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functions.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.
//! Declaration of built-in (scalar) functions.
//! This module contains built-in functions' enumeration and metadata.
//!
//! Generally, a function has:
//! * a signature
//! * a return type, that is a function of the incoming argument's types
//! * the computation, that must accept each valid signature
//!
//! * Signature: see `Signature`
//! * Return type: a function `(arg_types) -> return_type`. E.g. for sqrt, ([f32]) -> f32, ([f64]) -> f64.
//!
//! This module also supports coercion to improve user experience: if
//! an argument i32 is passed to a function that supports f64, the
//! argument is automatically is coerced to f64.
use crate::execution_props::ExecutionProps;
use crate::{
array_expressions, conditional_expressions, datetime_expressions,
expressions::{cast_column, nullif_func, DEFAULT_DATAFUSION_CAST_OPTIONS},
math_expressions, string_expressions, struct_expressions, PhysicalExpr,
ScalarFunctionExpr,
};
use arrow::{
array::ArrayRef,
compute::kernels::length::{bit_length, length},
datatypes::TimeUnit,
datatypes::{DataType, Int32Type, Int64Type, Schema},
};
use datafusion_common::{DataFusionError, Result, ScalarValue};
use datafusion_expr::{
function, BuiltinScalarFunction, ColumnarValue, ScalarFunctionImplementation,
};
use std::sync::Arc;
/// Create a physical (function) expression.
/// This function errors when `args`' can't be coerced to a valid argument type of the function.
pub fn create_physical_expr(
fun: &BuiltinScalarFunction,
input_phy_exprs: &[Arc<dyn PhysicalExpr>],
input_schema: &Schema,
execution_props: &ExecutionProps,
) -> Result<Arc<dyn PhysicalExpr>> {
let input_expr_types = input_phy_exprs
.iter()
.map(|e| e.data_type(input_schema))
.collect::<Result<Vec<_>>>()?;
let data_type = function::return_type(fun, &input_expr_types)?;
let fun_expr: ScalarFunctionImplementation = match fun {
// These functions need args and input schema to pick an implementation
// Unlike the string functions, which actually figure out the function to use with each array,
// here we return either a cast fn or string timestamp translation based on the expression data type
// so we don't have to pay a per-array/batch cost.
BuiltinScalarFunction::ToTimestamp => {
Arc::new(match input_phy_exprs[0].data_type(input_schema) {
Ok(DataType::Int64) | Ok(DataType::Timestamp(_, None)) => {
|col_values: &[ColumnarValue]| {
cast_column(
&col_values[0],
&DataType::Timestamp(TimeUnit::Nanosecond, None),
&DEFAULT_DATAFUSION_CAST_OPTIONS,
)
}
}
Ok(DataType::Utf8) => datetime_expressions::to_timestamp,
other => {
return Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function to_timestamp",
other,
)));
}
})
}
BuiltinScalarFunction::ToTimestampMillis => {
Arc::new(match input_phy_exprs[0].data_type(input_schema) {
Ok(DataType::Int64) | Ok(DataType::Timestamp(_, None)) => {
|col_values: &[ColumnarValue]| {
cast_column(
&col_values[0],
&DataType::Timestamp(TimeUnit::Millisecond, None),
&DEFAULT_DATAFUSION_CAST_OPTIONS,
)
}
}
Ok(DataType::Utf8) => datetime_expressions::to_timestamp_millis,
other => {
return Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function to_timestamp_millis",
other,
)));
}
})
}
BuiltinScalarFunction::ToTimestampMicros => {
Arc::new(match input_phy_exprs[0].data_type(input_schema) {
Ok(DataType::Int64) | Ok(DataType::Timestamp(_, None)) => {
|col_values: &[ColumnarValue]| {
cast_column(
&col_values[0],
&DataType::Timestamp(TimeUnit::Microsecond, None),
&DEFAULT_DATAFUSION_CAST_OPTIONS,
)
}
}
Ok(DataType::Utf8) => datetime_expressions::to_timestamp_micros,
other => {
return Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function to_timestamp_micros",
other,
)));
}
})
}
BuiltinScalarFunction::ToTimestampSeconds => Arc::new({
match input_phy_exprs[0].data_type(input_schema) {
Ok(DataType::Int64) | Ok(DataType::Timestamp(_, None)) => {
|col_values: &[ColumnarValue]| {
cast_column(
&col_values[0],
&DataType::Timestamp(TimeUnit::Second, None),
&DEFAULT_DATAFUSION_CAST_OPTIONS,
)
}
}
Ok(DataType::Utf8) => datetime_expressions::to_timestamp_seconds,
other => {
return Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function to_timestamp_seconds",
other,
)));
}
}
}),
BuiltinScalarFunction::FromUnixtime => Arc::new({
match input_phy_exprs[0].data_type(input_schema) {
Ok(DataType::Int64) => |col_values: &[ColumnarValue]| {
cast_column(
&col_values[0],
&DataType::Timestamp(TimeUnit::Second, None),
&DEFAULT_DATAFUSION_CAST_OPTIONS,
)
},
other => {
return Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function from_unixtime",
other,
)));
}
}
}),
BuiltinScalarFunction::ArrowTypeof => {
let input_data_type = input_phy_exprs[0].data_type(input_schema)?;
Arc::new(move |_| {
Ok(ColumnarValue::Scalar(ScalarValue::Utf8(Some(format!(
"{}",
input_data_type
)))))
})
}
// These don't need args and input schema
_ => create_physical_fun(fun, execution_props)?,
};
Ok(Arc::new(ScalarFunctionExpr::new(
&format!("{}", fun),
fun_expr,
input_phy_exprs.to_vec(),
&data_type,
)))
}
#[cfg(feature = "crypto_expressions")]
macro_rules! invoke_if_crypto_expressions_feature_flag {
($FUNC:ident, $NAME:expr) => {{
use crate::crypto_expressions;
crypto_expressions::$FUNC
}};
}
#[cfg(not(feature = "crypto_expressions"))]
macro_rules! invoke_if_crypto_expressions_feature_flag {
($FUNC:ident, $NAME:expr) => {
|_: &[ColumnarValue]| -> Result<ColumnarValue> {
Err(DataFusionError::Internal(format!(
"function {} requires compilation with feature flag: crypto_expressions.",
$NAME
)))
}
};
}
#[cfg(feature = "regex_expressions")]
macro_rules! invoke_if_regex_expressions_feature_flag {
($FUNC:ident, $T:tt, $NAME:expr) => {{
use crate::regex_expressions;
regex_expressions::$FUNC::<$T>
}};
}
#[cfg(not(feature = "regex_expressions"))]
macro_rules! invoke_if_regex_expressions_feature_flag {
($FUNC:ident, $T:tt, $NAME:expr) => {
|_: &[ArrayRef]| -> Result<ArrayRef> {
Err(DataFusionError::Internal(format!(
"function {} requires compilation with feature flag: regex_expressions.",
$NAME
)))
}
};
}
#[cfg(feature = "unicode_expressions")]
macro_rules! invoke_if_unicode_expressions_feature_flag {
($FUNC:ident, $T:tt, $NAME:expr) => {{
use crate::unicode_expressions;
unicode_expressions::$FUNC::<$T>
}};
}
#[cfg(not(feature = "unicode_expressions"))]
macro_rules! invoke_if_unicode_expressions_feature_flag {
($FUNC:ident, $T:tt, $NAME:expr) => {
|_: &[ArrayRef]| -> Result<ArrayRef> {
Err(DataFusionError::Internal(format!(
"function {} requires compilation with feature flag: unicode_expressions.",
$NAME
)))
}
};
}
#[derive(Debug, Clone, Copy)]
pub(crate) enum Hint {
/// Indicates the argument needs to be padded if it is scalar
Pad,
/// Indicates the argument can be converted to an array of length 1
AcceptsSingular,
}
/// decorates a function to handle [`ScalarValue`]s by converting them to arrays before calling the function
/// and vice-versa after evaluation.
pub fn make_scalar_function<F>(inner: F) -> ScalarFunctionImplementation
where
F: Fn(&[ArrayRef]) -> Result<ArrayRef> + Sync + Send + 'static,
{
make_scalar_function_with_hints(inner, vec![])
}
/// Just like [`make_scalar_function`], decorates the given function to handle both [`ScalarValue`]s and arrays.
/// Additionally can receive a `hints` vector which can be used to control the output arrays when generating them
/// from [`ScalarValue`]s.
///
/// Each element of the `hints` vector gets mapped to the corresponding argument of the function. The number of hints
/// can be less or greater than the number of arguments (for functions with variable number of arguments). Each unmapped
/// argument will assume the default hint (for padding, it is [`Hint::Pad`]).
pub(crate) fn make_scalar_function_with_hints<F>(
inner: F,
hints: Vec<Hint>,
) -> ScalarFunctionImplementation
where
F: Fn(&[ArrayRef]) -> Result<ArrayRef> + Sync + Send + 'static,
{
Arc::new(move |args: &[ColumnarValue]| {
// first, identify if any of the arguments is an Array. If yes, store its `len`,
// as any scalar will need to be converted to an array of len `len`.
let len = args
.iter()
.fold(Option::<usize>::None, |acc, arg| match arg {
ColumnarValue::Scalar(_) => acc,
ColumnarValue::Array(a) => Some(a.len()),
});
let inferred_length = len.unwrap_or(1);
let args = args
.iter()
.zip(hints.iter().chain(std::iter::repeat(&Hint::Pad)))
.map(|(arg, hint)| {
// Decide on the length to expand this scalar to depending
// on the given hints.
let expansion_len = match hint {
Hint::AcceptsSingular => 1,
Hint::Pad => inferred_length,
};
arg.clone().into_array(expansion_len)
})
.collect::<Vec<ArrayRef>>();
let result = (inner)(&args);
// maybe back to scalar
if len.is_some() {
result.map(ColumnarValue::Array)
} else {
ScalarValue::try_from_array(&result?, 0).map(ColumnarValue::Scalar)
}
})
}
/// Create a physical scalar function.
pub fn create_physical_fun(
fun: &BuiltinScalarFunction,
execution_props: &ExecutionProps,
) -> Result<ScalarFunctionImplementation> {
Ok(match fun {
// math functions
BuiltinScalarFunction::Abs => Arc::new(math_expressions::abs),
BuiltinScalarFunction::Acos => Arc::new(math_expressions::acos),
BuiltinScalarFunction::Asin => Arc::new(math_expressions::asin),
BuiltinScalarFunction::Atan => Arc::new(math_expressions::atan),
BuiltinScalarFunction::Ceil => Arc::new(math_expressions::ceil),
BuiltinScalarFunction::Cos => Arc::new(math_expressions::cos),
BuiltinScalarFunction::Exp => Arc::new(math_expressions::exp),
BuiltinScalarFunction::Floor => Arc::new(math_expressions::floor),
BuiltinScalarFunction::Log => Arc::new(math_expressions::log10),
BuiltinScalarFunction::Ln => Arc::new(math_expressions::ln),
BuiltinScalarFunction::Log10 => Arc::new(math_expressions::log10),
BuiltinScalarFunction::Log2 => Arc::new(math_expressions::log2),
BuiltinScalarFunction::Random => Arc::new(math_expressions::random),
BuiltinScalarFunction::Round => Arc::new(math_expressions::round),
BuiltinScalarFunction::Signum => Arc::new(math_expressions::signum),
BuiltinScalarFunction::Sin => Arc::new(math_expressions::sin),
BuiltinScalarFunction::Sqrt => Arc::new(math_expressions::sqrt),
BuiltinScalarFunction::Tan => Arc::new(math_expressions::tan),
BuiltinScalarFunction::Trunc => Arc::new(math_expressions::trunc),
BuiltinScalarFunction::Power => {
Arc::new(|args| make_scalar_function(math_expressions::power)(args))
}
BuiltinScalarFunction::Atan2 => {
Arc::new(|args| make_scalar_function(math_expressions::atan2)(args))
}
// string functions
BuiltinScalarFunction::MakeArray => Arc::new(array_expressions::array),
BuiltinScalarFunction::Struct => Arc::new(struct_expressions::struct_expr),
BuiltinScalarFunction::Ascii => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
make_scalar_function(string_expressions::ascii::<i32>)(args)
}
DataType::LargeUtf8 => {
make_scalar_function(string_expressions::ascii::<i64>)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function ascii",
other,
))),
}),
BuiltinScalarFunction::BitLength => Arc::new(|args| match &args[0] {
ColumnarValue::Array(v) => Ok(ColumnarValue::Array(bit_length(v.as_ref())?)),
ColumnarValue::Scalar(v) => match v {
ScalarValue::Utf8(v) => Ok(ColumnarValue::Scalar(ScalarValue::Int32(
v.as_ref().map(|x| (x.len() * 8) as i32),
))),
ScalarValue::LargeUtf8(v) => Ok(ColumnarValue::Scalar(
ScalarValue::Int64(v.as_ref().map(|x| (x.len() * 8) as i64)),
)),
_ => unreachable!(),
},
}),
BuiltinScalarFunction::Btrim => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
make_scalar_function(string_expressions::btrim::<i32>)(args)
}
DataType::LargeUtf8 => {
make_scalar_function(string_expressions::btrim::<i64>)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function btrim",
other,
))),
}),
BuiltinScalarFunction::CharacterLength => {
Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
let func = invoke_if_unicode_expressions_feature_flag!(
character_length,
Int32Type,
"character_length"
);
make_scalar_function(func)(args)
}
DataType::LargeUtf8 => {
let func = invoke_if_unicode_expressions_feature_flag!(
character_length,
Int64Type,
"character_length"
);
make_scalar_function(func)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function character_length",
other,
))),
})
}
BuiltinScalarFunction::Chr => {
Arc::new(|args| make_scalar_function(string_expressions::chr)(args))
}
BuiltinScalarFunction::Coalesce => Arc::new(conditional_expressions::coalesce),
BuiltinScalarFunction::Concat => Arc::new(string_expressions::concat),
BuiltinScalarFunction::ConcatWithSeparator => {
Arc::new(|args| make_scalar_function(string_expressions::concat_ws)(args))
}
BuiltinScalarFunction::DatePart => Arc::new(datetime_expressions::date_part),
BuiltinScalarFunction::DateTrunc => Arc::new(datetime_expressions::date_trunc),
BuiltinScalarFunction::DateBin => Arc::new(datetime_expressions::date_bin),
BuiltinScalarFunction::Now => {
// bind value for now at plan time
Arc::new(datetime_expressions::make_now(
execution_props.query_execution_start_time,
))
}
BuiltinScalarFunction::InitCap => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
make_scalar_function(string_expressions::initcap::<i32>)(args)
}
DataType::LargeUtf8 => {
make_scalar_function(string_expressions::initcap::<i64>)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function initcap",
other,
))),
}),
BuiltinScalarFunction::Left => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
let func = invoke_if_unicode_expressions_feature_flag!(left, i32, "left");
make_scalar_function(func)(args)
}
DataType::LargeUtf8 => {
let func = invoke_if_unicode_expressions_feature_flag!(left, i64, "left");
make_scalar_function(func)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function left",
other,
))),
}),
BuiltinScalarFunction::Lower => Arc::new(string_expressions::lower),
BuiltinScalarFunction::Lpad => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
let func = invoke_if_unicode_expressions_feature_flag!(lpad, i32, "lpad");
make_scalar_function(func)(args)
}
DataType::LargeUtf8 => {
let func = invoke_if_unicode_expressions_feature_flag!(lpad, i64, "lpad");
make_scalar_function(func)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function lpad",
other,
))),
}),
BuiltinScalarFunction::Ltrim => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
make_scalar_function(string_expressions::ltrim::<i32>)(args)
}
DataType::LargeUtf8 => {
make_scalar_function(string_expressions::ltrim::<i64>)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function ltrim",
other,
))),
}),
BuiltinScalarFunction::MD5 => {
Arc::new(invoke_if_crypto_expressions_feature_flag!(md5, "md5"))
}
BuiltinScalarFunction::Digest => {
Arc::new(invoke_if_crypto_expressions_feature_flag!(digest, "digest"))
}
BuiltinScalarFunction::NullIf => Arc::new(nullif_func),
BuiltinScalarFunction::OctetLength => Arc::new(|args| match &args[0] {
ColumnarValue::Array(v) => Ok(ColumnarValue::Array(length(v.as_ref())?)),
ColumnarValue::Scalar(v) => match v {
ScalarValue::Utf8(v) => Ok(ColumnarValue::Scalar(ScalarValue::Int32(
v.as_ref().map(|x| x.len() as i32),
))),
ScalarValue::LargeUtf8(v) => Ok(ColumnarValue::Scalar(
ScalarValue::Int64(v.as_ref().map(|x| x.len() as i64)),
)),
_ => unreachable!(),
},
}),
BuiltinScalarFunction::RegexpMatch => {
Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
let func = invoke_if_regex_expressions_feature_flag!(
regexp_match,
i32,
"regexp_match"
);
make_scalar_function(func)(args)
}
DataType::LargeUtf8 => {
let func = invoke_if_regex_expressions_feature_flag!(
regexp_match,
i64,
"regexp_match"
);
make_scalar_function(func)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function regexp_match",
other
))),
})
}
BuiltinScalarFunction::RegexpReplace => {
Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
let specializer_func = invoke_if_regex_expressions_feature_flag!(
specialize_regexp_replace,
i32,
"regexp_replace"
);
let func = specializer_func(args)?;
func(args)
}
DataType::LargeUtf8 => {
let specializer_func = invoke_if_regex_expressions_feature_flag!(
specialize_regexp_replace,
i64,
"regexp_replace"
);
let func = specializer_func(args)?;
func(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function regexp_replace",
other,
))),
})
}
BuiltinScalarFunction::Repeat => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
make_scalar_function(string_expressions::repeat::<i32>)(args)
}
DataType::LargeUtf8 => {
make_scalar_function(string_expressions::repeat::<i64>)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function repeat",
other,
))),
}),
BuiltinScalarFunction::Replace => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
make_scalar_function(string_expressions::replace::<i32>)(args)
}
DataType::LargeUtf8 => {
make_scalar_function(string_expressions::replace::<i64>)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function replace",
other,
))),
}),
BuiltinScalarFunction::Reverse => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
let func =
invoke_if_unicode_expressions_feature_flag!(reverse, i32, "reverse");
make_scalar_function(func)(args)
}
DataType::LargeUtf8 => {
let func =
invoke_if_unicode_expressions_feature_flag!(reverse, i64, "reverse");
make_scalar_function(func)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function reverse",
other,
))),
}),
BuiltinScalarFunction::Right => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
let func =
invoke_if_unicode_expressions_feature_flag!(right, i32, "right");
make_scalar_function(func)(args)
}
DataType::LargeUtf8 => {
let func =
invoke_if_unicode_expressions_feature_flag!(right, i64, "right");
make_scalar_function(func)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function right",
other,
))),
}),
BuiltinScalarFunction::Rpad => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
let func = invoke_if_unicode_expressions_feature_flag!(rpad, i32, "rpad");
make_scalar_function(func)(args)
}
DataType::LargeUtf8 => {
let func = invoke_if_unicode_expressions_feature_flag!(rpad, i64, "rpad");
make_scalar_function(func)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function rpad",
other,
))),
}),
BuiltinScalarFunction::Rtrim => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
make_scalar_function(string_expressions::rtrim::<i32>)(args)
}
DataType::LargeUtf8 => {
make_scalar_function(string_expressions::rtrim::<i64>)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function rtrim",
other,
))),
}),
BuiltinScalarFunction::SHA224 => {
Arc::new(invoke_if_crypto_expressions_feature_flag!(sha224, "sha224"))
}
BuiltinScalarFunction::SHA256 => {
Arc::new(invoke_if_crypto_expressions_feature_flag!(sha256, "sha256"))
}
BuiltinScalarFunction::SHA384 => {
Arc::new(invoke_if_crypto_expressions_feature_flag!(sha384, "sha384"))
}
BuiltinScalarFunction::SHA512 => {
Arc::new(invoke_if_crypto_expressions_feature_flag!(sha512, "sha512"))
}
BuiltinScalarFunction::SplitPart => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
make_scalar_function(string_expressions::split_part::<i32>)(args)
}
DataType::LargeUtf8 => {
make_scalar_function(string_expressions::split_part::<i64>)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function split_part",
other,
))),
}),
BuiltinScalarFunction::StartsWith => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
make_scalar_function(string_expressions::starts_with::<i32>)(args)
}
DataType::LargeUtf8 => {
make_scalar_function(string_expressions::starts_with::<i64>)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function starts_with",
other,
))),
}),
BuiltinScalarFunction::Strpos => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
let func = invoke_if_unicode_expressions_feature_flag!(
strpos, Int32Type, "strpos"
);
make_scalar_function(func)(args)
}
DataType::LargeUtf8 => {
let func = invoke_if_unicode_expressions_feature_flag!(
strpos, Int64Type, "strpos"
);
make_scalar_function(func)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function strpos",
other,
))),
}),
BuiltinScalarFunction::Substr => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
let func =
invoke_if_unicode_expressions_feature_flag!(substr, i32, "substr");
make_scalar_function(func)(args)
}
DataType::LargeUtf8 => {
let func =
invoke_if_unicode_expressions_feature_flag!(substr, i64, "substr");
make_scalar_function(func)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function substr",
other,
))),
}),
BuiltinScalarFunction::ToHex => Arc::new(|args| match args[0].data_type() {
DataType::Int32 => {
make_scalar_function(string_expressions::to_hex::<Int32Type>)(args)
}
DataType::Int64 => {
make_scalar_function(string_expressions::to_hex::<Int64Type>)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function to_hex",
other,
))),
}),
BuiltinScalarFunction::Translate => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
let func = invoke_if_unicode_expressions_feature_flag!(
translate,
i32,
"translate"
);
make_scalar_function(func)(args)
}
DataType::LargeUtf8 => {
let func = invoke_if_unicode_expressions_feature_flag!(
translate,
i64,
"translate"
);
make_scalar_function(func)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function translate",
other,
))),
}),
BuiltinScalarFunction::Trim => Arc::new(|args| match args[0].data_type() {
DataType::Utf8 => {
make_scalar_function(string_expressions::btrim::<i32>)(args)
}
DataType::LargeUtf8 => {
make_scalar_function(string_expressions::btrim::<i64>)(args)
}
other => Err(DataFusionError::Internal(format!(
"Unsupported data type {:?} for function trim",
other,
))),
}),
BuiltinScalarFunction::Upper => Arc::new(string_expressions::upper),
_ => {
return Err(DataFusionError::Internal(format!(
"create_physical_fun: Unsupported scalar function {:?}",
fun
)));
}
})
}
#[cfg(test)]
mod tests {
use super::*;
use crate::expressions::{col, lit};
use crate::from_slice::FromSlice;
use crate::type_coercion::coerce;
use arrow::{
array::{
Array, ArrayRef, BinaryArray, BooleanArray, FixedSizeListArray, Float32Array,
Float64Array, Int32Array, StringArray, UInt32Array, UInt64Array,
},
datatypes::Field,
record_batch::RecordBatch,
};
use datafusion_common::{Result, ScalarValue};
/// $FUNC function to test
/// $ARGS arguments (vec) to pass to function
/// $EXPECTED a Result<Option<$EXPECTED_TYPE>> where Result allows testing errors and Option allows testing Null
/// $EXPECTED_TYPE is the expected value type
/// $DATA_TYPE is the function to test result type
/// $ARRAY_TYPE is the column type after function applied
macro_rules! test_function {
($FUNC:ident, $ARGS:expr, $EXPECTED:expr, $EXPECTED_TYPE:ty, $DATA_TYPE: ident, $ARRAY_TYPE:ident) => {
// used to provide type annotation
let expected: Result<Option<$EXPECTED_TYPE>> = $EXPECTED;
let execution_props = ExecutionProps::new();
// any type works here: we evaluate against a literal of `value`
let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]);
let columns: Vec<ArrayRef> = vec![Arc::new(Int32Array::from_slice(&[1]))];
let expr =
create_physical_expr_with_type_coercion(&BuiltinScalarFunction::$FUNC, $ARGS, &schema, &execution_props)?;
// type is correct
assert_eq!(expr.data_type(&schema)?, DataType::$DATA_TYPE);
let batch = RecordBatch::try_new(Arc::new(schema.clone()), columns)?;
match expected {
Ok(expected) => {
let result = expr.evaluate(&batch)?;
let result = result.into_array(batch.num_rows());
let result = result.as_any().downcast_ref::<$ARRAY_TYPE>().unwrap();
// value is correct
match expected {
Some(v) => assert_eq!(result.value(0), v),
None => assert!(result.is_null(0)),
};
}
Err(expected_error) => {
// evaluate is expected error - cannot use .expect_err() due to Debug not being implemented
match expr.evaluate(&batch) {
Ok(_) => assert!(false, "expected error"),
Err(error) => {
assert_eq!(error.to_string(), expected_error.to_string());
}
}
}
};
};
}
#[test]
fn test_functions() -> Result<()> {
test_function!(Ascii, &[lit("x")], Ok(Some(120)), i32, Int32, Int32Array);
test_function!(Ascii, &[lit("ésoj")], Ok(Some(233)), i32, Int32, Int32Array);
test_function!(
Ascii,
&[lit("💯")],
Ok(Some(128175)),
i32,
Int32,
Int32Array
);
test_function!(
Ascii,
&[lit("💯a")],
Ok(Some(128175)),
i32,
Int32,
Int32Array
);
test_function!(Ascii, &[lit("")], Ok(Some(0)), i32, Int32, Int32Array);
test_function!(
Ascii,
&[lit(ScalarValue::Utf8(None))],
Ok(None),
i32,
Int32,
Int32Array
);
test_function!(
BitLength,
&[lit("chars")],
Ok(Some(40)),
i32,
Int32,
Int32Array
);
test_function!(
BitLength,
&[lit("josé")],
Ok(Some(40)),
i32,
Int32,
Int32Array
);
test_function!(BitLength, &[lit("")], Ok(Some(0)), i32, Int32, Int32Array);
test_function!(
Btrim,
&[lit(" trim ")],
Ok(Some("trim")),
&str,
Utf8,
StringArray
);
test_function!(
Btrim,
&[lit(" trim")],
Ok(Some("trim")),
&str,
Utf8,
StringArray
);
test_function!(
Btrim,
&[lit("trim ")],
Ok(Some("trim")),
&str,
Utf8,
StringArray
);
test_function!(
Btrim,
&[lit("\n trim \n")],
Ok(Some("\n trim \n")),
&str,
Utf8,
StringArray
);
test_function!(
Btrim,
&[lit("xyxtrimyyx"), lit("xyz"),],
Ok(Some("trim")),
&str,
Utf8,
StringArray
);
test_function!(
Btrim,
&[lit("\nxyxtrimyyx\n"), lit("xyz\n"),],
Ok(Some("trim")),
&str,
Utf8,
StringArray
);
test_function!(
Btrim,
&[lit(ScalarValue::Utf8(None)), lit("xyz"),],
Ok(None),
&str,
Utf8,
StringArray
);
test_function!(
Btrim,
&[lit("xyxtrimyyx"), lit(ScalarValue::Utf8(None)),],
Ok(None),
&str,
Utf8,
StringArray
);
#[cfg(feature = "unicode_expressions")]
test_function!(
CharacterLength,
&[lit("chars")],
Ok(Some(5)),
i32,
Int32,
Int32Array
);
#[cfg(feature = "unicode_expressions")]
test_function!(
CharacterLength,
&[lit("josé")],
Ok(Some(4)),
i32,
Int32,
Int32Array
);
#[cfg(feature = "unicode_expressions")]
test_function!(
CharacterLength,
&[lit("")],
Ok(Some(0)),
i32,
Int32,
Int32Array
);
#[cfg(feature = "unicode_expressions")]
test_function!(
CharacterLength,
&[lit(ScalarValue::Utf8(None))],
Ok(None),
i32,
Int32,
Int32Array
);
#[cfg(not(feature = "unicode_expressions"))]
test_function!(
CharacterLength,
&[lit("josé")],
Err(DataFusionError::Internal(
"function character_length requires compilation with feature flag: unicode_expressions.".to_string()
)),
i32,
Int32,
Int32Array
);
test_function!(
Chr,
&[lit(ScalarValue::Int64(Some(128175)))],
Ok(Some("💯")),
&str,
Utf8,
StringArray
);
test_function!(
Chr,
&[lit(ScalarValue::Int64(None))],
Ok(None),
&str,
Utf8,
StringArray
);
test_function!(
Chr,
&[lit(ScalarValue::Int64(Some(120)))],