datafusion_spark/function/math/
factorial.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 std::sync::Arc;
19
20use arrow::array::{Array, Int64Array};
21use arrow::datatypes::DataType;
22use arrow::datatypes::DataType::{Int32, Int64};
23use datafusion_common::cast::as_int32_array;
24use datafusion_common::{
25    DataFusionError, Result, ScalarValue, exec_err, utils::take_function_args,
26};
27use datafusion_expr::Signature;
28use datafusion_expr::{ColumnarValue, ScalarFunctionArgs, ScalarUDFImpl, Volatility};
29
30/// <https://spark.apache.org/docs/latest/api/sql/index.html#factorial>
31#[derive(Debug, PartialEq, Eq, Hash)]
32pub struct SparkFactorial {
33    signature: Signature,
34    aliases: Vec<String>,
35}
36
37impl Default for SparkFactorial {
38    fn default() -> Self {
39        Self::new()
40    }
41}
42
43impl SparkFactorial {
44    pub fn new() -> Self {
45        Self {
46            signature: Signature::exact(vec![Int32], Volatility::Immutable),
47            aliases: vec![],
48        }
49    }
50}
51
52impl ScalarUDFImpl for SparkFactorial {
53    fn name(&self) -> &str {
54        "factorial"
55    }
56
57    fn signature(&self) -> &Signature {
58        &self.signature
59    }
60
61    fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
62        Ok(Int64)
63    }
64
65    fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> {
66        spark_factorial(&args.args)
67    }
68
69    fn aliases(&self) -> &[String] {
70        &self.aliases
71    }
72}
73
74const FACTORIALS: [i64; 21] = [
75    1,
76    1,
77    2,
78    6,
79    24,
80    120,
81    720,
82    5040,
83    40320,
84    362880,
85    3628800,
86    39916800,
87    479001600,
88    6227020800,
89    87178291200,
90    1307674368000,
91    20922789888000,
92    355687428096000,
93    6402373705728000,
94    121645100408832000,
95    2432902008176640000,
96];
97
98pub fn spark_factorial(args: &[ColumnarValue]) -> Result<ColumnarValue, DataFusionError> {
99    let [arg] = take_function_args("factorial", args)?;
100
101    match arg {
102        ColumnarValue::Scalar(ScalarValue::Int32(value)) => {
103            let result = compute_factorial(*value);
104            Ok(ColumnarValue::Scalar(ScalarValue::Int64(result)))
105        }
106        ColumnarValue::Scalar(other) => {
107            exec_err!("`factorial` got an unexpected scalar type: {}", other)
108        }
109        ColumnarValue::Array(array) => match array.data_type() {
110            Int32 => {
111                let array = as_int32_array(array)?;
112
113                let result: Int64Array = array.iter().map(compute_factorial).collect();
114
115                Ok(ColumnarValue::Array(Arc::new(result)))
116            }
117            other => {
118                exec_err!("`factorial` got an unexpected argument type: {}", other)
119            }
120        },
121    }
122}
123
124#[inline]
125fn compute_factorial(num: Option<i32>) -> Option<i64> {
126    num.filter(|&v| (0..=20).contains(&v))
127        .map(|v| FACTORIALS[v as usize])
128}
129
130#[cfg(test)]
131mod test {
132    use crate::function::math::factorial::spark_factorial;
133    use arrow::array::{Int32Array, Int64Array};
134    use datafusion_common::ScalarValue;
135    use datafusion_common::cast::as_int64_array;
136    use datafusion_expr::ColumnarValue;
137    use std::sync::Arc;
138
139    #[test]
140    fn test_spark_factorial_array() {
141        let input = Int32Array::from(vec![
142            Some(-1),
143            Some(0),
144            Some(1),
145            Some(2),
146            Some(4),
147            Some(20),
148            Some(21),
149            None,
150        ]);
151
152        let args = ColumnarValue::Array(Arc::new(input));
153        let result = spark_factorial(&[args]).unwrap();
154        let result = match result {
155            ColumnarValue::Array(array) => array,
156            _ => panic!("Expected array"),
157        };
158
159        let actual = as_int64_array(&result).unwrap();
160        let expected = Int64Array::from(vec![
161            None,
162            Some(1),
163            Some(1),
164            Some(2),
165            Some(24),
166            Some(2432902008176640000),
167            None,
168            None,
169        ]);
170
171        assert_eq!(actual, &expected);
172    }
173
174    #[test]
175    fn test_spark_factorial_scalar() {
176        let input = ScalarValue::Int32(Some(5));
177
178        let args = ColumnarValue::Scalar(input);
179        let result = spark_factorial(&[args]).unwrap();
180        let result = match result {
181            ColumnarValue::Scalar(ScalarValue::Int64(val)) => val,
182            _ => panic!("Expected scalar"),
183        };
184        let actual = result.unwrap();
185        let expected = 120_i64;
186
187        assert_eq!(actual, expected);
188    }
189}
datafusion_spark/function/math/factorial.rs

datafusion_spark/function/math/
factorial.rs
