Support for contextual dot and implicits #395

christoph-dfinity · 2025-09-15T14:30:45Z

Adds Self members to enable contextual dot resolution
Updates List tests to use contextual dot resolution

github-actions · 2025-09-15T14:31:57Z

✨ Documentation preview for bd8e69e:

https://dfinity.github.io/motoko-core/pull/395 (source code)

github-actions · 2025-09-25T17:53:05Z

Benchmark Results

bench/ArrayBuilding.bench.mo $({\color{gray}0\%})$

Large known-size array building

Compares performance of different data structures for building arrays of known size.

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Instructions

	1000	100000	1000000
List	548_233 $({\color{gray}0\%})$	48_324_535 $({\color{gray}0\%})$	478_161_875 $({\color{gray}0\%})$
Buffer	342_005 $({\color{gray}0\%})$	33_903_435 $({\color{gray}0\%})$	339_003_650 $({\color{gray}0\%})$
pure/List	302_135 $({\color{gray}0\%})$	30_003_590 $({\color{gray}0\%})$	300_055_972 $({\color{gray}0\%})$
VarArray ?T	180_526 $({\color{gray}0\%})$	17_802_956 $({\color{gray}0\%})$	178_003_171 $({\color{gray}0\%})$
VarArray T	160_813 $({\color{gray}0\%})$	15_803_243 $({\color{gray}0\%})$	158_003_458 $({\color{gray}0\%})$
Array (baseline)	42_695 $({\color{gray}0\%})$	4_003_125 $({\color{gray}0\%})$	40_003_340 $({\color{gray}0\%})$

Heap

	1000	100000	1000000
List	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
Buffer	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
pure/List	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
VarArray ?T	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
VarArray T	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
Array (baseline)	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$

Garbage Collection

	1000	100000	1000000
List	10.05 KiB $({\color{gray}0\%})$	797.56 KiB $({\color{gray}0\%})$	7.67 MiB $({\color{gray}0\%})$
Buffer	8.71 KiB $({\color{gray}0\%})$	782.15 KiB $({\color{gray}0\%})$	7.63 MiB $({\color{gray}0\%})$
pure/List	19.95 KiB $({\color{gray}0\%})$	1.91 MiB $({\color{gray}0\%})$	19.07 MiB $({\color{gray}0\%})$
VarArray ?T	8.24 KiB $({\color{gray}0\%})$	781.68 KiB $({\color{gray}0\%})$	7.63 MiB $({\color{gray}0\%})$
VarArray T	8.23 KiB $({\color{gray}0\%})$	781.67 KiB $({\color{gray}0\%})$	7.63 MiB $({\color{gray}0\%})$
Array (baseline)	4.3 KiB $({\color{gray}0\%})$	391.02 KiB $({\color{gray}0\%})$	3.82 MiB $({\color{gray}0\%})$

bench/FromIters.bench.mo $({\color{gray}0\%})$

Benchmarking the fromIter functions

Columns describe the number of elements in the input iter.

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Instructions

	100	10_000	100_000
Array.fromIter	48_764 $({\color{gray}0\%})$	4_712_025 $({\color{gray}0\%})$	47_103_135 $({\color{gray}0\%})$
List.fromIter	31_698 $({\color{gray}0\%})$	3_061_541 $({\color{gray}0\%})$	30_603_553 $({\color{gray}0\%})$
List.fromIter . Iter.reverse	50_297 $({\color{gray}0\%})$	4_832_563 $({\color{gray}0\%})$	48_305_477 $({\color{gray}0\%})$

Heap

	100	10_000	100_000
Array.fromIter	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
List.fromIter	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
List.fromIter . Iter.reverse	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$

Garbage Collection

	100	10_000	100_000
Array.fromIter	2.76 KiB $({\color{gray}0\%})$	234.79 KiB $({\color{gray}0\%})$	2.29 MiB $({\color{gray}0\%})$
List.fromIter	3.51 KiB $({\color{gray}0\%})$	312.88 KiB $({\color{gray}0\%})$	3.05 MiB $({\color{gray}0\%})$
List.fromIter . Iter.reverse	5.11 KiB $({\color{gray}0\%})$	469.17 KiB $({\color{gray}0\%})$	4.58 MiB $({\color{gray}0\%})$

bench/ListBufferNewArray.bench.mo $({\color{gray}0\%})$

List vs. Buffer for creating known-size arrays

Performance comparison between List and Buffer for creating a new array.

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Instructions

	0 (baseline)	1	5	10	100 (for loop)
List	1_547 $({\color{gray}0\%})$	2_916 $({\color{gray}0\%})$	9_046 $({\color{gray}0\%})$	13_948 $({\color{gray}0\%})$	74_564 $({\color{gray}0\%})$
pure/List	1_247 $({\color{gray}0\%})$	1_355 $({\color{gray}0\%})$	2_439 $({\color{gray}0\%})$	3_801 $({\color{gray}0\%})$	31_868 $({\color{gray}0\%})$
Buffer	2_119 $({\color{gray}0\%})$	2_271 $({\color{gray}0\%})$	3_518 $({\color{gray}0\%})$	5_085 $({\color{gray}0\%})$	36_640 $({\color{gray}0\%})$

Heap

	0 (baseline)	1	5	10	100 (for loop)
List	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
pure/List	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
Buffer	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$

Garbage Collection

	0 (baseline)	1	5	10	100 (for loop)
List	576 B $({\color{gray}0\%})$	616 B $({\color{gray}0\%})$	776 B $({\color{gray}0\%})$	884 B $({\color{gray}0\%})$	1.93 KiB $({\color{gray}0\%})$
pure/List	360 B $({\color{gray}0\%})$	380 B $({\color{gray}0\%})$	460 B $({\color{gray}0\%})$	560 B $({\color{gray}0\%})$	2.3 KiB $({\color{gray}0\%})$
Buffer	856 B $({\color{gray}0\%})$	864 B $({\color{gray}0\%})$	896 B $({\color{gray}0\%})$	936 B $({\color{gray}0\%})$	1.62 KiB $({\color{gray}0\%})$

bench/PriorityQueues.bench.mo $({\color{gray}0\%})$

Different priority queue implementations

_Compare the performance of the following priority queue implementations:

PriorityQueue: Binary heap implementation over List.
PriorityQueueSet: Wrapper over Set<(T, Nat)>._

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Instructions

	A) PriorityQueue	B) PriorityQueueSet
1.) 100000 operations (push:pop = 1:1)	597_528_283 $({\color{gray}0\%})$	522_729_861 $({\color{gray}0\%})$
2.) 100000 operations (push:pop = 2:1)	742_952_999 $({\color{gray}0\%})$	809_693_415 $({\color{gray}0\%})$
3.) 100000 operations (push:pop = 10:1)	357_911_737 $({\color{gray}0\%})$	873_181_028 $({\color{gray}0\%})$
4.) 100000 operations (only push)	192_422_882 $({\color{gray}0\%})$	886_824_792 $({\color{gray}0\%})$
5.) 50000 pushes, then 50000 pops	776_632_572 $({\color{gray}0\%})$	961_776_534 $({\color{gray}0\%})$
6.) 50000 pushes, then 25000 "pop;push"es	529_475_053 $({\color{gray}0\%})$	922_137_111 $({\color{gray}0\%})$

Heap

	A) PriorityQueue	B) PriorityQueueSet
1.) 100000 operations (push:pop = 1:1)	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
2.) 100000 operations (push:pop = 2:1)	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
3.) 100000 operations (push:pop = 10:1)	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
4.) 100000 operations (only push)	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
5.) 50000 pushes, then 50000 pops	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
6.) 50000 pushes, then 25000 "pop;push"es	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$

Garbage Collection

	A) PriorityQueue	B) PriorityQueueSet
1.) 100000 operations (push:pop = 1:1)	15.03 MiB $({\color{gray}0\%})$	17.43 MiB $({\color{gray}0\%})$
2.) 100000 operations (push:pop = 2:1)	19.73 MiB $({\color{gray}0\%})$	19.32 MiB $({\color{gray}0\%})$
3.) 100000 operations (push:pop = 10:1)	8.67 MiB $({\color{gray}0\%})$	12.64 MiB $({\color{gray}0\%})$
4.) 100000 operations (only push)	3.87 MiB $({\color{gray}0\%})$	9.96 MiB $({\color{gray}0\%})$
5.) 50000 pushes, then 50000 pops	22.03 MiB $({\color{gray}0\%})$	26.2 MiB $({\color{gray}0\%})$
6.) 50000 pushes, then 25000 "pop;push"es	14.22 MiB $({\color{gray}0\%})$	18.44 MiB $({\color{gray}0\%})$

bench/PureListStackSafety.bench.mo $({\color{gray}0\%})$

List Stack safety

Check stack-safety of the following pure/List-related functions.

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Instructions


pure/List.split	24_602_524 $({\color{gray}0\%})$
pure/List.all	7_901_014 $({\color{gray}0\%})$
pure/List.any	8_001_390 $({\color{gray}0\%})$
pure/List.map	23_103_767 $({\color{gray}0\%})$
pure/List.filter	21_104_188 $({\color{gray}0\%})$
pure/List.filterMap	27_404_742 $({\color{gray}0\%})$
pure/List.partition	21_304_994 $({\color{gray}0\%})$
pure/List.join	33_105_326 $({\color{gray}0\%})$
pure/List.flatten	24_805_667 $({\color{gray}0\%})$
pure/List.take	24_605_664 $({\color{gray}0\%})$
pure/List.drop	9_904_119 $({\color{gray}0\%})$
pure/List.foldRight	19_105_768 $({\color{gray}0\%})$
pure/List.merge	31_808_584 $({\color{gray}0\%})$
pure/List.chunks	51_510_344 $({\color{gray}0\%})$
pure/Queue	142_662_505 $({\color{gray}0\%})$

Heap


pure/List.split	272 B $({\color{gray}0\%})$
pure/List.all	272 B $({\color{gray}0\%})$
pure/List.any	272 B $({\color{gray}0\%})$
pure/List.map	272 B $({\color{gray}0\%})$
pure/List.filter	272 B $({\color{gray}0\%})$
pure/List.filterMap	272 B $({\color{gray}0\%})$
pure/List.partition	272 B $({\color{gray}0\%})$
pure/List.join	272 B $({\color{gray}0\%})$
pure/List.flatten	272 B $({\color{gray}0\%})$
pure/List.take	272 B $({\color{gray}0\%})$
pure/List.drop	272 B $({\color{gray}0\%})$
pure/List.foldRight	272 B $({\color{gray}0\%})$
pure/List.merge	272 B $({\color{gray}0\%})$
pure/List.chunks	272 B $({\color{gray}0\%})$
pure/Queue	272 B $({\color{gray}0\%})$

Garbage Collection


pure/List.split	3.05 MiB $({\color{gray}0\%})$
pure/List.all	328 B $({\color{gray}0\%})$
pure/List.any	328 B $({\color{gray}0\%})$
pure/List.map	3.05 MiB $({\color{gray}0\%})$
pure/List.filter	3.05 MiB $({\color{gray}0\%})$
pure/List.filterMap	3.05 MiB $({\color{gray}0\%})$
pure/List.partition	3.05 MiB $({\color{gray}0\%})$
pure/List.join	3.05 MiB $({\color{gray}0\%})$
pure/List.flatten	3.05 MiB $({\color{gray}0\%})$
pure/List.take	3.05 MiB $({\color{gray}0\%})$
pure/List.drop	328 B $({\color{gray}0\%})$
pure/List.foldRight	1.53 MiB $({\color{gray}0\%})$
pure/List.merge	4.58 MiB $({\color{gray}0\%})$
pure/List.chunks	7.63 MiB $({\color{gray}0\%})$
pure/Queue	18.31 MiB $({\color{gray}0\%})$

bench/Queues.bench.mo $({\color{gray}0\%})$

Different queue implementations

Compare the performance of the following queue implementations:

pure/Queue: The default immutable double-ended queue implementation.
- Pros: Good amortized performance, meaning that the average cost of operations is low O(1).
- Cons: In worst case, an operation can take O(size) time rebuilding the queue as demonstrated in the Pop front 2 elements scenario.
pure/RealTimeQueue
- Pros: Every operation is guaranteed to take at most O(1) time and space.
- Cons: Poor amortized performance: Instruction cost is on average 3x for pop and 8x for push compared to pure/Queue.
mutable Queue
- Pros: Also O(1) guarantees with a lower constant factor than pure/RealTimeQueue. Amortized performance is comparable to pure/Queue.
- Cons: It is mutable and cannot be used in shared types (not shareable).

Instructions: ${\color{gray}0\%}$
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Instructions

	pure/Queue	pure/RealTimeQueue	mutable Queue
Initialize with 2 elements	3_092 $({\color{gray}0\%})$	2_304 $({\color{gray}0\%})$	3_040 $({\color{gray}0\%})$
Push 500 elements	90_713 $({\color{gray}0\%})$	744_219 $({\color{gray}0\%})$	219_284 $({\color{gray}0\%})$
Pop front 2 elements	86_966 $({\color{gray}0\%})$	4_446 $({\color{gray}0\%})$	3_847 $({\color{gray}0\%})$
Pop 150 front&back	92_095 $({\color{gray}0\%})$	304_908 $({\color{gray}0\%})$	124_581 $({\color{gray}0\%})$

Heap

	pure/Queue	pure/RealTimeQueue	mutable Queue
Initialize with 2 elements	324 B $({\color{gray}0\%})$	300 B $({\color{gray}0\%})$	352 B $({\color{gray}0\%})$
Push 500 elements	8.08 KiB $({\color{gray}0\%})$	8.17 KiB $({\color{gray}0\%})$	19.8 KiB $({\color{gray}0\%})$
Pop front 2 elements	240 B $({\color{gray}0\%})$	240 B $({\color{gray}0\%})$	192 B $({\color{gray}0\%})$
Pop 150 front&back	-4.42 KiB $({\color{gray}0\%})$	-492 B $({\color{gray}0\%})$	-11.45 KiB $({\color{gray}0\%})$

Garbage Collection

	pure/Queue	pure/RealTimeQueue	mutable Queue
Initialize with 2 elements	508 B $({\color{gray}0\%})$	444 B $({\color{gray}0\%})$	456 B $({\color{gray}0\%})$
Push 500 elements	10.1 KiB $({\color{gray}0\%})$	137.84 KiB $({\color{gray}0\%})$	344 B $({\color{gray}0\%})$
Pop front 2 elements	12.19 KiB $({\color{gray}0\%})$	528 B $({\color{gray}0\%})$	424 B $({\color{gray}0\%})$
Pop 150 front&back	15.61 KiB $({\color{gray}0\%})$	49.66 KiB $({\color{gray}0\%})$	12.1 KiB $({\color{gray}0\%})$

Note: Renamed benchmarks cannot be compared. Refer to the current baseline for manual comparison.

At a high level this allows writing a function call like `List.concat(list1, list2)` as `list1.concat(list2)`, by finding the `List.concat` function in the context using the inferred type of `list1`. This allows for a more fluent style of using code defined with data types and functions as opposed to classes and methods. This implements the design in #2537 with its first two variations. It extends it slightly by not only taking the `Self` type of the module into account, but also checking compatibility with the first argument of the to-be-picked resolution. ### Todo - [x] Decide if the `Self` module type is the way we want to go. I went with it over searching through every function in scope with the hope of keeping compile times in check. - [x] Write up proper spec, by adjusting the design in #2537 to the current implementation - [x] Make a branch of `core` that uses this throughout to see if we like the impact (dfinity/motoko-core#395) - Documentation - [x] Language manual ### Future - Integration with the language server. Don't want to block this PR on this item and I'm not sure what it involves, so I'll defer to the future. - More informal prose with examples (For now just the Language manual has been updated)

* chore: updates matchers package to mops release (#394) * makes comparison arguments to Map functions implicit * use implicits in tests for Map * make comparison arguments to Set functions implicit * update Set tests to use implicits * remove local bindings, now that we can disambiguate Nat and Int * make equal arguments implicit * rename internal Map and Set size to size_ to avoid clash with contextual dot * Implicit pattern across entire `core` package (#398) * First pass * Another pass * Fix stragglers * Fix more stragglers * Apply suggestions from code review Co-authored-by: Christoph <christoph.hegemann@dfinity.org> Co-authored-by: Claudio Russo <claudio@dfinity.org> * Apply suggestions from code review Co-authored-by: Claudio Russo <claudio@dfinity.org> * Various fixes * Apply suggestions from code review Co-authored-by: Claudio Russo <claudio@dfinity.org> * Fix * Update API lockfile --------- Co-authored-by: Christoph <christoph.hegemann@dfinity.org> Co-authored-by: Claudio Russo <claudio@dfinity.org> * rename .size on Queue * add TODO comment * I was using a stale compiler * updates the toolchain to a pre-release * updated mops to pull down draft branch and adjust tests (#399) * feat: Add PriorityQueue (#392) * Partial implementation. * Fix bug and add peek. * Started with tests, and more functions. * Towards more tests, adding SetPriorityQueue. * Completing first implementation, with tests. * Add comments to PriorityQueue.mo * Move set implementation and add benchmark. * Better holes. * Extra benchmark. * More tests. * . * Comments. * Optimization. * Comments for SetWrapper. * Move SetWrapper. * Run format. * . * npm run validate * Fix imports in docstrings. * Add Changelog entry. * Fix Changelog entry. * Remove inefficient push and pop operations (and benchmarks for them). * Move PriorityQueueSet.mo * Fix validation. * Update Changelog. * Update implicits for RealTimeQueue * Update implicits for PriorityQueue * Update API lockfile --------- Co-authored-by: Ryan Vandersmith <ryan.vandersmith@dfinity.org> Co-authored-by: Claudio Russo <claudio@dfinity.org> Co-authored-by: Andrei Constantinescu <andrei.constantinescu@dfinity.org>

christoph-dfinity · 2025-10-13T14:06:48Z

Closing in favor of #404

christoph-dfinity added 2 commits September 15, 2025 09:51

adds Self members to enable contextual dot resolution

ff241c9

update List tests to use contextual dot resolution

f528fa7

christoph-dfinity mentioned this pull request Sep 22, 2025

feat: implements contextual dot resolution dfinity/motoko#5458

Merged

4 tasks

fix typos

596b378

adds toArray for Map (needs tests and docs)

fe3bcfc

christoph-dfinity and others added 3 commits September 27, 2025 19:57

adds toArray for Set

9c55f80

adds Self member to Blob

bd8e69e

christoph-dfinity changed the title ~~experiment: Contextual dot resolution~~ Support for contextual dot and implicits Oct 13, 2025

christoph-dfinity closed this Oct 13, 2025

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Support for contextual dot and implicits #395

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Large known-size array building

Benchmarking the fromIter functions

List vs. Buffer for creating known-size arrays

Different priority queue implementations

List Stack safety

Different queue implementations

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Benchmark Results

Large known-size array building

Benchmarking the fromIter functions

List vs. Buffer for creating known-size arrays

Different priority queue implementations

List Stack safety

Different queue implementations

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