forked from apache/arrow-rs
/
levels.rs
1397 lines (1204 loc) · 49.2 KB
/
levels.rs
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.
//! Parquet definition and repetition levels
//!
//! Contains the algorithm for computing definition and repetition levels.
//! The algorithm works by tracking the slots of an array that should
//! ultimately be populated when writing to Parquet.
//! Parquet achieves nesting through definition levels and repetition levels \[1\].
//! Definition levels specify how many optional fields in the part for the column
//! are defined.
//! Repetition levels specify at what repeated field (list) in the path a column
//! is defined.
//!
//! In a nested data structure such as `a.b.c`, one can see levels as defining
//! whether a record is defined at `a`, `a.b`, or `a.b.c`.
//! Optional fields are nullable fields, thus if all 3 fields
//! are nullable, the maximum definition could be = 3 if there are no lists.
//!
//! The algorithm in this module computes the necessary information to enable
//! the writer to keep track of which columns are at which levels, and to extract
//! the correct values at the correct slots from Arrow arrays.
//!
//! It works by walking a record batch's arrays, keeping track of what values
//! are non-null, their positions and computing what their levels are.
//!
//! \[1\] [parquet-format#nested-encoding](https://github.com/apache/parquet-format#nested-encoding)
use crate::errors::{ParquetError, Result};
use arrow_array::{
make_array, Array, ArrayRef, GenericListArray, MapArray, OffsetSizeTrait, StructArray,
};
use arrow_data::ArrayData;
use arrow_schema::{DataType, Field};
use std::ops::Range;
/// Performs a depth-first scan of the children of `array`, constructing [`LevelInfo`]
/// for each leaf column encountered
pub(crate) fn calculate_array_levels(
array: &ArrayRef,
field: &Field,
) -> Result<Vec<LevelInfo>> {
let mut builder = LevelInfoBuilder::try_new(field, Default::default())?;
builder.write(array, 0..array.len());
Ok(builder.finish())
}
/// Returns true if the DataType can be represented as a primitive parquet column,
/// i.e. a leaf array with no children
fn is_leaf(data_type: &DataType) -> bool {
matches!(
data_type,
DataType::Null
| DataType::Boolean
| DataType::Int8
| DataType::Int16
| DataType::Int32
| DataType::Int64
| DataType::UInt8
| DataType::UInt16
| DataType::UInt32
| DataType::UInt64
| DataType::Float16
| DataType::Float32
| DataType::Float64
| DataType::Utf8
| DataType::LargeUtf8
| DataType::Timestamp(_, _)
| DataType::Date32
| DataType::Date64
| DataType::Time32(_)
| DataType::Time64(_)
| DataType::Duration(_)
| DataType::Interval(_)
| DataType::Binary
| DataType::LargeBinary
| DataType::Decimal128(_, _)
| DataType::FixedSizeBinary(_)
)
}
/// The definition and repetition level of an array within a potentially nested hierarchy
#[derive(Debug, Default, Clone, Copy)]
struct LevelContext {
/// The current repetition level
rep_level: i16,
/// The current definition level
def_level: i16,
}
/// A helper to construct [`LevelInfo`] from a potentially nested [`Field`]
enum LevelInfoBuilder {
/// A primitive, leaf array
Primitive(LevelInfo),
/// A list array, contains the [`LevelInfoBuilder`] of the child and
/// the [`LevelContext`] of this list
List(Box<LevelInfoBuilder>, LevelContext),
/// A list array, contains the [`LevelInfoBuilder`] of its children and
/// the [`LevelContext`] of this struct array
Struct(Vec<LevelInfoBuilder>, LevelContext),
}
impl LevelInfoBuilder {
/// Create a new [`LevelInfoBuilder`] for the given [`Field`] and parent [`LevelContext`]
fn try_new(field: &Field, parent_ctx: LevelContext) -> Result<Self> {
match field.data_type() {
d if is_leaf(d) => Ok(Self::Primitive(LevelInfo::new(
parent_ctx,
field.is_nullable(),
))),
DataType::Dictionary(_, v) if is_leaf(v.as_ref()) => Ok(Self::Primitive(
LevelInfo::new(parent_ctx, field.is_nullable()),
)),
DataType::Struct(children) => {
let def_level = match field.is_nullable() {
true => parent_ctx.def_level + 1,
false => parent_ctx.def_level,
};
let ctx = LevelContext {
rep_level: parent_ctx.rep_level,
def_level,
};
let children = children
.iter()
.map(|f| Self::try_new(f, ctx))
.collect::<Result<_>>()?;
Ok(Self::Struct(children, ctx))
}
DataType::List(child)
| DataType::LargeList(child)
| DataType::Map(child, _) => {
let def_level = match field.is_nullable() {
true => parent_ctx.def_level + 2,
false => parent_ctx.def_level + 1,
};
let ctx = LevelContext {
rep_level: parent_ctx.rep_level + 1,
def_level,
};
let child = Self::try_new(child.as_ref(), ctx)?;
Ok(Self::List(Box::new(child), ctx))
}
d => Err(nyi_err!("Datatype {} is not yet supported", d)),
}
}
/// Finish this [`LevelInfoBuilder`] returning the [`LevelInfo`] for the leaf columns
/// as enumerated by a depth-first search
fn finish(self) -> Vec<LevelInfo> {
match self {
LevelInfoBuilder::Primitive(v) => vec![v],
LevelInfoBuilder::List(v, _) => v.finish(),
LevelInfoBuilder::Struct(v, _) => {
v.into_iter().flat_map(|l| l.finish()).collect()
}
}
}
/// Given an `array`, write the level data for the elements in `range`
fn write(&mut self, array: &ArrayRef, range: Range<usize>) {
match array.data_type() {
d if is_leaf(d) => self.write_leaf(array, range),
DataType::Dictionary(_, v) if is_leaf(v.as_ref()) => {
self.write_leaf(array, range)
}
DataType::Struct(_) => {
let array = array.as_any().downcast_ref::<StructArray>().unwrap();
self.write_struct(array, range)
}
DataType::List(_) => {
let array = array
.as_any()
.downcast_ref::<GenericListArray<i32>>()
.unwrap();
self.write_list(array.value_offsets(), array.data(), range)
}
DataType::LargeList(_) => {
let array = array
.as_any()
.downcast_ref::<GenericListArray<i64>>()
.unwrap();
self.write_list(array.value_offsets(), array.data(), range)
}
DataType::Map(_, _) => {
let array = array.as_any().downcast_ref::<MapArray>().unwrap();
// A Map is just as ListArray<i32> with a StructArray child, we therefore
// treat it as such to avoid code duplication
self.write_list(array.value_offsets(), array.data(), range)
}
_ => unreachable!(),
}
}
/// Write `range` elements from ListArray `array`
///
/// Note: MapArrays are `ListArray<i32>` under the hood and so are dispatched to this method
fn write_list<O: OffsetSizeTrait>(
&mut self,
offsets: &[O],
list_data: &ArrayData,
range: Range<usize>,
) {
let (child, ctx) = match self {
Self::List(child, ctx) => (child, ctx),
_ => unreachable!(),
};
let offsets = &offsets[range.start..range.end + 1];
let child_array = make_array(list_data.child_data()[0].clone());
let write_non_null_slice =
|child: &mut LevelInfoBuilder, start_idx: usize, end_idx: usize| {
child.write(&child_array, start_idx..end_idx);
child.visit_leaves(|leaf| {
let rep_levels = leaf.rep_levels.as_mut().unwrap();
let mut rev = rep_levels.iter_mut().rev();
let mut remaining = end_idx - start_idx;
loop {
let next = rev.next().unwrap();
if *next > ctx.rep_level {
// Nested element - ignore
continue;
}
remaining -= 1;
if remaining == 0 {
*next = ctx.rep_level - 1;
break;
}
}
})
};
let write_empty_slice = |child: &mut LevelInfoBuilder| {
child.visit_leaves(|leaf| {
let rep_levels = leaf.rep_levels.as_mut().unwrap();
rep_levels.push(ctx.rep_level - 1);
let def_levels = leaf.def_levels.as_mut().unwrap();
def_levels.push(ctx.def_level - 1);
})
};
let write_null_slice = |child: &mut LevelInfoBuilder| {
child.visit_leaves(|leaf| {
let rep_levels = leaf.rep_levels.as_mut().unwrap();
rep_levels.push(ctx.rep_level - 1);
let def_levels = leaf.def_levels.as_mut().unwrap();
def_levels.push(ctx.def_level - 2);
})
};
match list_data.null_bitmap() {
Some(nulls) => {
let null_offset = list_data.offset() + range.start;
// TODO: Faster bitmask iteration (#1757)
for (idx, w) in offsets.windows(2).enumerate() {
let is_valid = nulls.is_set(idx + null_offset);
let start_idx = w[0].as_usize();
let end_idx = w[1].as_usize();
if !is_valid {
write_null_slice(child)
} else if start_idx == end_idx {
write_empty_slice(child)
} else {
write_non_null_slice(child, start_idx, end_idx)
}
}
}
None => {
for w in offsets.windows(2) {
let start_idx = w[0].as_usize();
let end_idx = w[1].as_usize();
if start_idx == end_idx {
write_empty_slice(child)
} else {
write_non_null_slice(child, start_idx, end_idx)
}
}
}
}
}
/// Write `range` elements from StructArray `array`
fn write_struct(&mut self, array: &StructArray, range: Range<usize>) {
let (children, ctx) = match self {
Self::Struct(children, ctx) => (children, ctx),
_ => unreachable!(),
};
let write_null = |children: &mut [LevelInfoBuilder], range: Range<usize>| {
for child in children {
child.visit_leaves(|info| {
let len = range.end - range.start;
let def_levels = info.def_levels.as_mut().unwrap();
def_levels.extend(std::iter::repeat(ctx.def_level - 1).take(len));
if let Some(rep_levels) = info.rep_levels.as_mut() {
rep_levels.extend(std::iter::repeat(ctx.rep_level).take(len));
}
})
}
};
let write_non_null = |children: &mut [LevelInfoBuilder], range: Range<usize>| {
for (child_array, child) in array.columns().iter().zip(children) {
child.write(child_array, range.clone())
}
};
match array.data().null_bitmap() {
Some(validity) => {
let null_offset = array.data().offset();
let mut last_non_null_idx = None;
let mut last_null_idx = None;
// TODO: Faster bitmask iteration (#1757)
for i in range.clone() {
match validity.is_set(i + null_offset) {
true => {
if let Some(last_idx) = last_null_idx.take() {
write_null(children, last_idx..i)
}
last_non_null_idx.get_or_insert(i);
}
false => {
if let Some(last_idx) = last_non_null_idx.take() {
write_non_null(children, last_idx..i)
}
last_null_idx.get_or_insert(i);
}
}
}
if let Some(last_idx) = last_null_idx.take() {
write_null(children, last_idx..range.end)
}
if let Some(last_idx) = last_non_null_idx.take() {
write_non_null(children, last_idx..range.end)
}
}
None => write_non_null(children, range),
}
}
/// Write a primitive array, as defined by [`is_leaf`]
fn write_leaf(&mut self, array: &ArrayRef, range: Range<usize>) {
let info = match self {
Self::Primitive(info) => info,
_ => unreachable!(),
};
let len = range.end - range.start;
match &mut info.def_levels {
Some(def_levels) => {
def_levels.reserve(len);
info.non_null_indices.reserve(len);
match array.data().null_bitmap() {
Some(nulls) => {
let nulls_offset = array.data().offset();
// TODO: Faster bitmask iteration (#1757)
for i in range {
match nulls.is_set(i + nulls_offset) {
true => {
def_levels.push(info.max_def_level);
info.non_null_indices.push(i)
}
false => def_levels.push(info.max_def_level - 1),
}
}
}
None => {
let iter = std::iter::repeat(info.max_def_level).take(len);
def_levels.extend(iter);
info.non_null_indices.extend(range);
}
}
}
None => info.non_null_indices.extend(range),
}
if let Some(rep_levels) = &mut info.rep_levels {
rep_levels.extend(std::iter::repeat(info.max_rep_level).take(len))
}
}
/// Visits all children of this node in depth first order
fn visit_leaves(&mut self, visit: impl Fn(&mut LevelInfo) + Copy) {
match self {
LevelInfoBuilder::Primitive(info) => visit(info),
LevelInfoBuilder::List(c, _) => c.visit_leaves(visit),
LevelInfoBuilder::Struct(children, _) => {
for c in children {
c.visit_leaves(visit)
}
}
}
}
}
/// The data necessary to write a primitive Arrow array to parquet, taking into account
/// any non-primitive parents it may have in the arrow representation
#[derive(Debug, Eq, PartialEq, Clone)]
pub(crate) struct LevelInfo {
/// Array's definition levels
///
/// Present if `max_def_level != 0`
def_levels: Option<Vec<i16>>,
/// Array's optional repetition levels
///
/// Present if `max_rep_level != 0`
rep_levels: Option<Vec<i16>>,
/// The corresponding array identifying non-null slices of data
/// from the primitive array
non_null_indices: Vec<usize>,
/// The maximum definition level for this leaf column
max_def_level: i16,
/// The maximum repetition for this leaf column
max_rep_level: i16,
}
impl LevelInfo {
fn new(ctx: LevelContext, is_nullable: bool) -> Self {
let max_rep_level = ctx.rep_level;
let max_def_level = match is_nullable {
true => ctx.def_level + 1,
false => ctx.def_level,
};
Self {
def_levels: (max_def_level != 0).then(Vec::new),
rep_levels: (max_rep_level != 0).then(Vec::new),
non_null_indices: vec![],
max_def_level,
max_rep_level,
}
}
pub fn def_levels(&self) -> Option<&[i16]> {
self.def_levels.as_deref()
}
pub fn rep_levels(&self) -> Option<&[i16]> {
self.rep_levels.as_deref()
}
pub fn non_null_indices(&self) -> &[usize] {
&self.non_null_indices
}
}
#[cfg(test)]
mod tests {
use super::*;
use std::sync::Arc;
use arrow_array::builder::*;
use arrow_array::types::Int32Type;
use arrow_array::*;
use arrow_buffer::{Buffer, ToByteSlice};
use arrow_cast::display::array_value_to_string;
use arrow_data::ArrayDataBuilder;
use arrow_schema::Schema;
#[test]
fn test_calculate_array_levels_twitter_example() {
// based on the example at https://blog.twitter.com/engineering/en_us/a/2013/dremel-made-simple-with-parquet.html
// [[a, b, c], [d, e, f, g]], [[h], [i,j]]
let leaf_type = Field::new("item", DataType::Int32, false);
let inner_type = DataType::List(Box::new(leaf_type));
let inner_field = Field::new("l2", inner_type.clone(), false);
let outer_type = DataType::List(Box::new(inner_field));
let outer_field = Field::new("l1", outer_type.clone(), false);
let primitives = Int32Array::from_iter(0..10);
// Cannot use from_iter_primitive as always infers nullable
let offsets = Buffer::from_iter([0_i32, 3, 7, 8, 10]);
let inner_list = ArrayDataBuilder::new(inner_type)
.len(4)
.add_buffer(offsets)
.add_child_data(primitives.into_data())
.build()
.unwrap();
let offsets = Buffer::from_iter([0_i32, 2, 4]);
let outer_list = ArrayDataBuilder::new(outer_type)
.len(2)
.add_buffer(offsets)
.add_child_data(inner_list)
.build()
.unwrap();
let outer_list = make_array(outer_list);
let levels = calculate_array_levels(&outer_list, &outer_field).unwrap();
assert_eq!(levels.len(), 1);
let expected = LevelInfo {
def_levels: Some(vec![2; 10]),
rep_levels: Some(vec![0, 2, 2, 1, 2, 2, 2, 0, 1, 2]),
non_null_indices: vec![0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
max_def_level: 2,
max_rep_level: 2,
};
assert_eq!(&levels[0], &expected);
}
#[test]
fn test_calculate_one_level_1() {
// This test calculates the levels for a non-null primitive array
let array = Arc::new(Int32Array::from_iter(0..10)) as ArrayRef;
let field = Field::new("item", DataType::Int32, false);
let levels = calculate_array_levels(&array, &field).unwrap();
assert_eq!(levels.len(), 1);
let expected_levels = LevelInfo {
def_levels: None,
rep_levels: None,
non_null_indices: (0..10).collect(),
max_def_level: 0,
max_rep_level: 0,
};
assert_eq!(&levels[0], &expected_levels);
}
#[test]
fn test_calculate_one_level_2() {
// This test calculates the levels for a nullable primitive array
let array = Arc::new(Int32Array::from_iter([
Some(0),
None,
Some(0),
Some(0),
None,
])) as ArrayRef;
let field = Field::new("item", DataType::Int32, true);
let levels = calculate_array_levels(&array, &field).unwrap();
assert_eq!(levels.len(), 1);
let expected_levels = LevelInfo {
def_levels: Some(vec![1, 0, 1, 1, 0]),
rep_levels: None,
non_null_indices: vec![0, 2, 3],
max_def_level: 1,
max_rep_level: 0,
};
assert_eq!(&levels[0], &expected_levels);
}
#[test]
fn test_calculate_array_levels_1() {
let leaf_field = Field::new("item", DataType::Int32, false);
let list_type = DataType::List(Box::new(leaf_field));
// if all array values are defined (e.g. batch<list<_>>)
// [[0], [1], [2], [3], [4]]
let leaf_array = Int32Array::from_iter(0..5);
// Cannot use from_iter_primitive as always infers nullable
let offsets = Buffer::from_iter(0_i32..6);
let list = ArrayDataBuilder::new(list_type.clone())
.len(5)
.add_buffer(offsets)
.add_child_data(leaf_array.into_data())
.build()
.unwrap();
let list = make_array(list);
let list_field = Field::new("list", list_type.clone(), false);
let levels = calculate_array_levels(&list, &list_field).unwrap();
assert_eq!(levels.len(), 1);
let expected_levels = LevelInfo {
def_levels: Some(vec![1; 5]),
rep_levels: Some(vec![0; 5]),
non_null_indices: (0..5).collect(),
max_def_level: 1,
max_rep_level: 1,
};
assert_eq!(&levels[0], &expected_levels);
// array: [[0, 0], NULL, [2, 2], [3, 3, 3, 3], [4, 4, 4]]
// all values are defined as we do not have nulls on the root (batch)
// repetition:
// 0: 0, 1
// 1: 0
// 2: 0, 1
// 3: 0, 1, 1, 1
// 4: 0, 1, 1
let leaf_array = Int32Array::from_iter([0, 0, 2, 2, 3, 3, 3, 3, 4, 4, 4]);
let offsets = Buffer::from_iter([0_i32, 2, 2, 4, 8, 11]);
let list = ArrayDataBuilder::new(list_type.clone())
.len(5)
.add_buffer(offsets)
.add_child_data(leaf_array.into_data())
.null_bit_buffer(Some(Buffer::from([0b00011101])))
.build()
.unwrap();
let list = make_array(list);
let list_field = Field::new("list", list_type, true);
let levels = calculate_array_levels(&list, &list_field).unwrap();
assert_eq!(levels.len(), 1);
let expected_levels = LevelInfo {
def_levels: Some(vec![2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2]),
rep_levels: Some(vec![0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1]),
non_null_indices: (0..11).collect(),
max_def_level: 2,
max_rep_level: 1,
};
assert_eq!(&levels[0], &expected_levels);
}
#[test]
fn test_calculate_array_levels_2() {
// If some values are null
//
// This emulates an array in the form: <struct<list<?>>
// with values:
// - 0: [0, 1], but is null because of the struct
// - 1: []
// - 2: [2, 3], but is null because of the struct
// - 3: [4, 5, 6, 7]
// - 4: [8, 9, 10]
//
// If the first values of a list are null due to a parent, we have to still account for them
// while indexing, because they would affect the way the child is indexed
// i.e. in the above example, we have to know that [0, 1] has to be skipped
let leaf = Int32Array::from_iter(0..11);
let leaf_field = Field::new("leaf", DataType::Int32, false);
let list_type = DataType::List(Box::new(leaf_field));
let list = ArrayData::builder(list_type.clone())
.len(5)
.add_child_data(leaf.into_data())
.add_buffer(Buffer::from_iter([0_i32, 2, 2, 4, 8, 11]))
.build()
.unwrap();
let list = make_array(list);
let list_field = Field::new("list", list_type, true);
let struct_array =
StructArray::from((vec![(list_field, list)], Buffer::from([0b00011010])));
let array = Arc::new(struct_array) as ArrayRef;
let struct_field = Field::new("struct", array.data_type().clone(), true);
let levels = calculate_array_levels(&array, &struct_field).unwrap();
assert_eq!(levels.len(), 1);
let expected_levels = LevelInfo {
def_levels: Some(vec![0, 2, 0, 3, 3, 3, 3, 3, 3, 3]),
rep_levels: Some(vec![0, 0, 0, 0, 1, 1, 1, 0, 1, 1]),
non_null_indices: (4..11).collect(),
max_def_level: 3,
max_rep_level: 1,
};
assert_eq!(&levels[0], &expected_levels);
// nested lists
// 0: [[100, 101], [102, 103]]
// 1: []
// 2: [[104, 105], [106, 107]]
// 3: [[108, 109], [110, 111], [112, 113], [114, 115]]
// 4: [[116, 117], [118, 119], [120, 121]]
let leaf = Int32Array::from_iter(100..122);
let leaf_field = Field::new("leaf", DataType::Int32, true);
let l1_type = DataType::List(Box::new(leaf_field));
let offsets = Buffer::from_iter([0_i32, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22]);
let l1 = ArrayData::builder(l1_type.clone())
.len(11)
.add_child_data(leaf.into_data())
.add_buffer(offsets)
.build()
.unwrap();
let l1_field = Field::new("l1", l1_type, true);
let l2_type = DataType::List(Box::new(l1_field));
let l2 = ArrayData::builder(l2_type)
.len(5)
.add_child_data(l1)
.add_buffer(Buffer::from_iter([0, 2, 2, 4, 8, 11]))
.build()
.unwrap();
let l2 = make_array(l2);
let l2_field = Field::new("l2", l2.data_type().clone(), true);
let levels = calculate_array_levels(&l2, &l2_field).unwrap();
assert_eq!(levels.len(), 1);
let expected_levels = LevelInfo {
def_levels: Some(vec![
5, 5, 5, 5, 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
]),
rep_levels: Some(vec![
0, 2, 1, 2, 0, 0, 2, 1, 2, 0, 2, 1, 2, 1, 2, 1, 2, 0, 2, 1, 2, 1, 2,
]),
non_null_indices: (0..22).collect(),
max_def_level: 5,
max_rep_level: 2,
};
assert_eq!(&levels[0], &expected_levels);
}
#[test]
fn test_calculate_array_levels_nested_list() {
let leaf_field = Field::new("leaf", DataType::Int32, false);
let list_type = DataType::List(Box::new(leaf_field));
// if all array values are defined (e.g. batch<list<_>>)
// The array at this level looks like:
// 0: [a]
// 1: [a]
// 2: [a]
// 3: [a]
let leaf = Int32Array::from_iter([0; 4]);
let list = ArrayData::builder(list_type.clone())
.len(4)
.add_buffer(Buffer::from_iter(0_i32..5))
.add_child_data(leaf.into_data())
.build()
.unwrap();
let list = make_array(list);
let list_field = Field::new("list", list_type.clone(), false);
let levels = calculate_array_levels(&list, &list_field).unwrap();
assert_eq!(levels.len(), 1);
let expected_levels = LevelInfo {
def_levels: Some(vec![1; 4]),
rep_levels: Some(vec![0; 4]),
non_null_indices: (0..4).collect(),
max_def_level: 1,
max_rep_level: 1,
};
assert_eq!(&levels[0], &expected_levels);
// 0: null
// 1: [1, 2, 3]
// 2: [4, 5]
// 3: [6, 7]
let leaf = Int32Array::from_iter(0..8);
let list = ArrayData::builder(list_type.clone())
.len(4)
.add_buffer(Buffer::from_iter([0_i32, 0, 3, 5, 7]))
.null_bit_buffer(Some(Buffer::from([0b00001110])))
.add_child_data(leaf.into_data())
.build()
.unwrap();
let list = make_array(list);
let list_field = Field::new("list", list_type, true);
let struct_array = StructArray::from(vec![(list_field, list)]);
let array = Arc::new(struct_array) as ArrayRef;
let struct_field = Field::new("struct", array.data_type().clone(), true);
let levels = calculate_array_levels(&array, &struct_field).unwrap();
assert_eq!(levels.len(), 1);
let expected_levels = LevelInfo {
def_levels: Some(vec![1, 3, 3, 3, 3, 3, 3, 3]),
rep_levels: Some(vec![0, 0, 1, 1, 0, 1, 0, 1]),
non_null_indices: (0..7).collect(),
max_def_level: 3,
max_rep_level: 1,
};
assert_eq!(&levels[0], &expected_levels);
// nested lists
// In a JSON syntax with the schema: <struct<list<list<primitive>>>>, this translates into:
// 0: {"struct": null }
// 1: {"struct": [ [201], [202, 203], [] ]}
// 2: {"struct": [ [204, 205, 206], [207, 208, 209, 210] ]}
// 3: {"struct": [ [], [211, 212, 213, 214, 215] ]}
let leaf = Int32Array::from_iter(201..216);
let leaf_field = Field::new("leaf", DataType::Int32, false);
let list_1_type = DataType::List(Box::new(leaf_field));
let list_1 = ArrayData::builder(list_1_type.clone())
.len(7)
.add_buffer(Buffer::from_iter([0_i32, 1, 3, 3, 6, 10, 10, 15]))
.add_child_data(leaf.into_data())
.build()
.unwrap();
let list_1_field = Field::new("l1", list_1_type, true);
let list_2_type = DataType::List(Box::new(list_1_field));
let list_2 = ArrayData::builder(list_2_type.clone())
.len(4)
.add_buffer(Buffer::from_iter([0_i32, 0, 3, 5, 7]))
.null_bit_buffer(Some(Buffer::from([0b00001110])))
.add_child_data(list_1)
.build()
.unwrap();
let list_2 = make_array(list_2);
let list_2_field = Field::new("list_2", list_2_type, true);
let struct_array =
StructArray::from((vec![(list_2_field, list_2)], Buffer::from([0b00001111])));
let struct_field = Field::new("struct", struct_array.data_type().clone(), true);
let array = Arc::new(struct_array) as ArrayRef;
let levels = calculate_array_levels(&array, &struct_field).unwrap();
assert_eq!(levels.len(), 1);
let expected_levels = LevelInfo {
def_levels: Some(vec![1, 5, 5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5]),
rep_levels: Some(vec![0, 0, 1, 2, 1, 0, 2, 2, 1, 2, 2, 2, 0, 1, 2, 2, 2, 2]),
non_null_indices: (0..15).collect(),
max_def_level: 5,
max_rep_level: 2,
};
assert_eq!(&levels[0], &expected_levels);
}
#[test]
fn test_calculate_nested_struct_levels() {
// tests a <struct[a]<struct[b]<int[c]>>
// array:
// - {a: {b: {c: 1}}}
// - {a: {b: {c: null}}}
// - {a: {b: {c: 3}}}
// - {a: {b: null}}
// - {a: null}}
// - {a: {b: {c: 6}}}
let c = Int32Array::from_iter([Some(1), None, Some(3), None, Some(5), Some(6)]);
let c_field = Field::new("c", DataType::Int32, true);
let b = StructArray::from((
(vec![(c_field, Arc::new(c) as ArrayRef)]),
Buffer::from([0b00110111]),
));
let b_field = Field::new("b", b.data_type().clone(), true);
let a = StructArray::from((
(vec![(b_field, Arc::new(b) as ArrayRef)]),
Buffer::from([0b00101111]),
));
let a_field = Field::new("a", a.data_type().clone(), true);
let a_array = Arc::new(a) as ArrayRef;
let levels = calculate_array_levels(&a_array, &a_field).unwrap();
assert_eq!(levels.len(), 1);
let expected_levels = LevelInfo {
def_levels: Some(vec![3, 2, 3, 1, 0, 3]),
rep_levels: None,
non_null_indices: vec![0, 2, 5],
max_def_level: 3,
max_rep_level: 0,
};
assert_eq!(&levels[0], &expected_levels);
}
#[test]
fn list_single_column() {
// this tests the level generation from the arrow_writer equivalent test
let a_values = Int32Array::from(vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
let a_value_offsets = arrow::buffer::Buffer::from_iter([0_i32, 1, 3, 3, 6, 10]);
let a_list_type =
DataType::List(Box::new(Field::new("item", DataType::Int32, true)));
let a_list_data = ArrayData::builder(a_list_type.clone())
.len(5)
.add_buffer(a_value_offsets)
.null_bit_buffer(Some(Buffer::from(vec![0b00011011])))
.add_child_data(a_values.into_data())
.build()
.unwrap();
assert_eq!(a_list_data.null_count(), 1);
let a = ListArray::from(a_list_data);
let values = Arc::new(a) as _;
let item_field = Field::new("item", a_list_type, true);
let mut builder =
LevelInfoBuilder::try_new(&item_field, Default::default()).unwrap();
builder.write(&values, 2..4);
let levels = builder.finish();
assert_eq!(levels.len(), 1);
let list_level = levels.get(0).unwrap();
let expected_level = LevelInfo {
def_levels: Some(vec![0, 3, 3, 3]),
rep_levels: Some(vec![0, 0, 1, 1]),
non_null_indices: vec![3, 4, 5],
max_def_level: 3,
max_rep_level: 1,
};
assert_eq!(list_level, &expected_level);
}
#[test]
fn mixed_struct_list() {
// this tests the level generation from the equivalent arrow_writer_complex test
// define schema
let struct_field_d = Field::new("d", DataType::Float64, true);
let struct_field_f = Field::new("f", DataType::Float32, true);
let struct_field_g = Field::new(
"g",
DataType::List(Box::new(Field::new("items", DataType::Int16, false))),
false,
);
let struct_field_e = Field::new(
"e",
DataType::Struct(vec![struct_field_f.clone(), struct_field_g.clone()]),
true,
);
let schema = Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, true),
Field::new(
"c",
DataType::Struct(vec![struct_field_d.clone(), struct_field_e.clone()]),
true, // https://github.com/apache/arrow-rs/issues/245
),
]);
// create some data
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
let b = Int32Array::from(vec![Some(1), None, None, Some(4), Some(5)]);
let d = Float64Array::from(vec![None, None, None, Some(1.0), None]);
let f = Float32Array::from(vec![Some(0.0), None, Some(333.3), None, Some(5.25)]);
let g_value = Int16Array::from(vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
// Construct a buffer for value offsets, for the nested array:
// [[1], [2, 3], null, [4, 5, 6], [7, 8, 9, 10]]
let g_value_offsets =
arrow::buffer::Buffer::from(&[0, 1, 3, 3, 6, 10].to_byte_slice());
// Construct a list array from the above two
let g_list_data = ArrayData::builder(struct_field_g.data_type().clone())
.len(5)
.add_buffer(g_value_offsets)
.add_child_data(g_value.into_data())
.build()
.unwrap();
let g = ListArray::from(g_list_data);
let e = StructArray::from(vec![
(struct_field_f, Arc::new(f) as ArrayRef),
(struct_field_g, Arc::new(g) as ArrayRef),
]);
let c = StructArray::from(vec![
(struct_field_d, Arc::new(d) as ArrayRef),
(struct_field_e, Arc::new(e) as ArrayRef),
]);
// build a record batch
let batch = RecordBatch::try_new(
Arc::new(schema),
vec![Arc::new(a), Arc::new(b), Arc::new(c)],