Note Diffing the performance result against the published result from main branch. Unchanged benchmarks are omitted.

Map

	binary_size	generate 1m	max mem	batch_get 50	batch_put 50	batch_remove 50	upgrade
hashmap	189_608 ($\textcolor{red}{0.37\%}$)	8_370_836_596 ($\textcolor{green}{-0.00\%}$)	61_987_852	344_911	6_593_214_552 ($\textcolor{green}{-0.00\%}$)	371_223	11_026_879_813 ($\textcolor{green}{-0.00\%}$)
triemap	195_507 ($\textcolor{red}{0.35\%}$)	13_855_038_807 ($\textcolor{green}{-0.00\%}$)	74_216_172	254_589	661_468	650_820 ($\textcolor{red}{0.01\%}$)	15_817_661_582 ($\textcolor{green}{-0.00\%}$)
rbtree	186_562 ($\textcolor{red}{0.40\%}$)	7_127_318_517 ($\textcolor{red}{0.00\%}$)	57_996_060	114_300	318_352 ($\textcolor{green}{-0.01\%}$)	328_277 ($\textcolor{red}{0.01\%}$)	7_169_325_607 ($\textcolor{red}{0.00\%}$)
splay	190_525 ($\textcolor{red}{0.36\%}$)	13_247_297_529 ($\textcolor{green}{-0.00\%}$)	53_995_996	628_661 ($\textcolor{red}{0.01\%}$)	661_579 ($\textcolor{green}{-0.01\%}$)	921_933	4_567_871_575 ($\textcolor{red}{0.00\%}$)
btree	229_870 ($\textcolor{red}{0.30\%}$)	10_266_014_811 ($\textcolor{red}{0.00\%}$)	31_104_012	353_622	482_125	533_935	3_134_168_915 ($\textcolor{red}{0.00\%}$)
zhenya_hashmap	189_285 ($\textcolor{red}{0.36\%}$)	2_570_554_540 ($\textcolor{red}{0.00\%}$)	22_773_100	60_196	70_137	82_453	3_305_550_277 ($\textcolor{green}{-0.00\%}$)
btreemap_rs	537_393 ($\textcolor{red}{13.95\%}$)	1_793_333_047 ($\textcolor{green}{-0.21\%}$)	27_590_656 ($\textcolor{red}{0.24\%}$)	75_328 ($\textcolor{green}{-0.32\%}$)	125_166 ($\textcolor{green}{-0.22\%}$)	86_260 ($\textcolor{green}{-0.07\%}$)	2_937_041_107 ($\textcolor{green}{-0.14\%}$)
imrc_hashmap_rs	542_882 ($\textcolor{red}{13.78\%}$)	2_584_501_850 ($\textcolor{green}{-0.01\%}$)	244_973_568	37_762 ($\textcolor{green}{-1.70\%}$)	178_926 ($\textcolor{green}{-0.19\%}$)	115_385 ($\textcolor{green}{-0.16\%}$)	5_796_587_958 ($\textcolor{red}{0.00\%}$)
hashmap_rs	529_458 ($\textcolor{red}{13.86\%}$)	439_248_112 ($\textcolor{red}{1.26\%}$)	73_138_176 ($\textcolor{red}{0.09\%}$)	21_501 ($\textcolor{green}{-1.62\%}$)	26_711 ($\textcolor{green}{-0.91\%}$)	25_024 ($\textcolor{green}{-0.79\%}$)	1_298_646_667 ($\textcolor{red}{0.40\%}$)

Priority queue

	binary_size	heapify 1m	max mem	pop_min 50	put 50	pop_min 50.1	upgrade
heap	167_497 ($\textcolor{red}{0.41\%}$)	5_697_880_501 ($\textcolor{red}{0.00\%}$)	29_995_956	621_338	228_674	592_198	3_309_807_456 ($\textcolor{green}{-0.00\%}$)
heap_rs	525_853 ($\textcolor{red}{14.49\%}$)	139_669_830 ($\textcolor{red}{0.72\%}$)	18_284_544 ($\textcolor{red}{0.36\%}$)	57_419 ($\textcolor{green}{-0.25\%}$)	23_051 ($\textcolor{green}{-1.26\%}$)	57_545 ($\textcolor{green}{-0.26\%}$)	510_960_192 ($\textcolor{green}{-0.20\%}$)

Growable array

	binary_size	generate 5k	max mem	batch_get 500	batch_put 500	batch_remove 500	upgrade
buffer	173_906 ($\textcolor{red}{0.40\%}$)	2_570_947	65_644	95_490	800_452	170_490	3_061_307
vector	172_249 ($\textcolor{red}{0.40\%}$)	1_920_997	24_580	126_114	183_385	175_981	4_695_522
vec_rs	520_881 ($\textcolor{red}{14.88\%}$)	289_040 ($\textcolor{green}{-0.02\%}$)	1_376_256 ($\textcolor{red}{5.00\%}$)	17_251 ($\textcolor{green}{-0.27\%}$)	30_571 ($\textcolor{green}{-0.17\%}$)	23_331 ($\textcolor{green}{-9.42\%}$)	3_161_017 ($\textcolor{green}{-0.34\%}$)

Stable structures

	binary_size	generate 50k	max mem	batch_get 50	batch_put 50	batch_remove 50	upgrade
btreemap_rs	537_393 ($\textcolor{red}{13.95\%}$)	76_200_333 ($\textcolor{green}{-0.25\%}$)	2_555_904 ($\textcolor{red}{2.63\%}$)	64_886 ($\textcolor{green}{-0.37\%}$)	97_044 ($\textcolor{green}{-0.29\%}$)	85_272 ($\textcolor{green}{-0.07\%}$)	126_265_270 ($\textcolor{green}{-0.16\%}$)
btreemap_stable_rs	543_609 ($\textcolor{red}{12.43\%}$)	4_561_985_735 ($\textcolor{green}{-13.20\%}$)	2_031_616	2_707_064 ($\textcolor{green}{-14.53\%}$)	5_026_642 ($\textcolor{green}{-13.10\%}$)	8_594_683 ($\textcolor{green}{-13.08\%}$)	729_311 ($\textcolor{green}{-0.25\%}$)
heap_rs	525_853 ($\textcolor{red}{14.49\%}$)	7_051_730 ($\textcolor{red}{0.71\%}$)	2_293_760 ($\textcolor{red}{2.94\%}$)	49_928 ($\textcolor{green}{-0.29\%}$)	23_299 ($\textcolor{green}{-1.25\%}$)	49_894 ($\textcolor{green}{-0.30\%}$)	26_768_703 ($\textcolor{green}{-0.19\%}$)
heap_stable_rs	506_559 ($\textcolor{red}{13.95\%}$)	271_553_517 ($\textcolor{green}{-15.04\%}$)	458_752	2_294_851 ($\textcolor{green}{-14.20\%}$)	238_596 ($\textcolor{green}{-14.29\%}$)	2_277_771 ($\textcolor{green}{-14.19\%}$)	729_317 ($\textcolor{green}{-0.27\%}$)
vec_rs	520_881 ($\textcolor{red}{14.88\%}$)	3_079_382 ($\textcolor{green}{-0.00\%}$)	2_293_760 ($\textcolor{red}{2.94\%}$)	17_251 ($\textcolor{green}{-0.27\%}$)	18_421 ($\textcolor{green}{-0.28\%}$)	17_719 ($\textcolor{green}{-1.56\%}$)	24_671_551 ($\textcolor{green}{-0.41\%}$)
vec_stable_rs	503_829 ($\textcolor{red}{14.19\%}$)	63_394_912 ($\textcolor{green}{-15.64\%}$)	458_752	62_491 ($\textcolor{green}{-12.81\%}$)	79_685 ($\textcolor{green}{-12.93\%}$)	81_633 ($\textcolor{green}{-13.99\%}$)	729_320 ($\textcolor{green}{-0.27\%}$)

Statistics

binary_size: 7.91% [5.20%, 10.62%]
max_mem: 2.03% [0.67%, 3.39%]
cycles: -2.52% [-3.51%, -1.53%]

SHA-2

	binary_size	SHA-256	SHA-512	account_id	neuron_id
Motoko	196_492 ($\textcolor{red}{0.35\%}$)	273_037_090 ($\textcolor{red}{0.00\%}$)	259_835_230 ($\textcolor{red}{0.00\%}$)	34_373	24_897
Rust	537_397 ($\textcolor{red}{12.89\%}$)	82_787_911 ($\textcolor{green}{-0.00\%}$)	56_792_991 ($\textcolor{green}{-0.00\%}$)	47_914 ($\textcolor{green}{-1.48\%}$)	50_388 ($\textcolor{green}{-1.51\%}$)

Certified map

	binary_size	generate 10k	max mem	inc	witness	upgrade
Motoko	245_183 ($\textcolor{red}{0.28\%}$)	4_763_349_709 ($\textcolor{red}{0.00\%}$)	3_430_044	565_117	402_073	274_276_383 ($\textcolor{red}{0.00\%}$)
Rust	565_792 ($\textcolor{red}{13.57\%}$)	6_409_147_805 ($\textcolor{red}{1.58\%}$)	2_228_224	1_019_959 ($\textcolor{red}{1.57\%}$)	303_897 ($\textcolor{red}{0.85\%}$)	6_019_483_730 ($\textcolor{red}{1.57\%}$)

Statistics

binary_size: 6.77% [-2.01%, 15.55%]
max_mem: no change
cycles: 0.21% [-0.33%, 0.76%]

Basic DAO

	binary_size	init	transfer_token	submit_proposal	vote_proposal	upgrade
Motoko	275_458 ($\textcolor{red}{0.25\%}$)	510_765 ($\textcolor{red}{0.03\%}$)	22_312 ($\textcolor{green}{-0.07\%}$)	18_606 ($\textcolor{red}{0.29\%}$)	19_662 ($\textcolor{red}{0.48\%}$)	157_656 ($\textcolor{red}{0.03\%}$)
Rust	849_921 ($\textcolor{red}{9.21\%}$)	599_916 ($\textcolor{green}{-1.86\%}$)	99_156 ($\textcolor{green}{-2.19\%}$)	123_702 ($\textcolor{green}{-1.16\%}$)	136_655 ($\textcolor{green}{-0.57\%}$)	1_799_828 ($\textcolor{green}{-0.92\%}$)

DIP721 NFT

	binary_size	init	mint_token	transfer_token	upgrade
Motoko	222_607 ($\textcolor{red}{0.31\%}$)	481_158	29_810	8_776	89_459
Rust	869_104 ($\textcolor{red}{9.92\%}$)	236_542 ($\textcolor{green}{-1.15\%}$)	368_044 ($\textcolor{green}{-0.15\%}$)	91_941 ($\textcolor{red}{3.29\%}$)	1_999_207 ($\textcolor{green}{-0.14\%}$)

Statistics

binary_size: 4.92% [-1.40%, 11.24%]
max_mem: no change
cycles: -0.29% [-0.91%, 0.33%]

Heartbeat

	binary_size	heartbeat
Motoko	137_899 ($\textcolor{red}{0.51\%}$)	19_511 ($\textcolor{green}{-15.84\%}$)
Rust	23_637 ($\textcolor{green}{-0.99\%}$)	480 ($\textcolor{green}{-59.04\%}$)

Timer

	binary_size	setTimer	cancelTimer
Motoko	146_290 ($\textcolor{red}{0.49\%}$)	51_655 ($\textcolor{red}{0.37\%}$)	4_610
Rust	487_585 ($\textcolor{red}{15.90\%}$)	68_173 ($\textcolor{red}{0.03\%}$)	11_184 ($\textcolor{red}{0.21\%}$)

Statistics

binary_size: 8.19% [-40.45%, 56.84%]
max_mem: no change
cycles: 0.20% [-0.08%, 0.48%]

Garbage Collection

	generate 700k	max mem	batch_get 50	batch_put 50	batch_remove 50
default	1_171_395_786	51_991_392	119	119	119
copying	1_171_395_668	51_991_392	1_171_104_136	1_171_195_696	1_171_106_949
compacting	1_672_114_659	51_991_392	1_290_052_154	1_533_417_828	1_564_512_242
generational	2_529_073_372	51_999_856	999_515_201	1_232_756 ($\textcolor{red}{0.00\%}$)	1_103_869 ($\textcolor{red}{0.00\%}$)
incremental	29_503_170 ($\textcolor{red}{0.00\%}$)	985_890_644 ($\textcolor{red}{0.00\%}$)	477_971_911 ($\textcolor{red}{43.21\%}$)	493_638_503 ($\textcolor{red}{46.53\%}$)	1_092_284_316 ($\textcolor{red}{224.24\%}$)

Actor class

	binary size	put new bucket	put existing bucket	get
Map	300_186 ($\textcolor{red}{0.38\%}$)	816_032 ($\textcolor{red}{0.31\%}$)	16_099	16_644

Statistics

binary_size: no change
max_mem: 0.00%
cycles: 39.33% [-12.51%, 91.18%]

Publisher & Subscriber

	pub_binary_size	sub_binary_size	subscribe_caller	subscribe_callee	publish_caller	publish_callee
Motoko	161_935 ($\textcolor{red}{0.42\%}$)	146_225 ($\textcolor{red}{0.47\%}$)	28_593	11_963	22_864	6_430
Rust	519_866 ($\textcolor{red}{13.85\%}$)	570_028 ($\textcolor{red}{12.26\%}$)	68_903 ($\textcolor{green}{-0.41\%}$)	42_634 ($\textcolor{green}{-0.54\%}$)	92_131 ($\textcolor{green}{-0.69\%}$)	51_818 ($\textcolor{green}{-0.57\%}$)

Statistics

binary_size: 6.75% [-1.85%, 15.35%]
max_mem: no change
cycles: -0.55% [-0.68%, -0.42%]

Overall Statistics
binary_size: 7.31% [5.35%, 9.26%]
max_mem: 1.78% [0.53%, 3.02%]
cycles: 1.06% [-2.36%, 4.47%]

Note The flamegraph link only works after you merge. Unchanged benchmarks are omitted.

Collection libraries

Measure different collection libraries written in both Motoko and Rust. The library names with _rs suffix are written in Rust; the rest are written in Motoko. The _stable and _stable_rs suffix represents that the library directly writes the state to stable memory using Region in Motoko and ic-stable-stuctures in Rust.

We use the same random number generator with fixed seed to ensure that all collections contain the same elements, and the queries are exactly the same. Below we explain the measurements of each column in the table:

generate 1m. Insert 1m Nat64 integers into the collection. For Motoko collections, it usually triggers the GC; the rest of the column are not likely to trigger GC.
max mem. For Motoko, it reports rts_max_heap_size after generate call; For Rust, it reports the Wasm's memory page * 32Kb.
batch_get 50. Find 50 elements from the collection.
batch_put 50. Insert 50 elements to the collection.
batch_remove 50. Remove 50 elements from the collection.
upgrade. Upgrade the canister with the same Wasm module. For non-stable benchmarks, the map state is persisted by serializing and deserializing states into stable memory. For stable benchmarks, the upgrade takes no cycles, as the state is already in the stable memory.

💎 Takeaways

The platform only charges for instruction count. Data structures which make use of caching and locality have no impact on the cost.
We have a limit on the maximal cycles per round. This means asymptotic behavior doesn't matter much. We care more about the performance up to a fixed N. In the extreme cases, you may see an $O(10000 n\log n)$ algorithm hitting the limit, while an $O(n^2)$ algorithm runs just fine.
Amortized algorithms/GC may need to be more eager to avoid hitting the cycle limit on a particular round.
Rust costs more cycles to process complicated Candid data, but it is more efficient in performing core computations.

Note

The Candid interface of the benchmark is minimal, therefore the serialization cost is negligible in this measurement.

Due to the instrumentation overhead and cycle limit, we cannot profile computations with very large collections.

The upgrade column uses Candid for serializing stable data. In Rust, you may get better cycle cost by using a different serialization format. Another slowdown in Rust is that ic-stable-structures tends to be slower than the region memory in Motoko.

Different library has different ways for persisting data during upgrades, there are mainly three categories:

Use stable variable directly in Motoko: zhenya_hashmap, btree, vector

Expose and serialize external state (share/unshare in Motoko, candid::Encode in Rust): rbtree, heap, btreemap_rs, hashmap_rs, heap_rs, vector_rs

Use pre/post-upgrade hooks to convert data into an array: hashmap, splay, triemap, buffer, imrc_hashmap_rs

The stable benchmarks are much more expensive than their non-stable counterpart, because the stable memory API is much more expensive. The benefit is that they get fast upgrade. The upgrade still needs to parse the metadata when initializing the upgraded Wasm module.

hashmap uses amortized data structure. When the initial capacity is reached, it has to copy the whole array, thus the cost of batch_put 50 is much higher than other data structures.

btree comes from mops.one/stableheapbtreemap.

zhenya_hashmap comes from mops.one/map.

vector comes from mops.one/vector. Compare with buffer, put has better worst case time and space complexity ($O(\sqrt{n})$ vs $O(n)$); get has a slightly larger constant overhead.

hashmap_rs uses the fxhash crate, which is the same as std::collections::HashMap, but with a deterministic hasher. This ensures reproducible result.

imrc_hashmap_rs uses the im-rc crate, which is the immutable version hashmap in Rust.

Map

	binary_size	generate 1m	max mem	batch_get 50	batch_put 50	batch_remove 50	upgrade
hashmap	189_608	8_370_836_596	61_987_852	344_911	6_593_214_552	371_223	11_026_879_813
triemap	195_507	13_855_038_807	74_216_172	254_589	661_468	650_820	15_817_661_582
rbtree	186_562	7_127_318_517	57_996_060	114_300	318_352	328_277	7_169_325_607
splay	190_525	13_247_297_529	53_995_996	628_661	661_579	921_933	4_567_871_575
btree	229_870	10_266_014_811	31_104_012	353_622	482_125	533_935	3_134_168_915
zhenya_hashmap	189_285	2_570_554_540	22_773_100	60_196	70_137	82_453	3_305_550_277
btreemap_rs	537_393	1_793_333_047	27_590_656	75_328	125_166	86_260	2_937_041_107
imrc_hashmap_rs	542_882	2_584_501_850	244_973_568	37_762	178_926	115_385	5_796_587_958
hashmap_rs	529_458	439_248_112	73_138_176	21_501	26_711	25_024	1_298_646_667

Priority queue

	binary_size	heapify 1m	max mem	pop_min 50	put 50	pop_min 50	upgrade
heap	167_497	5_697_880_501	29_995_956	621_338	228_674	592_198	3_309_807_456
heap_rs	525_853	139_669_830	18_284_544	57_419	23_051	57_545	510_960_192

Growable array

	binary_size	generate 5k	max mem	batch_get 500	batch_put 500	batch_remove 500	upgrade
buffer	173_906	2_570_947	65_644	95_490	800_452	170_490	3_061_307
vector	172_249	1_920_997	24_580	126_114	183_385	175_981	4_695_522
vec_rs	520_881	289_040	1_376_256	17_251	30_571	23_331	3_161_017

Stable structures

	binary_size	generate 50k	max mem	batch_get 50	batch_put 50	batch_remove 50	upgrade
btreemap_rs	537_393	76_200_333	2_555_904	64_886	97_044	85_272	126_265_270
btreemap_stable_rs	543_609	4_561_985_735	2_031_616	2_707_064	5_026_642	8_594_683	729_311
heap_rs	525_853	7_051_730	2_293_760	49_928	23_299	49_894	26_768_703
heap_stable_rs	506_559	271_553_517	458_752	2_294_851	238_596	2_277_771	729_317
vec_rs	520_881	3_079_382	2_293_760	17_251	18_421	17_719	24_671_551
vec_stable_rs	503_829	63_394_912	458_752	62_491	79_685	81_633	729_320

Environment

dfx 0.16.1

Motoko compiler 0.10.4 (source js20w7g2-ysgfrqd0-1cmy11nb-3wdy9y1k)

rustc 1.75.0 (82e1608df 2023-12-21)

ic-repl 0.6.2

ic-wasm 0.7.0
Cryptographic libraries

Measure different cryptographic libraries written in both Motoko and Rust.

SHA-2 benchmarks
- SHA-256/SHA-512. Compute the hash of a 1M Wasm binary.
- account_id. Compute the ledger account id from principal, based on SHA-224.
- neuron_id. Compute the NNS neuron id from principal, based on SHA-256.
Certified map. Merkle Tree for storing key-value pairs and generate witness according to the IC Interface Specification.
- generate 10k. Insert 10k 7-character word as both key and value into the certified map.
- max mem. For Motoko, it reports rts_max_heap_size after generate call; For Rust, it reports the Wasm's memory page * 32Kb.
- inc. Increment a counter and insert the counter value into the map.
- witness. Generate the root hash and a witness for the counter.
- upgrade. Upgrade the canister with the same Wasm. In Motoko, we use stable variable. In Rust, we convert the tree to a vector before serialization.

SHA-2

	binary_size	SHA-256	SHA-512	account_id	neuron_id
Motoko	196_492	273_037_090	259_835_230	34_373	24_897
Rust	537_397	82_787_911	56_792_991	47_914	50_388

Certified map

	binary_size	generate 10k	max mem	inc	witness	upgrade
Motoko	245_183	4_763_349_709	3_430_044	565_117	402_073	274_276_383
Rust	565_792	6_409_147_805	2_228_224	1_019_959	303_897	6_019_483_730

Environment

dfx 0.16.1

Motoko compiler 0.10.4 (source js20w7g2-ysgfrqd0-1cmy11nb-3wdy9y1k)

rustc 1.75.0 (82e1608df 2023-12-21)

ic-repl 0.6.2

ic-wasm 0.7.0
Sample Dapps

Measure the performance of some typical dapps:

Basic DAO, with heartbeat disabled to make profiling easier. We have a separate benchmark to measure heartbeat performance.
DIP721 NFT

Note

The cost difference is mainly due to the Candid serialization cost.

Motoko statically compiles/specializes the serialization code for each method, whereas in Rust, we use serde to dynamically deserialize data based on data on the wire.

We could improve the performance on the Rust side by using parser combinators. But it is a challenge to maintain the ergonomics provided by serde.

For real-world applications, we tend to send small data for each endpoint, which makes the Candid overhead in Rust tolerable.

Basic DAO

	binary_size	init	transfer_token	submit_proposal	vote_proposal	upgrade
Motoko	275_458	510_765	22_312	18_606	19_662	157_656
Rust	849_921	599_916	99_156	123_702	136_655	1_799_828

DIP721 NFT

	binary_size	init	mint_token	transfer_token	upgrade
Motoko	222_607	481_158	29_810	8_776	89_459
Rust	869_104	236_542	368_044	91_941	1_999_207

Environment

dfx 0.16.1

Motoko compiler 0.10.4 (source js20w7g2-ysgfrqd0-1cmy11nb-3wdy9y1k)

rustc 1.75.0 (82e1608df 2023-12-21)

ic-repl 0.6.2

ic-wasm 0.7.0
Heartbeat / Timer

Measure the cost of empty heartbeat and timer job.

setTimer measures both the setTimer(0) method and the execution of empty job.
It is not easy to reliably capture the above events in one flamegraph, as the implementation detail of the replica can affect how we measure this. Typically, a correct flamegraph contains both setTimer and canister_global_timer function. If it's not there, we may need to adjust the script.

Heartbeat

	binary_size	heartbeat
Motoko	137_899	19_511
Rust	23_637	480

Timer

	binary_size	setTimer	cancelTimer
Motoko	146_290	51_655	4_610
Rust	487_585	68_173	11_184

Environment

dfx 0.16.1

Motoko compiler 0.10.4 (source js20w7g2-ysgfrqd0-1cmy11nb-3wdy9y1k)

rustc 1.75.0 (82e1608df 2023-12-21)

ic-repl 0.6.2

ic-wasm 0.7.0
Motoko Specific Benchmarks

Measure various features only available in Motoko.

Garbage Collection. Measure Motoko garbage collection cost using the Triemap benchmark. The max mem column reports rts_max_heap_size after generate call. The cycle cost numbers reported here are garbage collection cost only. Some flamegraphs are truncated due to the 2M log size limit. The dfx/ic-wasm optimizer is disabled for the garbage collection test cases due to how the optimizer affects function names, making profiling trickier.
- default. Compile with the default GC option. With the current GC scheduler, generate will trigger the copying GC. The rest of the methods will not trigger GC.
- copying. Compile with --force-gc --copying-gc.
- compacting. Compile with --force-gc --compacting-gc.
- generational. Compile with --force-gc --generational-gc.
- incremental. Compile with --force-gc --incremental-gc.
Actor class. Measure the cost of spawning actor class, using the Actor classes example.

Garbage Collection

	generate 700k	max mem	batch_get 50	batch_put 50	batch_remove 50
default	1_171_395_786	51_991_392	119	119	119
copying	1_171_395_668	51_991_392	1_171_104_136	1_171_195_696	1_171_106_949
compacting	1_672_114_659	51_991_392	1_290_052_154	1_533_417_828	1_564_512_242
generational	2_529_073_372	51_999_856	999_515_201	1_232_756	1_103_869
incremental	29_503_170	985_890_644	477_971_911	493_638_503	1_092_284_316

Actor class

	binary size	put new bucket	put existing bucket	get
Map	300_186	816_032	16_099	16_644

Environment

dfx 0.16.1

Motoko compiler 0.10.4 (source js20w7g2-ysgfrqd0-1cmy11nb-3wdy9y1k)

rustc 1.75.0 (82e1608df 2023-12-21)

ic-repl 0.6.2

ic-wasm 0.7.0
Publisher & Subscriber

Measure the cost of inter-canister calls from the Publisher & Subscriber example.

	pub_binary_size	sub_binary_size	subscribe_caller	subscribe_callee	publish_caller	publish_callee
Motoko	161_935	146_225	28_593	11_963	22_864	6_430
Rust	519_866	570_028	68_903	42_634	92_131	51_818

Environment

dfx 0.16.1

Motoko compiler 0.10.4 (source js20w7g2-ysgfrqd0-1cmy11nb-3wdy9y1k)

rustc 1.75.0 (82e1608df 2023-12-21)

ic-repl 0.6.2

ic-wasm 0.7.0

dfinity / canister-profiling

Bump dependencies #104

Map

Priority queue

Growable array

Stable structures

Statistics

SHA-2

Certified map

Statistics

Basic DAO

DIP721 NFT

Statistics

Heartbeat

Timer

Statistics

Garbage Collection

Actor class

Statistics

Publisher & Subscriber

Statistics

Overall Statistics

Collection libraries

💎 Takeaways

Map

Priority queue

Growable array

Stable structures

Environment

Cryptographic libraries

SHA-2

Certified map

Environment

Sample Dapps

Basic DAO

DIP721 NFT

Environment

Heartbeat / Timer

Heartbeat

Timer

Environment

Motoko Specific Benchmarks

Garbage Collection

Actor class

Environment

Publisher & Subscriber

Environment