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binary.rs
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// 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,
eq_decimal_scalar, gt_decimal_scalar, gt_eq_decimal_scalar, 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,
lt_decimal_scalar, lt_eq_decimal_scalar, modulus_decimal, modulus_decimal_scalar,
multiply_decimal, multiply_decimal_scalar, neq_decimal_scalar, subtract_decimal,
subtract_decimal_scalar,
};
use arrow::datatypes::{DataType, Schema, TimeUnit};
use arrow::record_batch::RecordBatch;
use crate::PhysicalExpr;
use datafusion_common::ScalarValue;
use datafusion_common::{DataFusionError, Result};
use datafusion_expr::binary_rule::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_scalar {
($LEFT:expr, $RIGHT:expr, $OP:ident, $DT:ident) => {{
let ll = $LEFT.as_any().downcast_ref::<$DT>().unwrap();
Ok(Arc::new(paste::expr! {[<$OP _decimal_scalar>]}(
ll,
$RIGHT.try_into()?,
)?))
}};
}
macro_rules! compute_decimal_op {
($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 $LEFT.data_type() {
// TODO support decimal type
// which is not the primitive type
DataType::Decimal128(_,_) => compute_decimal_op!($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, $OP, Date64Array)
}
DataType::Boolean => compute_bool_op!($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 = $LEFT
.as_any()
.downcast_ref::<BooleanArray>()
.expect("boolean_op failed to downcast array");
let rr = $RIGHT
.as_any()
.downcast_ref::<BooleanArray>()
.expect("boolean_op failed to downcast array");
Ok(Arc::new($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))
}
}
/// 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, $OP_TYPE),
ScalarValue::Decimal128(..) => compute_decimal_op_scalar!($LEFT, right, $OP, Decimal128Array),
ScalarValue::Utf8(v) => compute_utf8_op_dyn_scalar!($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, $OP, Date64Array),
ScalarValue::TimestampSecond(..) => compute_op_scalar!($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::Result;
use datafusion_expr::binary_rule::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 = result
.as_any()
.downcast_ref::<BooleanArray>()
.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);