datafusion_spark/function/math/
expm1.rs

1// Licensed to the Apache Software Foundation (ASF) under one
2// or more contributor license agreements.  See the NOTICE file
3// distributed with this work for additional information
4// regarding copyright ownership.  The ASF licenses this file
5// to you under the Apache License, Version 2.0 (the
6// "License"); you may not use this file except in compliance
7// with the License.  You may obtain a copy of the License at
8//
9//   http://www.apache.org/licenses/LICENSE-2.0
10//
11// Unless required by applicable law or agreed to in writing,
12// software distributed under the License is distributed on an
13// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
14// KIND, either express or implied.  See the License for the
15// specific language governing permissions and limitations
16// under the License.
17
18use crate::function::error_utils::{
19    invalid_arg_count_exec_err, unsupported_data_type_exec_err,
20};
21use arrow::array::{ArrayRef, AsArray};
22use arrow::datatypes::{DataType, Float64Type};
23use datafusion_common::{Result, ScalarValue};
24use datafusion_expr::{
25    ColumnarValue, ScalarFunctionArgs, ScalarUDFImpl, Signature, Volatility,
26};
27use std::any::Any;
28use std::sync::Arc;
29
30/// <https://spark.apache.org/docs/latest/api/sql/index.html#expm1>
31#[derive(Debug)]
32pub struct SparkExpm1 {
33    signature: Signature,
34    aliases: Vec<String>,
35}
36
37impl Default for SparkExpm1 {
38    fn default() -> Self {
39        Self::new()
40    }
41}
42
43impl SparkExpm1 {
44    pub fn new() -> Self {
45        Self {
46            signature: Signature::user_defined(Volatility::Immutable),
47            aliases: vec![],
48        }
49    }
50}
51
52impl ScalarUDFImpl for SparkExpm1 {
53    fn as_any(&self) -> &dyn Any {
54        self
55    }
56
57    fn name(&self) -> &str {
58        "expm1"
59    }
60
61    fn signature(&self) -> &Signature {
62        &self.signature
63    }
64
65    fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
66        Ok(DataType::Float64)
67    }
68
69    fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> {
70        if args.args.len() != 1 {
71            return Err(invalid_arg_count_exec_err("expm1", (1, 1), args.args.len()));
72        }
73        match &args.args[0] {
74            ColumnarValue::Scalar(ScalarValue::Float64(value)) => Ok(
75                ColumnarValue::Scalar(ScalarValue::Float64(value.map(|x| x.exp_m1()))),
76            ),
77            ColumnarValue::Array(array) => match array.data_type() {
78                DataType::Float64 => Ok(ColumnarValue::Array(Arc::new(
79                    array
80                        .as_primitive::<Float64Type>()
81                        .unary::<_, Float64Type>(|x| x.exp_m1()),
82                )
83                    as ArrayRef)),
84                other => Err(unsupported_data_type_exec_err(
85                    "expm1",
86                    format!("{}", DataType::Float64).as_str(),
87                    other,
88                )),
89            },
90            other => Err(unsupported_data_type_exec_err(
91                "expm1",
92                format!("{}", DataType::Float64).as_str(),
93                &other.data_type(),
94            )),
95        }
96    }
97
98    fn aliases(&self) -> &[String] {
99        &self.aliases
100    }
101
102    fn coerce_types(&self, arg_types: &[DataType]) -> Result<Vec<DataType>> {
103        if arg_types.len() != 1 {
104            return Err(invalid_arg_count_exec_err("expm1", (1, 1), arg_types.len()));
105        }
106        if arg_types[0].is_numeric() {
107            Ok(vec![DataType::Float64])
108        } else {
109            Err(unsupported_data_type_exec_err(
110                "expm1",
111                "Numeric Type",
112                &arg_types[0],
113            ))
114        }
115    }
116}
117
118#[cfg(test)]
119mod tests {
120    use crate::function::math::expm1::SparkExpm1;
121    use crate::function::utils::test::test_scalar_function;
122    use arrow::array::{Array, Float64Array};
123    use arrow::datatypes::DataType::Float64;
124    use datafusion_common::{Result, ScalarValue};
125    use datafusion_expr::{ColumnarValue, ScalarUDFImpl};
126
127    macro_rules! test_expm1_float64_invoke {
128        ($INPUT:expr, $EXPECTED:expr) => {
129            test_scalar_function!(
130                SparkExpm1::new(),
131                vec![ColumnarValue::Scalar(ScalarValue::Float64($INPUT))],
132                $EXPECTED,
133                f64,
134                Float64,
135                Float64Array
136            );
137        };
138    }
139
140    #[test]
141    fn test_expm1_invoke() -> Result<()> {
142        test_expm1_float64_invoke!(Some(0f64), Ok(Some(0.0f64)));
143        Ok(())
144    }
145}
datafusion_spark/function/math/expm1.rs

datafusion_spark/function/math/
expm1.rs
