datafusion_functions_aggregate_common/
merge_arrays.rs

1// Licensed to the Apache Software Foundation (ASF) under one
2// or more contributor license agreements.  See the NOTICE file
3// distributed with this work for additional information
4// regarding copyright ownership.  The ASF licenses this file
5// to you under the Apache License, Version 2.0 (the
6// "License"); you may not use this file except in compliance
7// with the License.  You may obtain a copy of the License at
8//
9//   http://www.apache.org/licenses/LICENSE-2.0
10//
11// Unless required by applicable law or agreed to in writing,
12// software distributed under the License is distributed on an
13// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
14// KIND, either express or implied.  See the License for the
15// specific language governing permissions and limitations
16// under the License.
17
18use arrow::compute::SortOptions;
19use datafusion_common::utils::compare_rows;
20use datafusion_common::{exec_err, ScalarValue};
21use std::cmp::Ordering;
22use std::collections::{BinaryHeap, VecDeque};
23
24/// This is a wrapper struct to be able to correctly merge `ARRAY_AGG` data from
25/// multiple partitions using `BinaryHeap`. When used inside `BinaryHeap`, this
26/// struct returns smallest `CustomElement`, where smallest is determined by
27/// `ordering` values (`Vec<ScalarValue>`) according to `sort_options`.
28#[derive(Debug, PartialEq, Eq)]
29struct CustomElement<'a> {
30    /// Stores the partition this entry came from
31    branch_idx: usize,
32    /// Values to merge
33    value: ScalarValue,
34    // Comparison "key"
35    ordering: Vec<ScalarValue>,
36    /// Options defining the ordering semantics
37    sort_options: &'a [SortOptions],
38}
39
40impl<'a> CustomElement<'a> {
41    fn new(
42        branch_idx: usize,
43        value: ScalarValue,
44        ordering: Vec<ScalarValue>,
45        sort_options: &'a [SortOptions],
46    ) -> Self {
47        Self {
48            branch_idx,
49            value,
50            ordering,
51            sort_options,
52        }
53    }
54
55    fn ordering(
56        &self,
57        current: &[ScalarValue],
58        target: &[ScalarValue],
59    ) -> datafusion_common::Result<Ordering> {
60        // Calculate ordering according to `sort_options`
61        compare_rows(current, target, self.sort_options)
62    }
63}
64
65// Overwrite ordering implementation such that
66// - `self.ordering` values are used for comparison,
67// - When used inside `BinaryHeap` it is a min-heap.
68impl Ord for CustomElement<'_> {
69    fn cmp(&self, other: &Self) -> Ordering {
70        // TODO Ord/PartialOrd is not consistent with PartialEq; PartialOrd contract is violated
71        // Compares according to custom ordering
72        self.ordering(&self.ordering, &other.ordering)
73            // Convert max heap to min heap
74            .map(|ordering| ordering.reverse())
75            // This function return error, when `self.ordering` and `other.ordering`
76            // have different types (such as one is `ScalarValue::Int64`, other is `ScalarValue::Float32`)
77            // Here this case won't happen, because data from each partition will have same type
78            .unwrap()
79    }
80}
81
82impl PartialOrd for CustomElement<'_> {
83    fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
84        Some(self.cmp(other))
85    }
86}
87
88/// This functions merges `values` array (`&[Vec<ScalarValue>]`) into single array `Vec<ScalarValue>`
89/// Merging done according to ordering values stored inside `ordering_values` (`&[Vec<Vec<ScalarValue>>]`)
90/// Inner `Vec<ScalarValue>` in the `ordering_values` can be thought as ordering information for the
91/// each `ScalarValue` in the `values` array.
92/// Desired ordering specified by `sort_options` argument (Should have same size with inner `Vec<ScalarValue>`
93/// of the `ordering_values` array).
94///
95/// As an example
96/// values can be \[
97///      \[1, 2, 3, 4, 5\],
98///      \[1, 2, 3, 4\],
99///      \[1, 2, 3, 4, 5, 6\],
100/// \]
101/// In this case we will be merging three arrays (doesn't have to be same size)
102/// and produce a merged array with size 15 (sum of 5+4+6)
103/// Merging will be done according to ordering at `ordering_values` vector.
104/// As an example `ordering_values` can be [
105///      \[(1, a), (2, b), (3, b), (4, a), (5, b) \],
106///      \[(1, a), (2, b), (3, b), (4, a) \],
107///      \[(1, b), (2, c), (3, d), (4, e), (5, a), (6, b) \],
108/// ]
109/// For each ScalarValue in the `values` we have a corresponding `Vec<ScalarValue>` (like timestamp of it)
110/// for the example above `sort_options` will have size two, that defines ordering requirement of the merge.
111/// Inner `Vec<ScalarValue>`s of the `ordering_values` will be compared according `sort_options` (Their sizes should match)
112pub fn merge_ordered_arrays(
113    // We will merge values into single `Vec<ScalarValue>`.
114    values: &mut [VecDeque<ScalarValue>],
115    // `values` will be merged according to `ordering_values`.
116    // Inner `Vec<ScalarValue>` can be thought as ordering information for the
117    // each `ScalarValue` in the values`.
118    ordering_values: &mut [VecDeque<Vec<ScalarValue>>],
119    // Defines according to which ordering comparisons should be done.
120    sort_options: &[SortOptions],
121) -> datafusion_common::Result<(Vec<ScalarValue>, Vec<Vec<ScalarValue>>)> {
122    // Keep track the most recent data of each branch, in binary heap data structure.
123    let mut heap = BinaryHeap::<CustomElement>::new();
124
125    if values.len() != ordering_values.len()
126        || values
127            .iter()
128            .zip(ordering_values.iter())
129            .any(|(vals, ordering_vals)| vals.len() != ordering_vals.len())
130    {
131        return exec_err!(
132            "Expects values arguments and/or ordering_values arguments to have same size"
133        );
134    }
135    let n_branch = values.len();
136    let mut merged_values = vec![];
137    let mut merged_orderings = vec![];
138    // Continue iterating the loop until consuming data of all branches.
139    loop {
140        let minimum = if let Some(minimum) = heap.pop() {
141            minimum
142        } else {
143            // Heap is empty, fill it with the next entries from each branch.
144            for branch_idx in 0..n_branch {
145                if let Some(orderings) = ordering_values[branch_idx].pop_front() {
146                    // Their size should be same, we can safely .unwrap here.
147                    let value = values[branch_idx].pop_front().unwrap();
148                    // Push the next element to the heap:
149                    heap.push(CustomElement::new(
150                        branch_idx,
151                        value,
152                        orderings,
153                        sort_options,
154                    ));
155                }
156                // If None, we consumed this branch, skip it.
157            }
158
159            // Now we have filled the heap, get the largest entry (this will be
160            // the next element in merge).
161            if let Some(minimum) = heap.pop() {
162                minimum
163            } else {
164                // Heap is empty, this means that all indices are same with
165                // `end_indices`. We have consumed all of the branches, merge
166                // is completed, exit from the loop:
167                break;
168            }
169        };
170        let CustomElement {
171            branch_idx,
172            value,
173            ordering,
174            ..
175        } = minimum;
176        // Add minimum value in the heap to the result
177        merged_values.push(value);
178        merged_orderings.push(ordering);
179
180        // If there is an available entry, push next entry in the most
181        // recently consumed branch to the heap.
182        if let Some(orderings) = ordering_values[branch_idx].pop_front() {
183            // Their size should be same, we can safely .unwrap here.
184            let value = values[branch_idx].pop_front().unwrap();
185            // Push the next element to the heap:
186            heap.push(CustomElement::new(
187                branch_idx,
188                value,
189                orderings,
190                sort_options,
191            ));
192        }
193    }
194
195    Ok((merged_values, merged_orderings))
196}
197
198#[cfg(test)]
199mod tests {
200    use super::*;
201
202    use std::collections::VecDeque;
203    use std::sync::Arc;
204
205    use arrow::array::{ArrayRef, Int64Array};
206
207    use datafusion_common::utils::get_row_at_idx;
208    use datafusion_common::{Result, ScalarValue};
209
210    #[test]
211    fn test_merge_asc() -> Result<()> {
212        let lhs_arrays: Vec<ArrayRef> = vec![
213            Arc::new(Int64Array::from(vec![0, 0, 1, 1, 2])),
214            Arc::new(Int64Array::from(vec![0, 1, 2, 3, 4])),
215        ];
216        let n_row = lhs_arrays[0].len();
217        let lhs_orderings = (0..n_row)
218            .map(|idx| get_row_at_idx(&lhs_arrays, idx))
219            .collect::<Result<VecDeque<_>>>()?;
220
221        let rhs_arrays: Vec<ArrayRef> = vec![
222            Arc::new(Int64Array::from(vec![0, 0, 1, 1, 2])),
223            Arc::new(Int64Array::from(vec![0, 1, 2, 3, 4])),
224        ];
225        let n_row = rhs_arrays[0].len();
226        let rhs_orderings = (0..n_row)
227            .map(|idx| get_row_at_idx(&rhs_arrays, idx))
228            .collect::<Result<VecDeque<_>>>()?;
229        let sort_options = vec![
230            SortOptions {
231                descending: false,
232                nulls_first: false,
233            },
234            SortOptions {
235                descending: false,
236                nulls_first: false,
237            },
238        ];
239
240        let lhs_vals_arr = Arc::new(Int64Array::from(vec![0, 1, 2, 3, 4])) as ArrayRef;
241        let lhs_vals = (0..lhs_vals_arr.len())
242            .map(|idx| ScalarValue::try_from_array(&lhs_vals_arr, idx))
243            .collect::<Result<VecDeque<_>>>()?;
244
245        let rhs_vals_arr = Arc::new(Int64Array::from(vec![0, 1, 2, 3, 4])) as ArrayRef;
246        let rhs_vals = (0..rhs_vals_arr.len())
247            .map(|idx| ScalarValue::try_from_array(&rhs_vals_arr, idx))
248            .collect::<Result<VecDeque<_>>>()?;
249        let expected =
250            Arc::new(Int64Array::from(vec![0, 0, 1, 1, 2, 2, 3, 3, 4, 4])) as ArrayRef;
251        let expected_ts = vec![
252            Arc::new(Int64Array::from(vec![0, 0, 0, 0, 1, 1, 1, 1, 2, 2])) as ArrayRef,
253            Arc::new(Int64Array::from(vec![0, 0, 1, 1, 2, 2, 3, 3, 4, 4])) as ArrayRef,
254        ];
255
256        let (merged_vals, merged_ts) = merge_ordered_arrays(
257            &mut [lhs_vals, rhs_vals],
258            &mut [lhs_orderings, rhs_orderings],
259            &sort_options,
260        )?;
261        let merged_vals = ScalarValue::iter_to_array(merged_vals.into_iter())?;
262        let merged_ts = (0..merged_ts[0].len())
263            .map(|col_idx| {
264                ScalarValue::iter_to_array(
265                    (0..merged_ts.len())
266                        .map(|row_idx| merged_ts[row_idx][col_idx].clone()),
267                )
268            })
269            .collect::<Result<Vec<_>>>()?;
270
271        assert_eq!(&merged_vals, &expected);
272        assert_eq!(&merged_ts, &expected_ts);
273
274        Ok(())
275    }
276
277    #[test]
278    fn test_merge_desc() -> Result<()> {
279        let lhs_arrays: Vec<ArrayRef> = vec![
280            Arc::new(Int64Array::from(vec![2, 1, 1, 0, 0])),
281            Arc::new(Int64Array::from(vec![4, 3, 2, 1, 0])),
282        ];
283        let n_row = lhs_arrays[0].len();
284        let lhs_orderings = (0..n_row)
285            .map(|idx| get_row_at_idx(&lhs_arrays, idx))
286            .collect::<Result<VecDeque<_>>>()?;
287
288        let rhs_arrays: Vec<ArrayRef> = vec![
289            Arc::new(Int64Array::from(vec![2, 1, 1, 0, 0])),
290            Arc::new(Int64Array::from(vec![4, 3, 2, 1, 0])),
291        ];
292        let n_row = rhs_arrays[0].len();
293        let rhs_orderings = (0..n_row)
294            .map(|idx| get_row_at_idx(&rhs_arrays, idx))
295            .collect::<Result<VecDeque<_>>>()?;
296        let sort_options = vec![
297            SortOptions {
298                descending: true,
299                nulls_first: false,
300            },
301            SortOptions {
302                descending: true,
303                nulls_first: false,
304            },
305        ];
306
307        // Values (which will be merged) doesn't have to be ordered.
308        let lhs_vals_arr = Arc::new(Int64Array::from(vec![0, 1, 2, 1, 2])) as ArrayRef;
309        let lhs_vals = (0..lhs_vals_arr.len())
310            .map(|idx| ScalarValue::try_from_array(&lhs_vals_arr, idx))
311            .collect::<Result<VecDeque<_>>>()?;
312
313        let rhs_vals_arr = Arc::new(Int64Array::from(vec![0, 1, 2, 1, 2])) as ArrayRef;
314        let rhs_vals = (0..rhs_vals_arr.len())
315            .map(|idx| ScalarValue::try_from_array(&rhs_vals_arr, idx))
316            .collect::<Result<VecDeque<_>>>()?;
317        let expected =
318            Arc::new(Int64Array::from(vec![0, 0, 1, 1, 2, 2, 1, 1, 2, 2])) as ArrayRef;
319        let expected_ts = vec![
320            Arc::new(Int64Array::from(vec![2, 2, 1, 1, 1, 1, 0, 0, 0, 0])) as ArrayRef,
321            Arc::new(Int64Array::from(vec![4, 4, 3, 3, 2, 2, 1, 1, 0, 0])) as ArrayRef,
322        ];
323        let (merged_vals, merged_ts) = merge_ordered_arrays(
324            &mut [lhs_vals, rhs_vals],
325            &mut [lhs_orderings, rhs_orderings],
326            &sort_options,
327        )?;
328        let merged_vals = ScalarValue::iter_to_array(merged_vals.into_iter())?;
329        let merged_ts = (0..merged_ts[0].len())
330            .map(|col_idx| {
331                ScalarValue::iter_to_array(
332                    (0..merged_ts.len())
333                        .map(|row_idx| merged_ts[row_idx][col_idx].clone()),
334                )
335            })
336            .collect::<Result<Vec<_>>>()?;
337
338        assert_eq!(&merged_vals, &expected);
339        assert_eq!(&merged_ts, &expected_ts);
340        Ok(())
341    }
342}
datafusion_functions_aggregate_common/merge_arrays.rs

datafusion_functions_aggregate_common/
merge_arrays.rs
