1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155

// 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.

//! Negation (-) expression

use std::any::Any;
use std::sync::Arc;

use arrow::array::ArrayRef;
use arrow::compute::kernels::arithmetic::negate;
use arrow::{
    array::{Float32Array, Float64Array, Int16Array, Int32Array, Int64Array, Int8Array},
    datatypes::{DataType, Schema},
    record_batch::RecordBatch,
};

use crate::physical_expr::down_cast_any_ref;
use crate::PhysicalExpr;
use datafusion_common::{DataFusionError, Result};
use datafusion_expr::{
    type_coercion::{is_null, is_signed_numeric},
    ColumnarValue,
};

/// Invoke a compute kernel on array(s)
macro_rules! compute_op {
    // invoke unary operator
    ($OPERAND:expr, $OP:ident, $DT:ident) => {{
        let operand = $OPERAND
            .as_any()
            .downcast_ref::<$DT>()
            .expect("compute_op failed to downcast array");
        Ok(Arc::new($OP(&operand)?))
    }};
}

/// Negative expression
#[derive(Debug)]
pub struct NegativeExpr {
    /// Input expression
    arg: Arc<dyn PhysicalExpr>,
}

impl NegativeExpr {
    /// Create new not expression
    pub fn new(arg: Arc<dyn PhysicalExpr>) -> Self {
        Self { arg }
    }

    /// Get the input expression
    pub fn arg(&self) -> &Arc<dyn PhysicalExpr> {
        &self.arg
    }
}

impl std::fmt::Display for NegativeExpr {
    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
        write!(f, "(- {})", self.arg)
    }
}

impl PhysicalExpr for NegativeExpr {
    /// Return a reference to Any that can be used for downcasting
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn data_type(&self, input_schema: &Schema) -> Result<DataType> {
        self.arg.data_type(input_schema)
    }

    fn nullable(&self, input_schema: &Schema) -> Result<bool> {
        self.arg.nullable(input_schema)
    }

    fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue> {
        let arg = self.arg.evaluate(batch)?;
        match arg {
            ColumnarValue::Array(array) => {
                let result: Result<ArrayRef> = match array.data_type() {
                    DataType::Int8 => compute_op!(array, negate, Int8Array),
                    DataType::Int16 => compute_op!(array, negate, Int16Array),
                    DataType::Int32 => compute_op!(array, negate, Int32Array),
                    DataType::Int64 => compute_op!(array, negate, Int64Array),
                    DataType::Float32 => compute_op!(array, negate, Float32Array),
                    DataType::Float64 => compute_op!(array, negate, Float64Array),
                    _ => Err(DataFusionError::Internal(format!(
                        "(- '{:?}') can't be evaluated because the expression's type is {:?}, not signed numeric",
                        self,
                        array.data_type(),
                    ))),
                };
                result.map(|a| ColumnarValue::Array(a))
            }
            ColumnarValue::Scalar(scalar) => {
                Ok(ColumnarValue::Scalar((scalar.arithmetic_negate())?))
            }
        }
    }

    fn children(&self) -> Vec<Arc<dyn PhysicalExpr>> {
        vec![self.arg.clone()]
    }

    fn with_new_children(
        self: Arc<Self>,
        children: Vec<Arc<dyn PhysicalExpr>>,
    ) -> Result<Arc<dyn PhysicalExpr>> {
        Ok(Arc::new(NegativeExpr::new(children[0].clone())))
    }
}

impl PartialEq<dyn Any> for NegativeExpr {
    fn eq(&self, other: &dyn Any) -> bool {
        down_cast_any_ref(other)
            .downcast_ref::<Self>()
            .map(|x| self.arg.eq(&x.arg))
            .unwrap_or(false)
    }
}

/// Creates a unary expression NEGATIVE
///
/// # Errors
///
/// This function errors when the argument's type is not signed numeric
pub fn negative(
    arg: Arc<dyn PhysicalExpr>,
    input_schema: &Schema,
) -> Result<Arc<dyn PhysicalExpr>> {
    let data_type = arg.data_type(input_schema)?;
    if is_null(&data_type) {
        Ok(arg)
    } else if !is_signed_numeric(&data_type) {
        Err(DataFusionError::Internal(
            format!("Can't create negative physical expr for (- '{arg:?}'), the type of child expr is {data_type}, not signed numeric"),
        ))
    } else {
        Ok(Arc::new(NegativeExpr::new(arg)))
    }
}