/Users/andrewlamb/Software/datafusion/datafusion/physical-expr/src/expressions/binary.rs
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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 | | |
18 | | mod kernels; |
19 | | |
20 | | use std::hash::{Hash, Hasher}; |
21 | | use std::{any::Any, sync::Arc}; |
22 | | |
23 | | use crate::intervals::cp_solver::{propagate_arithmetic, propagate_comparison}; |
24 | | use crate::physical_expr::down_cast_any_ref; |
25 | | use crate::PhysicalExpr; |
26 | | |
27 | | use arrow::array::*; |
28 | | use arrow::compute::kernels::boolean::{and_kleene, not, or_kleene}; |
29 | | use arrow::compute::kernels::cmp::*; |
30 | | use arrow::compute::kernels::comparison::{regexp_is_match, regexp_is_match_scalar}; |
31 | | use arrow::compute::kernels::concat_elements::concat_elements_utf8; |
32 | | use arrow::compute::{cast, ilike, like, nilike, nlike}; |
33 | | use arrow::datatypes::*; |
34 | | use arrow_schema::ArrowError; |
35 | | use datafusion_common::cast::as_boolean_array; |
36 | | use datafusion_common::{internal_err, Result, ScalarValue}; |
37 | | use datafusion_expr::interval_arithmetic::{apply_operator, Interval}; |
38 | | use datafusion_expr::sort_properties::ExprProperties; |
39 | | use datafusion_expr::type_coercion::binary::get_result_type; |
40 | | use datafusion_expr::{ColumnarValue, Operator}; |
41 | | use datafusion_physical_expr_common::datum::{apply, apply_cmp, apply_cmp_for_nested}; |
42 | | |
43 | | use crate::expressions::binary::kernels::concat_elements_utf8view; |
44 | | use kernels::{ |
45 | | bitwise_and_dyn, bitwise_and_dyn_scalar, bitwise_or_dyn, bitwise_or_dyn_scalar, |
46 | | bitwise_shift_left_dyn, bitwise_shift_left_dyn_scalar, bitwise_shift_right_dyn, |
47 | | bitwise_shift_right_dyn_scalar, bitwise_xor_dyn, bitwise_xor_dyn_scalar, |
48 | | }; |
49 | | |
50 | | /// Binary expression |
51 | | #[derive(Debug, Hash, Clone)] |
52 | | pub struct BinaryExpr { |
53 | | left: Arc<dyn PhysicalExpr>, |
54 | | op: Operator, |
55 | | right: Arc<dyn PhysicalExpr>, |
56 | | /// Specifies whether an error is returned on overflow or not |
57 | | fail_on_overflow: bool, |
58 | | } |
59 | | |
60 | | impl BinaryExpr { |
61 | | /// Create new binary expression |
62 | 2.70k | pub fn new( |
63 | 2.70k | left: Arc<dyn PhysicalExpr>, |
64 | 2.70k | op: Operator, |
65 | 2.70k | right: Arc<dyn PhysicalExpr>, |
66 | 2.70k | ) -> Self { |
67 | 2.70k | Self { |
68 | 2.70k | left, |
69 | 2.70k | op, |
70 | 2.70k | right, |
71 | 2.70k | fail_on_overflow: false, |
72 | 2.70k | } |
73 | 2.70k | } |
74 | | |
75 | | /// Create new binary expression with explicit fail_on_overflow value |
76 | 66 | pub fn with_fail_on_overflow(self, fail_on_overflow: bool) -> Self { |
77 | 66 | Self { |
78 | 66 | left: self.left, |
79 | 66 | op: self.op, |
80 | 66 | right: self.right, |
81 | 66 | fail_on_overflow, |
82 | 66 | } |
83 | 66 | } |
84 | | |
85 | | /// Get the left side of the binary expression |
86 | 98 | pub fn left(&self) -> &Arc<dyn PhysicalExpr> { |
87 | 98 | &self.left |
88 | 98 | } |
89 | | |
90 | | /// Get the right side of the binary expression |
91 | 91 | pub fn right(&self) -> &Arc<dyn PhysicalExpr> { |
92 | 91 | &self.right |
93 | 91 | } |
94 | | |
95 | | /// Get the operator for this binary expression |
96 | 183 | pub fn op(&self) -> &Operator { |
97 | 183 | &self.op |
98 | 183 | } |
99 | | } |
100 | | |
101 | | impl std::fmt::Display for BinaryExpr { |
102 | 0 | fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { |
103 | | // Put parentheses around child binary expressions so that we can see the difference |
104 | | // between `(a OR b) AND c` and `a OR (b AND c)`. We only insert parentheses when needed, |
105 | | // based on operator precedence. For example, `(a AND b) OR c` and `a AND b OR c` are |
106 | | // equivalent and the parentheses are not necessary. |
107 | | |
108 | 0 | fn write_child( |
109 | 0 | f: &mut std::fmt::Formatter, |
110 | 0 | expr: &dyn PhysicalExpr, |
111 | 0 | precedence: u8, |
112 | 0 | ) -> std::fmt::Result { |
113 | 0 | if let Some(child) = expr.as_any().downcast_ref::<BinaryExpr>() { |
114 | 0 | let p = child.op.precedence(); |
115 | 0 | if p == 0 || p < precedence { |
116 | 0 | write!(f, "({child})")?; |
117 | | } else { |
118 | 0 | write!(f, "{child}")?; |
119 | | } |
120 | | } else { |
121 | 0 | write!(f, "{expr}")?; |
122 | | } |
123 | | |
124 | 0 | Ok(()) |
125 | 0 | } |
126 | | |
127 | 0 | let precedence = self.op.precedence(); |
128 | 0 | write_child(f, self.left.as_ref(), precedence)?; |
129 | 0 | write!(f, " {} ", self.op)?; |
130 | 0 | write_child(f, self.right.as_ref(), precedence) |
131 | 0 | } |
132 | | } |
133 | | |
134 | | /// Invoke a boolean kernel on a pair of arrays |
135 | | #[inline] |
136 | 22.1k | fn boolean_op( |
137 | 22.1k | left: &dyn Array, |
138 | 22.1k | right: &dyn Array, |
139 | 22.1k | op: impl FnOnce(&BooleanArray, &BooleanArray) -> Result<BooleanArray, ArrowError>, |
140 | 22.1k | ) -> Result<Arc<(dyn Array + 'static)>, ArrowError> { |
141 | 22.1k | let ll = as_boolean_array(left).expect("boolean_op failed to downcast left array"); |
142 | 22.1k | let rr = as_boolean_array(right).expect("boolean_op failed to downcast right array"); |
143 | 22.1k | op(ll, rr).map(|t| Arc::new(t) as _) |
144 | 22.1k | } |
145 | | |
146 | | macro_rules! binary_string_array_flag_op { |
147 | | ($LEFT:expr, $RIGHT:expr, $OP:ident, $NOT:expr, $FLAG:expr) => {{ |
148 | | match $LEFT.data_type() { |
149 | | DataType::Utf8View | DataType::Utf8 => { |
150 | | compute_utf8_flag_op!($LEFT, $RIGHT, $OP, StringArray, $NOT, $FLAG) |
151 | | }, |
152 | | DataType::LargeUtf8 => { |
153 | | compute_utf8_flag_op!($LEFT, $RIGHT, $OP, LargeStringArray, $NOT, $FLAG) |
154 | | }, |
155 | | other => internal_err!( |
156 | | "Data type {:?} not supported for binary_string_array_flag_op operation '{}' on string array", |
157 | | other, stringify!($OP) |
158 | | ), |
159 | | } |
160 | | }}; |
161 | | } |
162 | | |
163 | | /// Invoke a compute kernel on a pair of binary data arrays with flags |
164 | | macro_rules! compute_utf8_flag_op { |
165 | | ($LEFT:expr, $RIGHT:expr, $OP:ident, $ARRAYTYPE:ident, $NOT:expr, $FLAG:expr) => {{ |
166 | | let ll = $LEFT |
167 | | .as_any() |
168 | | .downcast_ref::<$ARRAYTYPE>() |
169 | | .expect("compute_utf8_flag_op failed to downcast array"); |
170 | | let rr = $RIGHT |
171 | | .as_any() |
172 | | .downcast_ref::<$ARRAYTYPE>() |
173 | | .expect("compute_utf8_flag_op failed to downcast array"); |
174 | | |
175 | | let flag = if $FLAG { |
176 | | Some($ARRAYTYPE::from(vec!["i"; ll.len()])) |
177 | | } else { |
178 | | None |
179 | | }; |
180 | | let mut array = $OP(ll, rr, flag.as_ref())?; |
181 | | if $NOT { |
182 | | array = not(&array).unwrap(); |
183 | | } |
184 | | Ok(Arc::new(array)) |
185 | | }}; |
186 | | } |
187 | | |
188 | | macro_rules! binary_string_array_flag_op_scalar { |
189 | | ($LEFT:expr, $RIGHT:expr, $OP:ident, $NOT:expr, $FLAG:expr) => {{ |
190 | | let result: Result<Arc<dyn Array>> = match $LEFT.data_type() { |
191 | | DataType::Utf8View | DataType::Utf8 => { |
192 | | compute_utf8_flag_op_scalar!($LEFT, $RIGHT, $OP, StringArray, $NOT, $FLAG) |
193 | | }, |
194 | | DataType::LargeUtf8 => { |
195 | | compute_utf8_flag_op_scalar!($LEFT, $RIGHT, $OP, LargeStringArray, $NOT, $FLAG) |
196 | | }, |
197 | | other => internal_err!( |
198 | | "Data type {:?} not supported for binary_string_array_flag_op_scalar operation '{}' on string array", |
199 | | other, stringify!($OP) |
200 | | ), |
201 | | }; |
202 | | Some(result) |
203 | | }}; |
204 | | } |
205 | | |
206 | | /// Invoke a compute kernel on a data array and a scalar value with flag |
207 | | macro_rules! compute_utf8_flag_op_scalar { |
208 | | ($LEFT:expr, $RIGHT:expr, $OP:ident, $ARRAYTYPE:ident, $NOT:expr, $FLAG:expr) => {{ |
209 | | let ll = $LEFT |
210 | | .as_any() |
211 | | .downcast_ref::<$ARRAYTYPE>() |
212 | | .expect("compute_utf8_flag_op_scalar failed to downcast array"); |
213 | | |
214 | | if let ScalarValue::Utf8(Some(string_value)) | ScalarValue::LargeUtf8(Some(string_value)) = $RIGHT { |
215 | | let flag = $FLAG.then_some("i"); |
216 | | let mut array = |
217 | | paste::expr! {[<$OP _scalar>]}(ll, &string_value, flag)?; |
218 | | if $NOT { |
219 | | array = not(&array).unwrap(); |
220 | | } |
221 | | Ok(Arc::new(array)) |
222 | | } else { |
223 | | internal_err!( |
224 | | "compute_utf8_flag_op_scalar failed to cast literal value {} for operation '{}'", |
225 | | $RIGHT, stringify!($OP) |
226 | | ) |
227 | | } |
228 | | }}; |
229 | | } |
230 | | |
231 | | impl PhysicalExpr for BinaryExpr { |
232 | | /// Return a reference to Any that can be used for downcasting |
233 | 48.4k | fn as_any(&self) -> &dyn Any { |
234 | 48.4k | self |
235 | 48.4k | } |
236 | | |
237 | 147k | fn data_type(&self, input_schema: &Schema) -> Result<DataType> { |
238 | 147k | get_result_type( |
239 | 147k | &self.left.data_type(input_schema)?0 , |
240 | 147k | &self.op, |
241 | 147k | &self.right.data_type(input_schema)?0 , |
242 | | ) |
243 | 147k | } |
244 | | |
245 | 0 | fn nullable(&self, input_schema: &Schema) -> Result<bool> { |
246 | 0 | Ok(self.left.nullable(input_schema)? || self.right.nullable(input_schema)?) |
247 | 0 | } |
248 | | |
249 | 132k | fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue> { |
250 | | use arrow::compute::kernels::numeric::*; |
251 | | |
252 | 132k | let lhs = self.left.evaluate(batch)?0 ; |
253 | 132k | let rhs = self.right.evaluate(batch)?0 ; |
254 | 132k | let left_data_type = lhs.data_type(); |
255 | 132k | let right_data_type = rhs.data_type(); |
256 | 132k | |
257 | 132k | let schema = batch.schema(); |
258 | 132k | let input_schema = schema.as_ref(); |
259 | 132k | |
260 | 132k | if left_data_type.is_nested() { |
261 | 0 | if right_data_type != left_data_type { |
262 | 0 | return internal_err!("type mismatch"); |
263 | 0 | } |
264 | 0 | return apply_cmp_for_nested(self.op, &lhs, &rhs); |
265 | 132k | } |
266 | | |
267 | 18.9k | match self.op { |
268 | 0 | Operator::Plus if self.fail_on_overflow => return apply(&lhs, &rhs, add), |
269 | 18.9k | Operator::Plus => return apply(&lhs, &rhs, add_wrapping), |
270 | 0 | Operator::Minus if self.fail_on_overflow => return apply(&lhs, &rhs, sub), |
271 | 22.5k | Operator::Minus => return apply(&lhs, &rhs, sub_wrapping), |
272 | 0 | Operator::Multiply if self.fail_on_overflow => return apply(&lhs, &rhs, mul), |
273 | 0 | Operator::Multiply => return apply(&lhs, &rhs, mul_wrapping), |
274 | 0 | Operator::Divide => return apply(&lhs, &rhs, div), |
275 | 24.2k | Operator::Modulo => return apply(&lhs, &rhs, rem), |
276 | 0 | Operator::Eq => return apply_cmp(&lhs, &rhs, eq), |
277 | 24.3k | Operator::NotEq => return apply_cmp(&lhs, &rhs, neq), |
278 | 8.76k | Operator::Lt => return apply_cmp(&lhs, &rhs, lt), |
279 | 8.82k | Operator::Gt => return apply_cmp(&lhs, &rhs, gt), |
280 | 1.19k | Operator::LtEq => return apply_cmp(&lhs, &rhs, lt_eq), |
281 | 1.19k | Operator::GtEq => return apply_cmp(&lhs, &rhs, gt_eq), |
282 | 0 | Operator::IsDistinctFrom => return apply_cmp(&lhs, &rhs, distinct), |
283 | 0 | Operator::IsNotDistinctFrom => return apply_cmp(&lhs, &rhs, not_distinct), |
284 | 0 | Operator::LikeMatch => return apply_cmp(&lhs, &rhs, like), |
285 | 0 | Operator::ILikeMatch => return apply_cmp(&lhs, &rhs, ilike), |
286 | 0 | Operator::NotLikeMatch => return apply_cmp(&lhs, &rhs, nlike), |
287 | 0 | Operator::NotILikeMatch => return apply_cmp(&lhs, &rhs, nilike), |
288 | 22.1k | _ => {} |
289 | | } |
290 | | |
291 | 22.1k | let result_type = self.data_type(input_schema)?0 ; |
292 | | |
293 | | // Attempt to use special kernels if one input is scalar and the other is an array |
294 | 22.1k | let scalar_result = match (&lhs, &rhs) { |
295 | 0 | (ColumnarValue::Array(array), ColumnarValue::Scalar(scalar)) => { |
296 | 0 | // if left is array and right is literal(not NULL) - use scalar operations |
297 | 0 | if scalar.is_null() { |
298 | 0 | None |
299 | | } else { |
300 | 0 | self.evaluate_array_scalar(array, scalar.clone())?.map(|r| { |
301 | 0 | r.and_then(|a| to_result_type_array(&self.op, a, &result_type)) |
302 | 0 | }) |
303 | | } |
304 | | } |
305 | 22.1k | (_, _) => None, // default to array implementation |
306 | | }; |
307 | | |
308 | 22.1k | if let Some(result0 ) = scalar_result { |
309 | 0 | return result.map(ColumnarValue::Array); |
310 | 22.1k | } |
311 | | |
312 | | // if both arrays or both literals - extract arrays and continue execution |
313 | 22.1k | let (left, right) = ( |
314 | 22.1k | lhs.into_array(batch.num_rows())?0 , |
315 | 22.1k | rhs.into_array(batch.num_rows())?0 , |
316 | | ); |
317 | 22.1k | self.evaluate_with_resolved_args(left, &left_data_type, right, &right_data_type) |
318 | 22.1k | .map(ColumnarValue::Array) |
319 | 132k | } |
320 | | |
321 | 23.6k | fn children(&self) -> Vec<&Arc<dyn PhysicalExpr>> { |
322 | 23.6k | vec![&self.left, &self.right] |
323 | 23.6k | } |
324 | | |
325 | 66 | fn with_new_children( |
326 | 66 | self: Arc<Self>, |
327 | 66 | children: Vec<Arc<dyn PhysicalExpr>>, |
328 | 66 | ) -> Result<Arc<dyn PhysicalExpr>> { |
329 | 66 | Ok(Arc::new( |
330 | 66 | BinaryExpr::new(Arc::clone(&children[0]), self.op, Arc::clone(&children[1])) |
331 | 66 | .with_fail_on_overflow(self.fail_on_overflow), |
332 | 66 | )) |
333 | 66 | } |
334 | | |
335 | 39.4k | fn evaluate_bounds(&self, children: &[&Interval]) -> Result<Interval> { |
336 | 39.4k | // Get children intervals: |
337 | 39.4k | let left_interval = children[0]; |
338 | 39.4k | let right_interval = children[1]; |
339 | 39.4k | // Calculate current node's interval: |
340 | 39.4k | apply_operator(&self.op, left_interval, right_interval) |
341 | 39.4k | } |
342 | | |
343 | 39.3k | fn propagate_constraints( |
344 | 39.3k | &self, |
345 | 39.3k | interval: &Interval, |
346 | 39.3k | children: &[&Interval], |
347 | 39.3k | ) -> Result<Option<Vec<Interval>>> { |
348 | 39.3k | // Get children intervals. |
349 | 39.3k | let left_interval = children[0]; |
350 | 39.3k | let right_interval = children[1]; |
351 | 39.3k | |
352 | 39.3k | if self.op.eq(&Operator::And) { |
353 | 5.73k | if interval.eq(&Interval::CERTAINLY_TRUE) { |
354 | | // A certainly true logical conjunction can only derive from possibly |
355 | | // true operands. Otherwise, we prove infeasability. |
356 | 5.73k | Ok((!left_interval.eq(&Interval::CERTAINLY_FALSE) |
357 | 5.73k | && !right_interval.eq(&Interval::CERTAINLY_FALSE)) |
358 | 5.73k | .then(|| vec![Interval::CERTAINLY_TRUE, Interval::CERTAINLY_TRUE])) |
359 | 0 | } else if interval.eq(&Interval::CERTAINLY_FALSE) { |
360 | | // If the logical conjunction is certainly false, one of the |
361 | | // operands must be false. However, it's not always possible to |
362 | | // determine which operand is false, leading to different scenarios. |
363 | | |
364 | | // If one operand is certainly true and the other one is uncertain, |
365 | | // then the latter must be certainly false. |
366 | 0 | if left_interval.eq(&Interval::CERTAINLY_TRUE) |
367 | 0 | && right_interval.eq(&Interval::UNCERTAIN) |
368 | | { |
369 | 0 | Ok(Some(vec![ |
370 | 0 | Interval::CERTAINLY_TRUE, |
371 | 0 | Interval::CERTAINLY_FALSE, |
372 | 0 | ])) |
373 | 0 | } else if right_interval.eq(&Interval::CERTAINLY_TRUE) |
374 | 0 | && left_interval.eq(&Interval::UNCERTAIN) |
375 | | { |
376 | 0 | Ok(Some(vec![ |
377 | 0 | Interval::CERTAINLY_FALSE, |
378 | 0 | Interval::CERTAINLY_TRUE, |
379 | 0 | ])) |
380 | | } |
381 | | // If both children are uncertain, or if one is certainly false, |
382 | | // we cannot conclusively refine their intervals. In this case, |
383 | | // propagation does not result in any interval changes. |
384 | | else { |
385 | 0 | Ok(Some(vec![])) |
386 | | } |
387 | | } else { |
388 | | // An uncertain logical conjunction result can not shrink the |
389 | | // end-points of its children. |
390 | 0 | Ok(Some(vec![])) |
391 | | } |
392 | 33.6k | } else if self.op.eq(&Operator::Or) { |
393 | 0 | if interval.eq(&Interval::CERTAINLY_FALSE) { |
394 | | // A certainly false logical conjunction can only derive from certainly |
395 | | // false operands. Otherwise, we prove infeasability. |
396 | 0 | Ok((!left_interval.eq(&Interval::CERTAINLY_TRUE) |
397 | 0 | && !right_interval.eq(&Interval::CERTAINLY_TRUE)) |
398 | 0 | .then(|| vec![Interval::CERTAINLY_FALSE, Interval::CERTAINLY_FALSE])) |
399 | 0 | } else if interval.eq(&Interval::CERTAINLY_TRUE) { |
400 | | // If the logical disjunction is certainly true, one of the |
401 | | // operands must be true. However, it's not always possible to |
402 | | // determine which operand is true, leading to different scenarios. |
403 | | |
404 | | // If one operand is certainly false and the other one is uncertain, |
405 | | // then the latter must be certainly true. |
406 | 0 | if left_interval.eq(&Interval::CERTAINLY_FALSE) |
407 | 0 | && right_interval.eq(&Interval::UNCERTAIN) |
408 | | { |
409 | 0 | Ok(Some(vec![ |
410 | 0 | Interval::CERTAINLY_FALSE, |
411 | 0 | Interval::CERTAINLY_TRUE, |
412 | 0 | ])) |
413 | 0 | } else if right_interval.eq(&Interval::CERTAINLY_FALSE) |
414 | 0 | && left_interval.eq(&Interval::UNCERTAIN) |
415 | | { |
416 | 0 | Ok(Some(vec![ |
417 | 0 | Interval::CERTAINLY_TRUE, |
418 | 0 | Interval::CERTAINLY_FALSE, |
419 | 0 | ])) |
420 | | } |
421 | | // If both children are uncertain, or if one is certainly true, |
422 | | // we cannot conclusively refine their intervals. In this case, |
423 | | // propagation does not result in any interval changes. |
424 | | else { |
425 | 0 | Ok(Some(vec![])) |
426 | | } |
427 | | } else { |
428 | | // An uncertain logical disjunction result can not shrink the |
429 | | // end-points of its children. |
430 | 0 | Ok(Some(vec![])) |
431 | | } |
432 | 33.6k | } else if self.op.is_comparison_operator() { |
433 | | Ok( |
434 | 11.4k | propagate_comparison(&self.op, interval, left_interval, right_interval)?0 |
435 | 11.4k | .map(|(left, right)| vec![left, right]), |
436 | 11.4k | ) |
437 | | } else { |
438 | | Ok( |
439 | 22.1k | propagate_arithmetic(&self.op, interval, left_interval, right_interval)?0 |
440 | 22.1k | .map(|(left, right)| vec![left, right]), |
441 | 22.1k | ) |
442 | | } |
443 | 39.3k | } |
444 | | |
445 | 0 | fn dyn_hash(&self, state: &mut dyn Hasher) { |
446 | 0 | let mut s = state; |
447 | 0 | self.hash(&mut s); |
448 | 0 | } |
449 | | |
450 | | /// For each operator, [`BinaryExpr`] has distinct rules. |
451 | | /// TODO: There may be rules specific to some data types and expression ranges. |
452 | 0 | fn get_properties(&self, children: &[ExprProperties]) -> Result<ExprProperties> { |
453 | 0 | let (l_order, l_range) = (children[0].sort_properties, &children[0].range); |
454 | 0 | let (r_order, r_range) = (children[1].sort_properties, &children[1].range); |
455 | 0 | match self.op() { |
456 | | Operator::Plus => Ok(ExprProperties { |
457 | 0 | sort_properties: l_order.add(&r_order), |
458 | 0 | range: l_range.add(r_range)?, |
459 | | }), |
460 | | Operator::Minus => Ok(ExprProperties { |
461 | 0 | sort_properties: l_order.sub(&r_order), |
462 | 0 | range: l_range.sub(r_range)?, |
463 | | }), |
464 | | Operator::Gt => Ok(ExprProperties { |
465 | 0 | sort_properties: l_order.gt_or_gteq(&r_order), |
466 | 0 | range: l_range.gt(r_range)?, |
467 | | }), |
468 | | Operator::GtEq => Ok(ExprProperties { |
469 | 0 | sort_properties: l_order.gt_or_gteq(&r_order), |
470 | 0 | range: l_range.gt_eq(r_range)?, |
471 | | }), |
472 | | Operator::Lt => Ok(ExprProperties { |
473 | 0 | sort_properties: r_order.gt_or_gteq(&l_order), |
474 | 0 | range: l_range.lt(r_range)?, |
475 | | }), |
476 | | Operator::LtEq => Ok(ExprProperties { |
477 | 0 | sort_properties: r_order.gt_or_gteq(&l_order), |
478 | 0 | range: l_range.lt_eq(r_range)?, |
479 | | }), |
480 | | Operator::And => Ok(ExprProperties { |
481 | 0 | sort_properties: r_order.and_or(&l_order), |
482 | 0 | range: l_range.and(r_range)?, |
483 | | }), |
484 | | Operator::Or => Ok(ExprProperties { |
485 | 0 | sort_properties: r_order.and_or(&l_order), |
486 | 0 | range: l_range.or(r_range)?, |
487 | | }), |
488 | 0 | _ => Ok(ExprProperties::new_unknown()), |
489 | | } |
490 | 0 | } |
491 | | } |
492 | | |
493 | | impl PartialEq<dyn Any> for BinaryExpr { |
494 | 99.2k | fn eq(&self, other: &dyn Any) -> bool { |
495 | 99.2k | down_cast_any_ref(other) |
496 | 99.2k | .downcast_ref::<Self>() |
497 | 99.2k | .map(|x| { |
498 | 26.8k | self.left.eq(&x.left) |
499 | 3.60k | && self.op == x.op |
500 | 2.73k | && self.right.eq(&x.right) |
501 | 348 | && self.fail_on_overflow.eq(&x.fail_on_overflow) |
502 | 99.2k | }26.8k ) |
503 | 99.2k | .unwrap_or(false) |
504 | 99.2k | } |
505 | | } |
506 | | |
507 | | /// Casts dictionary array to result type for binary numerical operators. Such operators |
508 | | /// between array and scalar produce a dictionary array other than primitive array of the |
509 | | /// same operators between array and array. This leads to inconsistent result types causing |
510 | | /// errors in the following query execution. For such operators between array and scalar, |
511 | | /// we cast the dictionary array to primitive array. |
512 | 0 | fn to_result_type_array( |
513 | 0 | op: &Operator, |
514 | 0 | array: ArrayRef, |
515 | 0 | result_type: &DataType, |
516 | 0 | ) -> Result<ArrayRef> { |
517 | 0 | if array.data_type() == result_type { |
518 | 0 | Ok(array) |
519 | 0 | } else if op.is_numerical_operators() { |
520 | 0 | match array.data_type() { |
521 | 0 | DataType::Dictionary(_, value_type) => { |
522 | 0 | if value_type.as_ref() == result_type { |
523 | 0 | Ok(cast(&array, result_type)?) |
524 | | } else { |
525 | 0 | internal_err!( |
526 | 0 | "Incompatible Dictionary value type {value_type:?} with result type {result_type:?} of Binary operator {op:?}" |
527 | 0 | ) |
528 | | } |
529 | | } |
530 | 0 | _ => Ok(array), |
531 | | } |
532 | | } else { |
533 | 0 | Ok(array) |
534 | | } |
535 | 0 | } |
536 | | |
537 | | impl BinaryExpr { |
538 | | /// Evaluate the expression of the left input is an array and |
539 | | /// right is literal - use scalar operations |
540 | 0 | fn evaluate_array_scalar( |
541 | 0 | &self, |
542 | 0 | array: &dyn Array, |
543 | 0 | scalar: ScalarValue, |
544 | 0 | ) -> Result<Option<Result<ArrayRef>>> { |
545 | | use Operator::*; |
546 | 0 | let scalar_result = match &self.op { |
547 | 0 | RegexMatch => binary_string_array_flag_op_scalar!( |
548 | 0 | array, |
549 | 0 | scalar, |
550 | | regexp_is_match, |
551 | 0 | false, |
552 | | false |
553 | | ), |
554 | 0 | RegexIMatch => binary_string_array_flag_op_scalar!( |
555 | 0 | array, |
556 | 0 | scalar, |
557 | | regexp_is_match, |
558 | 0 | false, |
559 | | true |
560 | | ), |
561 | 0 | RegexNotMatch => binary_string_array_flag_op_scalar!( |
562 | 0 | array, |
563 | 0 | scalar, |
564 | | regexp_is_match, |
565 | 0 | true, |
566 | | false |
567 | | ), |
568 | 0 | RegexNotIMatch => binary_string_array_flag_op_scalar!( |
569 | 0 | array, |
570 | 0 | scalar, |
571 | | regexp_is_match, |
572 | 0 | true, |
573 | | true |
574 | | ), |
575 | 0 | BitwiseAnd => bitwise_and_dyn_scalar(array, scalar), |
576 | 0 | BitwiseOr => bitwise_or_dyn_scalar(array, scalar), |
577 | 0 | BitwiseXor => bitwise_xor_dyn_scalar(array, scalar), |
578 | 0 | BitwiseShiftRight => bitwise_shift_right_dyn_scalar(array, scalar), |
579 | 0 | BitwiseShiftLeft => bitwise_shift_left_dyn_scalar(array, scalar), |
580 | | // if scalar operation is not supported - fallback to array implementation |
581 | 0 | _ => None, |
582 | | }; |
583 | | |
584 | 0 | Ok(scalar_result) |
585 | 0 | } |
586 | | |
587 | 22.1k | fn evaluate_with_resolved_args( |
588 | 22.1k | &self, |
589 | 22.1k | left: Arc<dyn Array>, |
590 | 22.1k | left_data_type: &DataType, |
591 | 22.1k | right: Arc<dyn Array>, |
592 | 22.1k | right_data_type: &DataType, |
593 | 22.1k | ) -> Result<ArrayRef> { |
594 | | use Operator::*; |
595 | 22.1k | match &self.op { |
596 | | IsDistinctFrom | IsNotDistinctFrom | Lt | LtEq | Gt | GtEq | Eq | NotEq |
597 | | | Plus | Minus | Multiply | Divide | Modulo | LikeMatch | ILikeMatch |
598 | 0 | | NotLikeMatch | NotILikeMatch => unreachable!(), |
599 | | And => { |
600 | 22.1k | if left_data_type == &DataType::Boolean { |
601 | 22.1k | Ok(boolean_op(&left, &right, and_kleene)?0 ) |
602 | | } else { |
603 | 0 | internal_err!( |
604 | 0 | "Cannot evaluate binary expression {:?} with types {:?} and {:?}", |
605 | 0 | self.op, |
606 | 0 | left.data_type(), |
607 | 0 | right.data_type() |
608 | 0 | ) |
609 | | } |
610 | | } |
611 | | Or => { |
612 | 0 | if left_data_type == &DataType::Boolean { |
613 | 0 | Ok(boolean_op(&left, &right, or_kleene)?) |
614 | | } else { |
615 | 0 | internal_err!( |
616 | 0 | "Cannot evaluate binary expression {:?} with types {:?} and {:?}", |
617 | 0 | self.op, |
618 | 0 | left_data_type, |
619 | 0 | right_data_type |
620 | 0 | ) |
621 | | } |
622 | | } |
623 | | RegexMatch => { |
624 | 0 | binary_string_array_flag_op!(left, right, regexp_is_match, false, false) |
625 | | } |
626 | | RegexIMatch => { |
627 | 0 | binary_string_array_flag_op!(left, right, regexp_is_match, false, true) |
628 | | } |
629 | | RegexNotMatch => { |
630 | 0 | binary_string_array_flag_op!(left, right, regexp_is_match, true, false) |
631 | | } |
632 | | RegexNotIMatch => { |
633 | 0 | binary_string_array_flag_op!(left, right, regexp_is_match, true, true) |
634 | | } |
635 | 0 | BitwiseAnd => bitwise_and_dyn(left, right), |
636 | 0 | BitwiseOr => bitwise_or_dyn(left, right), |
637 | 0 | BitwiseXor => bitwise_xor_dyn(left, right), |
638 | 0 | BitwiseShiftRight => bitwise_shift_right_dyn(left, right), |
639 | 0 | BitwiseShiftLeft => bitwise_shift_left_dyn(left, right), |
640 | 0 | StringConcat => concat_elements(left, right), |
641 | | AtArrow | ArrowAt => { |
642 | 0 | unreachable!("ArrowAt and AtArrow should be rewritten to function") |
643 | | } |
644 | | } |
645 | 22.1k | } |
646 | | } |
647 | | |
648 | 0 | fn concat_elements(left: Arc<dyn Array>, right: Arc<dyn Array>) -> Result<ArrayRef> { |
649 | 0 | Ok(match left.data_type() { |
650 | 0 | DataType::Utf8 => Arc::new(concat_elements_utf8( |
651 | 0 | left.as_string::<i32>(), |
652 | 0 | right.as_string::<i32>(), |
653 | 0 | )?), |
654 | 0 | DataType::LargeUtf8 => Arc::new(concat_elements_utf8( |
655 | 0 | left.as_string::<i64>(), |
656 | 0 | right.as_string::<i64>(), |
657 | 0 | )?), |
658 | 0 | DataType::Utf8View => Arc::new(concat_elements_utf8view( |
659 | 0 | left.as_string_view(), |
660 | 0 | right.as_string_view(), |
661 | 0 | )?), |
662 | 0 | other => { |
663 | 0 | return internal_err!( |
664 | 0 | "Data type {other:?} not supported for binary operation 'concat_elements' on string arrays" |
665 | 0 | ); |
666 | | } |
667 | | }) |
668 | 0 | } |
669 | | |
670 | | /// Create a binary expression whose arguments are correctly coerced. |
671 | | /// This function errors if it is not possible to coerce the arguments |
672 | | /// to computational types supported by the operator. |
673 | 458 | pub fn binary( |
674 | 458 | lhs: Arc<dyn PhysicalExpr>, |
675 | 458 | op: Operator, |
676 | 458 | rhs: Arc<dyn PhysicalExpr>, |
677 | 458 | _input_schema: &Schema, |
678 | 458 | ) -> Result<Arc<dyn PhysicalExpr>> { |
679 | 458 | Ok(Arc::new(BinaryExpr::new(lhs, op, rhs))) |
680 | 458 | } |
681 | | |
682 | | /// Create a similar to expression |
683 | 0 | pub fn similar_to( |
684 | 0 | negated: bool, |
685 | 0 | case_insensitive: bool, |
686 | 0 | expr: Arc<dyn PhysicalExpr>, |
687 | 0 | pattern: Arc<dyn PhysicalExpr>, |
688 | 0 | ) -> Result<Arc<dyn PhysicalExpr>> { |
689 | 0 | let binary_op = match (negated, case_insensitive) { |
690 | 0 | (false, false) => Operator::RegexMatch, |
691 | 0 | (false, true) => Operator::RegexIMatch, |
692 | 0 | (true, false) => Operator::RegexNotMatch, |
693 | 0 | (true, true) => Operator::RegexNotIMatch, |
694 | | }; |
695 | 0 | Ok(Arc::new(BinaryExpr::new(expr, binary_op, pattern))) |
696 | 0 | } |
697 | | |
698 | | #[cfg(test)] |
699 | | mod tests { |
700 | | use super::*; |
701 | | use crate::expressions::{col, lit, try_cast, Column, Literal}; |
702 | | use datafusion_common::plan_datafusion_err; |
703 | | use datafusion_expr::type_coercion::binary::get_input_types; |
704 | | |
705 | | /// Performs a binary operation, applying any type coercion necessary |
706 | | fn binary_op( |
707 | | left: Arc<dyn PhysicalExpr>, |
708 | | op: Operator, |
709 | | right: Arc<dyn PhysicalExpr>, |
710 | | schema: &Schema, |
711 | | ) -> Result<Arc<dyn PhysicalExpr>> { |
712 | | let left_type = left.data_type(schema)?; |
713 | | let right_type = right.data_type(schema)?; |
714 | | let (lhs, rhs) = get_input_types(&left_type, &op, &right_type)?; |
715 | | |
716 | | let left_expr = try_cast(left, schema, lhs)?; |
717 | | let right_expr = try_cast(right, schema, rhs)?; |
718 | | binary(left_expr, op, right_expr, schema) |
719 | | } |
720 | | |
721 | | #[test] |
722 | | fn binary_comparison() -> Result<()> { |
723 | | let schema = Schema::new(vec![ |
724 | | Field::new("a", DataType::Int32, false), |
725 | | Field::new("b", DataType::Int32, false), |
726 | | ]); |
727 | | let a = Int32Array::from(vec![1, 2, 3, 4, 5]); |
728 | | let b = Int32Array::from(vec![1, 2, 4, 8, 16]); |
729 | | |
730 | | // expression: "a < b" |
731 | | let lt = binary( |
732 | | col("a", &schema)?, |
733 | | Operator::Lt, |
734 | | col("b", &schema)?, |
735 | | &schema, |
736 | | )?; |
737 | | let batch = |
738 | | RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a), Arc::new(b)])?; |
739 | | |
740 | | let result = lt |
741 | | .evaluate(&batch)? |
742 | | .into_array(batch.num_rows()) |
743 | | .expect("Failed to convert to array"); |
744 | | assert_eq!(result.len(), 5); |
745 | | |
746 | | let expected = [false, false, true, true, true]; |
747 | | let result = |
748 | | as_boolean_array(&result).expect("failed to downcast to BooleanArray"); |
749 | | for (i, &expected_item) in expected.iter().enumerate().take(5) { |
750 | | assert_eq!(result.value(i), expected_item); |
751 | | } |
752 | | |
753 | | Ok(()) |
754 | | } |
755 | | |
756 | | #[test] |
757 | | fn binary_nested() -> Result<()> { |
758 | | let schema = Schema::new(vec![ |
759 | | Field::new("a", DataType::Int32, false), |
760 | | Field::new("b", DataType::Int32, false), |
761 | | ]); |
762 | | let a = Int32Array::from(vec![2, 4, 6, 8, 10]); |
763 | | let b = Int32Array::from(vec![2, 5, 4, 8, 8]); |
764 | | |
765 | | // expression: "a < b OR a == b" |
766 | | let expr = binary( |
767 | | binary( |
768 | | col("a", &schema)?, |
769 | | Operator::Lt, |
770 | | col("b", &schema)?, |
771 | | &schema, |
772 | | )?, |
773 | | Operator::Or, |
774 | | binary( |
775 | | col("a", &schema)?, |
776 | | Operator::Eq, |
777 | | col("b", &schema)?, |
778 | | &schema, |
779 | | )?, |
780 | | &schema, |
781 | | )?; |
782 | | let batch = |
783 | | RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a), Arc::new(b)])?; |
784 | | |
785 | | assert_eq!("a@0 < b@1 OR a@0 = b@1", format!("{expr}")); |
786 | | |
787 | | let result = expr |
788 | | .evaluate(&batch)? |
789 | | .into_array(batch.num_rows()) |
790 | | .expect("Failed to convert to array"); |
791 | | assert_eq!(result.len(), 5); |
792 | | |
793 | | let expected = [true, true, false, true, false]; |
794 | | let result = |
795 | | as_boolean_array(&result).expect("failed to downcast to BooleanArray"); |
796 | | for (i, &expected_item) in expected.iter().enumerate().take(5) { |
797 | | assert_eq!(result.value(i), expected_item); |
798 | | } |
799 | | |
800 | | Ok(()) |
801 | | } |
802 | | |
803 | | // runs an end-to-end test of physical type coercion: |
804 | | // 1. construct a record batch with two columns of type A and B |
805 | | // (*_ARRAY is the Rust Arrow array type, and *_TYPE is the DataType of the elements) |
806 | | // 2. construct a physical expression of A OP B |
807 | | // 3. evaluate the expression |
808 | | // 4. verify that the resulting expression is of type C |
809 | | // 5. verify that the results of evaluation are $VEC |
810 | | macro_rules! test_coercion { |
811 | | ($A_ARRAY:ident, $A_TYPE:expr, $A_VEC:expr, $B_ARRAY:ident, $B_TYPE:expr, $B_VEC:expr, $OP:expr, $C_ARRAY:ident, $C_TYPE:expr, $VEC:expr,) => {{ |
812 | | let schema = Schema::new(vec![ |
813 | | Field::new("a", $A_TYPE, false), |
814 | | Field::new("b", $B_TYPE, false), |
815 | | ]); |
816 | | let a = $A_ARRAY::from($A_VEC); |
817 | | let b = $B_ARRAY::from($B_VEC); |
818 | | let (lhs, rhs) = get_input_types(&$A_TYPE, &$OP, &$B_TYPE)?; |
819 | | |
820 | | let left = try_cast(col("a", &schema)?, &schema, lhs)?; |
821 | | let right = try_cast(col("b", &schema)?, &schema, rhs)?; |
822 | | |
823 | | // verify that we can construct the expression |
824 | | let expression = binary(left, $OP, right, &schema)?; |
825 | | let batch = RecordBatch::try_new( |
826 | | Arc::new(schema.clone()), |
827 | | vec![Arc::new(a), Arc::new(b)], |
828 | | )?; |
829 | | |
830 | | // verify that the expression's type is correct |
831 | | assert_eq!(expression.data_type(&schema)?, $C_TYPE); |
832 | | |
833 | | // compute |
834 | | let result = expression.evaluate(&batch)?.into_array(batch.num_rows()).expect("Failed to convert to array"); |
835 | | |
836 | | // verify that the array's data_type is correct |
837 | | assert_eq!(*result.data_type(), $C_TYPE); |
838 | | |
839 | | // verify that the data itself is downcastable |
840 | | let result = result |
841 | | .as_any() |
842 | | .downcast_ref::<$C_ARRAY>() |
843 | | .expect("failed to downcast"); |
844 | | // verify that the result itself is correct |
845 | | for (i, x) in $VEC.iter().enumerate() { |
846 | | let v = result.value(i); |
847 | | assert_eq!( |
848 | | v, |
849 | | *x, |
850 | | "Unexpected output at position {i}:\n\nActual:\n{v}\n\nExpected:\n{x}" |
851 | | ); |
852 | | } |
853 | | }}; |
854 | | } |
855 | | |
856 | | #[test] |
857 | | fn test_type_coercion() -> Result<()> { |
858 | | test_coercion!( |
859 | | Int32Array, |
860 | | DataType::Int32, |
861 | | vec![1i32, 2i32], |
862 | | UInt32Array, |
863 | | DataType::UInt32, |
864 | | vec![1u32, 2u32], |
865 | | Operator::Plus, |
866 | | Int32Array, |
867 | | DataType::Int32, |
868 | | [2i32, 4i32], |
869 | | ); |
870 | | test_coercion!( |
871 | | Int32Array, |
872 | | DataType::Int32, |
873 | | vec![1i32], |
874 | | UInt16Array, |
875 | | DataType::UInt16, |
876 | | vec![1u16], |
877 | | Operator::Plus, |
878 | | Int32Array, |
879 | | DataType::Int32, |
880 | | [2i32], |
881 | | ); |
882 | | test_coercion!( |
883 | | Float32Array, |
884 | | DataType::Float32, |
885 | | vec![1f32], |
886 | | UInt16Array, |
887 | | DataType::UInt16, |
888 | | vec![1u16], |
889 | | Operator::Plus, |
890 | | Float32Array, |
891 | | DataType::Float32, |
892 | | [2f32], |
893 | | ); |
894 | | test_coercion!( |
895 | | Float32Array, |
896 | | DataType::Float32, |
897 | | vec![2f32], |
898 | | UInt16Array, |
899 | | DataType::UInt16, |
900 | | vec![1u16], |
901 | | Operator::Multiply, |
902 | | Float32Array, |
903 | | DataType::Float32, |
904 | | [2f32], |
905 | | ); |
906 | | test_coercion!( |
907 | | StringArray, |
908 | | DataType::Utf8, |
909 | | vec!["1994-12-13", "1995-01-26"], |
910 | | Date32Array, |
911 | | DataType::Date32, |
912 | | vec![9112, 9156], |
913 | | Operator::Eq, |
914 | | BooleanArray, |
915 | | DataType::Boolean, |
916 | | [true, true], |
917 | | ); |
918 | | test_coercion!( |
919 | | StringArray, |
920 | | DataType::Utf8, |
921 | | vec!["1994-12-13", "1995-01-26"], |
922 | | Date32Array, |
923 | | DataType::Date32, |
924 | | vec![9113, 9154], |
925 | | Operator::Lt, |
926 | | BooleanArray, |
927 | | DataType::Boolean, |
928 | | [true, false], |
929 | | ); |
930 | | test_coercion!( |
931 | | StringArray, |
932 | | DataType::Utf8, |
933 | | vec!["1994-12-13T12:34:56", "1995-01-26T01:23:45"], |
934 | | Date64Array, |
935 | | DataType::Date64, |
936 | | vec![787322096000, 791083425000], |
937 | | Operator::Eq, |
938 | | BooleanArray, |
939 | | DataType::Boolean, |
940 | | [true, true], |
941 | | ); |
942 | | test_coercion!( |
943 | | StringArray, |
944 | | DataType::Utf8, |
945 | | vec!["1994-12-13T12:34:56", "1995-01-26T01:23:45"], |
946 | | Date64Array, |
947 | | DataType::Date64, |
948 | | vec![787322096001, 791083424999], |
949 | | Operator::Lt, |
950 | | BooleanArray, |
951 | | DataType::Boolean, |
952 | | [true, false], |
953 | | ); |
954 | | test_coercion!( |
955 | | StringViewArray, |
956 | | DataType::Utf8View, |
957 | | vec!["abc"; 5], |
958 | | StringArray, |
959 | | DataType::Utf8, |
960 | | vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"], |
961 | | Operator::RegexMatch, |
962 | | BooleanArray, |
963 | | DataType::Boolean, |
964 | | [true, false, true, false, false], |
965 | | ); |
966 | | test_coercion!( |
967 | | StringViewArray, |
968 | | DataType::Utf8View, |
969 | | vec!["abc"; 5], |
970 | | StringArray, |
971 | | DataType::Utf8, |
972 | | vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"], |
973 | | Operator::RegexIMatch, |
974 | | BooleanArray, |
975 | | DataType::Boolean, |
976 | | [true, true, true, true, false], |
977 | | ); |
978 | | test_coercion!( |
979 | | StringArray, |
980 | | DataType::Utf8, |
981 | | vec!["abc"; 5], |
982 | | StringViewArray, |
983 | | DataType::Utf8View, |
984 | | vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"], |
985 | | Operator::RegexNotMatch, |
986 | | BooleanArray, |
987 | | DataType::Boolean, |
988 | | [false, true, false, true, true], |
989 | | ); |
990 | | test_coercion!( |
991 | | StringArray, |
992 | | DataType::Utf8, |
993 | | vec!["abc"; 5], |
994 | | StringViewArray, |
995 | | DataType::Utf8View, |
996 | | vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"], |
997 | | Operator::RegexNotIMatch, |
998 | | BooleanArray, |
999 | | DataType::Boolean, |
1000 | | [false, false, false, false, true], |
1001 | | ); |
1002 | | test_coercion!( |
1003 | | StringArray, |
1004 | | DataType::Utf8, |
1005 | | vec!["abc"; 5], |
1006 | | StringArray, |
1007 | | DataType::Utf8, |
1008 | | vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"], |
1009 | | Operator::RegexMatch, |
1010 | | BooleanArray, |
1011 | | DataType::Boolean, |
1012 | | [true, false, true, false, false], |
1013 | | ); |
1014 | | test_coercion!( |
1015 | | StringArray, |
1016 | | DataType::Utf8, |
1017 | | vec!["abc"; 5], |
1018 | | StringArray, |
1019 | | DataType::Utf8, |
1020 | | vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"], |
1021 | | Operator::RegexIMatch, |
1022 | | BooleanArray, |
1023 | | DataType::Boolean, |
1024 | | [true, true, true, true, false], |
1025 | | ); |
1026 | | test_coercion!( |
1027 | | StringArray, |
1028 | | DataType::Utf8, |
1029 | | vec!["abc"; 5], |
1030 | | StringArray, |
1031 | | DataType::Utf8, |
1032 | | vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"], |
1033 | | Operator::RegexNotMatch, |
1034 | | BooleanArray, |
1035 | | DataType::Boolean, |
1036 | | [false, true, false, true, true], |
1037 | | ); |
1038 | | test_coercion!( |
1039 | | StringArray, |
1040 | | DataType::Utf8, |
1041 | | vec!["abc"; 5], |
1042 | | StringArray, |
1043 | | DataType::Utf8, |
1044 | | vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"], |
1045 | | Operator::RegexNotIMatch, |
1046 | | BooleanArray, |
1047 | | DataType::Boolean, |
1048 | | [false, false, false, false, true], |
1049 | | ); |
1050 | | test_coercion!( |
1051 | | LargeStringArray, |
1052 | | DataType::LargeUtf8, |
1053 | | vec!["abc"; 5], |
1054 | | LargeStringArray, |
1055 | | DataType::LargeUtf8, |
1056 | | vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"], |
1057 | | Operator::RegexMatch, |
1058 | | BooleanArray, |
1059 | | DataType::Boolean, |
1060 | | [true, false, true, false, false], |
1061 | | ); |
1062 | | test_coercion!( |
1063 | | LargeStringArray, |
1064 | | DataType::LargeUtf8, |
1065 | | vec!["abc"; 5], |
1066 | | LargeStringArray, |
1067 | | DataType::LargeUtf8, |
1068 | | vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"], |
1069 | | Operator::RegexIMatch, |
1070 | | BooleanArray, |
1071 | | DataType::Boolean, |
1072 | | [true, true, true, true, false], |
1073 | | ); |
1074 | | test_coercion!( |
1075 | | LargeStringArray, |
1076 | | DataType::LargeUtf8, |
1077 | | vec!["abc"; 5], |
1078 | | LargeStringArray, |
1079 | | DataType::LargeUtf8, |
1080 | | vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"], |
1081 | | Operator::RegexNotMatch, |
1082 | | BooleanArray, |
1083 | | DataType::Boolean, |
1084 | | [false, true, false, true, true], |
1085 | | ); |
1086 | | test_coercion!( |
1087 | | LargeStringArray, |
1088 | | DataType::LargeUtf8, |
1089 | | vec!["abc"; 5], |
1090 | | LargeStringArray, |
1091 | | DataType::LargeUtf8, |
1092 | | vec!["^a", "^A", "(b|d)", "(B|D)", "^(b|c)"], |
1093 | | Operator::RegexNotIMatch, |
1094 | | BooleanArray, |
1095 | | DataType::Boolean, |
1096 | | [false, false, false, false, true], |
1097 | | ); |
1098 | | test_coercion!( |
1099 | | StringArray, |
1100 | | DataType::Utf8, |
1101 | | vec!["abc"; 5], |
1102 | | StringArray, |
1103 | | DataType::Utf8, |
1104 | | vec!["a__", "A%BC", "A_BC", "abc", "a%C"], |
1105 | | Operator::LikeMatch, |
1106 | | BooleanArray, |
1107 | | DataType::Boolean, |
1108 | | [true, false, false, true, false], |
1109 | | ); |
1110 | | test_coercion!( |
1111 | | StringArray, |
1112 | | DataType::Utf8, |
1113 | | vec!["abc"; 5], |
1114 | | StringArray, |
1115 | | DataType::Utf8, |
1116 | | vec!["a__", "A%BC", "A_BC", "abc", "a%C"], |
1117 | | Operator::ILikeMatch, |
1118 | | BooleanArray, |
1119 | | DataType::Boolean, |
1120 | | [true, true, false, true, true], |
1121 | | ); |
1122 | | test_coercion!( |
1123 | | StringArray, |
1124 | | DataType::Utf8, |
1125 | | vec!["abc"; 5], |
1126 | | StringArray, |
1127 | | DataType::Utf8, |
1128 | | vec!["a__", "A%BC", "A_BC", "abc", "a%C"], |
1129 | | Operator::NotLikeMatch, |
1130 | | BooleanArray, |
1131 | | DataType::Boolean, |
1132 | | [false, true, true, false, true], |
1133 | | ); |
1134 | | test_coercion!( |
1135 | | StringArray, |
1136 | | DataType::Utf8, |
1137 | | vec!["abc"; 5], |
1138 | | StringArray, |
1139 | | DataType::Utf8, |
1140 | | vec!["a__", "A%BC", "A_BC", "abc", "a%C"], |
1141 | | Operator::NotILikeMatch, |
1142 | | BooleanArray, |
1143 | | DataType::Boolean, |
1144 | | [false, false, true, false, false], |
1145 | | ); |
1146 | | test_coercion!( |
1147 | | LargeStringArray, |
1148 | | DataType::LargeUtf8, |
1149 | | vec!["abc"; 5], |
1150 | | LargeStringArray, |
1151 | | DataType::LargeUtf8, |
1152 | | vec!["a__", "A%BC", "A_BC", "abc", "a%C"], |
1153 | | Operator::LikeMatch, |
1154 | | BooleanArray, |
1155 | | DataType::Boolean, |
1156 | | [true, false, false, true, false], |
1157 | | ); |
1158 | | test_coercion!( |
1159 | | LargeStringArray, |
1160 | | DataType::LargeUtf8, |
1161 | | vec!["abc"; 5], |
1162 | | LargeStringArray, |
1163 | | DataType::LargeUtf8, |
1164 | | vec!["a__", "A%BC", "A_BC", "abc", "a%C"], |
1165 | | Operator::ILikeMatch, |
1166 | | BooleanArray, |
1167 | | DataType::Boolean, |
1168 | | [true, true, false, true, true], |
1169 | | ); |
1170 | | test_coercion!( |
1171 | | LargeStringArray, |
1172 | | DataType::LargeUtf8, |
1173 | | vec!["abc"; 5], |
1174 | | LargeStringArray, |
1175 | | DataType::LargeUtf8, |
1176 | | vec!["a__", "A%BC", "A_BC", "abc", "a%C"], |
1177 | | Operator::NotLikeMatch, |
1178 | | BooleanArray, |
1179 | | DataType::Boolean, |
1180 | | [false, true, true, false, true], |
1181 | | ); |
1182 | | test_coercion!( |
1183 | | LargeStringArray, |
1184 | | DataType::LargeUtf8, |
1185 | | vec!["abc"; 5], |
1186 | | LargeStringArray, |
1187 | | DataType::LargeUtf8, |
1188 | | vec!["a__", "A%BC", "A_BC", "abc", "a%C"], |
1189 | | Operator::NotILikeMatch, |
1190 | | BooleanArray, |
1191 | | DataType::Boolean, |
1192 | | [false, false, true, false, false], |
1193 | | ); |
1194 | | test_coercion!( |
1195 | | Int16Array, |
1196 | | DataType::Int16, |
1197 | | vec![1i16, 2i16, 3i16], |
1198 | | Int64Array, |
1199 | | DataType::Int64, |
1200 | | vec![10i64, 4i64, 5i64], |
1201 | | Operator::BitwiseAnd, |
1202 | | Int64Array, |
1203 | | DataType::Int64, |
1204 | | [0i64, 0i64, 1i64], |
1205 | | ); |
1206 | | test_coercion!( |
1207 | | UInt16Array, |
1208 | | DataType::UInt16, |
1209 | | vec![1u16, 2u16, 3u16], |
1210 | | UInt64Array, |
1211 | | DataType::UInt64, |
1212 | | vec![10u64, 4u64, 5u64], |
1213 | | Operator::BitwiseAnd, |
1214 | | UInt64Array, |
1215 | | DataType::UInt64, |
1216 | | [0u64, 0u64, 1u64], |
1217 | | ); |
1218 | | test_coercion!( |
1219 | | Int16Array, |
1220 | | DataType::Int16, |
1221 | | vec![3i16, 2i16, 3i16], |
1222 | | Int64Array, |
1223 | | DataType::Int64, |
1224 | | vec![10i64, 6i64, 5i64], |
1225 | | Operator::BitwiseOr, |
1226 | | Int64Array, |
1227 | | DataType::Int64, |
1228 | | [11i64, 6i64, 7i64], |
1229 | | ); |
1230 | | test_coercion!( |
1231 | | UInt16Array, |
1232 | | DataType::UInt16, |
1233 | | vec![1u16, 2u16, 3u16], |
1234 | | UInt64Array, |
1235 | | DataType::UInt64, |
1236 | | vec![10u64, 4u64, 5u64], |
1237 | | Operator::BitwiseOr, |
1238 | | UInt64Array, |
1239 | | DataType::UInt64, |
1240 | | [11u64, 6u64, 7u64], |
1241 | | ); |
1242 | | test_coercion!( |
1243 | | Int16Array, |
1244 | | DataType::Int16, |
1245 | | vec![3i16, 2i16, 3i16], |
1246 | | Int64Array, |
1247 | | DataType::Int64, |
1248 | | vec![10i64, 6i64, 5i64], |
1249 | | Operator::BitwiseXor, |
1250 | | Int64Array, |
1251 | | DataType::Int64, |
1252 | | [9i64, 4i64, 6i64], |
1253 | | ); |
1254 | | test_coercion!( |
1255 | | UInt16Array, |
1256 | | DataType::UInt16, |
1257 | | vec![3u16, 2u16, 3u16], |
1258 | | UInt64Array, |
1259 | | DataType::UInt64, |
1260 | | vec![10u64, 6u64, 5u64], |
1261 | | Operator::BitwiseXor, |
1262 | | UInt64Array, |
1263 | | DataType::UInt64, |
1264 | | [9u64, 4u64, 6u64], |
1265 | | ); |
1266 | | test_coercion!( |
1267 | | Int16Array, |
1268 | | DataType::Int16, |
1269 | | vec![4i16, 27i16, 35i16], |
1270 | | Int64Array, |
1271 | | DataType::Int64, |
1272 | | vec![2i64, 3i64, 4i64], |
1273 | | Operator::BitwiseShiftRight, |
1274 | | Int64Array, |
1275 | | DataType::Int64, |
1276 | | [1i64, 3i64, 2i64], |
1277 | | ); |
1278 | | test_coercion!( |
1279 | | UInt16Array, |
1280 | | DataType::UInt16, |
1281 | | vec![4u16, 27u16, 35u16], |
1282 | | UInt64Array, |
1283 | | DataType::UInt64, |
1284 | | vec![2u64, 3u64, 4u64], |
1285 | | Operator::BitwiseShiftRight, |
1286 | | UInt64Array, |
1287 | | DataType::UInt64, |
1288 | | [1u64, 3u64, 2u64], |
1289 | | ); |
1290 | | test_coercion!( |
1291 | | Int16Array, |
1292 | | DataType::Int16, |
1293 | | vec![2i16, 3i16, 4i16], |
1294 | | Int64Array, |
1295 | | DataType::Int64, |
1296 | | vec![4i64, 12i64, 7i64], |
1297 | | Operator::BitwiseShiftLeft, |
1298 | | Int64Array, |
1299 | | DataType::Int64, |
1300 | | [32i64, 12288i64, 512i64], |
1301 | | ); |
1302 | | test_coercion!( |
1303 | | UInt16Array, |
1304 | | DataType::UInt16, |
1305 | | vec![2u16, 3u16, 4u16], |
1306 | | UInt64Array, |
1307 | | DataType::UInt64, |
1308 | | vec![4u64, 12u64, 7u64], |
1309 | | Operator::BitwiseShiftLeft, |
1310 | | UInt64Array, |
1311 | | DataType::UInt64, |
1312 | | [32u64, 12288u64, 512u64], |
1313 | | ); |
1314 | | Ok(()) |
1315 | | } |
1316 | | |
1317 | | // Note it would be nice to use the same test_coercion macro as |
1318 | | // above, but sadly the type of the values of the dictionary are |
1319 | | // not encoded in the rust type of the DictionaryArray. Thus there |
1320 | | // is no way at the time of this writing to create a dictionary |
1321 | | // array using the `From` trait |
1322 | | #[test] |
1323 | | fn test_dictionary_type_to_array_coercion() -> Result<()> { |
1324 | | // Test string a string dictionary |
1325 | | let dict_type = |
1326 | | DataType::Dictionary(Box::new(DataType::Int32), Box::new(DataType::Utf8)); |
1327 | | let string_type = DataType::Utf8; |
1328 | | |
1329 | | // build dictionary |
1330 | | let mut dict_builder = StringDictionaryBuilder::<Int32Type>::new(); |
1331 | | |
1332 | | dict_builder.append("one")?; |
1333 | | dict_builder.append_null(); |
1334 | | dict_builder.append("three")?; |
1335 | | dict_builder.append("four")?; |
1336 | | let dict_array = Arc::new(dict_builder.finish()) as ArrayRef; |
1337 | | |
1338 | | let str_array = Arc::new(StringArray::from(vec![ |
1339 | | Some("not one"), |
1340 | | Some("two"), |
1341 | | None, |
1342 | | Some("four"), |
1343 | | ])) as ArrayRef; |
1344 | | |
1345 | | let schema = Arc::new(Schema::new(vec![ |
1346 | | Field::new("a", dict_type.clone(), true), |
1347 | | Field::new("b", string_type.clone(), true), |
1348 | | ])); |
1349 | | |
1350 | | // Test 1: a = b |
1351 | | let result = BooleanArray::from(vec![Some(false), None, None, Some(true)]); |
1352 | | apply_logic_op(&schema, &dict_array, &str_array, Operator::Eq, result)?; |
1353 | | |
1354 | | // Test 2: now test the other direction |
1355 | | // b = a |
1356 | | let schema = Arc::new(Schema::new(vec![ |
1357 | | Field::new("a", string_type, true), |
1358 | | Field::new("b", dict_type, true), |
1359 | | ])); |
1360 | | let result = BooleanArray::from(vec![Some(false), None, None, Some(true)]); |
1361 | | apply_logic_op(&schema, &str_array, &dict_array, Operator::Eq, result)?; |
1362 | | |
1363 | | Ok(()) |
1364 | | } |
1365 | | |
1366 | | #[test] |
1367 | | fn plus_op() -> Result<()> { |
1368 | | let schema = Schema::new(vec![ |
1369 | | Field::new("a", DataType::Int32, false), |
1370 | | Field::new("b", DataType::Int32, false), |
1371 | | ]); |
1372 | | let a = Int32Array::from(vec![1, 2, 3, 4, 5]); |
1373 | | let b = Int32Array::from(vec![1, 2, 4, 8, 16]); |
1374 | | |
1375 | | apply_arithmetic::<Int32Type>( |
1376 | | Arc::new(schema), |
1377 | | vec![Arc::new(a), Arc::new(b)], |
1378 | | Operator::Plus, |
1379 | | Int32Array::from(vec![2, 4, 7, 12, 21]), |
1380 | | )?; |
1381 | | |
1382 | | Ok(()) |
1383 | | } |
1384 | | |
1385 | | #[test] |
1386 | | fn plus_op_dict() -> Result<()> { |
1387 | | let schema = Schema::new(vec![ |
1388 | | Field::new( |
1389 | | "a", |
1390 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
1391 | | true, |
1392 | | ), |
1393 | | Field::new( |
1394 | | "b", |
1395 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
1396 | | true, |
1397 | | ), |
1398 | | ]); |
1399 | | |
1400 | | let a = Int32Array::from(vec![1, 2, 3, 4, 5]); |
1401 | | let keys = Int8Array::from(vec![Some(0), None, Some(1), Some(3), None]); |
1402 | | let a = DictionaryArray::try_new(keys, Arc::new(a))?; |
1403 | | |
1404 | | let b = Int32Array::from(vec![1, 2, 4, 8, 16]); |
1405 | | let keys = Int8Array::from(vec![0, 1, 1, 2, 1]); |
1406 | | let b = DictionaryArray::try_new(keys, Arc::new(b))?; |
1407 | | |
1408 | | apply_arithmetic::<Int32Type>( |
1409 | | Arc::new(schema), |
1410 | | vec![Arc::new(a), Arc::new(b)], |
1411 | | Operator::Plus, |
1412 | | Int32Array::from(vec![Some(2), None, Some(4), Some(8), None]), |
1413 | | )?; |
1414 | | |
1415 | | Ok(()) |
1416 | | } |
1417 | | |
1418 | | #[test] |
1419 | | fn plus_op_dict_decimal() -> Result<()> { |
1420 | | let schema = Schema::new(vec![ |
1421 | | Field::new( |
1422 | | "a", |
1423 | | DataType::Dictionary( |
1424 | | Box::new(DataType::Int8), |
1425 | | Box::new(DataType::Decimal128(10, 0)), |
1426 | | ), |
1427 | | true, |
1428 | | ), |
1429 | | Field::new( |
1430 | | "b", |
1431 | | DataType::Dictionary( |
1432 | | Box::new(DataType::Int8), |
1433 | | Box::new(DataType::Decimal128(10, 0)), |
1434 | | ), |
1435 | | true, |
1436 | | ), |
1437 | | ]); |
1438 | | |
1439 | | let value = 123; |
1440 | | let decimal_array = Arc::new(create_decimal_array( |
1441 | | &[ |
1442 | | Some(value), |
1443 | | Some(value + 2), |
1444 | | Some(value - 1), |
1445 | | Some(value + 1), |
1446 | | ], |
1447 | | 10, |
1448 | | 0, |
1449 | | )); |
1450 | | |
1451 | | let keys = Int8Array::from(vec![Some(0), Some(2), None, Some(3), Some(0)]); |
1452 | | let a = DictionaryArray::try_new(keys, decimal_array)?; |
1453 | | |
1454 | | let keys = Int8Array::from(vec![Some(0), None, Some(3), Some(2), Some(2)]); |
1455 | | let decimal_array = Arc::new(create_decimal_array( |
1456 | | &[ |
1457 | | Some(value + 1), |
1458 | | Some(value + 3), |
1459 | | Some(value), |
1460 | | Some(value + 2), |
1461 | | ], |
1462 | | 10, |
1463 | | 0, |
1464 | | )); |
1465 | | let b = DictionaryArray::try_new(keys, decimal_array)?; |
1466 | | |
1467 | | apply_arithmetic( |
1468 | | Arc::new(schema), |
1469 | | vec![Arc::new(a), Arc::new(b)], |
1470 | | Operator::Plus, |
1471 | | create_decimal_array(&[Some(247), None, None, Some(247), Some(246)], 11, 0), |
1472 | | )?; |
1473 | | |
1474 | | Ok(()) |
1475 | | } |
1476 | | |
1477 | | #[test] |
1478 | | fn plus_op_scalar() -> Result<()> { |
1479 | | let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]); |
1480 | | let a = Int32Array::from(vec![1, 2, 3, 4, 5]); |
1481 | | |
1482 | | apply_arithmetic_scalar( |
1483 | | Arc::new(schema), |
1484 | | vec![Arc::new(a)], |
1485 | | Operator::Plus, |
1486 | | ScalarValue::Int32(Some(1)), |
1487 | | Arc::new(Int32Array::from(vec![2, 3, 4, 5, 6])), |
1488 | | )?; |
1489 | | |
1490 | | Ok(()) |
1491 | | } |
1492 | | |
1493 | | #[test] |
1494 | | fn plus_op_dict_scalar() -> Result<()> { |
1495 | | let schema = Schema::new(vec![Field::new( |
1496 | | "a", |
1497 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
1498 | | true, |
1499 | | )]); |
1500 | | |
1501 | | let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new(); |
1502 | | |
1503 | | dict_builder.append(1)?; |
1504 | | dict_builder.append_null(); |
1505 | | dict_builder.append(2)?; |
1506 | | dict_builder.append(5)?; |
1507 | | |
1508 | | let a = dict_builder.finish(); |
1509 | | |
1510 | | let expected: PrimitiveArray<Int32Type> = |
1511 | | PrimitiveArray::from(vec![Some(2), None, Some(3), Some(6)]); |
1512 | | |
1513 | | apply_arithmetic_scalar( |
1514 | | Arc::new(schema), |
1515 | | vec![Arc::new(a)], |
1516 | | Operator::Plus, |
1517 | | ScalarValue::Dictionary( |
1518 | | Box::new(DataType::Int8), |
1519 | | Box::new(ScalarValue::Int32(Some(1))), |
1520 | | ), |
1521 | | Arc::new(expected), |
1522 | | )?; |
1523 | | |
1524 | | Ok(()) |
1525 | | } |
1526 | | |
1527 | | #[test] |
1528 | | fn plus_op_dict_scalar_decimal() -> Result<()> { |
1529 | | let schema = Schema::new(vec![Field::new( |
1530 | | "a", |
1531 | | DataType::Dictionary( |
1532 | | Box::new(DataType::Int8), |
1533 | | Box::new(DataType::Decimal128(10, 0)), |
1534 | | ), |
1535 | | true, |
1536 | | )]); |
1537 | | |
1538 | | let value = 123; |
1539 | | let decimal_array = Arc::new(create_decimal_array( |
1540 | | &[Some(value), None, Some(value - 1), Some(value + 1)], |
1541 | | 10, |
1542 | | 0, |
1543 | | )); |
1544 | | |
1545 | | let keys = Int8Array::from(vec![0, 2, 1, 3, 0]); |
1546 | | let a = DictionaryArray::try_new(keys, decimal_array)?; |
1547 | | |
1548 | | let decimal_array = Arc::new(create_decimal_array( |
1549 | | &[ |
1550 | | Some(value + 1), |
1551 | | Some(value), |
1552 | | None, |
1553 | | Some(value + 2), |
1554 | | Some(value + 1), |
1555 | | ], |
1556 | | 11, |
1557 | | 0, |
1558 | | )); |
1559 | | |
1560 | | apply_arithmetic_scalar( |
1561 | | Arc::new(schema), |
1562 | | vec![Arc::new(a)], |
1563 | | Operator::Plus, |
1564 | | ScalarValue::Dictionary( |
1565 | | Box::new(DataType::Int8), |
1566 | | Box::new(ScalarValue::Decimal128(Some(1), 10, 0)), |
1567 | | ), |
1568 | | decimal_array, |
1569 | | )?; |
1570 | | |
1571 | | Ok(()) |
1572 | | } |
1573 | | |
1574 | | #[test] |
1575 | | fn minus_op() -> Result<()> { |
1576 | | let schema = Arc::new(Schema::new(vec![ |
1577 | | Field::new("a", DataType::Int32, false), |
1578 | | Field::new("b", DataType::Int32, false), |
1579 | | ])); |
1580 | | let a = Arc::new(Int32Array::from(vec![1, 2, 4, 8, 16])); |
1581 | | let b = Arc::new(Int32Array::from(vec![1, 2, 3, 4, 5])); |
1582 | | |
1583 | | apply_arithmetic::<Int32Type>( |
1584 | | Arc::clone(&schema), |
1585 | | vec![ |
1586 | | Arc::clone(&a) as Arc<dyn Array>, |
1587 | | Arc::clone(&b) as Arc<dyn Array>, |
1588 | | ], |
1589 | | Operator::Minus, |
1590 | | Int32Array::from(vec![0, 0, 1, 4, 11]), |
1591 | | )?; |
1592 | | |
1593 | | // should handle have negative values in result (for signed) |
1594 | | apply_arithmetic::<Int32Type>( |
1595 | | schema, |
1596 | | vec![b, a], |
1597 | | Operator::Minus, |
1598 | | Int32Array::from(vec![0, 0, -1, -4, -11]), |
1599 | | )?; |
1600 | | |
1601 | | Ok(()) |
1602 | | } |
1603 | | |
1604 | | #[test] |
1605 | | fn minus_op_dict() -> Result<()> { |
1606 | | let schema = Schema::new(vec![ |
1607 | | Field::new( |
1608 | | "a", |
1609 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
1610 | | true, |
1611 | | ), |
1612 | | Field::new( |
1613 | | "b", |
1614 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
1615 | | true, |
1616 | | ), |
1617 | | ]); |
1618 | | |
1619 | | let a = Int32Array::from(vec![1, 2, 3, 4, 5]); |
1620 | | let keys = Int8Array::from(vec![Some(0), None, Some(1), Some(3), None]); |
1621 | | let a = DictionaryArray::try_new(keys, Arc::new(a))?; |
1622 | | |
1623 | | let b = Int32Array::from(vec![1, 2, 4, 8, 16]); |
1624 | | let keys = Int8Array::from(vec![0, 1, 1, 2, 1]); |
1625 | | let b = DictionaryArray::try_new(keys, Arc::new(b))?; |
1626 | | |
1627 | | apply_arithmetic::<Int32Type>( |
1628 | | Arc::new(schema), |
1629 | | vec![Arc::new(a), Arc::new(b)], |
1630 | | Operator::Minus, |
1631 | | Int32Array::from(vec![Some(0), None, Some(0), Some(0), None]), |
1632 | | )?; |
1633 | | |
1634 | | Ok(()) |
1635 | | } |
1636 | | |
1637 | | #[test] |
1638 | | fn minus_op_dict_decimal() -> Result<()> { |
1639 | | let schema = Schema::new(vec![ |
1640 | | Field::new( |
1641 | | "a", |
1642 | | DataType::Dictionary( |
1643 | | Box::new(DataType::Int8), |
1644 | | Box::new(DataType::Decimal128(10, 0)), |
1645 | | ), |
1646 | | true, |
1647 | | ), |
1648 | | Field::new( |
1649 | | "b", |
1650 | | DataType::Dictionary( |
1651 | | Box::new(DataType::Int8), |
1652 | | Box::new(DataType::Decimal128(10, 0)), |
1653 | | ), |
1654 | | true, |
1655 | | ), |
1656 | | ]); |
1657 | | |
1658 | | let value = 123; |
1659 | | let decimal_array = Arc::new(create_decimal_array( |
1660 | | &[ |
1661 | | Some(value), |
1662 | | Some(value + 2), |
1663 | | Some(value - 1), |
1664 | | Some(value + 1), |
1665 | | ], |
1666 | | 10, |
1667 | | 0, |
1668 | | )); |
1669 | | |
1670 | | let keys = Int8Array::from(vec![Some(0), Some(2), None, Some(3), Some(0)]); |
1671 | | let a = DictionaryArray::try_new(keys, decimal_array)?; |
1672 | | |
1673 | | let keys = Int8Array::from(vec![Some(0), None, Some(3), Some(2), Some(2)]); |
1674 | | let decimal_array = Arc::new(create_decimal_array( |
1675 | | &[ |
1676 | | Some(value + 1), |
1677 | | Some(value + 3), |
1678 | | Some(value), |
1679 | | Some(value + 2), |
1680 | | ], |
1681 | | 10, |
1682 | | 0, |
1683 | | )); |
1684 | | let b = DictionaryArray::try_new(keys, decimal_array)?; |
1685 | | |
1686 | | apply_arithmetic( |
1687 | | Arc::new(schema), |
1688 | | vec![Arc::new(a), Arc::new(b)], |
1689 | | Operator::Minus, |
1690 | | create_decimal_array(&[Some(-1), None, None, Some(1), Some(0)], 11, 0), |
1691 | | )?; |
1692 | | |
1693 | | Ok(()) |
1694 | | } |
1695 | | |
1696 | | #[test] |
1697 | | fn minus_op_scalar() -> Result<()> { |
1698 | | let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]); |
1699 | | let a = Int32Array::from(vec![1, 2, 3, 4, 5]); |
1700 | | |
1701 | | apply_arithmetic_scalar( |
1702 | | Arc::new(schema), |
1703 | | vec![Arc::new(a)], |
1704 | | Operator::Minus, |
1705 | | ScalarValue::Int32(Some(1)), |
1706 | | Arc::new(Int32Array::from(vec![0, 1, 2, 3, 4])), |
1707 | | )?; |
1708 | | |
1709 | | Ok(()) |
1710 | | } |
1711 | | |
1712 | | #[test] |
1713 | | fn minus_op_dict_scalar() -> Result<()> { |
1714 | | let schema = Schema::new(vec![Field::new( |
1715 | | "a", |
1716 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
1717 | | true, |
1718 | | )]); |
1719 | | |
1720 | | let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new(); |
1721 | | |
1722 | | dict_builder.append(1)?; |
1723 | | dict_builder.append_null(); |
1724 | | dict_builder.append(2)?; |
1725 | | dict_builder.append(5)?; |
1726 | | |
1727 | | let a = dict_builder.finish(); |
1728 | | |
1729 | | let expected: PrimitiveArray<Int32Type> = |
1730 | | PrimitiveArray::from(vec![Some(0), None, Some(1), Some(4)]); |
1731 | | |
1732 | | apply_arithmetic_scalar( |
1733 | | Arc::new(schema), |
1734 | | vec![Arc::new(a)], |
1735 | | Operator::Minus, |
1736 | | ScalarValue::Dictionary( |
1737 | | Box::new(DataType::Int8), |
1738 | | Box::new(ScalarValue::Int32(Some(1))), |
1739 | | ), |
1740 | | Arc::new(expected), |
1741 | | )?; |
1742 | | |
1743 | | Ok(()) |
1744 | | } |
1745 | | |
1746 | | #[test] |
1747 | | fn minus_op_dict_scalar_decimal() -> Result<()> { |
1748 | | let schema = Schema::new(vec![Field::new( |
1749 | | "a", |
1750 | | DataType::Dictionary( |
1751 | | Box::new(DataType::Int8), |
1752 | | Box::new(DataType::Decimal128(10, 0)), |
1753 | | ), |
1754 | | true, |
1755 | | )]); |
1756 | | |
1757 | | let value = 123; |
1758 | | let decimal_array = Arc::new(create_decimal_array( |
1759 | | &[Some(value), None, Some(value - 1), Some(value + 1)], |
1760 | | 10, |
1761 | | 0, |
1762 | | )); |
1763 | | |
1764 | | let keys = Int8Array::from(vec![0, 2, 1, 3, 0]); |
1765 | | let a = DictionaryArray::try_new(keys, decimal_array)?; |
1766 | | |
1767 | | let decimal_array = Arc::new(create_decimal_array( |
1768 | | &[ |
1769 | | Some(value - 1), |
1770 | | Some(value - 2), |
1771 | | None, |
1772 | | Some(value), |
1773 | | Some(value - 1), |
1774 | | ], |
1775 | | 11, |
1776 | | 0, |
1777 | | )); |
1778 | | |
1779 | | apply_arithmetic_scalar( |
1780 | | Arc::new(schema), |
1781 | | vec![Arc::new(a)], |
1782 | | Operator::Minus, |
1783 | | ScalarValue::Dictionary( |
1784 | | Box::new(DataType::Int8), |
1785 | | Box::new(ScalarValue::Decimal128(Some(1), 10, 0)), |
1786 | | ), |
1787 | | decimal_array, |
1788 | | )?; |
1789 | | |
1790 | | Ok(()) |
1791 | | } |
1792 | | |
1793 | | #[test] |
1794 | | fn multiply_op() -> Result<()> { |
1795 | | let schema = Arc::new(Schema::new(vec![ |
1796 | | Field::new("a", DataType::Int32, false), |
1797 | | Field::new("b", DataType::Int32, false), |
1798 | | ])); |
1799 | | let a = Arc::new(Int32Array::from(vec![4, 8, 16, 32, 64])); |
1800 | | let b = Arc::new(Int32Array::from(vec![2, 4, 8, 16, 32])); |
1801 | | |
1802 | | apply_arithmetic::<Int32Type>( |
1803 | | schema, |
1804 | | vec![a, b], |
1805 | | Operator::Multiply, |
1806 | | Int32Array::from(vec![8, 32, 128, 512, 2048]), |
1807 | | )?; |
1808 | | |
1809 | | Ok(()) |
1810 | | } |
1811 | | |
1812 | | #[test] |
1813 | | fn multiply_op_dict() -> Result<()> { |
1814 | | let schema = Schema::new(vec![ |
1815 | | Field::new( |
1816 | | "a", |
1817 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
1818 | | true, |
1819 | | ), |
1820 | | Field::new( |
1821 | | "b", |
1822 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
1823 | | true, |
1824 | | ), |
1825 | | ]); |
1826 | | |
1827 | | let a = Int32Array::from(vec![1, 2, 3, 4, 5]); |
1828 | | let keys = Int8Array::from(vec![Some(0), None, Some(1), Some(3), None]); |
1829 | | let a = DictionaryArray::try_new(keys, Arc::new(a))?; |
1830 | | |
1831 | | let b = Int32Array::from(vec![1, 2, 4, 8, 16]); |
1832 | | let keys = Int8Array::from(vec![0, 1, 1, 2, 1]); |
1833 | | let b = DictionaryArray::try_new(keys, Arc::new(b))?; |
1834 | | |
1835 | | apply_arithmetic::<Int32Type>( |
1836 | | Arc::new(schema), |
1837 | | vec![Arc::new(a), Arc::new(b)], |
1838 | | Operator::Multiply, |
1839 | | Int32Array::from(vec![Some(1), None, Some(4), Some(16), None]), |
1840 | | )?; |
1841 | | |
1842 | | Ok(()) |
1843 | | } |
1844 | | |
1845 | | #[test] |
1846 | | fn multiply_op_dict_decimal() -> Result<()> { |
1847 | | let schema = Schema::new(vec![ |
1848 | | Field::new( |
1849 | | "a", |
1850 | | DataType::Dictionary( |
1851 | | Box::new(DataType::Int8), |
1852 | | Box::new(DataType::Decimal128(10, 0)), |
1853 | | ), |
1854 | | true, |
1855 | | ), |
1856 | | Field::new( |
1857 | | "b", |
1858 | | DataType::Dictionary( |
1859 | | Box::new(DataType::Int8), |
1860 | | Box::new(DataType::Decimal128(10, 0)), |
1861 | | ), |
1862 | | true, |
1863 | | ), |
1864 | | ]); |
1865 | | |
1866 | | let value = 123; |
1867 | | let decimal_array = Arc::new(create_decimal_array( |
1868 | | &[ |
1869 | | Some(value), |
1870 | | Some(value + 2), |
1871 | | Some(value - 1), |
1872 | | Some(value + 1), |
1873 | | ], |
1874 | | 10, |
1875 | | 0, |
1876 | | )) as ArrayRef; |
1877 | | |
1878 | | let keys = Int8Array::from(vec![Some(0), Some(2), None, Some(3), Some(0)]); |
1879 | | let a = DictionaryArray::try_new(keys, decimal_array)?; |
1880 | | |
1881 | | let keys = Int8Array::from(vec![Some(0), None, Some(3), Some(2), Some(2)]); |
1882 | | let decimal_array = Arc::new(create_decimal_array( |
1883 | | &[ |
1884 | | Some(value + 1), |
1885 | | Some(value + 3), |
1886 | | Some(value), |
1887 | | Some(value + 2), |
1888 | | ], |
1889 | | 10, |
1890 | | 0, |
1891 | | )); |
1892 | | let b = DictionaryArray::try_new(keys, decimal_array)?; |
1893 | | |
1894 | | apply_arithmetic( |
1895 | | Arc::new(schema), |
1896 | | vec![Arc::new(a), Arc::new(b)], |
1897 | | Operator::Multiply, |
1898 | | create_decimal_array( |
1899 | | &[Some(15252), None, None, Some(15252), Some(15129)], |
1900 | | 21, |
1901 | | 0, |
1902 | | ), |
1903 | | )?; |
1904 | | |
1905 | | Ok(()) |
1906 | | } |
1907 | | |
1908 | | #[test] |
1909 | | fn multiply_op_scalar() -> Result<()> { |
1910 | | let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]); |
1911 | | let a = Int32Array::from(vec![1, 2, 3, 4, 5]); |
1912 | | |
1913 | | apply_arithmetic_scalar( |
1914 | | Arc::new(schema), |
1915 | | vec![Arc::new(a)], |
1916 | | Operator::Multiply, |
1917 | | ScalarValue::Int32(Some(2)), |
1918 | | Arc::new(Int32Array::from(vec![2, 4, 6, 8, 10])), |
1919 | | )?; |
1920 | | |
1921 | | Ok(()) |
1922 | | } |
1923 | | |
1924 | | #[test] |
1925 | | fn multiply_op_dict_scalar() -> Result<()> { |
1926 | | let schema = Schema::new(vec![Field::new( |
1927 | | "a", |
1928 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
1929 | | true, |
1930 | | )]); |
1931 | | |
1932 | | let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new(); |
1933 | | |
1934 | | dict_builder.append(1)?; |
1935 | | dict_builder.append_null(); |
1936 | | dict_builder.append(2)?; |
1937 | | dict_builder.append(5)?; |
1938 | | |
1939 | | let a = dict_builder.finish(); |
1940 | | |
1941 | | let expected: PrimitiveArray<Int32Type> = |
1942 | | PrimitiveArray::from(vec![Some(2), None, Some(4), Some(10)]); |
1943 | | |
1944 | | apply_arithmetic_scalar( |
1945 | | Arc::new(schema), |
1946 | | vec![Arc::new(a)], |
1947 | | Operator::Multiply, |
1948 | | ScalarValue::Dictionary( |
1949 | | Box::new(DataType::Int8), |
1950 | | Box::new(ScalarValue::Int32(Some(2))), |
1951 | | ), |
1952 | | Arc::new(expected), |
1953 | | )?; |
1954 | | |
1955 | | Ok(()) |
1956 | | } |
1957 | | |
1958 | | #[test] |
1959 | | fn multiply_op_dict_scalar_decimal() -> Result<()> { |
1960 | | let schema = Schema::new(vec![Field::new( |
1961 | | "a", |
1962 | | DataType::Dictionary( |
1963 | | Box::new(DataType::Int8), |
1964 | | Box::new(DataType::Decimal128(10, 0)), |
1965 | | ), |
1966 | | true, |
1967 | | )]); |
1968 | | |
1969 | | let value = 123; |
1970 | | let decimal_array = Arc::new(create_decimal_array( |
1971 | | &[Some(value), None, Some(value - 1), Some(value + 1)], |
1972 | | 10, |
1973 | | 0, |
1974 | | )); |
1975 | | |
1976 | | let keys = Int8Array::from(vec![0, 2, 1, 3, 0]); |
1977 | | let a = DictionaryArray::try_new(keys, decimal_array)?; |
1978 | | |
1979 | | let decimal_array = Arc::new(create_decimal_array( |
1980 | | &[Some(246), Some(244), None, Some(248), Some(246)], |
1981 | | 21, |
1982 | | 0, |
1983 | | )); |
1984 | | |
1985 | | apply_arithmetic_scalar( |
1986 | | Arc::new(schema), |
1987 | | vec![Arc::new(a)], |
1988 | | Operator::Multiply, |
1989 | | ScalarValue::Dictionary( |
1990 | | Box::new(DataType::Int8), |
1991 | | Box::new(ScalarValue::Decimal128(Some(2), 10, 0)), |
1992 | | ), |
1993 | | decimal_array, |
1994 | | )?; |
1995 | | |
1996 | | Ok(()) |
1997 | | } |
1998 | | |
1999 | | #[test] |
2000 | | fn divide_op() -> Result<()> { |
2001 | | let schema = Arc::new(Schema::new(vec![ |
2002 | | Field::new("a", DataType::Int32, false), |
2003 | | Field::new("b", DataType::Int32, false), |
2004 | | ])); |
2005 | | let a = Arc::new(Int32Array::from(vec![8, 32, 128, 512, 2048])); |
2006 | | let b = Arc::new(Int32Array::from(vec![2, 4, 8, 16, 32])); |
2007 | | |
2008 | | apply_arithmetic::<Int32Type>( |
2009 | | schema, |
2010 | | vec![a, b], |
2011 | | Operator::Divide, |
2012 | | Int32Array::from(vec![4, 8, 16, 32, 64]), |
2013 | | )?; |
2014 | | |
2015 | | Ok(()) |
2016 | | } |
2017 | | |
2018 | | #[test] |
2019 | | fn divide_op_dict() -> Result<()> { |
2020 | | let schema = Schema::new(vec![ |
2021 | | Field::new( |
2022 | | "a", |
2023 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
2024 | | true, |
2025 | | ), |
2026 | | Field::new( |
2027 | | "b", |
2028 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
2029 | | true, |
2030 | | ), |
2031 | | ]); |
2032 | | |
2033 | | let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new(); |
2034 | | |
2035 | | dict_builder.append(1)?; |
2036 | | dict_builder.append_null(); |
2037 | | dict_builder.append(2)?; |
2038 | | dict_builder.append(5)?; |
2039 | | dict_builder.append(0)?; |
2040 | | |
2041 | | let a = dict_builder.finish(); |
2042 | | |
2043 | | let b = Int32Array::from(vec![1, 2, 4, 8, 16]); |
2044 | | let keys = Int8Array::from(vec![0, 1, 1, 2, 1]); |
2045 | | let b = DictionaryArray::try_new(keys, Arc::new(b))?; |
2046 | | |
2047 | | apply_arithmetic::<Int32Type>( |
2048 | | Arc::new(schema), |
2049 | | vec![Arc::new(a), Arc::new(b)], |
2050 | | Operator::Divide, |
2051 | | Int32Array::from(vec![Some(1), None, Some(1), Some(1), Some(0)]), |
2052 | | )?; |
2053 | | |
2054 | | Ok(()) |
2055 | | } |
2056 | | |
2057 | | #[test] |
2058 | | fn divide_op_dict_decimal() -> Result<()> { |
2059 | | let schema = Schema::new(vec![ |
2060 | | Field::new( |
2061 | | "a", |
2062 | | DataType::Dictionary( |
2063 | | Box::new(DataType::Int8), |
2064 | | Box::new(DataType::Decimal128(10, 0)), |
2065 | | ), |
2066 | | true, |
2067 | | ), |
2068 | | Field::new( |
2069 | | "b", |
2070 | | DataType::Dictionary( |
2071 | | Box::new(DataType::Int8), |
2072 | | Box::new(DataType::Decimal128(10, 0)), |
2073 | | ), |
2074 | | true, |
2075 | | ), |
2076 | | ]); |
2077 | | |
2078 | | let value = 123; |
2079 | | let decimal_array = Arc::new(create_decimal_array( |
2080 | | &[ |
2081 | | Some(value), |
2082 | | Some(value + 2), |
2083 | | Some(value - 1), |
2084 | | Some(value + 1), |
2085 | | ], |
2086 | | 10, |
2087 | | 0, |
2088 | | )); |
2089 | | |
2090 | | let keys = Int8Array::from(vec![Some(0), Some(2), None, Some(3), Some(0)]); |
2091 | | let a = DictionaryArray::try_new(keys, decimal_array)?; |
2092 | | |
2093 | | let keys = Int8Array::from(vec![Some(0), None, Some(3), Some(2), Some(2)]); |
2094 | | let decimal_array = Arc::new(create_decimal_array( |
2095 | | &[ |
2096 | | Some(value + 1), |
2097 | | Some(value + 3), |
2098 | | Some(value), |
2099 | | Some(value + 2), |
2100 | | ], |
2101 | | 10, |
2102 | | 0, |
2103 | | )); |
2104 | | let b = DictionaryArray::try_new(keys, decimal_array)?; |
2105 | | |
2106 | | apply_arithmetic( |
2107 | | Arc::new(schema), |
2108 | | vec![Arc::new(a), Arc::new(b)], |
2109 | | Operator::Divide, |
2110 | | create_decimal_array( |
2111 | | &[ |
2112 | | Some(9919), // 0.9919 |
2113 | | None, |
2114 | | None, |
2115 | | Some(10081), // 1.0081 |
2116 | | Some(10000), // 1.0 |
2117 | | ], |
2118 | | 14, |
2119 | | 4, |
2120 | | ), |
2121 | | )?; |
2122 | | |
2123 | | Ok(()) |
2124 | | } |
2125 | | |
2126 | | #[test] |
2127 | | fn divide_op_scalar() -> Result<()> { |
2128 | | let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]); |
2129 | | let a = Int32Array::from(vec![1, 2, 3, 4, 5]); |
2130 | | |
2131 | | apply_arithmetic_scalar( |
2132 | | Arc::new(schema), |
2133 | | vec![Arc::new(a)], |
2134 | | Operator::Divide, |
2135 | | ScalarValue::Int32(Some(2)), |
2136 | | Arc::new(Int32Array::from(vec![0, 1, 1, 2, 2])), |
2137 | | )?; |
2138 | | |
2139 | | Ok(()) |
2140 | | } |
2141 | | |
2142 | | #[test] |
2143 | | fn divide_op_dict_scalar() -> Result<()> { |
2144 | | let schema = Schema::new(vec![Field::new( |
2145 | | "a", |
2146 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
2147 | | true, |
2148 | | )]); |
2149 | | |
2150 | | let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new(); |
2151 | | |
2152 | | dict_builder.append(1)?; |
2153 | | dict_builder.append_null(); |
2154 | | dict_builder.append(2)?; |
2155 | | dict_builder.append(5)?; |
2156 | | |
2157 | | let a = dict_builder.finish(); |
2158 | | |
2159 | | let expected: PrimitiveArray<Int32Type> = |
2160 | | PrimitiveArray::from(vec![Some(0), None, Some(1), Some(2)]); |
2161 | | |
2162 | | apply_arithmetic_scalar( |
2163 | | Arc::new(schema), |
2164 | | vec![Arc::new(a)], |
2165 | | Operator::Divide, |
2166 | | ScalarValue::Dictionary( |
2167 | | Box::new(DataType::Int8), |
2168 | | Box::new(ScalarValue::Int32(Some(2))), |
2169 | | ), |
2170 | | Arc::new(expected), |
2171 | | )?; |
2172 | | |
2173 | | Ok(()) |
2174 | | } |
2175 | | |
2176 | | #[test] |
2177 | | fn divide_op_dict_scalar_decimal() -> Result<()> { |
2178 | | let schema = Schema::new(vec![Field::new( |
2179 | | "a", |
2180 | | DataType::Dictionary( |
2181 | | Box::new(DataType::Int8), |
2182 | | Box::new(DataType::Decimal128(10, 0)), |
2183 | | ), |
2184 | | true, |
2185 | | )]); |
2186 | | |
2187 | | let value = 123; |
2188 | | let decimal_array = Arc::new(create_decimal_array( |
2189 | | &[Some(value), None, Some(value - 1), Some(value + 1)], |
2190 | | 10, |
2191 | | 0, |
2192 | | )); |
2193 | | |
2194 | | let keys = Int8Array::from(vec![0, 2, 1, 3, 0]); |
2195 | | let a = DictionaryArray::try_new(keys, decimal_array)?; |
2196 | | |
2197 | | let decimal_array = Arc::new(create_decimal_array( |
2198 | | &[Some(615000), Some(610000), None, Some(620000), Some(615000)], |
2199 | | 14, |
2200 | | 4, |
2201 | | )); |
2202 | | |
2203 | | apply_arithmetic_scalar( |
2204 | | Arc::new(schema), |
2205 | | vec![Arc::new(a)], |
2206 | | Operator::Divide, |
2207 | | ScalarValue::Dictionary( |
2208 | | Box::new(DataType::Int8), |
2209 | | Box::new(ScalarValue::Decimal128(Some(2), 10, 0)), |
2210 | | ), |
2211 | | decimal_array, |
2212 | | )?; |
2213 | | |
2214 | | Ok(()) |
2215 | | } |
2216 | | |
2217 | | #[test] |
2218 | | fn modulus_op() -> Result<()> { |
2219 | | let schema = Arc::new(Schema::new(vec![ |
2220 | | Field::new("a", DataType::Int32, false), |
2221 | | Field::new("b", DataType::Int32, false), |
2222 | | ])); |
2223 | | let a = Arc::new(Int32Array::from(vec![8, 32, 128, 512, 2048])); |
2224 | | let b = Arc::new(Int32Array::from(vec![2, 4, 7, 14, 32])); |
2225 | | |
2226 | | apply_arithmetic::<Int32Type>( |
2227 | | schema, |
2228 | | vec![a, b], |
2229 | | Operator::Modulo, |
2230 | | Int32Array::from(vec![0, 0, 2, 8, 0]), |
2231 | | )?; |
2232 | | |
2233 | | Ok(()) |
2234 | | } |
2235 | | |
2236 | | #[test] |
2237 | | fn modulus_op_dict() -> Result<()> { |
2238 | | let schema = Schema::new(vec![ |
2239 | | Field::new( |
2240 | | "a", |
2241 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
2242 | | true, |
2243 | | ), |
2244 | | Field::new( |
2245 | | "b", |
2246 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
2247 | | true, |
2248 | | ), |
2249 | | ]); |
2250 | | |
2251 | | let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new(); |
2252 | | |
2253 | | dict_builder.append(1)?; |
2254 | | dict_builder.append_null(); |
2255 | | dict_builder.append(2)?; |
2256 | | dict_builder.append(5)?; |
2257 | | dict_builder.append(0)?; |
2258 | | |
2259 | | let a = dict_builder.finish(); |
2260 | | |
2261 | | let b = Int32Array::from(vec![1, 2, 4, 8, 16]); |
2262 | | let keys = Int8Array::from(vec![0, 1, 1, 2, 1]); |
2263 | | let b = DictionaryArray::try_new(keys, Arc::new(b))?; |
2264 | | |
2265 | | apply_arithmetic::<Int32Type>( |
2266 | | Arc::new(schema), |
2267 | | vec![Arc::new(a), Arc::new(b)], |
2268 | | Operator::Modulo, |
2269 | | Int32Array::from(vec![Some(0), None, Some(0), Some(1), Some(0)]), |
2270 | | )?; |
2271 | | |
2272 | | Ok(()) |
2273 | | } |
2274 | | |
2275 | | #[test] |
2276 | | fn modulus_op_dict_decimal() -> Result<()> { |
2277 | | let schema = Schema::new(vec![ |
2278 | | Field::new( |
2279 | | "a", |
2280 | | DataType::Dictionary( |
2281 | | Box::new(DataType::Int8), |
2282 | | Box::new(DataType::Decimal128(10, 0)), |
2283 | | ), |
2284 | | true, |
2285 | | ), |
2286 | | Field::new( |
2287 | | "b", |
2288 | | DataType::Dictionary( |
2289 | | Box::new(DataType::Int8), |
2290 | | Box::new(DataType::Decimal128(10, 0)), |
2291 | | ), |
2292 | | true, |
2293 | | ), |
2294 | | ]); |
2295 | | |
2296 | | let value = 123; |
2297 | | let decimal_array = Arc::new(create_decimal_array( |
2298 | | &[ |
2299 | | Some(value), |
2300 | | Some(value + 2), |
2301 | | Some(value - 1), |
2302 | | Some(value + 1), |
2303 | | ], |
2304 | | 10, |
2305 | | 0, |
2306 | | )); |
2307 | | |
2308 | | let keys = Int8Array::from(vec![Some(0), Some(2), None, Some(3), Some(0)]); |
2309 | | let a = DictionaryArray::try_new(keys, decimal_array)?; |
2310 | | |
2311 | | let keys = Int8Array::from(vec![Some(0), None, Some(3), Some(2), Some(2)]); |
2312 | | let decimal_array = Arc::new(create_decimal_array( |
2313 | | &[ |
2314 | | Some(value + 1), |
2315 | | Some(value + 3), |
2316 | | Some(value), |
2317 | | Some(value + 2), |
2318 | | ], |
2319 | | 10, |
2320 | | 0, |
2321 | | )); |
2322 | | let b = DictionaryArray::try_new(keys, decimal_array)?; |
2323 | | |
2324 | | apply_arithmetic( |
2325 | | Arc::new(schema), |
2326 | | vec![Arc::new(a), Arc::new(b)], |
2327 | | Operator::Modulo, |
2328 | | create_decimal_array(&[Some(123), None, None, Some(1), Some(0)], 10, 0), |
2329 | | )?; |
2330 | | |
2331 | | Ok(()) |
2332 | | } |
2333 | | |
2334 | | #[test] |
2335 | | fn modulus_op_scalar() -> Result<()> { |
2336 | | let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]); |
2337 | | let a = Int32Array::from(vec![1, 2, 3, 4, 5]); |
2338 | | |
2339 | | apply_arithmetic_scalar( |
2340 | | Arc::new(schema), |
2341 | | vec![Arc::new(a)], |
2342 | | Operator::Modulo, |
2343 | | ScalarValue::Int32(Some(2)), |
2344 | | Arc::new(Int32Array::from(vec![1, 0, 1, 0, 1])), |
2345 | | )?; |
2346 | | |
2347 | | Ok(()) |
2348 | | } |
2349 | | |
2350 | | #[test] |
2351 | | fn modules_op_dict_scalar() -> Result<()> { |
2352 | | let schema = Schema::new(vec![Field::new( |
2353 | | "a", |
2354 | | DataType::Dictionary(Box::new(DataType::Int8), Box::new(DataType::Int32)), |
2355 | | true, |
2356 | | )]); |
2357 | | |
2358 | | let mut dict_builder = PrimitiveDictionaryBuilder::<Int8Type, Int32Type>::new(); |
2359 | | |
2360 | | dict_builder.append(1)?; |
2361 | | dict_builder.append_null(); |
2362 | | dict_builder.append(2)?; |
2363 | | dict_builder.append(5)?; |
2364 | | |
2365 | | let a = dict_builder.finish(); |
2366 | | |
2367 | | let expected: PrimitiveArray<Int32Type> = |
2368 | | PrimitiveArray::from(vec![Some(1), None, Some(0), Some(1)]); |
2369 | | |
2370 | | apply_arithmetic_scalar( |
2371 | | Arc::new(schema), |
2372 | | vec![Arc::new(a)], |
2373 | | Operator::Modulo, |
2374 | | ScalarValue::Dictionary( |
2375 | | Box::new(DataType::Int8), |
2376 | | Box::new(ScalarValue::Int32(Some(2))), |
2377 | | ), |
2378 | | Arc::new(expected), |
2379 | | )?; |
2380 | | |
2381 | | Ok(()) |
2382 | | } |
2383 | | |
2384 | | #[test] |
2385 | | fn modulus_op_dict_scalar_decimal() -> Result<()> { |
2386 | | let schema = Schema::new(vec![Field::new( |
2387 | | "a", |
2388 | | DataType::Dictionary( |
2389 | | Box::new(DataType::Int8), |
2390 | | Box::new(DataType::Decimal128(10, 0)), |
2391 | | ), |
2392 | | true, |
2393 | | )]); |
2394 | | |
2395 | | let value = 123; |
2396 | | let decimal_array = Arc::new(create_decimal_array( |
2397 | | &[Some(value), None, Some(value - 1), Some(value + 1)], |
2398 | | 10, |
2399 | | 0, |
2400 | | )); |
2401 | | |
2402 | | let keys = Int8Array::from(vec![0, 2, 1, 3, 0]); |
2403 | | let a = DictionaryArray::try_new(keys, decimal_array)?; |
2404 | | |
2405 | | let decimal_array = Arc::new(create_decimal_array( |
2406 | | &[Some(1), Some(0), None, Some(0), Some(1)], |
2407 | | 10, |
2408 | | 0, |
2409 | | )); |
2410 | | |
2411 | | apply_arithmetic_scalar( |
2412 | | Arc::new(schema), |
2413 | | vec![Arc::new(a)], |
2414 | | Operator::Modulo, |
2415 | | ScalarValue::Dictionary( |
2416 | | Box::new(DataType::Int8), |
2417 | | Box::new(ScalarValue::Decimal128(Some(2), 10, 0)), |
2418 | | ), |
2419 | | decimal_array, |
2420 | | )?; |
2421 | | |
2422 | | Ok(()) |
2423 | | } |
2424 | | |
2425 | | fn apply_arithmetic<T: ArrowNumericType>( |
2426 | | schema: SchemaRef, |
2427 | | data: Vec<ArrayRef>, |
2428 | | op: Operator, |
2429 | | expected: PrimitiveArray<T>, |
2430 | | ) -> Result<()> { |
2431 | | let arithmetic_op = |
2432 | | binary_op(col("a", &schema)?, op, col("b", &schema)?, &schema)?; |
2433 | | let batch = RecordBatch::try_new(schema, data)?; |
2434 | | let result = arithmetic_op |
2435 | | .evaluate(&batch)? |
2436 | | .into_array(batch.num_rows()) |
2437 | | .expect("Failed to convert to array"); |
2438 | | |
2439 | | assert_eq!(result.as_ref(), &expected); |
2440 | | Ok(()) |
2441 | | } |
2442 | | |
2443 | | fn apply_arithmetic_scalar( |
2444 | | schema: SchemaRef, |
2445 | | data: Vec<ArrayRef>, |
2446 | | op: Operator, |
2447 | | literal: ScalarValue, |
2448 | | expected: ArrayRef, |
2449 | | ) -> Result<()> { |
2450 | | let lit = Arc::new(Literal::new(literal)); |
2451 | | let arithmetic_op = binary_op(col("a", &schema)?, op, lit, &schema)?; |
2452 | | let batch = RecordBatch::try_new(schema, data)?; |
2453 | | let result = arithmetic_op |
2454 | | .evaluate(&batch)? |
2455 | | .into_array(batch.num_rows()) |
2456 | | .expect("Failed to convert to array"); |
2457 | | |
2458 | | assert_eq!(&result, &expected); |
2459 | | Ok(()) |
2460 | | } |
2461 | | |
2462 | | fn apply_logic_op( |
2463 | | schema: &SchemaRef, |
2464 | | left: &ArrayRef, |
2465 | | right: &ArrayRef, |
2466 | | op: Operator, |
2467 | | expected: BooleanArray, |
2468 | | ) -> Result<()> { |
2469 | | let op = binary_op(col("a", schema)?, op, col("b", schema)?, schema)?; |
2470 | | let data: Vec<ArrayRef> = vec![Arc::clone(left), Arc::clone(right)]; |
2471 | | let batch = RecordBatch::try_new(Arc::clone(schema), data)?; |
2472 | | let result = op |
2473 | | .evaluate(&batch)? |
2474 | | .into_array(batch.num_rows()) |
2475 | | .expect("Failed to convert to array"); |
2476 | | |
2477 | | assert_eq!(result.as_ref(), &expected); |
2478 | | Ok(()) |
2479 | | } |
2480 | | |
2481 | | // Test `scalar <op> arr` produces expected |
2482 | | fn apply_logic_op_scalar_arr( |
2483 | | schema: &SchemaRef, |
2484 | | scalar: &ScalarValue, |
2485 | | arr: &ArrayRef, |
2486 | | op: Operator, |
2487 | | expected: &BooleanArray, |
2488 | | ) -> Result<()> { |
2489 | | let scalar = lit(scalar.clone()); |
2490 | | let op = binary_op(scalar, op, col("a", schema)?, schema)?; |
2491 | | let batch = RecordBatch::try_new(Arc::clone(schema), vec![Arc::clone(arr)])?; |
2492 | | let result = op |
2493 | | .evaluate(&batch)? |
2494 | | .into_array(batch.num_rows()) |
2495 | | .expect("Failed to convert to array"); |
2496 | | assert_eq!(result.as_ref(), expected); |
2497 | | |
2498 | | Ok(()) |
2499 | | } |
2500 | | |
2501 | | // Test `arr <op> scalar` produces expected |
2502 | | fn apply_logic_op_arr_scalar( |
2503 | | schema: &SchemaRef, |
2504 | | arr: &ArrayRef, |
2505 | | scalar: &ScalarValue, |
2506 | | op: Operator, |
2507 | | expected: &BooleanArray, |
2508 | | ) -> Result<()> { |
2509 | | let scalar = lit(scalar.clone()); |
2510 | | let op = binary_op(col("a", schema)?, op, scalar, schema)?; |
2511 | | let batch = RecordBatch::try_new(Arc::clone(schema), vec![Arc::clone(arr)])?; |
2512 | | let result = op |
2513 | | .evaluate(&batch)? |
2514 | | .into_array(batch.num_rows()) |
2515 | | .expect("Failed to convert to array"); |
2516 | | assert_eq!(result.as_ref(), expected); |
2517 | | |
2518 | | Ok(()) |
2519 | | } |
2520 | | |
2521 | | #[test] |
2522 | | fn and_with_nulls_op() -> Result<()> { |
2523 | | let schema = Schema::new(vec![ |
2524 | | Field::new("a", DataType::Boolean, true), |
2525 | | Field::new("b", DataType::Boolean, true), |
2526 | | ]); |
2527 | | let a = Arc::new(BooleanArray::from(vec![ |
2528 | | Some(true), |
2529 | | Some(false), |
2530 | | None, |
2531 | | Some(true), |
2532 | | Some(false), |
2533 | | None, |
2534 | | Some(true), |
2535 | | Some(false), |
2536 | | None, |
2537 | | ])) as ArrayRef; |
2538 | | let b = Arc::new(BooleanArray::from(vec![ |
2539 | | Some(true), |
2540 | | Some(true), |
2541 | | Some(true), |
2542 | | Some(false), |
2543 | | Some(false), |
2544 | | Some(false), |
2545 | | None, |
2546 | | None, |
2547 | | None, |
2548 | | ])) as ArrayRef; |
2549 | | |
2550 | | let expected = BooleanArray::from(vec![ |
2551 | | Some(true), |
2552 | | Some(false), |
2553 | | None, |
2554 | | Some(false), |
2555 | | Some(false), |
2556 | | Some(false), |
2557 | | None, |
2558 | | Some(false), |
2559 | | None, |
2560 | | ]); |
2561 | | apply_logic_op(&Arc::new(schema), &a, &b, Operator::And, expected)?; |
2562 | | |
2563 | | Ok(()) |
2564 | | } |
2565 | | |
2566 | | #[test] |
2567 | | fn regex_with_nulls() -> Result<()> { |
2568 | | let schema = Schema::new(vec![ |
2569 | | Field::new("a", DataType::Utf8, true), |
2570 | | Field::new("b", DataType::Utf8, true), |
2571 | | ]); |
2572 | | let a = Arc::new(StringArray::from(vec![ |
2573 | | Some("abc"), |
2574 | | None, |
2575 | | Some("abc"), |
2576 | | None, |
2577 | | Some("abc"), |
2578 | | ])) as ArrayRef; |
2579 | | let b = Arc::new(StringArray::from(vec![ |
2580 | | Some("^a"), |
2581 | | Some("^A"), |
2582 | | None, |
2583 | | None, |
2584 | | Some("^(b|c)"), |
2585 | | ])) as ArrayRef; |
2586 | | |
2587 | | let regex_expected = |
2588 | | BooleanArray::from(vec![Some(true), None, None, None, Some(false)]); |
2589 | | let regex_not_expected = |
2590 | | BooleanArray::from(vec![Some(false), None, None, None, Some(true)]); |
2591 | | apply_logic_op( |
2592 | | &Arc::new(schema.clone()), |
2593 | | &a, |
2594 | | &b, |
2595 | | Operator::RegexMatch, |
2596 | | regex_expected.clone(), |
2597 | | )?; |
2598 | | apply_logic_op( |
2599 | | &Arc::new(schema.clone()), |
2600 | | &a, |
2601 | | &b, |
2602 | | Operator::RegexIMatch, |
2603 | | regex_expected.clone(), |
2604 | | )?; |
2605 | | apply_logic_op( |
2606 | | &Arc::new(schema.clone()), |
2607 | | &a, |
2608 | | &b, |
2609 | | Operator::RegexNotMatch, |
2610 | | regex_not_expected.clone(), |
2611 | | )?; |
2612 | | apply_logic_op( |
2613 | | &Arc::new(schema), |
2614 | | &a, |
2615 | | &b, |
2616 | | Operator::RegexNotIMatch, |
2617 | | regex_not_expected.clone(), |
2618 | | )?; |
2619 | | |
2620 | | let schema = Schema::new(vec![ |
2621 | | Field::new("a", DataType::LargeUtf8, true), |
2622 | | Field::new("b", DataType::LargeUtf8, true), |
2623 | | ]); |
2624 | | let a = Arc::new(LargeStringArray::from(vec![ |
2625 | | Some("abc"), |
2626 | | None, |
2627 | | Some("abc"), |
2628 | | None, |
2629 | | Some("abc"), |
2630 | | ])) as ArrayRef; |
2631 | | let b = Arc::new(LargeStringArray::from(vec![ |
2632 | | Some("^a"), |
2633 | | Some("^A"), |
2634 | | None, |
2635 | | None, |
2636 | | Some("^(b|c)"), |
2637 | | ])) as ArrayRef; |
2638 | | |
2639 | | apply_logic_op( |
2640 | | &Arc::new(schema.clone()), |
2641 | | &a, |
2642 | | &b, |
2643 | | Operator::RegexMatch, |
2644 | | regex_expected.clone(), |
2645 | | )?; |
2646 | | apply_logic_op( |
2647 | | &Arc::new(schema.clone()), |
2648 | | &a, |
2649 | | &b, |
2650 | | Operator::RegexIMatch, |
2651 | | regex_expected, |
2652 | | )?; |
2653 | | apply_logic_op( |
2654 | | &Arc::new(schema.clone()), |
2655 | | &a, |
2656 | | &b, |
2657 | | Operator::RegexNotMatch, |
2658 | | regex_not_expected.clone(), |
2659 | | )?; |
2660 | | apply_logic_op( |
2661 | | &Arc::new(schema), |
2662 | | &a, |
2663 | | &b, |
2664 | | Operator::RegexNotIMatch, |
2665 | | regex_not_expected, |
2666 | | )?; |
2667 | | |
2668 | | Ok(()) |
2669 | | } |
2670 | | |
2671 | | #[test] |
2672 | | fn or_with_nulls_op() -> Result<()> { |
2673 | | let schema = Schema::new(vec![ |
2674 | | Field::new("a", DataType::Boolean, true), |
2675 | | Field::new("b", DataType::Boolean, true), |
2676 | | ]); |
2677 | | let a = Arc::new(BooleanArray::from(vec![ |
2678 | | Some(true), |
2679 | | Some(false), |
2680 | | None, |
2681 | | Some(true), |
2682 | | Some(false), |
2683 | | None, |
2684 | | Some(true), |
2685 | | Some(false), |
2686 | | None, |
2687 | | ])) as ArrayRef; |
2688 | | let b = Arc::new(BooleanArray::from(vec![ |
2689 | | Some(true), |
2690 | | Some(true), |
2691 | | Some(true), |
2692 | | Some(false), |
2693 | | Some(false), |
2694 | | Some(false), |
2695 | | None, |
2696 | | None, |
2697 | | None, |
2698 | | ])) as ArrayRef; |
2699 | | |
2700 | | let expected = BooleanArray::from(vec![ |
2701 | | Some(true), |
2702 | | Some(true), |
2703 | | Some(true), |
2704 | | Some(true), |
2705 | | Some(false), |
2706 | | None, |
2707 | | Some(true), |
2708 | | None, |
2709 | | None, |
2710 | | ]); |
2711 | | apply_logic_op(&Arc::new(schema), &a, &b, Operator::Or, expected)?; |
2712 | | |
2713 | | Ok(()) |
2714 | | } |
2715 | | |
2716 | | /// Returns (schema, a: BooleanArray, b: BooleanArray) with all possible inputs |
2717 | | /// |
2718 | | /// a: [true, true, true, NULL, NULL, NULL, false, false, false] |
2719 | | /// b: [true, NULL, false, true, NULL, false, true, NULL, false] |
2720 | | fn bool_test_arrays() -> (SchemaRef, ArrayRef, ArrayRef) { |
2721 | | let schema = Schema::new(vec![ |
2722 | | Field::new("a", DataType::Boolean, true), |
2723 | | Field::new("b", DataType::Boolean, true), |
2724 | | ]); |
2725 | | let a: BooleanArray = [ |
2726 | | Some(true), |
2727 | | Some(true), |
2728 | | Some(true), |
2729 | | None, |
2730 | | None, |
2731 | | None, |
2732 | | Some(false), |
2733 | | Some(false), |
2734 | | Some(false), |
2735 | | ] |
2736 | | .iter() |
2737 | | .collect(); |
2738 | | let b: BooleanArray = [ |
2739 | | Some(true), |
2740 | | None, |
2741 | | Some(false), |
2742 | | Some(true), |
2743 | | None, |
2744 | | Some(false), |
2745 | | Some(true), |
2746 | | None, |
2747 | | Some(false), |
2748 | | ] |
2749 | | .iter() |
2750 | | .collect(); |
2751 | | (Arc::new(schema), Arc::new(a), Arc::new(b)) |
2752 | | } |
2753 | | |
2754 | | /// Returns (schema, BooleanArray) with [true, NULL, false] |
2755 | | fn scalar_bool_test_array() -> (SchemaRef, ArrayRef) { |
2756 | | let schema = Schema::new(vec![Field::new("a", DataType::Boolean, true)]); |
2757 | | let a: BooleanArray = [Some(true), None, Some(false)].iter().collect(); |
2758 | | (Arc::new(schema), Arc::new(a)) |
2759 | | } |
2760 | | |
2761 | | #[test] |
2762 | | fn eq_op_bool() { |
2763 | | let (schema, a, b) = bool_test_arrays(); |
2764 | | let expected = [ |
2765 | | Some(true), |
2766 | | None, |
2767 | | Some(false), |
2768 | | None, |
2769 | | None, |
2770 | | None, |
2771 | | Some(false), |
2772 | | None, |
2773 | | Some(true), |
2774 | | ] |
2775 | | .iter() |
2776 | | .collect(); |
2777 | | apply_logic_op(&schema, &a, &b, Operator::Eq, expected).unwrap(); |
2778 | | } |
2779 | | |
2780 | | #[test] |
2781 | | fn eq_op_bool_scalar() { |
2782 | | let (schema, a) = scalar_bool_test_array(); |
2783 | | let expected = [Some(true), None, Some(false)].iter().collect(); |
2784 | | apply_logic_op_scalar_arr( |
2785 | | &schema, |
2786 | | &ScalarValue::from(true), |
2787 | | &a, |
2788 | | Operator::Eq, |
2789 | | &expected, |
2790 | | ) |
2791 | | .unwrap(); |
2792 | | apply_logic_op_arr_scalar( |
2793 | | &schema, |
2794 | | &a, |
2795 | | &ScalarValue::from(true), |
2796 | | Operator::Eq, |
2797 | | &expected, |
2798 | | ) |
2799 | | .unwrap(); |
2800 | | |
2801 | | let expected = [Some(false), None, Some(true)].iter().collect(); |
2802 | | apply_logic_op_scalar_arr( |
2803 | | &schema, |
2804 | | &ScalarValue::from(false), |
2805 | | &a, |
2806 | | Operator::Eq, |
2807 | | &expected, |
2808 | | ) |
2809 | | .unwrap(); |
2810 | | apply_logic_op_arr_scalar( |
2811 | | &schema, |
2812 | | &a, |
2813 | | &ScalarValue::from(false), |
2814 | | Operator::Eq, |
2815 | | &expected, |
2816 | | ) |
2817 | | .unwrap(); |
2818 | | } |
2819 | | |
2820 | | #[test] |
2821 | | fn neq_op_bool() { |
2822 | | let (schema, a, b) = bool_test_arrays(); |
2823 | | let expected = [ |
2824 | | Some(false), |
2825 | | None, |
2826 | | Some(true), |
2827 | | None, |
2828 | | None, |
2829 | | None, |
2830 | | Some(true), |
2831 | | None, |
2832 | | Some(false), |
2833 | | ] |
2834 | | .iter() |
2835 | | .collect(); |
2836 | | apply_logic_op(&schema, &a, &b, Operator::NotEq, expected).unwrap(); |
2837 | | } |
2838 | | |
2839 | | #[test] |
2840 | | fn neq_op_bool_scalar() { |
2841 | | let (schema, a) = scalar_bool_test_array(); |
2842 | | let expected = [Some(false), None, Some(true)].iter().collect(); |
2843 | | apply_logic_op_scalar_arr( |
2844 | | &schema, |
2845 | | &ScalarValue::from(true), |
2846 | | &a, |
2847 | | Operator::NotEq, |
2848 | | &expected, |
2849 | | ) |
2850 | | .unwrap(); |
2851 | | apply_logic_op_arr_scalar( |
2852 | | &schema, |
2853 | | &a, |
2854 | | &ScalarValue::from(true), |
2855 | | Operator::NotEq, |
2856 | | &expected, |
2857 | | ) |
2858 | | .unwrap(); |
2859 | | |
2860 | | let expected = [Some(true), None, Some(false)].iter().collect(); |
2861 | | apply_logic_op_scalar_arr( |
2862 | | &schema, |
2863 | | &ScalarValue::from(false), |
2864 | | &a, |
2865 | | Operator::NotEq, |
2866 | | &expected, |
2867 | | ) |
2868 | | .unwrap(); |
2869 | | apply_logic_op_arr_scalar( |
2870 | | &schema, |
2871 | | &a, |
2872 | | &ScalarValue::from(false), |
2873 | | Operator::NotEq, |
2874 | | &expected, |
2875 | | ) |
2876 | | .unwrap(); |
2877 | | } |
2878 | | |
2879 | | #[test] |
2880 | | fn lt_op_bool() { |
2881 | | let (schema, a, b) = bool_test_arrays(); |
2882 | | let expected = [ |
2883 | | Some(false), |
2884 | | None, |
2885 | | Some(false), |
2886 | | None, |
2887 | | None, |
2888 | | None, |
2889 | | Some(true), |
2890 | | None, |
2891 | | Some(false), |
2892 | | ] |
2893 | | .iter() |
2894 | | .collect(); |
2895 | | apply_logic_op(&schema, &a, &b, Operator::Lt, expected).unwrap(); |
2896 | | } |
2897 | | |
2898 | | #[test] |
2899 | | fn lt_op_bool_scalar() { |
2900 | | let (schema, a) = scalar_bool_test_array(); |
2901 | | let expected = [Some(false), None, Some(false)].iter().collect(); |
2902 | | apply_logic_op_scalar_arr( |
2903 | | &schema, |
2904 | | &ScalarValue::from(true), |
2905 | | &a, |
2906 | | Operator::Lt, |
2907 | | &expected, |
2908 | | ) |
2909 | | .unwrap(); |
2910 | | |
2911 | | let expected = [Some(false), None, Some(true)].iter().collect(); |
2912 | | apply_logic_op_arr_scalar( |
2913 | | &schema, |
2914 | | &a, |
2915 | | &ScalarValue::from(true), |
2916 | | Operator::Lt, |
2917 | | &expected, |
2918 | | ) |
2919 | | .unwrap(); |
2920 | | |
2921 | | let expected = [Some(true), None, Some(false)].iter().collect(); |
2922 | | apply_logic_op_scalar_arr( |
2923 | | &schema, |
2924 | | &ScalarValue::from(false), |
2925 | | &a, |
2926 | | Operator::Lt, |
2927 | | &expected, |
2928 | | ) |
2929 | | .unwrap(); |
2930 | | |
2931 | | let expected = [Some(false), None, Some(false)].iter().collect(); |
2932 | | apply_logic_op_arr_scalar( |
2933 | | &schema, |
2934 | | &a, |
2935 | | &ScalarValue::from(false), |
2936 | | Operator::Lt, |
2937 | | &expected, |
2938 | | ) |
2939 | | .unwrap(); |
2940 | | } |
2941 | | |
2942 | | #[test] |
2943 | | fn lt_eq_op_bool() { |
2944 | | let (schema, a, b) = bool_test_arrays(); |
2945 | | let expected = [ |
2946 | | Some(true), |
2947 | | None, |
2948 | | Some(false), |
2949 | | None, |
2950 | | None, |
2951 | | None, |
2952 | | Some(true), |
2953 | | None, |
2954 | | Some(true), |
2955 | | ] |
2956 | | .iter() |
2957 | | .collect(); |
2958 | | apply_logic_op(&schema, &a, &b, Operator::LtEq, expected).unwrap(); |
2959 | | } |
2960 | | |
2961 | | #[test] |
2962 | | fn lt_eq_op_bool_scalar() { |
2963 | | let (schema, a) = scalar_bool_test_array(); |
2964 | | let expected = [Some(true), None, Some(false)].iter().collect(); |
2965 | | apply_logic_op_scalar_arr( |
2966 | | &schema, |
2967 | | &ScalarValue::from(true), |
2968 | | &a, |
2969 | | Operator::LtEq, |
2970 | | &expected, |
2971 | | ) |
2972 | | .unwrap(); |
2973 | | |
2974 | | let expected = [Some(true), None, Some(true)].iter().collect(); |
2975 | | apply_logic_op_arr_scalar( |
2976 | | &schema, |
2977 | | &a, |
2978 | | &ScalarValue::from(true), |
2979 | | Operator::LtEq, |
2980 | | &expected, |
2981 | | ) |
2982 | | .unwrap(); |
2983 | | |
2984 | | let expected = [Some(true), None, Some(true)].iter().collect(); |
2985 | | apply_logic_op_scalar_arr( |
2986 | | &schema, |
2987 | | &ScalarValue::from(false), |
2988 | | &a, |
2989 | | Operator::LtEq, |
2990 | | &expected, |
2991 | | ) |
2992 | | .unwrap(); |
2993 | | |
2994 | | let expected = [Some(false), None, Some(true)].iter().collect(); |
2995 | | apply_logic_op_arr_scalar( |
2996 | | &schema, |
2997 | | &a, |
2998 | | &ScalarValue::from(false), |
2999 | | Operator::LtEq, |
3000 | | &expected, |
3001 | | ) |
3002 | | .unwrap(); |
3003 | | } |
3004 | | |
3005 | | #[test] |
3006 | | fn gt_op_bool() { |
3007 | | let (schema, a, b) = bool_test_arrays(); |
3008 | | let expected = [ |
3009 | | Some(false), |
3010 | | None, |
3011 | | Some(true), |
3012 | | None, |
3013 | | None, |
3014 | | None, |
3015 | | Some(false), |
3016 | | None, |
3017 | | Some(false), |
3018 | | ] |
3019 | | .iter() |
3020 | | .collect(); |
3021 | | apply_logic_op(&schema, &a, &b, Operator::Gt, expected).unwrap(); |
3022 | | } |
3023 | | |
3024 | | #[test] |
3025 | | fn gt_op_bool_scalar() { |
3026 | | let (schema, a) = scalar_bool_test_array(); |
3027 | | let expected = [Some(false), None, Some(true)].iter().collect(); |
3028 | | apply_logic_op_scalar_arr( |
3029 | | &schema, |
3030 | | &ScalarValue::from(true), |
3031 | | &a, |
3032 | | Operator::Gt, |
3033 | | &expected, |
3034 | | ) |
3035 | | .unwrap(); |
3036 | | |
3037 | | let expected = [Some(false), None, Some(false)].iter().collect(); |
3038 | | apply_logic_op_arr_scalar( |
3039 | | &schema, |
3040 | | &a, |
3041 | | &ScalarValue::from(true), |
3042 | | Operator::Gt, |
3043 | | &expected, |
3044 | | ) |
3045 | | .unwrap(); |
3046 | | |
3047 | | let expected = [Some(false), None, Some(false)].iter().collect(); |
3048 | | apply_logic_op_scalar_arr( |
3049 | | &schema, |
3050 | | &ScalarValue::from(false), |
3051 | | &a, |
3052 | | Operator::Gt, |
3053 | | &expected, |
3054 | | ) |
3055 | | .unwrap(); |
3056 | | |
3057 | | let expected = [Some(true), None, Some(false)].iter().collect(); |
3058 | | apply_logic_op_arr_scalar( |
3059 | | &schema, |
3060 | | &a, |
3061 | | &ScalarValue::from(false), |
3062 | | Operator::Gt, |
3063 | | &expected, |
3064 | | ) |
3065 | | .unwrap(); |
3066 | | } |
3067 | | |
3068 | | #[test] |
3069 | | fn gt_eq_op_bool() { |
3070 | | let (schema, a, b) = bool_test_arrays(); |
3071 | | let expected = [ |
3072 | | Some(true), |
3073 | | None, |
3074 | | Some(true), |
3075 | | None, |
3076 | | None, |
3077 | | None, |
3078 | | Some(false), |
3079 | | None, |
3080 | | Some(true), |
3081 | | ] |
3082 | | .iter() |
3083 | | .collect(); |
3084 | | apply_logic_op(&schema, &a, &b, Operator::GtEq, expected).unwrap(); |
3085 | | } |
3086 | | |
3087 | | #[test] |
3088 | | fn gt_eq_op_bool_scalar() { |
3089 | | let (schema, a) = scalar_bool_test_array(); |
3090 | | let expected = [Some(true), None, Some(true)].iter().collect(); |
3091 | | apply_logic_op_scalar_arr( |
3092 | | &schema, |
3093 | | &ScalarValue::from(true), |
3094 | | &a, |
3095 | | Operator::GtEq, |
3096 | | &expected, |
3097 | | ) |
3098 | | .unwrap(); |
3099 | | |
3100 | | let expected = [Some(true), None, Some(false)].iter().collect(); |
3101 | | apply_logic_op_arr_scalar( |
3102 | | &schema, |
3103 | | &a, |
3104 | | &ScalarValue::from(true), |
3105 | | Operator::GtEq, |
3106 | | &expected, |
3107 | | ) |
3108 | | .unwrap(); |
3109 | | |
3110 | | let expected = [Some(false), None, Some(true)].iter().collect(); |
3111 | | apply_logic_op_scalar_arr( |
3112 | | &schema, |
3113 | | &ScalarValue::from(false), |
3114 | | &a, |
3115 | | Operator::GtEq, |
3116 | | &expected, |
3117 | | ) |
3118 | | .unwrap(); |
3119 | | |
3120 | | let expected = [Some(true), None, Some(true)].iter().collect(); |
3121 | | apply_logic_op_arr_scalar( |
3122 | | &schema, |
3123 | | &a, |
3124 | | &ScalarValue::from(false), |
3125 | | Operator::GtEq, |
3126 | | &expected, |
3127 | | ) |
3128 | | .unwrap(); |
3129 | | } |
3130 | | |
3131 | | #[test] |
3132 | | fn is_distinct_from_op_bool() { |
3133 | | let (schema, a, b) = bool_test_arrays(); |
3134 | | let expected = [ |
3135 | | Some(false), |
3136 | | Some(true), |
3137 | | Some(true), |
3138 | | Some(true), |
3139 | | Some(false), |
3140 | | Some(true), |
3141 | | Some(true), |
3142 | | Some(true), |
3143 | | Some(false), |
3144 | | ] |
3145 | | .iter() |
3146 | | .collect(); |
3147 | | apply_logic_op(&schema, &a, &b, Operator::IsDistinctFrom, expected).unwrap(); |
3148 | | } |
3149 | | |
3150 | | #[test] |
3151 | | fn is_not_distinct_from_op_bool() { |
3152 | | let (schema, a, b) = bool_test_arrays(); |
3153 | | let expected = [ |
3154 | | Some(true), |
3155 | | Some(false), |
3156 | | Some(false), |
3157 | | Some(false), |
3158 | | Some(true), |
3159 | | Some(false), |
3160 | | Some(false), |
3161 | | Some(false), |
3162 | | Some(true), |
3163 | | ] |
3164 | | .iter() |
3165 | | .collect(); |
3166 | | apply_logic_op(&schema, &a, &b, Operator::IsNotDistinctFrom, expected).unwrap(); |
3167 | | } |
3168 | | |
3169 | | #[test] |
3170 | | fn relatively_deeply_nested() { |
3171 | | // Reproducer for https://github.com/apache/datafusion/issues/419 |
3172 | | |
3173 | | // where even relatively shallow binary expressions overflowed |
3174 | | // the stack in debug builds |
3175 | | |
3176 | | let input: Vec<_> = vec![1, 2, 3, 4, 5].into_iter().map(Some).collect(); |
3177 | | let a: Int32Array = input.iter().collect(); |
3178 | | |
3179 | | let batch = RecordBatch::try_from_iter(vec![("a", Arc::new(a) as _)]).unwrap(); |
3180 | | let schema = batch.schema(); |
3181 | | |
3182 | | // build a left deep tree ((((a + a) + a) + a .... |
3183 | | let tree_depth: i32 = 100; |
3184 | | let expr = (0..tree_depth) |
3185 | | .map(|_| col("a", schema.as_ref()).unwrap()) |
3186 | | .reduce(|l, r| binary(l, Operator::Plus, r, &schema).unwrap()) |
3187 | | .unwrap(); |
3188 | | |
3189 | | let result = expr |
3190 | | .evaluate(&batch) |
3191 | | .expect("evaluation") |
3192 | | .into_array(batch.num_rows()) |
3193 | | .expect("Failed to convert to array"); |
3194 | | |
3195 | | let expected: Int32Array = input |
3196 | | .into_iter() |
3197 | | .map(|i| i.map(|i| i * tree_depth)) |
3198 | | .collect(); |
3199 | | assert_eq!(result.as_ref(), &expected); |
3200 | | } |
3201 | | |
3202 | | fn create_decimal_array( |
3203 | | array: &[Option<i128>], |
3204 | | precision: u8, |
3205 | | scale: i8, |
3206 | | ) -> Decimal128Array { |
3207 | | let mut decimal_builder = Decimal128Builder::with_capacity(array.len()); |
3208 | | for value in array.iter().copied() { |
3209 | | decimal_builder.append_option(value) |
3210 | | } |
3211 | | decimal_builder |
3212 | | .finish() |
3213 | | .with_precision_and_scale(precision, scale) |
3214 | | .unwrap() |
3215 | | } |
3216 | | |
3217 | | #[test] |
3218 | | fn comparison_dict_decimal_scalar_expr_test() -> Result<()> { |
3219 | | // scalar of decimal compare with dictionary decimal array |
3220 | | let value_i128 = 123; |
3221 | | let decimal_scalar = ScalarValue::Dictionary( |
3222 | | Box::new(DataType::Int8), |
3223 | | Box::new(ScalarValue::Decimal128(Some(value_i128), 25, 3)), |
3224 | | ); |
3225 | | let schema = Arc::new(Schema::new(vec![Field::new( |
3226 | | "a", |
3227 | | DataType::Dictionary( |
3228 | | Box::new(DataType::Int8), |
3229 | | Box::new(DataType::Decimal128(25, 3)), |
3230 | | ), |
3231 | | true, |
3232 | | )])); |
3233 | | let decimal_array = Arc::new(create_decimal_array( |
3234 | | &[ |
3235 | | Some(value_i128), |
3236 | | None, |
3237 | | Some(value_i128 - 1), |
3238 | | Some(value_i128 + 1), |
3239 | | ], |
3240 | | 25, |
3241 | | 3, |
3242 | | )); |
3243 | | |
3244 | | let keys = Int8Array::from(vec![Some(0), None, Some(2), Some(3)]); |
3245 | | let dictionary = |
3246 | | Arc::new(DictionaryArray::try_new(keys, decimal_array)?) as ArrayRef; |
3247 | | |
3248 | | // array = scalar |
3249 | | apply_logic_op_arr_scalar( |
3250 | | &schema, |
3251 | | &dictionary, |
3252 | | &decimal_scalar, |
3253 | | Operator::Eq, |
3254 | | &BooleanArray::from(vec![Some(true), None, Some(false), Some(false)]), |
3255 | | ) |
3256 | | .unwrap(); |
3257 | | // array != scalar |
3258 | | apply_logic_op_arr_scalar( |
3259 | | &schema, |
3260 | | &dictionary, |
3261 | | &decimal_scalar, |
3262 | | Operator::NotEq, |
3263 | | &BooleanArray::from(vec![Some(false), None, Some(true), Some(true)]), |
3264 | | ) |
3265 | | .unwrap(); |
3266 | | // array < scalar |
3267 | | apply_logic_op_arr_scalar( |
3268 | | &schema, |
3269 | | &dictionary, |
3270 | | &decimal_scalar, |
3271 | | Operator::Lt, |
3272 | | &BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]), |
3273 | | ) |
3274 | | .unwrap(); |
3275 | | |
3276 | | // array <= scalar |
3277 | | apply_logic_op_arr_scalar( |
3278 | | &schema, |
3279 | | &dictionary, |
3280 | | &decimal_scalar, |
3281 | | Operator::LtEq, |
3282 | | &BooleanArray::from(vec![Some(true), None, Some(true), Some(false)]), |
3283 | | ) |
3284 | | .unwrap(); |
3285 | | // array > scalar |
3286 | | apply_logic_op_arr_scalar( |
3287 | | &schema, |
3288 | | &dictionary, |
3289 | | &decimal_scalar, |
3290 | | Operator::Gt, |
3291 | | &BooleanArray::from(vec![Some(false), None, Some(false), Some(true)]), |
3292 | | ) |
3293 | | .unwrap(); |
3294 | | |
3295 | | // array >= scalar |
3296 | | apply_logic_op_arr_scalar( |
3297 | | &schema, |
3298 | | &dictionary, |
3299 | | &decimal_scalar, |
3300 | | Operator::GtEq, |
3301 | | &BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]), |
3302 | | ) |
3303 | | .unwrap(); |
3304 | | |
3305 | | Ok(()) |
3306 | | } |
3307 | | |
3308 | | #[test] |
3309 | | fn comparison_decimal_expr_test() -> Result<()> { |
3310 | | // scalar of decimal compare with decimal array |
3311 | | let value_i128 = 123; |
3312 | | let decimal_scalar = ScalarValue::Decimal128(Some(value_i128), 25, 3); |
3313 | | let schema = Arc::new(Schema::new(vec![Field::new( |
3314 | | "a", |
3315 | | DataType::Decimal128(25, 3), |
3316 | | true, |
3317 | | )])); |
3318 | | let decimal_array = Arc::new(create_decimal_array( |
3319 | | &[ |
3320 | | Some(value_i128), |
3321 | | None, |
3322 | | Some(value_i128 - 1), |
3323 | | Some(value_i128 + 1), |
3324 | | ], |
3325 | | 25, |
3326 | | 3, |
3327 | | )) as ArrayRef; |
3328 | | // array = scalar |
3329 | | apply_logic_op_arr_scalar( |
3330 | | &schema, |
3331 | | &decimal_array, |
3332 | | &decimal_scalar, |
3333 | | Operator::Eq, |
3334 | | &BooleanArray::from(vec![Some(true), None, Some(false), Some(false)]), |
3335 | | ) |
3336 | | .unwrap(); |
3337 | | // array != scalar |
3338 | | apply_logic_op_arr_scalar( |
3339 | | &schema, |
3340 | | &decimal_array, |
3341 | | &decimal_scalar, |
3342 | | Operator::NotEq, |
3343 | | &BooleanArray::from(vec![Some(false), None, Some(true), Some(true)]), |
3344 | | ) |
3345 | | .unwrap(); |
3346 | | // array < scalar |
3347 | | apply_logic_op_arr_scalar( |
3348 | | &schema, |
3349 | | &decimal_array, |
3350 | | &decimal_scalar, |
3351 | | Operator::Lt, |
3352 | | &BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]), |
3353 | | ) |
3354 | | .unwrap(); |
3355 | | |
3356 | | // array <= scalar |
3357 | | apply_logic_op_arr_scalar( |
3358 | | &schema, |
3359 | | &decimal_array, |
3360 | | &decimal_scalar, |
3361 | | Operator::LtEq, |
3362 | | &BooleanArray::from(vec![Some(true), None, Some(true), Some(false)]), |
3363 | | ) |
3364 | | .unwrap(); |
3365 | | // array > scalar |
3366 | | apply_logic_op_arr_scalar( |
3367 | | &schema, |
3368 | | &decimal_array, |
3369 | | &decimal_scalar, |
3370 | | Operator::Gt, |
3371 | | &BooleanArray::from(vec![Some(false), None, Some(false), Some(true)]), |
3372 | | ) |
3373 | | .unwrap(); |
3374 | | |
3375 | | // array >= scalar |
3376 | | apply_logic_op_arr_scalar( |
3377 | | &schema, |
3378 | | &decimal_array, |
3379 | | &decimal_scalar, |
3380 | | Operator::GtEq, |
3381 | | &BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]), |
3382 | | ) |
3383 | | .unwrap(); |
3384 | | |
3385 | | // scalar of different data type with decimal array |
3386 | | let decimal_scalar = ScalarValue::Decimal128(Some(123_456), 10, 3); |
3387 | | let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Int64, true)])); |
3388 | | // scalar == array |
3389 | | apply_logic_op_scalar_arr( |
3390 | | &schema, |
3391 | | &decimal_scalar, |
3392 | | &(Arc::new(Int64Array::from(vec![Some(124), None])) as ArrayRef), |
3393 | | Operator::Eq, |
3394 | | &BooleanArray::from(vec![Some(false), None]), |
3395 | | ) |
3396 | | .unwrap(); |
3397 | | |
3398 | | // array != scalar |
3399 | | apply_logic_op_arr_scalar( |
3400 | | &schema, |
3401 | | &(Arc::new(Int64Array::from(vec![Some(123), None, Some(1)])) as ArrayRef), |
3402 | | &decimal_scalar, |
3403 | | Operator::NotEq, |
3404 | | &BooleanArray::from(vec![Some(true), None, Some(true)]), |
3405 | | ) |
3406 | | .unwrap(); |
3407 | | |
3408 | | // array < scalar |
3409 | | apply_logic_op_arr_scalar( |
3410 | | &schema, |
3411 | | &(Arc::new(Int64Array::from(vec![Some(123), None, Some(124)])) as ArrayRef), |
3412 | | &decimal_scalar, |
3413 | | Operator::Lt, |
3414 | | &BooleanArray::from(vec![Some(true), None, Some(false)]), |
3415 | | ) |
3416 | | .unwrap(); |
3417 | | |
3418 | | // array > scalar |
3419 | | apply_logic_op_arr_scalar( |
3420 | | &schema, |
3421 | | &(Arc::new(Int64Array::from(vec![Some(123), None, Some(124)])) as ArrayRef), |
3422 | | &decimal_scalar, |
3423 | | Operator::Gt, |
3424 | | &BooleanArray::from(vec![Some(false), None, Some(true)]), |
3425 | | ) |
3426 | | .unwrap(); |
3427 | | |
3428 | | let schema = |
3429 | | Arc::new(Schema::new(vec![Field::new("a", DataType::Float64, true)])); |
3430 | | // array == scalar |
3431 | | apply_logic_op_arr_scalar( |
3432 | | &schema, |
3433 | | &(Arc::new(Float64Array::from(vec![Some(123.456), None, Some(123.457)])) |
3434 | | as ArrayRef), |
3435 | | &decimal_scalar, |
3436 | | Operator::Eq, |
3437 | | &BooleanArray::from(vec![Some(true), None, Some(false)]), |
3438 | | ) |
3439 | | .unwrap(); |
3440 | | |
3441 | | // array <= scalar |
3442 | | apply_logic_op_arr_scalar( |
3443 | | &schema, |
3444 | | &(Arc::new(Float64Array::from(vec![ |
3445 | | Some(123.456), |
3446 | | None, |
3447 | | Some(123.457), |
3448 | | Some(123.45), |
3449 | | ])) as ArrayRef), |
3450 | | &decimal_scalar, |
3451 | | Operator::LtEq, |
3452 | | &BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]), |
3453 | | ) |
3454 | | .unwrap(); |
3455 | | // array >= scalar |
3456 | | apply_logic_op_arr_scalar( |
3457 | | &schema, |
3458 | | &(Arc::new(Float64Array::from(vec![ |
3459 | | Some(123.456), |
3460 | | None, |
3461 | | Some(123.457), |
3462 | | Some(123.45), |
3463 | | ])) as ArrayRef), |
3464 | | &decimal_scalar, |
3465 | | Operator::GtEq, |
3466 | | &BooleanArray::from(vec![Some(true), None, Some(true), Some(false)]), |
3467 | | ) |
3468 | | .unwrap(); |
3469 | | |
3470 | | let value: i128 = 123; |
3471 | | let decimal_array = Arc::new(create_decimal_array( |
3472 | | &[Some(value), None, Some(value - 1), Some(value + 1)], |
3473 | | 10, |
3474 | | 0, |
3475 | | )) as ArrayRef; |
3476 | | |
3477 | | // comparison array op for decimal array |
3478 | | let schema = Arc::new(Schema::new(vec![ |
3479 | | Field::new("a", DataType::Decimal128(10, 0), true), |
3480 | | Field::new("b", DataType::Decimal128(10, 0), true), |
3481 | | ])); |
3482 | | let right_decimal_array = Arc::new(create_decimal_array( |
3483 | | &[ |
3484 | | Some(value - 1), |
3485 | | Some(value), |
3486 | | Some(value + 1), |
3487 | | Some(value + 1), |
3488 | | ], |
3489 | | 10, |
3490 | | 0, |
3491 | | )) as ArrayRef; |
3492 | | |
3493 | | apply_logic_op( |
3494 | | &schema, |
3495 | | &decimal_array, |
3496 | | &right_decimal_array, |
3497 | | Operator::Eq, |
3498 | | BooleanArray::from(vec![Some(false), None, Some(false), Some(true)]), |
3499 | | ) |
3500 | | .unwrap(); |
3501 | | |
3502 | | apply_logic_op( |
3503 | | &schema, |
3504 | | &decimal_array, |
3505 | | &right_decimal_array, |
3506 | | Operator::NotEq, |
3507 | | BooleanArray::from(vec![Some(true), None, Some(true), Some(false)]), |
3508 | | ) |
3509 | | .unwrap(); |
3510 | | |
3511 | | apply_logic_op( |
3512 | | &schema, |
3513 | | &decimal_array, |
3514 | | &right_decimal_array, |
3515 | | Operator::Lt, |
3516 | | BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]), |
3517 | | ) |
3518 | | .unwrap(); |
3519 | | |
3520 | | apply_logic_op( |
3521 | | &schema, |
3522 | | &decimal_array, |
3523 | | &right_decimal_array, |
3524 | | Operator::LtEq, |
3525 | | BooleanArray::from(vec![Some(false), None, Some(true), Some(true)]), |
3526 | | ) |
3527 | | .unwrap(); |
3528 | | |
3529 | | apply_logic_op( |
3530 | | &schema, |
3531 | | &decimal_array, |
3532 | | &right_decimal_array, |
3533 | | Operator::Gt, |
3534 | | BooleanArray::from(vec![Some(true), None, Some(false), Some(false)]), |
3535 | | ) |
3536 | | .unwrap(); |
3537 | | |
3538 | | apply_logic_op( |
3539 | | &schema, |
3540 | | &decimal_array, |
3541 | | &right_decimal_array, |
3542 | | Operator::GtEq, |
3543 | | BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]), |
3544 | | ) |
3545 | | .unwrap(); |
3546 | | |
3547 | | // compare decimal array with other array type |
3548 | | let value: i64 = 123; |
3549 | | let schema = Arc::new(Schema::new(vec![ |
3550 | | Field::new("a", DataType::Int64, true), |
3551 | | Field::new("b", DataType::Decimal128(10, 0), true), |
3552 | | ])); |
3553 | | |
3554 | | let int64_array = Arc::new(Int64Array::from(vec![ |
3555 | | Some(value), |
3556 | | Some(value - 1), |
3557 | | Some(value), |
3558 | | Some(value + 1), |
3559 | | ])) as ArrayRef; |
3560 | | |
3561 | | // eq: int64array == decimal array |
3562 | | apply_logic_op( |
3563 | | &schema, |
3564 | | &int64_array, |
3565 | | &decimal_array, |
3566 | | Operator::Eq, |
3567 | | BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]), |
3568 | | ) |
3569 | | .unwrap(); |
3570 | | // neq: int64array != decimal array |
3571 | | apply_logic_op( |
3572 | | &schema, |
3573 | | &int64_array, |
3574 | | &decimal_array, |
3575 | | Operator::NotEq, |
3576 | | BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]), |
3577 | | ) |
3578 | | .unwrap(); |
3579 | | |
3580 | | let schema = Arc::new(Schema::new(vec![ |
3581 | | Field::new("a", DataType::Float64, true), |
3582 | | Field::new("b", DataType::Decimal128(10, 2), true), |
3583 | | ])); |
3584 | | |
3585 | | let value: i128 = 123; |
3586 | | let decimal_array = Arc::new(create_decimal_array( |
3587 | | &[ |
3588 | | Some(value), // 1.23 |
3589 | | None, |
3590 | | Some(value - 1), // 1.22 |
3591 | | Some(value + 1), // 1.24 |
3592 | | ], |
3593 | | 10, |
3594 | | 2, |
3595 | | )) as ArrayRef; |
3596 | | let float64_array = Arc::new(Float64Array::from(vec![ |
3597 | | Some(1.23), |
3598 | | Some(1.22), |
3599 | | Some(1.23), |
3600 | | Some(1.24), |
3601 | | ])) as ArrayRef; |
3602 | | // lt: float64array < decimal array |
3603 | | apply_logic_op( |
3604 | | &schema, |
3605 | | &float64_array, |
3606 | | &decimal_array, |
3607 | | Operator::Lt, |
3608 | | BooleanArray::from(vec![Some(false), None, Some(false), Some(false)]), |
3609 | | ) |
3610 | | .unwrap(); |
3611 | | // lt_eq: float64array <= decimal array |
3612 | | apply_logic_op( |
3613 | | &schema, |
3614 | | &float64_array, |
3615 | | &decimal_array, |
3616 | | Operator::LtEq, |
3617 | | BooleanArray::from(vec![Some(true), None, Some(false), Some(true)]), |
3618 | | ) |
3619 | | .unwrap(); |
3620 | | // gt: float64array > decimal array |
3621 | | apply_logic_op( |
3622 | | &schema, |
3623 | | &float64_array, |
3624 | | &decimal_array, |
3625 | | Operator::Gt, |
3626 | | BooleanArray::from(vec![Some(false), None, Some(true), Some(false)]), |
3627 | | ) |
3628 | | .unwrap(); |
3629 | | apply_logic_op( |
3630 | | &schema, |
3631 | | &float64_array, |
3632 | | &decimal_array, |
3633 | | Operator::GtEq, |
3634 | | BooleanArray::from(vec![Some(true), None, Some(true), Some(true)]), |
3635 | | ) |
3636 | | .unwrap(); |
3637 | | // is distinct: float64array is distinct decimal array |
3638 | | // TODO: now we do not refactor the `is distinct or is not distinct` rule of coercion. |
3639 | | // traced by https://github.com/apache/datafusion/issues/1590 |
3640 | | // the decimal array will be casted to float64array |
3641 | | apply_logic_op( |
3642 | | &schema, |
3643 | | &float64_array, |
3644 | | &decimal_array, |
3645 | | Operator::IsDistinctFrom, |
3646 | | BooleanArray::from(vec![Some(false), Some(true), Some(true), Some(false)]), |
3647 | | ) |
3648 | | .unwrap(); |
3649 | | // is not distinct |
3650 | | apply_logic_op( |
3651 | | &schema, |
3652 | | &float64_array, |
3653 | | &decimal_array, |
3654 | | Operator::IsNotDistinctFrom, |
3655 | | BooleanArray::from(vec![Some(true), Some(false), Some(false), Some(true)]), |
3656 | | ) |
3657 | | .unwrap(); |
3658 | | |
3659 | | Ok(()) |
3660 | | } |
3661 | | |
3662 | | fn apply_decimal_arithmetic_op( |
3663 | | schema: &SchemaRef, |
3664 | | left: &ArrayRef, |
3665 | | right: &ArrayRef, |
3666 | | op: Operator, |
3667 | | expected: ArrayRef, |
3668 | | ) -> Result<()> { |
3669 | | let arithmetic_op = binary_op(col("a", schema)?, op, col("b", schema)?, schema)?; |
3670 | | let data: Vec<ArrayRef> = vec![Arc::clone(left), Arc::clone(right)]; |
3671 | | let batch = RecordBatch::try_new(Arc::clone(schema), data)?; |
3672 | | let result = arithmetic_op |
3673 | | .evaluate(&batch)? |
3674 | | .into_array(batch.num_rows()) |
3675 | | .expect("Failed to convert to array"); |
3676 | | |
3677 | | assert_eq!(result.as_ref(), expected.as_ref()); |
3678 | | Ok(()) |
3679 | | } |
3680 | | |
3681 | | #[test] |
3682 | | fn arithmetic_decimal_expr_test() -> Result<()> { |
3683 | | let schema = Arc::new(Schema::new(vec![ |
3684 | | Field::new("a", DataType::Int32, true), |
3685 | | Field::new("b", DataType::Decimal128(10, 2), true), |
3686 | | ])); |
3687 | | let value: i128 = 123; |
3688 | | let decimal_array = Arc::new(create_decimal_array( |
3689 | | &[ |
3690 | | Some(value), // 1.23 |
3691 | | None, |
3692 | | Some(value - 1), // 1.22 |
3693 | | Some(value + 1), // 1.24 |
3694 | | ], |
3695 | | 10, |
3696 | | 2, |
3697 | | )) as ArrayRef; |
3698 | | let int32_array = Arc::new(Int32Array::from(vec![ |
3699 | | Some(123), |
3700 | | Some(122), |
3701 | | Some(123), |
3702 | | Some(124), |
3703 | | ])) as ArrayRef; |
3704 | | |
3705 | | // add: Int32array add decimal array |
3706 | | let expect = Arc::new(create_decimal_array( |
3707 | | &[Some(12423), None, Some(12422), Some(12524)], |
3708 | | 13, |
3709 | | 2, |
3710 | | )) as ArrayRef; |
3711 | | apply_decimal_arithmetic_op( |
3712 | | &schema, |
3713 | | &int32_array, |
3714 | | &decimal_array, |
3715 | | Operator::Plus, |
3716 | | expect, |
3717 | | ) |
3718 | | .unwrap(); |
3719 | | |
3720 | | // subtract: decimal array subtract int32 array |
3721 | | let schema = Arc::new(Schema::new(vec![ |
3722 | | Field::new("a", DataType::Decimal128(10, 2), true), |
3723 | | Field::new("b", DataType::Int32, true), |
3724 | | ])); |
3725 | | let expect = Arc::new(create_decimal_array( |
3726 | | &[Some(-12177), None, Some(-12178), Some(-12276)], |
3727 | | 13, |
3728 | | 2, |
3729 | | )) as ArrayRef; |
3730 | | apply_decimal_arithmetic_op( |
3731 | | &schema, |
3732 | | &decimal_array, |
3733 | | &int32_array, |
3734 | | Operator::Minus, |
3735 | | expect, |
3736 | | ) |
3737 | | .unwrap(); |
3738 | | |
3739 | | // multiply: decimal array multiply int32 array |
3740 | | let expect = Arc::new(create_decimal_array( |
3741 | | &[Some(15129), None, Some(15006), Some(15376)], |
3742 | | 21, |
3743 | | 2, |
3744 | | )) as ArrayRef; |
3745 | | apply_decimal_arithmetic_op( |
3746 | | &schema, |
3747 | | &decimal_array, |
3748 | | &int32_array, |
3749 | | Operator::Multiply, |
3750 | | expect, |
3751 | | ) |
3752 | | .unwrap(); |
3753 | | |
3754 | | // divide: int32 array divide decimal array |
3755 | | let schema = Arc::new(Schema::new(vec![ |
3756 | | Field::new("a", DataType::Int32, true), |
3757 | | Field::new("b", DataType::Decimal128(10, 2), true), |
3758 | | ])); |
3759 | | let expect = Arc::new(create_decimal_array( |
3760 | | &[Some(1000000), None, Some(1008196), Some(1000000)], |
3761 | | 16, |
3762 | | 4, |
3763 | | )) as ArrayRef; |
3764 | | apply_decimal_arithmetic_op( |
3765 | | &schema, |
3766 | | &int32_array, |
3767 | | &decimal_array, |
3768 | | Operator::Divide, |
3769 | | expect, |
3770 | | ) |
3771 | | .unwrap(); |
3772 | | |
3773 | | // modulus: int32 array modulus decimal array |
3774 | | let schema = Arc::new(Schema::new(vec![ |
3775 | | Field::new("a", DataType::Int32, true), |
3776 | | Field::new("b", DataType::Decimal128(10, 2), true), |
3777 | | ])); |
3778 | | let expect = Arc::new(create_decimal_array( |
3779 | | &[Some(000), None, Some(100), Some(000)], |
3780 | | 10, |
3781 | | 2, |
3782 | | )) as ArrayRef; |
3783 | | apply_decimal_arithmetic_op( |
3784 | | &schema, |
3785 | | &int32_array, |
3786 | | &decimal_array, |
3787 | | Operator::Modulo, |
3788 | | expect, |
3789 | | ) |
3790 | | .unwrap(); |
3791 | | |
3792 | | Ok(()) |
3793 | | } |
3794 | | |
3795 | | #[test] |
3796 | | fn arithmetic_decimal_float_expr_test() -> Result<()> { |
3797 | | let schema = Arc::new(Schema::new(vec![ |
3798 | | Field::new("a", DataType::Float64, true), |
3799 | | Field::new("b", DataType::Decimal128(10, 2), true), |
3800 | | ])); |
3801 | | let value: i128 = 123; |
3802 | | let decimal_array = Arc::new(create_decimal_array( |
3803 | | &[ |
3804 | | Some(value), // 1.23 |
3805 | | None, |
3806 | | Some(value - 1), // 1.22 |
3807 | | Some(value + 1), // 1.24 |
3808 | | ], |
3809 | | 10, |
3810 | | 2, |
3811 | | )) as ArrayRef; |
3812 | | let float64_array = Arc::new(Float64Array::from(vec![ |
3813 | | Some(123.0), |
3814 | | Some(122.0), |
3815 | | Some(123.0), |
3816 | | Some(124.0), |
3817 | | ])) as ArrayRef; |
3818 | | |
3819 | | // add: float64 array add decimal array |
3820 | | let expect = Arc::new(Float64Array::from(vec![ |
3821 | | Some(124.23), |
3822 | | None, |
3823 | | Some(124.22), |
3824 | | Some(125.24), |
3825 | | ])) as ArrayRef; |
3826 | | apply_decimal_arithmetic_op( |
3827 | | &schema, |
3828 | | &float64_array, |
3829 | | &decimal_array, |
3830 | | Operator::Plus, |
3831 | | expect, |
3832 | | ) |
3833 | | .unwrap(); |
3834 | | |
3835 | | // subtract: decimal array subtract float64 array |
3836 | | let schema = Arc::new(Schema::new(vec![ |
3837 | | Field::new("a", DataType::Float64, true), |
3838 | | Field::new("b", DataType::Decimal128(10, 2), true), |
3839 | | ])); |
3840 | | let expect = Arc::new(Float64Array::from(vec![ |
3841 | | Some(121.77), |
3842 | | None, |
3843 | | Some(121.78), |
3844 | | Some(122.76), |
3845 | | ])) as ArrayRef; |
3846 | | apply_decimal_arithmetic_op( |
3847 | | &schema, |
3848 | | &float64_array, |
3849 | | &decimal_array, |
3850 | | Operator::Minus, |
3851 | | expect, |
3852 | | ) |
3853 | | .unwrap(); |
3854 | | |
3855 | | // multiply: decimal array multiply float64 array |
3856 | | let expect = Arc::new(Float64Array::from(vec![ |
3857 | | Some(151.29), |
3858 | | None, |
3859 | | Some(150.06), |
3860 | | Some(153.76), |
3861 | | ])) as ArrayRef; |
3862 | | apply_decimal_arithmetic_op( |
3863 | | &schema, |
3864 | | &float64_array, |
3865 | | &decimal_array, |
3866 | | Operator::Multiply, |
3867 | | expect, |
3868 | | ) |
3869 | | .unwrap(); |
3870 | | |
3871 | | // divide: float64 array divide decimal array |
3872 | | let schema = Arc::new(Schema::new(vec![ |
3873 | | Field::new("a", DataType::Float64, true), |
3874 | | Field::new("b", DataType::Decimal128(10, 2), true), |
3875 | | ])); |
3876 | | let expect = Arc::new(Float64Array::from(vec![ |
3877 | | Some(100.0), |
3878 | | None, |
3879 | | Some(100.81967213114754), |
3880 | | Some(100.0), |
3881 | | ])) as ArrayRef; |
3882 | | apply_decimal_arithmetic_op( |
3883 | | &schema, |
3884 | | &float64_array, |
3885 | | &decimal_array, |
3886 | | Operator::Divide, |
3887 | | expect, |
3888 | | ) |
3889 | | .unwrap(); |
3890 | | |
3891 | | // modulus: float64 array modulus decimal array |
3892 | | let schema = Arc::new(Schema::new(vec![ |
3893 | | Field::new("a", DataType::Float64, true), |
3894 | | Field::new("b", DataType::Decimal128(10, 2), true), |
3895 | | ])); |
3896 | | let expect = Arc::new(Float64Array::from(vec![ |
3897 | | Some(1.7763568394002505e-15), |
3898 | | None, |
3899 | | Some(1.0000000000000027), |
3900 | | Some(8.881784197001252e-16), |
3901 | | ])) as ArrayRef; |
3902 | | apply_decimal_arithmetic_op( |
3903 | | &schema, |
3904 | | &float64_array, |
3905 | | &decimal_array, |
3906 | | Operator::Modulo, |
3907 | | expect, |
3908 | | ) |
3909 | | .unwrap(); |
3910 | | |
3911 | | Ok(()) |
3912 | | } |
3913 | | |
3914 | | #[test] |
3915 | | fn arithmetic_divide_zero() -> Result<()> { |
3916 | | // other data type |
3917 | | let schema = Arc::new(Schema::new(vec![ |
3918 | | Field::new("a", DataType::Int32, true), |
3919 | | Field::new("b", DataType::Int32, true), |
3920 | | ])); |
3921 | | let a = Arc::new(Int32Array::from(vec![100])); |
3922 | | let b = Arc::new(Int32Array::from(vec![0])); |
3923 | | |
3924 | | let err = apply_arithmetic::<Int32Type>( |
3925 | | schema, |
3926 | | vec![a, b], |
3927 | | Operator::Divide, |
3928 | | Int32Array::from(vec![Some(4), Some(8), Some(16), Some(32), Some(64)]), |
3929 | | ) |
3930 | | .unwrap_err(); |
3931 | | |
3932 | | let _expected = plan_datafusion_err!("Divide by zero"); |
3933 | | |
3934 | | assert!(matches!(err, ref _expected), "{err}"); |
3935 | | |
3936 | | // decimal |
3937 | | let schema = Arc::new(Schema::new(vec![ |
3938 | | Field::new("a", DataType::Decimal128(25, 3), true), |
3939 | | Field::new("b", DataType::Decimal128(25, 3), true), |
3940 | | ])); |
3941 | | let left_decimal_array = Arc::new(create_decimal_array(&[Some(1234567)], 25, 3)); |
3942 | | let right_decimal_array = Arc::new(create_decimal_array(&[Some(0)], 25, 3)); |
3943 | | |
3944 | | let err = apply_arithmetic::<Decimal128Type>( |
3945 | | schema, |
3946 | | vec![left_decimal_array, right_decimal_array], |
3947 | | Operator::Divide, |
3948 | | create_decimal_array( |
3949 | | &[Some(12345670000000000000000000000000000), None], |
3950 | | 38, |
3951 | | 29, |
3952 | | ), |
3953 | | ) |
3954 | | .unwrap_err(); |
3955 | | |
3956 | | assert!(matches!(err, ref _expected), "{err}"); |
3957 | | |
3958 | | Ok(()) |
3959 | | } |
3960 | | |
3961 | | #[test] |
3962 | | fn bitwise_array_test() -> Result<()> { |
3963 | | let left = Arc::new(Int32Array::from(vec![Some(12), None, Some(11)])) as ArrayRef; |
3964 | | let right = |
3965 | | Arc::new(Int32Array::from(vec![Some(1), Some(3), Some(7)])) as ArrayRef; |
3966 | | let mut result = bitwise_and_dyn(Arc::clone(&left), Arc::clone(&right))?; |
3967 | | let expected = Int32Array::from(vec![Some(0), None, Some(3)]); |
3968 | | assert_eq!(result.as_ref(), &expected); |
3969 | | |
3970 | | result = bitwise_or_dyn(Arc::clone(&left), Arc::clone(&right))?; |
3971 | | let expected = Int32Array::from(vec![Some(13), None, Some(15)]); |
3972 | | assert_eq!(result.as_ref(), &expected); |
3973 | | |
3974 | | result = bitwise_xor_dyn(Arc::clone(&left), Arc::clone(&right))?; |
3975 | | let expected = Int32Array::from(vec![Some(13), None, Some(12)]); |
3976 | | assert_eq!(result.as_ref(), &expected); |
3977 | | |
3978 | | let left = |
3979 | | Arc::new(UInt32Array::from(vec![Some(12), None, Some(11)])) as ArrayRef; |
3980 | | let right = |
3981 | | Arc::new(UInt32Array::from(vec![Some(1), Some(3), Some(7)])) as ArrayRef; |
3982 | | let mut result = bitwise_and_dyn(Arc::clone(&left), Arc::clone(&right))?; |
3983 | | let expected = UInt32Array::from(vec![Some(0), None, Some(3)]); |
3984 | | assert_eq!(result.as_ref(), &expected); |
3985 | | |
3986 | | result = bitwise_or_dyn(Arc::clone(&left), Arc::clone(&right))?; |
3987 | | let expected = UInt32Array::from(vec![Some(13), None, Some(15)]); |
3988 | | assert_eq!(result.as_ref(), &expected); |
3989 | | |
3990 | | result = bitwise_xor_dyn(Arc::clone(&left), Arc::clone(&right))?; |
3991 | | let expected = UInt32Array::from(vec![Some(13), None, Some(12)]); |
3992 | | assert_eq!(result.as_ref(), &expected); |
3993 | | |
3994 | | Ok(()) |
3995 | | } |
3996 | | |
3997 | | #[test] |
3998 | | fn bitwise_shift_array_test() -> Result<()> { |
3999 | | let input = Arc::new(Int32Array::from(vec![Some(2), None, Some(10)])) as ArrayRef; |
4000 | | let modules = |
4001 | | Arc::new(Int32Array::from(vec![Some(2), Some(4), Some(8)])) as ArrayRef; |
4002 | | let mut result = |
4003 | | bitwise_shift_left_dyn(Arc::clone(&input), Arc::clone(&modules))?; |
4004 | | |
4005 | | let expected = Int32Array::from(vec![Some(8), None, Some(2560)]); |
4006 | | assert_eq!(result.as_ref(), &expected); |
4007 | | |
4008 | | result = bitwise_shift_right_dyn(Arc::clone(&result), Arc::clone(&modules))?; |
4009 | | assert_eq!(result.as_ref(), &input); |
4010 | | |
4011 | | let input = |
4012 | | Arc::new(UInt32Array::from(vec![Some(2), None, Some(10)])) as ArrayRef; |
4013 | | let modules = |
4014 | | Arc::new(UInt32Array::from(vec![Some(2), Some(4), Some(8)])) as ArrayRef; |
4015 | | let mut result = |
4016 | | bitwise_shift_left_dyn(Arc::clone(&input), Arc::clone(&modules))?; |
4017 | | |
4018 | | let expected = UInt32Array::from(vec![Some(8), None, Some(2560)]); |
4019 | | assert_eq!(result.as_ref(), &expected); |
4020 | | |
4021 | | result = bitwise_shift_right_dyn(Arc::clone(&result), Arc::clone(&modules))?; |
4022 | | assert_eq!(result.as_ref(), &input); |
4023 | | Ok(()) |
4024 | | } |
4025 | | |
4026 | | #[test] |
4027 | | fn bitwise_shift_array_overflow_test() -> Result<()> { |
4028 | | let input = Arc::new(Int32Array::from(vec![Some(2)])) as ArrayRef; |
4029 | | let modules = Arc::new(Int32Array::from(vec![Some(100)])) as ArrayRef; |
4030 | | let result = bitwise_shift_left_dyn(Arc::clone(&input), Arc::clone(&modules))?; |
4031 | | |
4032 | | let expected = Int32Array::from(vec![Some(32)]); |
4033 | | assert_eq!(result.as_ref(), &expected); |
4034 | | |
4035 | | let input = Arc::new(UInt32Array::from(vec![Some(2)])) as ArrayRef; |
4036 | | let modules = Arc::new(UInt32Array::from(vec![Some(100)])) as ArrayRef; |
4037 | | let result = bitwise_shift_left_dyn(Arc::clone(&input), Arc::clone(&modules))?; |
4038 | | |
4039 | | let expected = UInt32Array::from(vec![Some(32)]); |
4040 | | assert_eq!(result.as_ref(), &expected); |
4041 | | Ok(()) |
4042 | | } |
4043 | | |
4044 | | #[test] |
4045 | | fn bitwise_scalar_test() -> Result<()> { |
4046 | | let left = Arc::new(Int32Array::from(vec![Some(12), None, Some(11)])) as ArrayRef; |
4047 | | let right = ScalarValue::from(3i32); |
4048 | | let mut result = bitwise_and_dyn_scalar(&left, right.clone()).unwrap()?; |
4049 | | let expected = Int32Array::from(vec![Some(0), None, Some(3)]); |
4050 | | assert_eq!(result.as_ref(), &expected); |
4051 | | |
4052 | | result = bitwise_or_dyn_scalar(&left, right.clone()).unwrap()?; |
4053 | | let expected = Int32Array::from(vec![Some(15), None, Some(11)]); |
4054 | | assert_eq!(result.as_ref(), &expected); |
4055 | | |
4056 | | result = bitwise_xor_dyn_scalar(&left, right).unwrap()?; |
4057 | | let expected = Int32Array::from(vec![Some(15), None, Some(8)]); |
4058 | | assert_eq!(result.as_ref(), &expected); |
4059 | | |
4060 | | let left = |
4061 | | Arc::new(UInt32Array::from(vec![Some(12), None, Some(11)])) as ArrayRef; |
4062 | | let right = ScalarValue::from(3u32); |
4063 | | let mut result = bitwise_and_dyn_scalar(&left, right.clone()).unwrap()?; |
4064 | | let expected = UInt32Array::from(vec![Some(0), None, Some(3)]); |
4065 | | assert_eq!(result.as_ref(), &expected); |
4066 | | |
4067 | | result = bitwise_or_dyn_scalar(&left, right.clone()).unwrap()?; |
4068 | | let expected = UInt32Array::from(vec![Some(15), None, Some(11)]); |
4069 | | assert_eq!(result.as_ref(), &expected); |
4070 | | |
4071 | | result = bitwise_xor_dyn_scalar(&left, right).unwrap()?; |
4072 | | let expected = UInt32Array::from(vec![Some(15), None, Some(8)]); |
4073 | | assert_eq!(result.as_ref(), &expected); |
4074 | | Ok(()) |
4075 | | } |
4076 | | |
4077 | | #[test] |
4078 | | fn bitwise_shift_scalar_test() -> Result<()> { |
4079 | | let input = Arc::new(Int32Array::from(vec![Some(2), None, Some(4)])) as ArrayRef; |
4080 | | let module = ScalarValue::from(10i32); |
4081 | | let mut result = |
4082 | | bitwise_shift_left_dyn_scalar(&input, module.clone()).unwrap()?; |
4083 | | |
4084 | | let expected = Int32Array::from(vec![Some(2048), None, Some(4096)]); |
4085 | | assert_eq!(result.as_ref(), &expected); |
4086 | | |
4087 | | result = bitwise_shift_right_dyn_scalar(&result, module).unwrap()?; |
4088 | | assert_eq!(result.as_ref(), &input); |
4089 | | |
4090 | | let input = Arc::new(UInt32Array::from(vec![Some(2), None, Some(4)])) as ArrayRef; |
4091 | | let module = ScalarValue::from(10u32); |
4092 | | let mut result = |
4093 | | bitwise_shift_left_dyn_scalar(&input, module.clone()).unwrap()?; |
4094 | | |
4095 | | let expected = UInt32Array::from(vec![Some(2048), None, Some(4096)]); |
4096 | | assert_eq!(result.as_ref(), &expected); |
4097 | | |
4098 | | result = bitwise_shift_right_dyn_scalar(&result, module).unwrap()?; |
4099 | | assert_eq!(result.as_ref(), &input); |
4100 | | Ok(()) |
4101 | | } |
4102 | | |
4103 | | #[test] |
4104 | | fn test_display_and_or_combo() { |
4105 | | let expr = BinaryExpr::new( |
4106 | | Arc::new(BinaryExpr::new( |
4107 | | lit(ScalarValue::from(1)), |
4108 | | Operator::And, |
4109 | | lit(ScalarValue::from(2)), |
4110 | | )), |
4111 | | Operator::And, |
4112 | | Arc::new(BinaryExpr::new( |
4113 | | lit(ScalarValue::from(3)), |
4114 | | Operator::And, |
4115 | | lit(ScalarValue::from(4)), |
4116 | | )), |
4117 | | ); |
4118 | | assert_eq!(expr.to_string(), "1 AND 2 AND 3 AND 4"); |
4119 | | |
4120 | | let expr = BinaryExpr::new( |
4121 | | Arc::new(BinaryExpr::new( |
4122 | | lit(ScalarValue::from(1)), |
4123 | | Operator::Or, |
4124 | | lit(ScalarValue::from(2)), |
4125 | | )), |
4126 | | Operator::Or, |
4127 | | Arc::new(BinaryExpr::new( |
4128 | | lit(ScalarValue::from(3)), |
4129 | | Operator::Or, |
4130 | | lit(ScalarValue::from(4)), |
4131 | | )), |
4132 | | ); |
4133 | | assert_eq!(expr.to_string(), "1 OR 2 OR 3 OR 4"); |
4134 | | |
4135 | | let expr = BinaryExpr::new( |
4136 | | Arc::new(BinaryExpr::new( |
4137 | | lit(ScalarValue::from(1)), |
4138 | | Operator::And, |
4139 | | lit(ScalarValue::from(2)), |
4140 | | )), |
4141 | | Operator::Or, |
4142 | | Arc::new(BinaryExpr::new( |
4143 | | lit(ScalarValue::from(3)), |
4144 | | Operator::And, |
4145 | | lit(ScalarValue::from(4)), |
4146 | | )), |
4147 | | ); |
4148 | | assert_eq!(expr.to_string(), "1 AND 2 OR 3 AND 4"); |
4149 | | |
4150 | | let expr = BinaryExpr::new( |
4151 | | Arc::new(BinaryExpr::new( |
4152 | | lit(ScalarValue::from(1)), |
4153 | | Operator::Or, |
4154 | | lit(ScalarValue::from(2)), |
4155 | | )), |
4156 | | Operator::And, |
4157 | | Arc::new(BinaryExpr::new( |
4158 | | lit(ScalarValue::from(3)), |
4159 | | Operator::Or, |
4160 | | lit(ScalarValue::from(4)), |
4161 | | )), |
4162 | | ); |
4163 | | assert_eq!(expr.to_string(), "(1 OR 2) AND (3 OR 4)"); |
4164 | | } |
4165 | | |
4166 | | #[test] |
4167 | | fn test_to_result_type_array() { |
4168 | | let values = Arc::new(Int32Array::from(vec![1, 2, 3, 4])); |
4169 | | let keys = Int8Array::from(vec![Some(0), None, Some(2), Some(3)]); |
4170 | | let dictionary = |
4171 | | Arc::new(DictionaryArray::try_new(keys, values).unwrap()) as ArrayRef; |
4172 | | |
4173 | | // Casting Dictionary to Int32 |
4174 | | let casted = to_result_type_array( |
4175 | | &Operator::Plus, |
4176 | | Arc::clone(&dictionary), |
4177 | | &DataType::Int32, |
4178 | | ) |
4179 | | .unwrap(); |
4180 | | assert_eq!( |
4181 | | &casted, |
4182 | | &(Arc::new(Int32Array::from(vec![Some(1), None, Some(3), Some(4)])) |
4183 | | as ArrayRef) |
4184 | | ); |
4185 | | |
4186 | | // Array has same datatype as result type, no casting |
4187 | | let casted = to_result_type_array( |
4188 | | &Operator::Plus, |
4189 | | Arc::clone(&dictionary), |
4190 | | dictionary.data_type(), |
4191 | | ) |
4192 | | .unwrap(); |
4193 | | assert_eq!(&casted, &dictionary); |
4194 | | |
4195 | | // Not numerical operator, no casting |
4196 | | let casted = to_result_type_array( |
4197 | | &Operator::Eq, |
4198 | | Arc::clone(&dictionary), |
4199 | | &DataType::Int32, |
4200 | | ) |
4201 | | .unwrap(); |
4202 | | assert_eq!(&casted, &dictionary); |
4203 | | } |
4204 | | |
4205 | | #[test] |
4206 | | fn test_add_with_overflow() -> Result<()> { |
4207 | | // create test data |
4208 | | let l = Arc::new(Int32Array::from(vec![1, i32::MAX])); |
4209 | | let r = Arc::new(Int32Array::from(vec![2, 1])); |
4210 | | let schema = Arc::new(Schema::new(vec![ |
4211 | | Field::new("l", DataType::Int32, false), |
4212 | | Field::new("r", DataType::Int32, false), |
4213 | | ])); |
4214 | | let batch = RecordBatch::try_new(schema, vec![l, r])?; |
4215 | | |
4216 | | // create expression |
4217 | | let expr = BinaryExpr::new( |
4218 | | Arc::new(Column::new("l", 0)), |
4219 | | Operator::Plus, |
4220 | | Arc::new(Column::new("r", 1)), |
4221 | | ) |
4222 | | .with_fail_on_overflow(true); |
4223 | | |
4224 | | // evaluate expression |
4225 | | let result = expr.evaluate(&batch); |
4226 | | assert!(result |
4227 | | .err() |
4228 | | .unwrap() |
4229 | | .to_string() |
4230 | | .contains("Overflow happened on: 2147483647 + 1")); |
4231 | | Ok(()) |
4232 | | } |
4233 | | |
4234 | | #[test] |
4235 | | fn test_subtract_with_overflow() -> Result<()> { |
4236 | | // create test data |
4237 | | let l = Arc::new(Int32Array::from(vec![1, i32::MIN])); |
4238 | | let r = Arc::new(Int32Array::from(vec![2, 1])); |
4239 | | let schema = Arc::new(Schema::new(vec![ |
4240 | | Field::new("l", DataType::Int32, false), |
4241 | | Field::new("r", DataType::Int32, false), |
4242 | | ])); |
4243 | | let batch = RecordBatch::try_new(schema, vec![l, r])?; |
4244 | | |
4245 | | // create expression |
4246 | | let expr = BinaryExpr::new( |
4247 | | Arc::new(Column::new("l", 0)), |
4248 | | Operator::Minus, |
4249 | | Arc::new(Column::new("r", 1)), |
4250 | | ) |
4251 | | .with_fail_on_overflow(true); |
4252 | | |
4253 | | // evaluate expression |
4254 | | let result = expr.evaluate(&batch); |
4255 | | assert!(result |
4256 | | .err() |
4257 | | .unwrap() |
4258 | | .to_string() |
4259 | | .contains("Overflow happened on: -2147483648 - 1")); |
4260 | | Ok(()) |
4261 | | } |
4262 | | |
4263 | | #[test] |
4264 | | fn test_mul_with_overflow() -> Result<()> { |
4265 | | // create test data |
4266 | | let l = Arc::new(Int32Array::from(vec![1, i32::MAX])); |
4267 | | let r = Arc::new(Int32Array::from(vec![2, 2])); |
4268 | | let schema = Arc::new(Schema::new(vec![ |
4269 | | Field::new("l", DataType::Int32, false), |
4270 | | Field::new("r", DataType::Int32, false), |
4271 | | ])); |
4272 | | let batch = RecordBatch::try_new(schema, vec![l, r])?; |
4273 | | |
4274 | | // create expression |
4275 | | let expr = BinaryExpr::new( |
4276 | | Arc::new(Column::new("l", 0)), |
4277 | | Operator::Multiply, |
4278 | | Arc::new(Column::new("r", 1)), |
4279 | | ) |
4280 | | .with_fail_on_overflow(true); |
4281 | | |
4282 | | // evaluate expression |
4283 | | let result = expr.evaluate(&batch); |
4284 | | assert!(result |
4285 | | .err() |
4286 | | .unwrap() |
4287 | | .to_string() |
4288 | | .contains("Overflow happened on: 2147483647 * 2")); |
4289 | | Ok(()) |
4290 | | } |
4291 | | |
4292 | | /// Test helper for SIMILAR TO binary operation |
4293 | | fn apply_similar_to( |
4294 | | schema: &SchemaRef, |
4295 | | va: Vec<&str>, |
4296 | | vb: Vec<&str>, |
4297 | | negated: bool, |
4298 | | case_insensitive: bool, |
4299 | | expected: &BooleanArray, |
4300 | | ) -> Result<()> { |
4301 | | let a = StringArray::from(va); |
4302 | | let b = StringArray::from(vb); |
4303 | | let op = similar_to( |
4304 | | negated, |
4305 | | case_insensitive, |
4306 | | col("a", schema)?, |
4307 | | col("b", schema)?, |
4308 | | )?; |
4309 | | let batch = |
4310 | | RecordBatch::try_new(Arc::clone(schema), vec![Arc::new(a), Arc::new(b)])?; |
4311 | | let result = op |
4312 | | .evaluate(&batch)? |
4313 | | .into_array(batch.num_rows()) |
4314 | | .expect("Failed to convert to array"); |
4315 | | assert_eq!(result.as_ref(), expected); |
4316 | | |
4317 | | Ok(()) |
4318 | | } |
4319 | | |
4320 | | #[test] |
4321 | | fn test_similar_to() { |
4322 | | let schema = Arc::new(Schema::new(vec![ |
4323 | | Field::new("a", DataType::Utf8, false), |
4324 | | Field::new("b", DataType::Utf8, false), |
4325 | | ])); |
4326 | | |
4327 | | let expected = [Some(true), Some(false)].iter().collect(); |
4328 | | // case-sensitive |
4329 | | apply_similar_to( |
4330 | | &schema, |
4331 | | vec!["hello world", "Hello World"], |
4332 | | vec!["hello.*", "hello.*"], |
4333 | | false, |
4334 | | false, |
4335 | | &expected, |
4336 | | ) |
4337 | | .unwrap(); |
4338 | | // case-insensitive |
4339 | | apply_similar_to( |
4340 | | &schema, |
4341 | | vec!["hello world", "bye"], |
4342 | | vec!["hello.*", "hello.*"], |
4343 | | false, |
4344 | | true, |
4345 | | &expected, |
4346 | | ) |
4347 | | .unwrap(); |
4348 | | } |
4349 | | } |