datafusion_spark/function/math/
rint.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, ArrayRef, AsArray};
21use arrow::compute::cast;
22use arrow::datatypes::DataType::{
23    Float32, Float64, Int8, Int16, Int32, Int64, UInt8, UInt16, UInt32, UInt64,
24};
25use arrow::datatypes::{DataType, Float32Type, Float64Type};
26use datafusion_common::{Result, assert_eq_or_internal_err, exec_err};
27use datafusion_expr::sort_properties::{ExprProperties, SortProperties};
28use datafusion_expr::{
29    ColumnarValue, ScalarFunctionArgs, ScalarUDFImpl, Signature, Volatility,
30};
31use datafusion_functions::utils::make_scalar_function;
32
33#[derive(Debug, PartialEq, Eq, Hash)]
34pub struct SparkRint {
35    signature: Signature,
36}
37
38impl Default for SparkRint {
39    fn default() -> Self {
40        Self::new()
41    }
42}
43
44impl SparkRint {
45    pub fn new() -> Self {
46        Self {
47            signature: Signature::numeric(1, Volatility::Immutable),
48        }
49    }
50}
51
52impl ScalarUDFImpl for SparkRint {
53    fn name(&self) -> &str {
54        "rint"
55    }
56
57    fn signature(&self) -> &Signature {
58        &self.signature
59    }
60
61    fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
62        Ok(Float64)
63    }
64
65    fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> {
66        make_scalar_function(spark_rint, vec![])(&args.args)
67    }
68
69    fn output_ordering(&self, input: &[ExprProperties]) -> Result<SortProperties> {
70        // round preserves the order of the first argument
71        if input.len() == 1 {
72            let value = &input[0];
73            Ok(value.sort_properties)
74        } else {
75            Ok(SortProperties::default())
76        }
77    }
78}
79
80pub fn spark_rint(args: &[ArrayRef]) -> Result<ArrayRef> {
81    assert_eq_or_internal_err!(args.len(), 1, "`rint` expects exactly one argument");
82
83    let array: &dyn Array = args[0].as_ref();
84    match args[0].data_type() {
85        Int8 | Int16 | Int32 | Int64 | UInt8 | UInt16 | UInt32 | UInt64 => {
86            Ok(cast(array, &Float64)?)
87        }
88        Float64 => {
89            let array = array
90                .as_primitive::<Float64Type>()
91                .unary::<_, Float64Type>(|value: f64| value.round_ties_even());
92            Ok(Arc::new(array))
93        }
94        Float32 => {
95            let array = array
96                .as_primitive::<Float32Type>()
97                .unary::<_, Float64Type>(|value: f32| value.round_ties_even() as f64);
98            Ok(Arc::new(array))
99        }
100        _ => {
101            exec_err!(
102                "rint expects a numeric argument, got {}",
103                args[0].data_type()
104            )
105        }
106    }
107}
108
109#[cfg(test)]
110mod tests {
111    use super::*;
112    use arrow::array::Float64Array;
113
114    #[test]
115    fn test_rint_positive_decimals() {
116        // Test positive decimal rounding
117        let result = spark_rint(&[Arc::new(Float64Array::from(vec![12.3456]))]).unwrap();
118        assert_eq!(result.as_ref(), &Float64Array::from(vec![12.0]));
119
120        // Test rounding to nearest even (banker's rounding)
121        let result = spark_rint(&[Arc::new(Float64Array::from(vec![2.5]))]).unwrap();
122        assert_eq!(result.as_ref(), &Float64Array::from(vec![2.0]));
123
124        let result = spark_rint(&[Arc::new(Float64Array::from(vec![3.5]))]).unwrap();
125        assert_eq!(result.as_ref(), &Float64Array::from(vec![4.0]));
126    }
127
128    #[test]
129    fn test_rint_negative_decimals() {
130        // Test negative decimal rounding
131        let result = spark_rint(&[Arc::new(Float64Array::from(vec![-12.3456]))]).unwrap();
132        assert_eq!(result.as_ref(), &Float64Array::from(vec![-12.0]));
133
134        // Test negative rounding to nearest even
135        let result = spark_rint(&[Arc::new(Float64Array::from(vec![-2.5]))]).unwrap();
136        assert_eq!(result.as_ref(), &Float64Array::from(vec![-2.0]));
137    }
138
139    #[test]
140    fn test_rint_integers() {
141        // Test integer input (should return as float64)
142        let result = spark_rint(&[Arc::new(Float64Array::from(vec![42.0]))]).unwrap();
143        assert_eq!(result.as_ref(), &Float64Array::from(vec![42.0]));
144    }
145
146    #[test]
147    fn test_rint_null() {
148        let result = spark_rint(&[Arc::new(Float64Array::from(vec![None]))]).unwrap();
149        assert_eq!(result.as_ref(), &Float64Array::from(vec![None]));
150    }
151
152    #[test]
153    fn test_rint_zero() {
154        // Test zero
155        let result = spark_rint(&[Arc::new(Float64Array::from(vec![0.0]))]).unwrap();
156        assert_eq!(result.as_ref(), &Float64Array::from(vec![0.0]));
157    }
158}
datafusion_spark/function/math/rint.rs

datafusion_spark/function/math/
rint.rs
