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Remove temporal to kernels_arrow #6069

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merged 10 commits into from
Apr 24, 2023
Merged

Remove temporal to kernels_arrow #6069

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1b79bd5
ready to review
berkaysynnada Apr 19, 2023
e7662af
clippy fix
berkaysynnada Apr 19, 2023
0fb5c0d
Merge branch 'apache:main' into feature/removal-temporal-arith-to-ker…
berkaysynnada Apr 19, 2023
ef50aab
clippy fix
berkaysynnada Apr 19, 2023
6e7311e
Simple code refactor
metesynnada Apr 19, 2023
5f19ad3
Merge branch 'main' into feature/removal-temporal-arith-to-kernels-arrow
berkaysynnada Apr 20, 2023
84afde7
fix clippy
berkaysynnada Apr 20, 2023
890e277
Merge branch 'main' into feature/removal-temporal-arith-to-kernels-arrow
berkaysynnada Apr 22, 2023
f6e6751
Merge branch 'main' into feature/removal-temporal-arith-to-kernels-arrow
berkaysynnada Apr 22, 2023
67592e5
Update datafusion/physical-expr/src/expressions/datetime.rs
berkaysynnada Apr 23, 2023
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52 changes: 41 additions & 11 deletions datafusion/physical-expr/src/expressions/binary.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -68,14 +68,15 @@ use kernels::{
bitwise_xor, bitwise_xor_scalar,
};
use kernels_arrow::{
add_decimal_dyn_scalar, add_dyn_decimal, divide_decimal_dyn_scalar,
divide_dyn_opt_decimal, is_distinct_from, is_distinct_from_bool,
is_distinct_from_decimal, is_distinct_from_f32, is_distinct_from_f64,
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_f32,
is_not_distinct_from_f64, is_not_distinct_from_null, is_not_distinct_from_utf8,
modulus_decimal_dyn_scalar, modulus_dyn_decimal, multiply_decimal_dyn_scalar,
multiply_dyn_decimal, subtract_decimal_dyn_scalar, subtract_dyn_decimal,
add_decimal_dyn_scalar, add_dyn_decimal, add_dyn_temporal, add_dyn_temporal_scalar,
divide_decimal_dyn_scalar, divide_dyn_opt_decimal, is_distinct_from,
is_distinct_from_bool, is_distinct_from_decimal, is_distinct_from_f32,
is_distinct_from_f64, 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_f32, is_not_distinct_from_f64, is_not_distinct_from_null,
is_not_distinct_from_utf8, modulus_decimal_dyn_scalar, modulus_dyn_decimal,
multiply_decimal_dyn_scalar, multiply_dyn_decimal, subtract_decimal_dyn_scalar,
subtract_dyn_decimal, subtract_dyn_temporal, subtract_dyn_temporal_scalar,
};

use arrow::datatypes::{DataType, Schema, TimeUnit};
Expand Down Expand Up @@ -1312,10 +1313,39 @@ macro_rules! sub_timestamp_macro {
Arc::new(ret) as ArrayRef
}};
}

pub fn resolve_temporal_op(
lhs: &ArrayRef,
sign: i32,
rhs: &ArrayRef,
) -> Result<ArrayRef> {
match sign {
1 => add_dyn_temporal(lhs, rhs),
-1 => subtract_dyn_temporal(lhs, rhs),
other => Err(DataFusionError::Internal(format!(
"Undefined operation for temporal types {other}"
))),
}
}

pub fn resolve_temporal_op_scalar(
lhs: &ArrayRef,
sign: i32,
rhs: &ScalarValue,
) -> Result<ColumnarValue> {
match sign {
1 => add_dyn_temporal_scalar(lhs, rhs),
-1 => subtract_dyn_temporal_scalar(lhs, rhs),
other => Err(DataFusionError::Internal(format!(
"Undefined operation for temporal types {other}"
))),
}
}

/// This function handles the Timestamp - Timestamp operations,
/// where the first one is an array, and the second one is a scalar,
/// hence the result is also an array.
pub fn ts_scalar_ts_op(array: ArrayRef, scalar: &ScalarValue) -> Result<ColumnarValue> {
pub fn ts_scalar_ts_op(array: &ArrayRef, scalar: &ScalarValue) -> Result<ColumnarValue> {
let ret = match (array.data_type(), scalar) {
(
DataType::Timestamp(TimeUnit::Second, opt_tz_lhs),
Expand Down Expand Up @@ -1410,7 +1440,7 @@ macro_rules! sub_timestamp_interval_macro {
/// where the first one is an array, and the second one is a scalar,
/// hence the result is also an array.
pub fn ts_scalar_interval_op(
array: ArrayRef,
array: &ArrayRef,
sign: i32,
scalar: &ScalarValue,
) -> Result<ColumnarValue> {
Expand Down Expand Up @@ -1494,7 +1524,7 @@ macro_rules! sub_interval_cross_macro {
/// where the first one is an array, and the second one is a scalar,
/// hence the result is also an interval array.
pub fn interval_scalar_interval_op(
array: ArrayRef,
array: &ArrayRef,
sign: i32,
scalar: &ScalarValue,
) -> Result<ColumnarValue> {
Expand Down
123 changes: 119 additions & 4 deletions datafusion/physical-expr/src/expressions/binary/kernels_arrow.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -21,17 +21,24 @@
use arrow::compute::{
add_dyn, add_scalar_dyn, divide_dyn_opt, divide_scalar_dyn, modulus_dyn,
modulus_scalar_dyn, multiply_dyn, multiply_scalar_dyn, subtract_dyn,
subtract_scalar_dyn,
subtract_scalar_dyn, try_unary,
};
use arrow::datatypes::Decimal128Type;
use arrow::datatypes::{Date32Type, Date64Type, Decimal128Type};
use arrow::{array::*, datatypes::ArrowNumericType, downcast_dictionary_array};
use arrow_schema::DataType;
use datafusion_common::cast::as_decimal128_array;
use datafusion_common::{DataFusionError, Result};
use datafusion_common::cast::{as_date32_array, as_date64_array, as_decimal128_array};
use datafusion_common::scalar::{date32_add, date64_add};
use datafusion_common::{DataFusionError, Result, ScalarValue};
use datafusion_expr::type_coercion::binary::decimal_op_mathematics_type;
use datafusion_expr::ColumnarValue;
use datafusion_expr::Operator;
use std::sync::Arc;

use super::{
interval_array_op, interval_scalar_interval_op, ts_array_op, ts_interval_array_op,
ts_scalar_interval_op, ts_scalar_ts_op,
};

// Simple (low performance) kernels until optimized kernels are added to arrow
// See https://github.com/apache/arrow-rs/issues/960

Expand Down Expand Up @@ -280,6 +287,57 @@ pub(crate) fn add_decimal_dyn_scalar(
decimal_array_with_precision_scale(array, precision, scale)
}

pub(crate) fn add_dyn_temporal(left: &ArrayRef, right: &ArrayRef) -> Result<ArrayRef> {
match (left.data_type(), right.data_type()) {
(DataType::Timestamp(..), DataType::Timestamp(..)) => ts_array_op(left, right),
(DataType::Interval(..), DataType::Interval(..)) => {
interval_array_op(left, right, 1)
}
(DataType::Timestamp(..), DataType::Interval(..)) => {
ts_interval_array_op(left, 1, right)
}
(DataType::Interval(..), DataType::Timestamp(..)) => {
ts_interval_array_op(right, 1, left)
}
_ => {
// fall back to kernels in arrow-rs
Ok(add_dyn(left, right)?)
}
}
}

pub(crate) fn add_dyn_temporal_scalar(
left: &ArrayRef,
right: &ScalarValue,
) -> Result<ColumnarValue> {
match (left.data_type(), right.get_datatype()) {
(DataType::Date32, DataType::Interval(..)) => {
let left = as_date32_array(&left)?;
let ret = Arc::new(try_unary::<Date32Type, _, Date32Type>(left, |days| {
Ok(date32_add(days, right, 1)?)
})?) as ArrayRef;
Ok(ColumnarValue::Array(ret))
}
(DataType::Date64, DataType::Interval(..)) => {
let left = as_date64_array(&left)?;
let ret = Arc::new(try_unary::<Date64Type, _, Date64Type>(left, |ms| {
Ok(date64_add(ms, right, 1)?)
})?) as ArrayRef;
Ok(ColumnarValue::Array(ret))
}
(DataType::Interval(..), DataType::Interval(..)) => {
interval_scalar_interval_op(left, 1, right)
}
(DataType::Timestamp(..), DataType::Interval(..)) => {
ts_scalar_interval_op(left, 1, right)
}
_ => {
// fall back to kernels in arrow-rs
Ok(ColumnarValue::Array(add_dyn(left, &right.to_array())?))
}
}
}

pub(crate) fn subtract_decimal_dyn_scalar(
left: &dyn Array,
right: i128,
Expand All @@ -291,6 +349,63 @@ pub(crate) fn subtract_decimal_dyn_scalar(
decimal_array_with_precision_scale(array, precision, scale)
}

pub(crate) fn subtract_dyn_temporal(
left: &ArrayRef,
right: &ArrayRef,
) -> Result<ArrayRef> {
match (left.data_type(), right.data_type()) {
(DataType::Timestamp(..), DataType::Timestamp(..)) => ts_array_op(left, right),
(DataType::Interval(..), DataType::Interval(..)) => {
interval_array_op(left, right, -1)
}
(DataType::Timestamp(..), DataType::Interval(..)) => {
ts_interval_array_op(left, -1, right)
}
(DataType::Interval(..), DataType::Timestamp(..)) => {
ts_interval_array_op(right, -1, left)
}
_ => {
// fall back to kernels in arrow-rs
Ok(subtract_dyn(left, right)?)
}
}
}

pub(crate) fn subtract_dyn_temporal_scalar(
left: &ArrayRef,
right: &ScalarValue,
) -> Result<ColumnarValue> {
match (left.data_type(), right.get_datatype()) {
(DataType::Date32, DataType::Interval(..)) => {
let left = as_date32_array(&left)?;
let ret = Arc::new(try_unary::<Date32Type, _, Date32Type>(left, |days| {
Ok(date32_add(days, right, -1)?)
})?) as ArrayRef;
Ok(ColumnarValue::Array(ret))
}
(DataType::Date64, DataType::Interval(..)) => {
let left = as_date64_array(&left)?;
let ret = Arc::new(try_unary::<Date64Type, _, Date64Type>(left, |ms| {
Ok(date64_add(ms, right, -1)?)
})?) as ArrayRef;
Ok(ColumnarValue::Array(ret))
}
(DataType::Timestamp(..), DataType::Timestamp(..)) => {
ts_scalar_ts_op(left, right)
}
(DataType::Interval(..), DataType::Interval(..)) => {
interval_scalar_interval_op(left, -1, right)
}
(DataType::Timestamp(..), DataType::Interval(..)) => {
ts_scalar_interval_op(left, -1, right)
}
_ => {
// fall back to kernels in arrow-rs
Ok(ColumnarValue::Array(subtract_dyn(left, &right.to_array())?))
}
}
}

fn get_precision_scale(data_type: &DataType) -> Result<(u8, i8)> {
match data_type {
DataType::Decimal128(precision, scale) => Ok((*precision, *scale)),
Expand Down
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