match op {
                Operator::Lt => right.distance(&left_min)?,
                Operator::Gt => left_max.distance(&right)?,
                Operator::LtEq => right.distance(&left_min)? + 1,
                Operator::GtEq => left_max.distance(&right)? + 1,
                Operator::Eq => 1,
                _ => unreachable!(),
            };

            Some(overlap_between_boundaries as f64 / total_range as f64)
        }
    }?;

    // The selectivity can't be be greater than 1.0.
    assert!(selectivity <= 1.0);

    let (pred_min, pred_max, pred_distinct) = match (always_selects, never_selects) {
        (false, true) => (false, false, 1),
        (true, false) => (true, true, 1),
        _ => (false, true, 2),
    };

    Some(ExprBoundaries::new_with_selectivity(
        ScalarValue::Boolean(Some(pred_min)),
        ScalarValue::Boolean(Some(pred_max)),
        Some(pred_distinct),
        Some(selectivity),
    ))
}

/// unwrap underlying (non dictionary) value, if any, to pass to a scalar kernel
fn unwrap_dict_value(v: ScalarValue) -> ScalarValue {
    if let ScalarValue::Dictionary(_key_type, v) = v {
        unwrap_dict_value(*v)
    } else {
        v
    }
}

/// The binary_array_op_dyn_scalar macro includes types that extend
/// beyond the primitive, such as Utf8 strings.
#[macro_export]
macro_rules! binary_array_op_dyn_scalar {
    ($LEFT:expr, $RIGHT:expr, $OP:ident, $OP_TYPE:expr) => {{
        // unwrap underlying (non dictionary) value
        let right = unwrap_dict_value($RIGHT);

        let result: Result<Arc<dyn Array>> = match right {
            ScalarValue::Boolean(b) => compute_bool_op_dyn_scalar!($LEFT, b, $OP, $OP_TYPE),
            ScalarValue::Decimal128(..) => compute_decimal_op_dyn_scalar!($LEFT, right, $OP, $OP_TYPE),
            ScalarValue::Utf8(v) => compute_utf8_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::LargeUtf8(v) => compute_utf8_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::Binary(v) => compute_binary_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::LargeBinary(v) => compute_binary_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::Int8(v) => compute_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::Int16(v) => compute_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::Int32(v) => compute_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::Int64(v) => compute_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::UInt8(v) => compute_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::UInt16(v) => compute_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::UInt32(v) => compute_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::UInt64(v) => compute_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::Float32(v) => compute_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::Float64(v) => compute_op_dyn_scalar!($LEFT, v, $OP, $OP_TYPE),
            ScalarValue::Date32(_) => compute_op_scalar!($LEFT, right, $OP, Date32Array),
            ScalarValue::Date64(_) => compute_op_scalar!($LEFT, right, $OP, Date64Array),
            ScalarValue::Time32Second(_) => compute_op_scalar!($LEFT, right, $OP, Time32SecondArray),
            ScalarValue::Time32Millisecond(_) => compute_op_scalar!($LEFT, right, $OP, Time32MillisecondArray),
            ScalarValue::Time64Microsecond(_) => compute_op_scalar!($LEFT, right, $OP, Time64MicrosecondArray),
            ScalarValue::Time64Nanosecond(_) => compute_op_scalar!($LEFT, right, $OP, Time64NanosecondArray),
            ScalarValue::TimestampSecond(..) => compute_op_scalar!($LEFT, right, $OP, TimestampSecondArray),
            ScalarValue::TimestampMillisecond(..) => compute_op_scalar!($LEFT, right, $OP, TimestampMillisecondArray),
            ScalarValue::TimestampMicrosecond(..) => compute_op_scalar!($LEFT, right, $OP, TimestampMicrosecondArray),
            ScalarValue::TimestampNanosecond(..) => compute_op_scalar!($LEFT, right, $OP, TimestampNanosecondArray),
            other => Err(DataFusionError::Internal(format!(
                "Data type {:?} not supported for scalar operation '{}' on dyn array",
                other, stringify!($OP)))
            )
        };
        Some(result)
    }}
}

/// Compares the array with the scalar value for equality, sometimes
/// used in other kernels
pub(crate) fn array_eq_scalar(lhs: &dyn Array, rhs: &ScalarValue) -> Result<ArrayRef> {
    binary_array_op_dyn_scalar!(lhs, rhs.clone(), eq, &DataType::Boolean).ok_or_else(
        || {
            DataFusionError::Internal(format!(
                "Data type {:?} and scalar {:?} not supported for array_eq_scalar",
                lhs.data_type(),
                rhs.get_datatype()
            ))
        },
    )?
}

impl BinaryExpr {
    /// Evaluate the expression of the left input is an array and
    /// right is literal - use scalar operations
    fn evaluate_array_scalar(
        &self,
        array: &dyn Array,
        scalar: &ScalarValue,
    ) -> Result<Option<Result<ArrayRef>>> {
        let bool_type = &DataType::Boolean;
        let scalar_result = match &self.op {
            Operator::Lt => {
                binary_array_op_dyn_scalar!(array, scalar.clone(), lt, bool_type)
            }
            Operator::LtEq => {
                binary_array_op_dyn_scalar!(array, scalar.clone(), lt_eq, bool_type)
            }
            Operator::Gt => {
                binary_array_op_dyn_scalar!(array, scalar.clone(), gt, bool_type)
            }
            Operator::GtEq => {
                binary_array_op_dyn_scalar!(array, scalar.clone(), gt_eq, bool_type)
            }
            Operator::Eq => {
                binary_array_op_dyn_scalar!(array, scalar.clone(), eq, bool_type)
            }
            Operator::NotEq => {
                binary_array_op_dyn_scalar!(array, scalar.clone(), neq, bool_type)
            }
            Operator::Like => {
                binary_string_array_op_scalar!(array, scalar.clone(), like, bool_type)
            }
            Operator::NotLike => {
                binary_string_array_op_scalar!(array, scalar.clone(), nlike, bool_type)
            }
            Operator::Plus => {
                binary_primitive_array_op_scalar!(array, scalar.clone(), add)
            }
            Operator::Minus => {
                binary_primitive_array_op_scalar!(array, scalar.clone(), subtract)
            }
            Operator::Multiply => {
                binary_primitive_array_op_scalar!(array, scalar.clone(), multiply)
            }
            Operator::Divide => {
                binary_primitive_array_op_scalar!(array, scalar.clone(), divide)
            }
            Operator::Modulo => {
                binary_primitive_array_op_scalar!(array, scalar.clone(), modulus)
            }
            Operator::RegexMatch => binary_string_array_flag_op_scalar!(
                array,
                scalar.clone(),
                regexp_is_match,
                false,
                false
            ),
            Operator::RegexIMatch => binary_string_array_flag_op_scalar!(
                array,
                scalar.clone(),
                regexp_is_match,
                false,
                true
            ),
            Operator::RegexNotMatch => binary_string_array_flag_op_scalar!(
                array,
                scalar.clone(),
                regexp_is_match,
                true,
                false
            ),
            Operator::RegexNotIMatch => binary_string_array_flag_op_scalar!(
                array,
                scalar.clone(),
                regexp_is_match,
                true,
                true
            ),
            Operator::BitwiseAnd => bitwise_and_scalar(array, scalar.clone()),
            Operator::BitwiseOr => bitwise_or_scalar(array, scalar.clone()),
            Operator::BitwiseXor => bitwise_xor_scalar(array, scalar.clone()),
            Operator::BitwiseShiftRight => {
                bitwise_shift_right_scalar(array, scalar.clone())
            }
            Operator::BitwiseShiftLeft => {
                bitwise_shift_left_scalar(array, scalar.clone())
            }
            // if scalar operation is not supported - fallback to array implementation
            _ => None,
        };

        Ok(scalar_result)
    }

    /// Evaluate the expression if the left input is a literal and the
    /// right is an array - reverse operator and parameters
    fn evaluate_scalar_array(
        &self,
        scalar: &ScalarValue,
        array: &ArrayRef,
    ) -> Result<Option<Result<ArrayRef>>> {
        let bool_type = &DataType::Boolean;
        let scalar_result = match &self.op {
            Operator::Lt => {
                binary_array_op_dyn_scalar!(array, scalar.clone(), gt, bool_type)
            }
            Operator::LtEq => {
                binary_array_op_dyn_scalar!(array, scalar.clone(), gt_eq, bool_type)
            }
            Operator::Gt => {
                binary_array_op_dyn_scalar!(array, scalar.clone(), lt, bool_type)
            }
            Operator::GtEq => {
                binary_array_op_dyn_scalar!(array, scalar.clone(), lt_eq, bool_type)
            }
            Operator::Eq => {
                binary_array_op_dyn_scalar!(array, scalar.clone(), eq, bool_type)
            }
            Operator::NotEq => {
                binary_array_op_dyn_scalar!(array, scalar.clone(), neq, bool_type)
            }
            // if scalar operation is not supported - fallback to array implementation
            _ => None,
        };
        Ok(scalar_result)
    }

    fn evaluate_with_resolved_args(
        &self,
        left: Arc<dyn Array>,
        left_data_type: &DataType,
        right: Arc<dyn Array>,
        right_data_type: &DataType,
    ) -> Result<ArrayRef> {
        match &self.op {
            Operator::Like => binary_string_array_op!(left, right, like),
            Operator::NotLike => binary_string_array_op!(left, right, nlike),
            Operator::Lt => lt_dyn(&left, &right),
            Operator::LtEq => lt_eq_dyn(&left, &right),
            Operator::Gt => gt_dyn(&left, &right),
            Operator::GtEq => gt_eq_dyn(&left, &right),
            Operator::Eq => eq_dyn(&left, &right),
            Operator::NotEq => neq_dyn(&left, &right),
            Operator::IsDistinctFrom => {
                match (left_data_type, right_data_type) {
                    // exchange lhs and rhs when lhs is Null, since `binary_array_op` is
                    // always try to down cast array according to $LEFT expression.
                    (DataType::Null, _) => {
                        binary_array_op!(right, left, is_distinct_from)
                    }
                    _ => binary_array_op!(left, right, is_distinct_from),
                }
            }
            Operator::IsNotDistinctFrom => {
                binary_array_op!(left, right, is_not_distinct_from)
            }
            Operator::Plus => binary_primitive_array_op!(left, right, add),
            Operator::Minus => binary_primitive_array_op!(left, right, subtract),
            Operator::Multiply => binary_primitive_array_op!(left, right, multiply),
            Operator::Divide => binary_primitive_array_op!(left, right, divide_opt),
            Operator::Modulo => binary_primitive_array_op!(left, right, modulus),
            Operator::And => {
                if left_data_type == &DataType::Boolean {
                    boolean_op!(&left, &right, and_kleene)
                } else {
                    Err(DataFusionError::Internal(format!(
                        "Cannot evaluate binary expression {:?} with types {:?} and {:?}",
                        self.op,
                        left.data_type(),
                        right.data_type()
                    )))
                }
            }
            Operator::Or => {
                if left_data_type == &DataType::Boolean {
                    boolean_op!(&left, &right, or_kleene)
                } else {
                    Err(DataFusionError::Internal(format!(
                        "Cannot evaluate binary expression {:?} with types {:?} and {:?}",
                        self.op, left_data_type, right_data_type
                    )))
                }
            }
            Operator::RegexMatch => {
                binary_string_array_flag_op!(left, right, regexp_is_match, false, false)
            }
            Operator::RegexIMatch => {
                binary_string_array_flag_op!(left, right, regexp_is_match, false, true)
            }
            Operator::RegexNotMatch => {
                binary_string_array_flag_op!(left, right, regexp_is_match, true, false)
            }
            Operator::RegexNotIMatch => {
                binary_string_array_flag_op!(left, right, regexp_is_match, true, true)
            }
            Operator::BitwiseAnd => bitwise_and(left, right),
            Operator::BitwiseOr => bitwise_or(left, right),
            Operator::BitwiseXor => bitwise_xor(left, right),
            Operator::BitwiseShiftRight => bitwise_shift_right(left, right),
            Operator::BitwiseShiftLeft => bitwise_shift_left(left, right),
            Operator::StringConcat => {
                binary_string_array_op!(left, right, concat_elements)
            }
        }
    }
}

/// Create a binary expression whose arguments are correctly coerced.
/// This function errors if it is not possible to coerce the arguments
/// to computational types supported by the operator.
pub fn binary(
    lhs: Arc<dyn PhysicalExpr>,
    op: Operator,
    rhs: Arc<dyn PhysicalExpr>,
    input_schema: &Schema,
) -> Result<Arc<dyn PhysicalExpr>> {
    let lhs_type = &lhs.data_type(input_schema)?;
    let rhs_type = &rhs.data_type(input_schema)?;
    if !lhs_type.eq(rhs_type) {
        return Err(DataFusionError::Internal(format!(
            "The type of {} {} {} of binary physical should be same",
            lhs_type, op, rhs_type
        )));
    }
    Ok(Arc::new(BinaryExpr::new(lhs, op, rhs)))
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::expressions::try_cast;
    use crate::expressions::{col, lit};
    use arrow::datatypes::{ArrowNumericType, Field, Int32Type, SchemaRef};
    use datafusion_common::{ColumnStatistics, Result, Statistics};
    use datafusion_expr::type_coercion::binary::coerce_types;

    // Create a binary expression without coercion. Used here when we do not want to coerce the expressions
    // to valid types. Usage can result in an execution (after plan) error.
    fn binary_simple(
        l: Arc<dyn PhysicalExpr>,
        op: Operator,
        r: Arc<dyn PhysicalExpr>,
        input_schema: &Schema,
    ) -> Arc<dyn PhysicalExpr> {
        binary(l, op, r, input_schema).unwrap()
    }

    #[test]
    fn binary_comparison() -> Result<()> {
        let schema = Schema::new(vec![
            Field::new("a", DataType::Int32, false),
            Field::new("b", DataType::Int32, false),
        ]);
        let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
        let b = Int32Array::from(vec![1, 2, 4, 8, 16]);

        // expression: "a < b"
        let lt = binary_simple(
            col("a", &schema)?,
            Operator::Lt,
            col("b", &schema)?,
            &schema,
        );
        let batch =
            RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a), Arc::new(b)])?;

        let result = lt.evaluate(&batch)?.into_array(batch.num_rows());
        assert_eq!(result.len(), 5);

        let expected = vec![false, false, true, true, true];
        let result =
            as_boolean_array(&result).expect("failed to downcast to BooleanArray");
        for (i, &expected_item) in expected.iter().enumerate().take(5) {
            assert_eq!(result.value(i), expected_item);
        }

        Ok(())
    }

    #[test]
    fn binary_nested() -> Result<()> {
        let schema = Schema::new(vec![
            Field::new("a", DataType::Int32, false),
            Field::new("b", DataType::Int32, false),
        ]);
        let a = Int32Array::from(vec![2, 4, 6, 8, 10]);
        let b = Int32Array::from(vec![2, 5, 4, 8, 8]);

        // expression: "a < b OR a == b"
        let expr = binary_simple(
            binary_simple(
                col("a", &schema)?,
                Operator::Lt,
                col("b", &schema)?,
                &schema,
            ),
            Operator::Or,
            binary_simple(
                col("a", &schema)?,
                Operator::Eq,
                col("b", &schema)?,
                &schema,
            ),
            &schema,
        );
        let batch =
            RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a), Arc::new(b)])?;

        assert_eq!("a@0 < b@1 OR a@0 = b@1", format!("{}", expr));

        let result = expr.evaluate(&batch)?.into_array(batch.num_rows());
        assert_eq!(result.len(), 5);

        let expected = vec![true, true, false, true, false];
        let result =
            as_boolean_array(&result).expect("failed to downcast to BooleanArray");
        for (i, &expected_item) in expected.iter().enumerate().take(5) {
            assert_eq!(result.value(i), expected_item);
        }

        Ok(())
    }

    // runs an end-to-end test of physical type coercion:
    // 1. construct a record batch with two columns of type A and B
    //  (*_ARRAY is the Rust Arrow array type, and *_TYPE is the DataType of the elements)
    // 2. construct a physical expression of A OP B
    // 3. evaluate the expression
    // 4. verify that the resulting expression is of type C
    // 5. verify that the results of evaluation are $VEC
    macro_rules! test_coercion {
        ($A_ARRAY:ident, $A_TYPE:expr, $A_VEC:expr, $B_ARRAY:ident, $B_TYPE:expr, $B_VEC:expr, $OP:expr, $C_ARRAY:ident, $C_TYPE:expr, $VEC:expr,) => {{
            let schema = Schema::new(vec![
                Field::new("a", $A_TYPE, false),
                Field::new("b", $B_TYPE, false),
            ]);
            let a = $A_ARRAY::from($A_VEC);
            let b = $B_ARRAY::from($B_VEC);
            let result_type = coerce_types(&$A_TYPE, &$OP, &$B_TYPE)?;

            let left = try_cast(col("a", &schema)?, &schema, result_type.clone())?;
            let right = try_cast(col("b", &schema)?, &schema, result_type)?;

            // verify that we can construct the expression
            let expression = binary(left, $OP, right, &schema)?;
            let batch = RecordBatch::try_new(
                Arc::new(schema.clone()),
                vec![Arc::new(a), Arc::new(b)],
            )?;

            // verify that the expression's type is correct
            assert_eq!(expression.data_type(&schema)?, $C_TYPE);

            // compute
            let result = expression.evaluate(&batch)?.into_array(batch.num_rows());

            // verify that the array's data_type is correct
            assert_eq!(*result.data_type(), $C_TYPE);

            // verify that the data itself is downcastable
            let result = result
                .as_any()
                .downcast_ref::<$C_ARRAY>()
                .expect("failed to downcast");
            // verify that the result itself is correct
            for (i, x) in $VEC.iter().enumerate() {
                assert_eq!(result.value(i), *x);
            }
        }};
    }

    #[test]
    fn test_type_coersion() -> Result<()> {
        test_coercion!(
            Int32Array,
            DataType::Int32,
            vec![1i32, 2i32],
            UInt32Array,
            DataType::UInt32,
            vec![1u32, 2u32],
            Operator::Plus,
            Int32Array,
            DataType::Int32,
            vec![2i32, 4i32],
        );
        test_coercion!(
            Int32Array,
            DataType::Int32,
            vec![1i32],
            UInt16Array,
            DataType::UInt16,
            vec![1u16],
            Operator::Plus,
            Int32Array,
            DataType::Int32,
            vec![2i32],
        );
        test_coercion!(
            Float32Array,
            DataType::Float32,
            vec![1f32],
            UInt16Array,
            DataType::UInt16,
            vec![1u16],
            Operator::Plus,
            Float32Array,
            DataType::Float32,
            vec![2f32],
        );
        test_coercion!(
            Float32Array,
            DataType::Float32,
            vec![2f32],
            UInt16Array,
            DataType::UInt16,
            vec![1u16],
            Operator::Multiply,
            Float32Array,
            DataType::Float32,
            vec![2f32],
        );
        test_coercion!(
            StringArray,
            DataType::Utf8,
            vec!["hello world", "world"],
            StringArray,
            DataType::Utf8,
            vec!["%hello%", "%hello%"],
            Operator::Like,
            BooleanArray,
            DataType::Boolean,
            vec![true, false],
        );
        test_coercion!(
            StringArray,
            DataType::Utf8,
            vec!["1994-12-13", "1995-01-26"],
            Date32Array,
            DataType::Date32,
            vec![9112, 9156],
            Operator::Eq,
            BooleanArray,
            DataType::Boolean,
            vec![true, true],
        );
        test_coercion!(
            StringArray,
            DataType::Utf8,
            vec!["1994-12-13", "1995-01-26"],
            Date32Array,
            DataType::Date32,
            vec![9113, 9154],
            Operator::Lt,
            BooleanArray,
            DataType::Boolean,
            vec![true, false],
        );
        test_coercion!(
            StringArray,
            DataType::Utf8,
            vec!["1994-12-13T12:34:56", "1995-01-26T01:23:45"],
            Date64Array,
            DataType::Date64,
            vec![787322096000, 791083425000],
            Operator::Eq,
            BooleanArray,
            DataType::Boolean,
            vec![true, true],
        );
        test_coercion!(
            StringArray,
            DataType::Utf8,
            vec!["1994-12-13T12:34:56", "1995-01-26T01:23:45"],
            Date64Array,
            DataType::Date64,
            vec![787322096001, 791083424999],
            Operator::Lt,
            BooleanArray,
            DataType::Boolean,
            vec![true, false],
        );
        test_coercion!(
            StringArray,
            DataType::Utf8,
            vec!["abc"; 5],
            StringArray,
            DataType::Utf8,
            vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
            Operator::RegexMatch,
            BooleanArray,
            DataType::Boolean,
            vec![true, false, true, false, false],
        );
        test_coercion!(
            StringArray,
            DataType::Utf8,
            vec!["abc"; 5],
            StringArray,
            DataType::Utf8,
            vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
            Operator::RegexIMatch,
            BooleanArray,
            DataType::Boolean,
            vec![true, true, true, true, false],
        );
        test_coercion!(
            StringArray,
            DataType::Utf8,
            vec!["abc"; 5],
            StringArray,
            DataType::Utf8,
            vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
            Operator::RegexNotMatch,
            BooleanArray,
            DataType::Boolean,
            vec![false, true, false, true, true],
        );
        test_coercion!(
            StringArray,
            DataType::Utf8,
            vec!["abc"; 5],
            StringArray,
            DataType::Utf8,
            vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
            Operator::RegexNotIMatch,
            BooleanArray,
            DataType::Boolean,
            vec![false, false, false, false, true],
        );
        test_coercion!(
            LargeStringArray,
            DataType::LargeUtf8,
            vec!["abc"; 5],
            LargeStringArray,
            DataType::LargeUtf8,
            vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
            Operator::RegexMatch,
            BooleanArray,
            DataType::Boolean,
            vec![true, false, true, false, false],
        );
        test_coercion!(
            LargeStringArray,
            DataType::LargeUtf8,
            vec!["abc"; 5],
            LargeStringArray,
            DataType::LargeUtf8,
            vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
            Operator::RegexIMatch,
            BooleanArray,
            DataType::Boolean,
            vec![true, true, true, true, false],
        );
        test_coercion!(
            LargeStringArray,
            DataType::LargeUtf8,
            vec!["abc"; 5],
            LargeStringArray,
            DataType::LargeUtf8,
            vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
            Operator::RegexNotMatch,
            BooleanArray,
            DataType::Boolean,
            vec![false, true, false, true, true],
        );
        test_coercion!(
            LargeStringArray,
            DataType::LargeUtf8,
            vec!["abc"; 5],
            LargeStringArray,
            DataType::LargeUtf8,
            vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"],
            Operator::RegexNotIMatch,
            BooleanArray,
            DataType::Boolean,
            vec![false, false, false, false, true],
        );
        test_coercion!(
            Int16Array,
            DataType::Int16,
            vec![1i16, 2i16, 3i16],
            Int64Array,
            DataType::Int64,
            vec![10i64, 4i64, 5i64],
            Operator::BitwiseAnd,
            Int64Array,
            DataType::Int64,
            vec![0i64, 0i64, 1i64],
        );
        test_coercion!(
            Int16Array,
            DataType::Int16,
            vec![1i16, 2i16, 3i16],
            Int64Array,
            DataType::Int64,
            vec![10i64, 4i64, 5i64],
            Operator::BitwiseOr,
            Int64Array,
            DataType::Int64,
            vec![11i64, 6i64, 7i64],
        );
        test_coercion!(
            Int16Array,
            DataType::Int16,
            vec![3i16, 2i16, 3i16],
            Int64Array,
            DataType::Int64,
            vec![10i64, 6i64, 5i64],
            Operator::BitwiseXor,
            Int64Array,
            DataType::Int64,
            vec![9i64, 4i64, 6i64],
        );
        Ok(())
    }

    // Note it would be nice to use the same test_coercion macro as
    // above, but sadly the type of the values of the dictionary are
    // not encoded in the rust type of the DictionaryArray. Thus there
    // is no way at the time of this writing to create a dictionary
    // array using the `From` trait
    #[test]
    fn test_dictionary_type_to_array_coersion() -> Result<()> {
        // Test string  a string dictionary
        let dict_type =
            DataType::Dictionary(Box::new(DataType::Int32), Box::new(DataType::Utf8));
        let string_type = DataType::Utf8;

        // build dictionary
        let mut dict_builder = StringDictionaryBuilder::<Int32Type>::new();

        dict_builder.append("one")?;
        dict_builder.append_null();
        dict_builder.append("three")?;
        dict_builder.append("four")?;
        let dict_array = Arc::new(dict_builder.finish()) as ArrayRef;

        let str_array = Arc::new(StringArray::from(vec![
            Some("not one"),
            Some("two"),
            None,
            Some("four"),
        ])) as ArrayRef;

        let schema = Arc::new(Schema::new(vec![
            Field::new("a", dict_type.clone(), true),
            Field::new("b", string_type.clone(), true),
        ]));

        // Test 1: a = b
        let result = BooleanArray::from(vec![Some(false), None, None, Some(true)]);
        apply_logic_op(&schema, &dict_array, &str_array, Operator::Eq, result)?;

        // Test 2: now test the other direction
        // b = a
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", string_type, true),
            Field::new("b", dict_type, true),
        ]));
        let result = BooleanArray::from(vec![Some(false), None, None, Some(true)]);
        apply_logic_op(&schema, &str_array, &dict_array, Operator::Eq, result)?;

        Ok(())
    }

    #[test]
    fn plus_op() -> Result<()> {
        let schema = Schema::new(vec![
            Field::new("a", DataType::Int32, false),
            Field::new("b", DataType::Int32, false),
        ]);
        let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
        let b = Int32Array::from(vec![1, 2, 4, 8, 16]);

        apply_arithmetic::<Int32Type>(
            Arc::new(schema),
            vec![Arc::new(a), Arc::new(b)],
            Operator::Plus,
            Int32Array::from(vec![2, 4, 7, 12, 21]),
        )?;

        Ok(())
    }

    #[test]
    fn minus_op() -> Result<()> {
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Int32, false),
            Field::new("b", DataType::Int32, false),
        ]));
        let a = Arc::new(Int32Array::from(vec![1, 2, 4, 8, 16]));
        let b = Arc::new(Int32Array::from(vec![1, 2, 3, 4, 5]));

        apply_arithmetic::<Int32Type>(
            schema.clone(),
            vec![a.clone(), b.clone()],
            Operator::Minus,
            Int32Array::from(vec![0, 0, 1, 4, 11]),
        )?;

        // should handle have negative values in result (for signed)
        apply_arithmetic::<Int32Type>(
            schema,
            vec![b, a],
            Operator::Minus,
            Int32Array::from(vec![0, 0, -1, -4, -11]),
        )?;

        Ok(())
    }

    #[test]
    fn multiply_op() -> Result<()> {
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Int32, false),
            Field::new("b", DataType::Int32, false),
        ]));
        let a = Arc::new(Int32Array::from(vec![4, 8, 16, 32, 64]));
        let b = Arc::new(Int32Array::from(vec![2, 4, 8, 16, 32]));

        apply_arithmetic::<Int32Type>(
            schema,
            vec![a, b],
            Operator::Multiply,
            Int32Array::from(vec![8, 32, 128, 512, 2048]),
        )?;

        Ok(())
    }

    #[test]
    fn divide_op() -> Result<()> {
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Int32, false),
            Field::new("b", DataType::Int32, false),
        ]));
        let a = Arc::new(Int32Array::from(vec![8, 32, 128, 512, 2048]));
        let b = Arc::new(Int32Array::from(vec![2, 4, 8, 16, 32]));

        apply_arithmetic::<Int32Type>(
            schema,
            vec![a, b],
            Operator::Divide,
            Int32Array::from(vec![4, 8, 16, 32, 64]),
        )?;

        Ok(())
    }

    #[test]
    fn modulus_op() -> Result<()> {
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Int32, false),
            Field::new("b", DataType::Int32, false),
        ]));
        let a = Arc::new(Int32Array::from(vec![8, 32, 128, 512, 2048]));
        let b = Arc::new(Int32Array::from(vec![2, 4, 7, 14, 32]));

        apply_arithmetic::<Int32Type>(
            schema,
            vec![a, b],
            Operator::Modulo,
            Int32Array::from(vec![0, 0, 2, 8, 0]),
        )?;

        Ok(())
    }

    fn apply_arithmetic<T: ArrowNumericType>(
        schema: SchemaRef,
        data: Vec<ArrayRef>,
        op: Operator,
        expected: PrimitiveArray<T>,
    ) -> Result<()> {
        let arithmetic_op =
            binary_simple(col("a", &schema)?, op, col("b", &schema)?, &schema);
        let batch = RecordBatch::try_new(schema, data)?;
        let result = arithmetic_op.evaluate(&batch)?.into_array(batch.num_rows());

        assert_eq!(result.as_ref(), &expected);
        Ok(())
    }

    fn apply_logic_op(
        schema: &SchemaRef,
        left: &ArrayRef,
        right: &ArrayRef,
        op: Operator,
        expected: BooleanArray,
    ) -> Result<()> {
        let left_type = left.data_type();
        let right_type = right.data_type();
        let result_type = coerce_types(left_type, &op, right_type)?;

        let left_expr = try_cast(col("a", schema)?, schema, result_type.clone())?;
        let right_expr = try_cast(col("b", schema)?, schema, result_type)?;
        let arithmetic_op = binary_simple(left_expr, op, right_expr, schema);
        let data: Vec<ArrayRef> = vec![left.clone(), right.clone()];
        let batch = RecordBatch::try_new(schema.clone(), data)?;
        let result = arithmetic_op.evaluate(&batch)?.into_array(batch.num_rows());

        assert_eq!(result.as_ref(), &expected);
        Ok(())
    }

    // Test `scalar <op> arr` produces expected
    fn apply_logic_op_scalar_arr(
        schema: &SchemaRef,
        scalar: &ScalarValue,
        arr: &ArrayRef,
        op: Operator,
        expected: &BooleanArray,
    ) -> Result<()> {
        let scalar = lit(scalar.clone());
        let op_type = coerce_types(&scalar.data_type(schema)?, &op, arr.data_type())?;
        let left_expr = if op_type.eq(&scalar.data_type(schema)?) {
            scalar
        } else {
            try_cast(scalar, schema, op_type.clone())?
        };
        let right_expr = if op_type.eq(arr.data_type()) {
            col("a", schema)?
        } else {
            try_cast(col("a", schema)?, schema, op_type)?
        };

        let arithmetic_op = binary_simple(left_expr, op, right_expr, schema);
        let batch = RecordBatch::try_new(Arc::clone(schema), vec![Arc::clone(arr)])?;
        let result = arithmetic_op.evaluate(&batch)?.into_array(batch.num_rows());
        assert_eq!(result.as_ref(), expected);

        Ok(())
    }

    // Test `arr <op> scalar` produces expected
    fn apply_logic_op_arr_scalar(
        schema: &SchemaRef,
        arr: &ArrayRef,
        scalar: &ScalarValue,
        op: Operator,
        expected: &BooleanArray,
    ) -> Result<()> {
        let scalar = lit(scalar.clone());
        let op_type = coerce_types(arr.data_type(), &op, &scalar.data_type(schema)?)?;
        let right_expr = if op_type.eq(&scalar.data_type(schema)?) {
            scalar
        } else {
            try_cast(scalar, schema, op_type.clone())?
        };
        let left_expr = if op_type.eq(arr.data_type()) {
            col("a", schema)?
        } else {
            try_cast(col("a", schema)?, schema, op_type)?
        };

        let arithmetic_op = binary_simple(left_expr, op, right_expr, schema);
        let batch = RecordBatch::try_new(Arc::clone(schema), vec![Arc::clone(arr)])?;
        let result = arithmetic_op.evaluate(&batch)?.into_array(batch.num_rows());
        assert_eq!(result.as_ref(), expected);

        Ok(())
    }

    #[test]
    fn and_with_nulls_op() -> Result<()> {
        let schema = Schema::new(vec![
            Field::new("a", DataType::Boolean, true),
            Field::new("b", DataType::Boolean, true),
        ]);
        let a = Arc::new(BooleanArray::from(vec![
            Some(true),
            Some(false),
            None,
            Some(true),
            Some(false),
            None,
            Some(true),
            Some(false),
            None,
        ])) as ArrayRef;
        let b = Arc::new(BooleanArray::from(vec![
            Some(true),
            Some(true),
            Some(true),
            Some(false),
            Some(false),
            Some(false),
            None,
            None,
            None,
        ])) as ArrayRef;

        let expected = BooleanArray::from(vec![
            Some(true),
            Some(false),
            None,
            Some(false),
            Some(false),
            Some(false),
            None,
            Some(false),
            None,
        ]);
        apply_logic_op(&Arc::new(schema), &a, &b, Operator::And, expected)?;

        Ok(())
    }

    #[test]
    fn or_with_nulls_op() -> Result<()> {
        let schema = Schema::new(vec![
            Field::new("a", DataType::Boolean, true),
            Field::new("b", DataType::Boolean, true),
        ]);
        let a = Arc::new(BooleanArray::from(vec![
            Some(true),
            Some(false),
            None,
            Some(true),
            Some(false),
            None,
            Some(true),
            Some(false),
            None,
        ])) as ArrayRef;
        let b = Arc::new(BooleanArray::from(vec![
            Some(true),
            Some(true),
            Some(true),
            Some(false),
            Some(false),
            Some(false),
            None,
            None,
            None,
        ])) as ArrayRef;

        let expected = BooleanArray::from(vec![
            Some(true),
            Some(true),
            Some(true),
            Some(true),
            Some(false),
            None,
            Some(true),
            None,
            None,
        ]);
        apply_logic_op(&Arc::new(schema), &a, &b, Operator::Or, expected)?;

        Ok(())
    }

    /// Returns (schema, a: BooleanArray, b: BooleanArray) with all possible inputs
    ///
    /// a: [true, true, true,  NULL, NULL, NULL,  false, false, false]
    /// b: [true, NULL, false, true, NULL, false, true,  NULL,  false]
    fn bool_test_arrays() -> (SchemaRef, ArrayRef, ArrayRef) {
        let schema = Schema::new(vec![
            Field::new("a", DataType::Boolean, true),
            Field::new("b", DataType::Boolean, true),
        ]);
        let a: BooleanArray = [
            Some(true),
            Some(true),
            Some(true),
            None,
            None,
            None,
            Some(false),
            Some(false),
            Some(false),
        ]
        .iter()
        .collect();
        let b: BooleanArray = [
            Some(true),
            None,
            Some(false),
            Some(true),
            None,
            Some(false),
            Some(true),
            None,
            Some(false),
        ]
        .iter()
        .collect();
        (Arc::new(schema), Arc::new(a), Arc::new(b))
    }

    /// Returns (schema, BooleanArray) with [true, NULL, false]
    fn scalar_bool_test_array() -> (SchemaRef, ArrayRef) {
        let schema = Schema::new(vec![Field::new("a", DataType::Boolean, true)]);
        let a: BooleanArray = vec![Some(true), None, Some(false)].iter().collect();
        (Arc::new(schema), Arc::new(a))
    }

    #[test]
    fn eq_op_bool() {
        let (schema, a, b) = bool_test_arrays();
        let expected = vec![
            Some(true),
            None,
            Some(false),
            None,
            None,
            None,
            Some(false),
            None,
            Some(true),
        ]
        .iter()
        .collect();
        apply_logic_op(&schema, &a, &b, Operator::Eq, expected).unwrap();
    }

    #[test]
    fn eq_op_bool_scalar() {
        let (schema, a) = scalar_bool_test_array();
        let expected = [Some(true), None, Some(false)].iter().collect();
        apply_logic_op_scalar_arr(
            &schema,
            &ScalarValue::from(true),
            &a,
            Operator::Eq,
            &expected,
        )
        .unwrap();
        apply_logic_op_arr_scalar(
            &schema,
            &a,
            &ScalarValue::from(true),
            Operator::Eq,
            &expected,
        )
        .unwrap();

        let expected = [Some(false), None, Some(true)].iter().collect();
        apply_logic_op_scalar_arr(
            &schema,
            &ScalarValue::from(false),
            &a,
            Operator::Eq,
            &expected,
        )
        .unwrap();
        apply_logic_op_arr_scalar(
            &schema,
            &a,
            &ScalarValue::from(false),
            Operator::Eq,
            &expected,
        )
        .unwrap();
    }

    #[test]
    fn neq_op_bool() {
        let (schema, a, b) = bool_test_arrays();
        let expected = [
            Some(false),
            None,
            Some(true),
            None,
            None,
            None,
            Some(true),
            None,
            Some(false),
        ]
        .iter()
        .collect();
        apply_logic_op(&schema, &a, &b, Operator::NotEq, expected).unwrap();
    }

    #[test]
    fn neq_op_bool_scalar() {
        let (schema, a) = scalar_bool_test_array();
        let expected = [Some(false), None, Some(true)].iter().collect();
        apply_logic_op_scalar_arr(
            &schema,
            &ScalarValue::from(true),
            &a,
            Operator::NotEq,
            &expected,
        )
        .unwrap();
        apply_logic_op_arr_scalar(
            &schema,
            &a,
            &ScalarValue::from(true),
            Operator::NotEq,
            &expected,
        )
        .unwrap();

        let expected = [Some(true), None, Some(false)].iter().collect();
        apply_logic_op_scalar_arr(
            &schema,
            &ScalarValue::from(false),
            &a,
            Operator::NotEq,
            &expected,
        )
        .unwrap();
        apply_logic_op_arr_scalar(
            &schema,
            &a,
            &ScalarValue::from(false),
            Operator::NotEq,
            &expected,
        )
        .unwrap();
    }

    #[test]
    fn lt_op_bool() {
        let (schema, a, b) = bool_test_arrays();
        let expected = [
            Some(false),
            None,
            Some(false),
            None,
            None,
            None,
            Some(true),
            None,
            Some(false),
        ]
        .iter()
        .collect();
        apply_logic_op(&schema, &a, &b, Operator::Lt, expected).unwrap();
    }

    #[test]
    fn lt_op_bool_scalar() {
        let (schema, a) = scalar_bool_test_array();
        let expected = [Some(false), None, Some(false)].iter().collect();
        apply_logic_op_scalar_arr(
            &schema,
            &ScalarValue::from(true),
            &a,
            Operator::Lt,
            &expected,
        )
        .unwrap();

        let expected = [Some(false), None, Some(true)].iter().collect();
        apply_logic_op_arr_scalar(
            &schema,
            &a,
            &ScalarValue::from(true),
            Operator::Lt,
            &expected,
        )
        .unwrap();

        let expected = [Some(true), None, Some(false)].iter().collect();
        apply_logic_op_scalar_arr(
            &schema,
            &ScalarValue::from(false),
            &a,
            Operator::Lt,
            &expected,
        )
        .unwrap();

        let expected = [Some(false), None, Some(false)].iter().collect();
        apply_logic_op_arr_scalar(
            &schema,
            &a,
            &ScalarValue::from(false),
            Operator::Lt,
            &expected,
        )
        .unwrap();
    }

    #[test]
    fn lt_eq_op_bool() {
        let (schema, a, b) = bool_test_arrays();
        let expected = [
            Some(true),
            None,
            Some(false),
            None,
            None,
            None,
            Some(true),
            None,
            Some(true),
        ]
        .iter()
        .collect();
        apply_logic_op(&schema, &a, &b, Operator::LtEq, expected).unwrap();
    }

    #[test]
    fn lt_eq_op_bool_scalar() {
        let (schema, a) = scalar_bool_test_array();
        let expected = [Some(true), None, Some(false)].iter().collect();
        apply_logic_op_scalar_arr(
            &schema,
            &ScalarValue::from(true),
            &a,
            Operator::LtEq,
            &expected,
        )
        .unwrap();

        let expected = [Some(true), None, Some(true)].iter().collect();
        apply_logic_op_arr_scalar(
            &schema,
            &a,
            &ScalarValue::from(true),
            Operator::LtEq,
            &expected,
        )
        .unwrap();

        let expected = [Some(true), None, Some(true)].iter().collect();
        apply_logic_op_scalar_arr(
            &schema,
            &ScalarValue::from(false),
            &a,
            Operator::LtEq,
            &expected,
        )
        .unwrap();

        let expected = [Some(false), None, Some(true)].iter().collect();
        apply_logic_op_arr_scalar(
            &schema,
            &a,
            &ScalarValue::from(false),
            Operator::LtEq,
            &expected,
        )
        .unwrap();
    }

    #[test]
    fn gt_op_bool() {
        let (schema, a, b) = bool_test_arrays();
        let expected = [
            Some(false),
            None,
            Some(true),
            None,
            None,
            None,
            Some(false),
            None,
            Some(false),
        ]
        .iter()
        .collect();
        apply_logic_op(&schema, &a, &b, Operator::Gt, expected).unwrap();
    }

    #[test]
    fn gt_op_bool_scalar() {
        let (schema, a) = scalar_bool_test_array();
        let expected = [Some(false), None, Some(true)].iter().collect();
        apply_logic_op_scalar_arr(
            &schema,
            &ScalarValue::from(true),
            &a,
            Operator::Gt,
            &expected,
        )
        .unwrap();

        let expected = [Some(false), None, Some(false)].iter().collect();
        apply_logic_op_arr_scalar(
            &schema,
            &a,
            &ScalarValue::from(true),
            Operator::Gt,
            &expected,
        )
        .unwrap();

        let expected = [Some(false), None, Some(false)].iter().collect();
        apply_logic_op_scalar_arr(
            &schema,
            &ScalarValue::from(false),
            &a,
            Operator::Gt,
            &expected,
        )
        .unwrap();

        let expected = [Some(true), None, Some(false)].iter().collect();
        apply_logic_op_arr_scalar(
            &schema,
            &a,
            &ScalarValue::from(false),
            Operator::Gt,
            &expected,
        )
        .unwrap();
    }

    #[test]
    fn gt_eq_op_bool() {
        let (schema, a, b) = bool_test_arrays();
        let expected = [
            Some(true),
            None,
            Some(true),
            None,
            None,
            None,
            Some(false),
            None,
            Some(true),
        ]
        .iter()
        .collect();
        apply_logic_op(&schema, &a, &b, Operator::GtEq, expected).unwrap();
    }

    #[test]
    fn gt_eq_op_bool_scalar() {
        let (schema, a) = scalar_bool_test_array();
        let expected = [Some(true), None, Some(true)].iter().collect();
        apply_logic_op_scalar_arr(
            &schema,
            &ScalarValue::from(true),
            &a,
            Operator::GtEq,
            &expected,
        )
        .unwrap();

        let expected = [Some(true), None, Some(false)].iter().collect();
        apply_logic_op_arr_scalar(
            &schema,
            &a,
            &ScalarValue::from(true),
            Operator::GtEq,
            &expected,
        )
        .unwrap();

        let expected = [Some(false), None, Some(true)].iter().collect();
        apply_logic_op_scalar_arr(
            &schema,
            &ScalarValue::from(false),
            &a,
            Operator::GtEq,
            &expected,
        )
        .unwrap();

        let expected = [Some(true), None, Some(true)].iter().collect();
        apply_logic_op_arr_scalar(
            &schema,
            &a,
            &ScalarValue::from(false),
            Operator::GtEq,
            &expected,
        )
        .unwrap();
    }

    #[test]
    fn is_distinct_from_op_bool() {
        let (schema, a, b) = bool_test_arrays();
        let expected = [
            Some(false),
            Some(true),
            Some(true),
            Some(true),
            Some(false),
            Some(true),
            Some(true),
            Some(true),
            Some(false),
        ]
        .iter()
        .collect();
        apply_logic_op(&schema, &a, &b, Operator::IsDistinctFrom, expected).unwrap();
    }

    #[test]
    fn is_not_distinct_from_op_bool() {
        let (schema, a, b) = bool_test_arrays();
        let expected = [
            Some(true),
            Some(false),
            Some(false),
            Some(false),
            Some(true),
            Some(false),
            Some(false),
            Some(false),
            Some(true),
        ]
        .iter()
        .collect();
        apply_logic_op(&schema, &a, &b, Operator::IsNotDistinctFrom, expected).unwrap();
    }

    #[test]
    fn relatively_deeply_nested() {
        // Reproducer for https://github.com/apache/arrow-datafusion/issues/419

        // where even relatively shallow binary expressions overflowed
        // the stack in debug builds

        let input: Vec<_> = vec![1, 2, 3, 4, 5].into_iter().map(Some).collect();
        let a: Int32Array = input.iter().collect();

        let batch = RecordBatch::try_from_iter(vec![("a", Arc::new(a) as _)]).unwrap();
        let schema = batch.schema();

        // build a left deep tree ((((a + a) + a) + a ....
        let tree_depth: i32 = 100;
        let expr = (0..tree_depth)
            .into_iter()
            .map(|_| col("a", schema.as_ref()).unwrap())
            .reduce(|l, r| binary_simple(l, Operator::Plus, r, &schema))
            .unwrap();

        let result = expr
            .evaluate(&batch)
            .expect("evaluation")
            .into_array(batch.num_rows());

        let expected: Int32Array = input
            .into_iter()
            .map(|i| i.map(|i| i * tree_depth))
            .collect();
        assert_eq!(result.as_ref(), &expected);
    }

    fn create_decimal_array(
        array: &[Option<i128>],
        precision: u8,
        scale: i8,
    ) -> Decimal128Array {
        let mut decimal_builder = Decimal128Builder::with_capacity(array.len());
        for value in array.iter().copied() {
            decimal_builder.append_option(value)
        }
        decimal_builder
            .finish()
            .with_precision_and_scale(precision, scale)
            .unwrap()
    }

    #[test]
    fn comparison_decimal_expr_test() -> Result<()> {
        // scalar of decimal compare with decimal array
        let value_i128 = 123;
        let decimal_scalar = ScalarValue::Decimal128(Some(value_i128), 25, 3);
        let schema = Arc::new(Schema::new(vec![Field::new(
            "a",
            DataType::Decimal128(25, 3),
            true,
        )]));
        let decimal_array = Arc::new(create_decimal_array(
            &[
                Some(value_i128),
                None,
                Some(value_i128 - 1),
                Some(value_i128 + 1),
            ],
            25,
            3,
        )) as ArrayRef;
        // array = scalar
        apply_logic_op_arr_scalar(
            &schema,
            &decimal_array,
            &decimal_scalar,
            Operator::Eq,
            &BooleanArray::from(vec![Some(true), None, Some(false), Some(false)]),
        )
        .unwrap();
        // array != scalar
        apply_logic_op_arr_scalar(
            &schema,
            &decimal_array,
            &decimal_scalar,
            Operator::NotEq,
            &BooleanArray::from(vec![Some(false), None, Some(true), Some(true)]),
        )
        .unwrap();
        //  array < scalar
        apply_logic_op_arr_scalar(
            &schema,
            &decimal_array,
            &decimal_scalar,
            Operator::Lt,
            &BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]),
        )
        .unwrap();

        //  array <= scalar
        apply_logic_op_arr_scalar(
            &schema,
            &decimal_array,
            &decimal_scalar,
            Operator::LtEq,
            &BooleanArray::from(vec![Some(true), None, Some(true), Some(false)]),
        )
        .unwrap();
        // array > scalar
        apply_logic_op_arr_scalar(
            &schema,
            &decimal_array,
            &decimal_scalar,
            Operator::Gt,
            &BooleanArray::from(vec![Some(false), None, Some(false), Some(true)]),
        )
        .unwrap();

        // array >= scalar
        apply_logic_op_arr_scalar(
            &schema,
            &decimal_array,
            &decimal_scalar,
            Operator::GtEq,
            &BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]),
        )
        .unwrap();

        // scalar of different data type with decimal array
        let decimal_scalar = ScalarValue::Decimal128(Some(123_456), 10, 3);
        let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Int64, true)]));
        // scalar == array
        apply_logic_op_scalar_arr(
            &schema,
            &decimal_scalar,
            &(Arc::new(Int64Array::from(vec![Some(124), None])) as ArrayRef),
            Operator::Eq,
            &BooleanArray::from(vec![Some(false), None]),
        )
        .unwrap();

        // array != scalar
        apply_logic_op_arr_scalar(
            &schema,
            &(Arc::new(Int64Array::from(vec![Some(123), None, Some(1)])) as ArrayRef),
            &decimal_scalar,
            Operator::NotEq,
            &BooleanArray::from(vec![Some(true), None, Some(true)]),
        )
        .unwrap();

        // array < scalar
        apply_logic_op_arr_scalar(
            &schema,
            &(Arc::new(Int64Array::from(vec![Some(123), None, Some(124)])) as ArrayRef),
            &decimal_scalar,
            Operator::Lt,
            &BooleanArray::from(vec![Some(true), None, Some(false)]),
        )
        .unwrap();

        // array > scalar
        apply_logic_op_arr_scalar(
            &schema,
            &(Arc::new(Int64Array::from(vec![Some(123), None, Some(124)])) as ArrayRef),
            &decimal_scalar,
            Operator::Gt,
            &BooleanArray::from(vec![Some(false), None, Some(true)]),
        )
        .unwrap();

        let schema =
            Arc::new(Schema::new(vec![Field::new("a", DataType::Float64, true)]));
        // array == scalar
        apply_logic_op_arr_scalar(
            &schema,
            &(Arc::new(Float64Array::from(vec![Some(123.456), None, Some(123.457)]))
                as ArrayRef),
            &decimal_scalar,
            Operator::Eq,
            &BooleanArray::from(vec![Some(true), None, Some(false)]),
        )
        .unwrap();

        // array <= scalar
        apply_logic_op_arr_scalar(
            &schema,
            &(Arc::new(Float64Array::from(vec![
                Some(123.456),
                None,
                Some(123.457),
                Some(123.45),
            ])) as ArrayRef),
            &decimal_scalar,
            Operator::LtEq,
            &BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]),
        )
        .unwrap();
        // array >= scalar
        apply_logic_op_arr_scalar(
            &schema,
            &(Arc::new(Float64Array::from(vec![
                Some(123.456),
                None,
                Some(123.457),
                Some(123.45),
            ])) as ArrayRef),
            &decimal_scalar,
            Operator::GtEq,
            &BooleanArray::from(vec![Some(true), None, Some(true), Some(false)]),
        )
        .unwrap();

        let value: i128 = 123;
        let decimal_array = Arc::new(create_decimal_array(
            &[Some(value), None, Some(value - 1), Some(value + 1)],
            10,
            0,
        )) as ArrayRef;

        // comparison array op for decimal array
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Decimal128(10, 0), true),
            Field::new("b", DataType::Decimal128(10, 0), true),
        ]));
        let right_decimal_array = Arc::new(create_decimal_array(
            &[
                Some(value - 1),
                Some(value),
                Some(value + 1),
                Some(value + 1),
            ],
            10,
            0,
        )) as ArrayRef;

        apply_logic_op(
            &schema,
            &decimal_array,
            &right_decimal_array,
            Operator::Eq,
            BooleanArray::from(vec![Some(false), None, Some(false), Some(true)]),
        )
        .unwrap();

        apply_logic_op(
            &schema,
            &decimal_array,
            &right_decimal_array,
            Operator::NotEq,
            BooleanArray::from(vec![Some(true), None, Some(true), Some(false)]),
        )
        .unwrap();

        apply_logic_op(
            &schema,
            &decimal_array,
            &right_decimal_array,
            Operator::Lt,
            BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]),
        )
        .unwrap();

        apply_logic_op(
            &schema,
            &decimal_array,
            &right_decimal_array,
            Operator::LtEq,
            BooleanArray::from(vec![Some(false), None, Some(true), Some(true)]),
        )
        .unwrap();

        apply_logic_op(
            &schema,
            &decimal_array,
            &right_decimal_array,
            Operator::Gt,
            BooleanArray::from(vec![Some(true), None, Some(false), Some(false)]),
        )
        .unwrap();

        apply_logic_op(
            &schema,
            &decimal_array,
            &right_decimal_array,
            Operator::GtEq,
            BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]),
        )
        .unwrap();

        // compare decimal array with other array type
        let value: i64 = 123;
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Int64, true),
            Field::new("b", DataType::Decimal128(10, 0), true),
        ]));

        let int64_array = Arc::new(Int64Array::from(vec![
            Some(value),
            Some(value - 1),
            Some(value),
            Some(value + 1),
        ])) as ArrayRef;

        // eq: int64array == decimal array
        apply_logic_op(
            &schema,
            &int64_array,
            &decimal_array,
            Operator::Eq,
            BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]),
        )
        .unwrap();
        // neq: int64array != decimal array
        apply_logic_op(
            &schema,
            &int64_array,
            &decimal_array,
            Operator::NotEq,
            BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]),
        )
        .unwrap();

        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Float64, true),
            Field::new("b", DataType::Decimal128(10, 2), true),
        ]));

        let value: i128 = 123;
        let decimal_array = Arc::new(create_decimal_array(
            &[
                Some(value), // 1.23
                None,
                Some(value - 1), // 1.22
                Some(value + 1), // 1.24
            ],
            10,
            2,
        )) as ArrayRef;
        let float64_array = Arc::new(Float64Array::from(vec![
            Some(1.23),
            Some(1.22),
            Some(1.23),
            Some(1.24),
        ])) as ArrayRef;
        // lt: float64array < decimal array
        apply_logic_op(
            &schema,
            &float64_array,
            &decimal_array,
            Operator::Lt,
            BooleanArray::from(vec![Some(false), None, Some(false), Some(false)]),
        )
        .unwrap();
        // lt_eq: float64array <= decimal array
        apply_logic_op(
            &schema,
            &float64_array,
            &decimal_array,
            Operator::LtEq,
            BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]),
        )
        .unwrap();
        // gt: float64array > decimal array
        apply_logic_op(
            &schema,
            &float64_array,
            &decimal_array,
            Operator::Gt,
            BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]),
        )
        .unwrap();
        apply_logic_op(
            &schema,
            &float64_array,
            &decimal_array,
            Operator::GtEq,
            BooleanArray::from(vec![Some(true), None, Some(true), Some(true)]),
        )
        .unwrap();
        // is distinct: float64array is distinct decimal array
        // TODO: now we do not refactor the `is distinct or is not distinct` rule of coercion.
        // traced by https://github.com/apache/arrow-datafusion/issues/1590
        // the decimal array will be casted to float64array
        apply_logic_op(
            &schema,
            &float64_array,
            &decimal_array,
            Operator::IsDistinctFrom,
            BooleanArray::from(vec![Some(false), Some(true), Some(true), Some(false)]),
        )
        .unwrap();
        // is not distinct
        apply_logic_op(
            &schema,
            &float64_array,
            &decimal_array,
            Operator::IsNotDistinctFrom,
            BooleanArray::from(vec![Some(true), Some(false), Some(false), Some(true)]),
        )
        .unwrap();

        Ok(())
    }

    fn apply_arithmetic_op(
        schema: &SchemaRef,
        left: &ArrayRef,
        right: &ArrayRef,
        op: Operator,
        expected: ArrayRef,
    ) -> Result<()> {
        let op_type = coerce_types(left.data_type(), &op, right.data_type())?;
        let left_expr = if left.data_type().eq(&op_type) {
            col("a", schema)?
        } else {
            try_cast(col("a", schema)?, schema, op_type.clone())?
        };

        let right_expr = if right.data_type().eq(&op_type) {
            col("b", schema)?
        } else {
            try_cast(col("b", schema)?, schema, op_type.clone())?
        };

        let coerced_schema = Schema::new(vec![
            Field::new(
                schema.field(0).name(),
                op_type.clone(),
                schema.field(0).is_nullable(),
            ),
            Field::new(
                schema.field(1).name(),
                op_type,
                schema.field(1).is_nullable(),
            ),
        ]);
        let arithmetic_op = binary_simple(left_expr, op, right_expr, &coerced_schema);
        let data: Vec<ArrayRef> = vec![left.clone(), right.clone()];
        let batch = RecordBatch::try_new(schema.clone(), data)?;
        let result = arithmetic_op.evaluate(&batch)?.into_array(batch.num_rows());

        assert_eq!(result.as_ref(), expected.as_ref());
        Ok(())
    }

    #[test]
    fn arithmetic_decimal_expr_test() -> Result<()> {
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Int32, true),
            Field::new("b", DataType::Decimal128(10, 2), true),
        ]));
        let value: i128 = 123;
        let decimal_array = Arc::new(create_decimal_array(
            &[
                Some(value), // 1.23
                None,
                Some(value - 1), // 1.22
                Some(value + 1), // 1.24
            ],
            10,
            2,
        )) as ArrayRef;
        let int32_array = Arc::new(Int32Array::from(vec![
            Some(123),
            Some(122),
            Some(123),
            Some(124),
        ])) as ArrayRef;

        // add: Int32array add decimal array
        let expect = Arc::new(create_decimal_array(
            &[Some(12423), None, Some(12422), Some(12524)],
            13,
            2,
        )) as ArrayRef;
        apply_arithmetic_op(
            &schema,
            &int32_array,
            &decimal_array,
            Operator::Plus,
            expect,
        )
        .unwrap();

        // subtract: decimal array subtract int32 array
        let schema = Arc::new(Schema::new(vec![
            Field::new("b", DataType::Int32, true),
            Field::new("a", DataType::Decimal128(10, 2), true),
        ]));
        let expect = Arc::new(create_decimal_array(
            &[Some(-12177), None, Some(-12178), Some(-12276)],
            13,
            2,
        )) as ArrayRef;
        apply_arithmetic_op(
            &schema,
            &int32_array,
            &decimal_array,
            Operator::Minus,
            expect,
        )
        .unwrap();

        // multiply: decimal array multiply int32 array
        let expect = Arc::new(create_decimal_array(
            &[Some(15129), None, Some(15006), Some(15376)],
            21,
            2,
        )) as ArrayRef;
        apply_arithmetic_op(
            &schema,
            &int32_array,
            &decimal_array,
            Operator::Multiply,
            expect,
        )
        .unwrap();

        // divide: int32 array divide decimal array
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Int32, true),
            Field::new("b", DataType::Decimal128(10, 2), true),
        ]));
        let expect = Arc::new(create_decimal_array(
            &[
                Some(10000000000000),
                None,
                Some(10081967213114),
                Some(10000000000000),
            ],
            23,
            11,
        )) as ArrayRef;
        apply_arithmetic_op(
            &schema,
            &int32_array,
            &decimal_array,
            Operator::Divide,
            expect,
        )
        .unwrap();

        // modulus: int32 array modulus decimal array
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Int32, true),
            Field::new("b", DataType::Decimal128(10, 2), true),
        ]));
        let expect = Arc::new(create_decimal_array(
            &[Some(000), None, Some(100), Some(000)],
            10,
            2,
        )) as ArrayRef;
        apply_arithmetic_op(
            &schema,
            &int32_array,
            &decimal_array,
            Operator::Modulo,
            expect,
        )
        .unwrap();

        Ok(())
    }

    #[test]
    fn arithmetic_decimal_float_expr_test() -> Result<()> {
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Float64, true),
            Field::new("b", DataType::Decimal128(10, 2), true),
        ]));
        let value: i128 = 123;
        let decimal_array = Arc::new(create_decimal_array(
            &[
                Some(value), // 1.23
                None,
                Some(value - 1), // 1.22
                Some(value + 1), // 1.24
            ],
            10,
            2,
        )) as ArrayRef;
        let float64_array = Arc::new(Float64Array::from(vec![
            Some(123.0),
            Some(122.0),
            Some(123.0),
            Some(124.0),
        ])) as ArrayRef;

        // add: float64 array add decimal array
        let expect = Arc::new(Float64Array::from(vec![
            Some(124.23),
            None,
            Some(124.22),
            Some(125.24),
        ])) as ArrayRef;
        apply_arithmetic_op(
            &schema,
            &float64_array,
            &decimal_array,
            Operator::Plus,
            expect,
        )
        .unwrap();

        // subtract: decimal array subtract float64 array
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Float64, true),
            Field::new("b", DataType::Decimal128(10, 2), true),
        ]));
        let expect = Arc::new(Float64Array::from(vec![
            Some(121.77),
            None,
            Some(121.78),
            Some(122.76),
        ])) as ArrayRef;
        apply_arithmetic_op(
            &schema,
            &float64_array,
            &decimal_array,
            Operator::Minus,
            expect,
        )
        .unwrap();

        // multiply: decimal array multiply float64 array
        let expect = Arc::new(Float64Array::from(vec![
            Some(151.29),
            None,
            Some(150.06),
            Some(153.76),
        ])) as ArrayRef;
        apply_arithmetic_op(
            &schema,
            &float64_array,
            &decimal_array,
            Operator::Multiply,
            expect,
        )
        .unwrap();

        // divide: float64 array divide decimal array
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Float64, true),
            Field::new("b", DataType::Decimal128(10, 2), true),
        ]));
        let expect = Arc::new(Float64Array::from(vec![
            Some(100.0),
            None,
            Some(100.81967213114754),
            Some(100.0),
        ])) as ArrayRef;
        apply_arithmetic_op(
            &schema,
            &float64_array,
            &decimal_array,
            Operator::Divide,
            expect,
        )
        .unwrap();

        // modulus: float64 array modulus decimal array
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Float64, true),
            Field::new("b", DataType::Decimal128(10, 2), true),
        ]));
        let expect = Arc::new(Float64Array::from(vec![
            Some(1.7763568394002505e-15),
            None,
            Some(1.0000000000000027),
            Some(8.881784197001252e-16),
        ])) as ArrayRef;
        apply_arithmetic_op(
            &schema,
            &float64_array,
            &decimal_array,
            Operator::Modulo,
            expect,
        )
        .unwrap();

        Ok(())
    }

    #[test]
    fn bitwise_array_test() -> Result<()> {
        let left = Arc::new(Int32Array::from(vec![Some(12), None, Some(11)])) as ArrayRef;
        let right =
            Arc::new(Int32Array::from(vec![Some(1), Some(3), Some(7)])) as ArrayRef;
        let mut result = bitwise_and(left.clone(), right.clone())?;
        let expected = Int32Array::from(vec![Some(0), None, Some(3)]);
        assert_eq!(result.as_ref(), &expected);

        result = bitwise_or(left.clone(), right.clone())?;
        let expected = Int32Array::from(vec![Some(13), None, Some(15)]);
        assert_eq!(result.as_ref(), &expected);

        result = bitwise_xor(left.clone(), right.clone())?;
        let expected = Int32Array::from(vec![Some(13), None, Some(12)]);
        assert_eq!(result.as_ref(), &expected);

        Ok(())
    }

    #[test]
    fn bitwise_shift_array_test() -> Result<()> {
        let input = Arc::new(Int32Array::from(vec![Some(2), None, Some(10)])) as ArrayRef;
        let modules =
            Arc::new(Int32Array::from(vec![Some(2), Some(4), Some(8)])) as ArrayRef;
        let mut result = bitwise_shift_left(input.clone(), modules.clone())?;

        let expected = Int32Array::from(vec![Some(8), None, Some(2560)]);
        assert_eq!(result.as_ref(), &expected);

        result = bitwise_shift_right(result.clone(), modules.clone())?;
        assert_eq!(result.as_ref(), &input);

        Ok(())
    }

    #[test]
    fn bitwise_shift_array_overflow_test() -> Result<()> {
        let input = Arc::new(Int32Array::from(vec![Some(2)])) as ArrayRef;
        let modules = Arc::new(Int32Array::from(vec![Some(100)])) as ArrayRef;
        let result = bitwise_shift_left(input.clone(), modules.clone())?;

        let expected = Int32Array::from(vec![Some(32)]);
        assert_eq!(result.as_ref(), &expected);

        Ok(())
    }

    #[test]
    fn bitwise_scalar_test() -> Result<()> {
        let left = Arc::new(Int32Array::from(vec![Some(12), None, Some(11)])) as ArrayRef;
        let right = ScalarValue::from(3i32);
        let mut result = bitwise_and_scalar(&left, right.clone()).unwrap()?;
        let expected = Int32Array::from(vec![Some(0), None, Some(3)]);
        assert_eq!(result.as_ref(), &expected);

        result = bitwise_or_scalar(&left, right.clone()).unwrap()?;
        let expected = Int32Array::from(vec![Some(15), None, Some(11)]);
        assert_eq!(result.as_ref(), &expected);

        result = bitwise_xor_scalar(&left, right).unwrap()?;
        let expected = Int32Array::from(vec![Some(15), None, Some(8)]);
        assert_eq!(result.as_ref(), &expected);
        Ok(())
    }

    #[test]
    fn bitwise_shift_scalar_test() -> Result<()> {
        let input = Arc::new(Int32Array::from(vec![Some(2), None, Some(4)])) as ArrayRef;
        let module = ScalarValue::from(10i32);
        let mut result = bitwise_shift_left_scalar(&input, module.clone()).unwrap()?;

        let expected = Int32Array::from(vec![Some(2048), None, Some(4096)]);
        assert_eq!(result.as_ref(), &expected);

        result = bitwise_shift_right_scalar(&result, module).unwrap()?;
        assert_eq!(result.as_ref(), &input);

        Ok(())
    }

    /// Return a pair of (schema, statistics) for a table with a single column (called "a") with
    /// the same type as the `min_value`/`max_value`.
    fn get_test_table_stats(
        min_value: ScalarValue,
        max_value: ScalarValue,
    ) -> (Schema, Statistics) {
        assert!(min_value.get_datatype() == max_value.get_datatype());
        let schema = Schema::new(vec![Field::new("a", min_value.get_datatype(), false)]);
        let columns = vec![ColumnStatistics {
            min_value: Some(min_value),
            max_value: Some(max_value),
            null_count: None,
            distinct_count: None,
        }];
        let statistics = Statistics {
            column_statistics: Some(columns),
            ..Default::default()
        };
        (schema, statistics)
    }

    #[test]
    fn test_analyze_expr_scalar_comparison() -> Result<()> {
        // A table where the column 'a' has a min of 1, a max of 100.
        let (schema, statistics) =
            get_test_table_stats(ScalarValue::from(1i64), ScalarValue::from(100i64));

        let cases = [
            // (operator, rhs), (expected selectivity, expected min, expected max)
            // -------------------------------------------------------------------
            //
            // Table:
            //   - a (min = 1, max = 100, distinct_count = null)
            //
            // Equality (a = $):
            //
            ((Operator::Eq, 1), (1.0 / 100.0, 1, 1)),
            ((Operator::Eq, 5), (1.0 / 100.0, 5, 5)),
            ((Operator::Eq, 99), (1.0 / 100.0, 99, 99)),
            ((Operator::Eq, 100), (1.0 / 100.0, 100, 100)),
            // For never matches like the following, we still produce the correct
            // min/max values since if this condition holds by an off chance, then
            // the result of expression will effectively become the = $limit.
            ((Operator::Eq, 0), (0.0, 0, 0)),
            ((Operator::Eq, -101), (0.0, -101, -101)),
            ((Operator::Eq, 101), (0.0, 101, 101)),
            //
            // Less than (a < $):
            //
            // Note: upper bounds for less than is currently overstated (by the closest value).
            // see the comment in `compare_left_boundaries` for the reason
            ((Operator::Lt, 5), (4.0 / 100.0, 1, 5)),
            ((Operator::Lt, 99), (98.0 / 100.0, 1, 99)),
            ((Operator::Lt, 101), (100.0 / 100.0, 1, 100)),
            // Unlike equality, we now have an obligation to provide a range of values here
            // so if "col < -100" expr is executed, we don't want to say col can take [0, -100].
            ((Operator::Lt, 0), (0.0, 1, 100)),
            ((Operator::Lt, 1), (0.0, 1, 100)),
            ((Operator::Lt, -100), (0.0, 1, 100)),
            ((Operator::Lt, -200), (0.0, 1, 100)),
            // We also don't want to expand the range unnecessarily even if the predicate is
            // successful.
            ((Operator::Lt, 200), (1.0, 1, 100)),
            //
            // Less than or equal (a <= $):
            //
            ((Operator::LtEq, -100), (0.0, 1, 100)),
            ((Operator::LtEq, 0), (0.0, 1, 100)),
            ((Operator::LtEq, 1), (1.0 / 100.0, 1, 1)),
            ((Operator::LtEq, 5), (5.0 / 100.0, 1, 5)),
            ((Operator::LtEq, 99), (99.0 / 100.0, 1, 99)),
            ((Operator::LtEq, 100), (100.0 / 100.0, 1, 100)),
            ((Operator::LtEq, 101), (1.0, 1, 100)),
            ((Operator::LtEq, 200), (1.0, 1, 100)),
            //
            // Greater than (a > $):
            //
            ((Operator::Gt, -100), (1.0, 1, 100)),
            ((Operator::Gt, 0), (1.0, 1, 100)),
            ((Operator::Gt, 1), (99.0 / 100.0, 1, 100)),
            ((Operator::Gt, 5), (95.0 / 100.0, 5, 100)),
            ((Operator::Gt, 99), (1.0 / 100.0, 99, 100)),
            ((Operator::Gt, 100), (0.0, 1, 100)),
            ((Operator::Gt, 101), (0.0, 1, 100)),
            ((Operator::Gt, 200), (0.0, 1, 100)),
            //
            // Greater than or equal (a >= $):
            //
            ((Operator::GtEq, -100), (1.0, 1, 100)),
            ((Operator::GtEq, 0), (1.0, 1, 100)),
            ((Operator::GtEq, 1), (1.0, 1, 100)),
            ((Operator::GtEq, 5), (96.0 / 100.0, 5, 100)),
            ((Operator::GtEq, 99), (2.0 / 100.0, 99, 100)),
            ((Operator::GtEq, 100), (1.0 / 100.0, 100, 100)),
            ((Operator::GtEq, 101), (0.0, 1, 100)),
            ((Operator::GtEq, 200), (0.0, 1, 100)),
        ];

        for ((operator, rhs), (exp_selectivity, _, _)) in cases {
            let context = AnalysisContext::from_statistics(&schema, &statistics);
            let left = col("a", &schema).unwrap();
            let right = ScalarValue::Int64(Some(rhs));
            let boundaries =
                analyze_expr_scalar_comparison(&operator, &context, &left, right)
                    .expect("this case should not return None");

            assert_eq!(
                boundaries
                    .selectivity
                    .expect("compare_left_boundaries must produce a selectivity value"),
                exp_selectivity
            );

            if exp_selectivity == 1.0 {
                // When the expected selectivity is 1.0, the resulting expression
                // should always be true.
                assert_eq!(boundaries.reduce(), Some(ScalarValue::Boolean(Some(true))));
            } else if exp_selectivity == 0.0 {
                // When the expected selectivity is 0.0, the resulting expression
                // should always be false.
                assert_eq!(boundaries.reduce(), Some(ScalarValue::Boolean(Some(false))));
            } else {
                // Otherwise, it should be [false, true] (since we don't know anything for sure)
                assert_eq!(boundaries.min_value, ScalarValue::Boolean(Some(false)));
                assert_eq!(boundaries.max_value, ScalarValue::Boolean(Some(true)));
            }
        }
        Ok(())
    }

    #[test]
    fn test_comparison_result_estimate_different_type() -> Result<()> {
        // A table where the column 'a' has a min of 1.3, a max of 50.7.
        let (schema, statistics) =
            get_test_table_stats(ScalarValue::from(1.3), ScalarValue::from(50.7));
        let distance = 50.0; // rounded distance is (max - min) + 1

        // Since the generic version already covers all the paths, we can just
        // test a small subset of the cases.
        let cases = [
            // (operator, rhs), (expected selectivity, expected min, expected max)
            // -------------------------------------------------------------------
            //
            // Table:
            //   - a (min = 1.3, max = 50.7, distinct_count = 25)
            //
            // Never selects (out of range)
            ((Operator::Eq, 1.1), (0.0, 1.1, 1.1)),
            ((Operator::Eq, 50.75), (0.0, 50.75, 50.75)),
            ((Operator::Lt, 1.3), (0.0, 1.3, 50.7)),
            ((Operator::LtEq, 1.29), (0.0, 1.3, 50.7)),
            ((Operator::Gt, 50.7), (0.0, 1.3, 50.7)),
            ((Operator::GtEq, 50.75), (0.0, 1.3, 50.7)),
            // Always selects
            ((Operator::Lt, 50.75), (1.0, 1.3, 50.7)),
            ((Operator::LtEq, 50.75), (1.0, 1.3, 50.7)),
            ((Operator::Gt, 1.29), (1.0, 1.3, 50.7)),
            ((Operator::GtEq, 1.3), (1.0, 1.3, 50.7)),
            // Partial selection (the x in 'x/distance' is basically the rounded version of
            // the bound distance, as per the implementation).
            ((Operator::Eq, 27.8), (1.0 / distance, 27.8, 27.8)),
            ((Operator::Lt, 5.2), (4.0 / distance, 1.3, 5.2)), // On a uniform distribution, this is {2.6, 3.9}
            ((Operator::LtEq, 1.3), (1.0 / distance, 1.3, 1.3)),
            ((Operator::Gt, 45.5), (5.0 / distance, 45.5, 50.7)), // On a uniform distribution, this is {46.8, 48.1, 49.4}
            ((Operator::GtEq, 50.7), (1.0 / distance, 50.7, 50.7)),
        ];

        for ((operator, rhs), (exp_selectivity, _, _)) in cases {
            let context = AnalysisContext::from_statistics(&schema, &statistics);
            let left = col("a", &schema).unwrap();
            let right = ScalarValue::from(rhs);
            let boundaries =
                analyze_expr_scalar_comparison(&operator, &context, &left, right)
                    .expect("this case should not return None");

            assert_eq!(
                boundaries
                    .selectivity
                    .expect("compare_left_boundaries must produce a selectivity value"),
                exp_selectivity
            );

            if exp_selectivity == 1.0 {
                // When the expected selectivity is 1.0, the resulting expression
                // should always be true.
                assert_eq!(boundaries.reduce(), Some(ScalarValue::from(true)));
            } else if exp_selectivity == 0.0 {
                // When the expected selectivity is 0.0, the resulting expression
                // should always be false.
                assert_eq!(boundaries.reduce(), Some(ScalarValue::from(false)));
            } else {
                // Otherwise, it should be [false, true] (since we don't know anything for sure)
                assert_eq!(boundaries.min_value, ScalarValue::from(false));
                assert_eq!(boundaries.max_value, ScalarValue::from(true));
            }
        }
        Ok(())
    }

    #[test]
    fn test_binary_expression_boundaries() -> Result<()> {
        // A table where the column 'a' has a min of 1, a max of 100.
        let (schema, statistics) =
            get_test_table_stats(ScalarValue::from(1), ScalarValue::from(100));

        // expression: "a >= 25"
        let a = col("a", &schema).unwrap();
        let gt = binary_simple(
            a.clone(),
            Operator::GtEq,
            lit(ScalarValue::from(25)),
            &schema,
        );

        let context = AnalysisContext::from_statistics(&schema, &statistics);
        let predicate_boundaries = gt
            .boundaries(&context)
            .expect("boundaries should not be None");
        assert_eq!(predicate_boundaries.selectivity, Some(0.76));

        Ok(())
    }

    #[test]
    fn test_binary_expression_boundaries_rhs() -> Result<()> {
        // This test is about the column rewriting feature in the boundary provider
        // (e.g. if the lhs is a literal and rhs is the column, then we swap them when
        // doing the computation).

        // A table where the column 'a' has a min of 1, a max of 100.
        let (schema, statistics) =
            get_test_table_stats(ScalarValue::from(1), ScalarValue::from(100));

        // expression: "50 >= a"
        let a = col("a", &schema).unwrap();
        let gt = binary_simple(
            lit(ScalarValue::from(50)),
            Operator::GtEq,
            a.clone(),
            &schema,
        );

        let context = AnalysisContext::from_statistics(&schema, &statistics);
        let predicate_boundaries = gt
            .boundaries(&context)
            .expect("boundaries should not be None");
        assert_eq!(predicate_boundaries.selectivity, Some(0.5));

        Ok(())
    }
}