The original, unoptimized stable B-Tree (with new metering) (c.f. #97)

[crusso]

Sames as #97 but using unoptimized version of stable btree (to see perf improvements relative to #97.

[github-actions]

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	160_146 ($\textcolor{green}{-0.05\%}$)	8_415_853_637 ($\textcolor{red}{20.50\%}$)	61_987_852	344_668 ($\textcolor{red}{19.40\%}$)	6_554_314_393 ($\textcolor{red}{18.38\%}$)	372_027 ($\textcolor{red}{19.93\%}$)	11_086_889_804 ($\textcolor{red}{21.45\%}$)
triemap	163_317 ($\textcolor{green}{-0.10\%}$)	14_814_581_233 ($\textcolor{red}{29.23\%}$)	74_216_172	291_744 ($\textcolor{red}{30.87\%}$)	714_312 ($\textcolor{red}{30.01\%}$)	700_163 ($\textcolor{red}{29.61\%}$)	16_814_045_622 ($\textcolor{red}{28.60\%}$)
rbtree	157_922 ($\textcolor{green}{-0.14\%}$)	7_517_002_376 ($\textcolor{red}{25.72\%}$)	57_996_060	113_955 ($\textcolor{red}{28.18\%}$)	339_364 ($\textcolor{red}{26.36\%}$)	362_212 ($\textcolor{red}{30.13\%}$)	7_125_606_793 ($\textcolor{red}{23.45\%}$)
splay	159_759 ($\textcolor{green}{-0.12\%}$)	15_011_765_466 ($\textcolor{red}{29.77\%}$)	53_995_996	719_957 ($\textcolor{red}{30.42\%}$)	758_215 ($\textcolor{red}{30.33\%}$)	1_051_199 ($\textcolor{red}{29.73\%}$)	4_732_822_678 ($\textcolor{red}{27.14\%}$)
btree	187_666 ($\textcolor{green}{-0.12\%}$)	10_705_101_825 ($\textcolor{red}{30.17\%}$)	31_104_012	365_187 ($\textcolor{red}{31.58\%}$)	503_327 ($\textcolor{red}{31.02\%}$)	559_436 ($\textcolor{red}{30.39\%}$)	3_120_410_218 ($\textcolor{red}{23.93\%}$)
zhenya_hashmap	160_516 ($\textcolor{red}{0.00\%}$)	2_776_814_274 ($\textcolor{red}{26.13\%}$)	22_773_100	64_709 ($\textcolor{red}{33.07\%}$)	82_032 ($\textcolor{red}{32.65\%}$)	94_090 ($\textcolor{red}{32.76\%}$)	3_319_787_035 ($\textcolor{red}{23.16\%}$)
btreemap_rs	477_037 ($\textcolor{green}{-0.12\%}$)	1_793_694_183 ($\textcolor{red}{8.60\%}$)	27_590_656	75_427 ($\textcolor{red}{12.81\%}$)	125_068 ($\textcolor{red}{11.19\%}$)	86_003 ($\textcolor{red}{12.81\%}$)	2_927_540_279 ($\textcolor{red}{10.02\%}$)
imrc_hashmap_rs	479_206 ($\textcolor{green}{-0.12\%}$)	2_580_421_602 ($\textcolor{red}{7.84\%}$)	244_973_568	38_236 ($\textcolor{red}{16.70\%}$)	178_903 ($\textcolor{red}{9.59\%}$)	114_251 ($\textcolor{red}{16.12\%}$)	5_754_488_547 ($\textcolor{red}{10.84\%}$)
hashmap_rs	467_420 ($\textcolor{green}{-0.12\%}$)	432_765_549 ($\textcolor{red}{7.31\%}$)	73_138_176	21_704 ($\textcolor{red}{28.80\%}$)	26_758 ($\textcolor{red}{23.42\%}$)	25_073 ($\textcolor{red}{23.74\%}$)	1_280_477_959 ($\textcolor{red}{11.85\%}$)

Priority queue

	binary_size	heapify 1m	max mem	pop_min 50	put 50	pop_min 50.1	upgrade
heap	147_633 ($\textcolor{green}{-0.00\%}$)	6_206_169_472 ($\textcolor{red}{32.48\%}$)	29_995_956	680_841 ($\textcolor{red}{33.11\%}$)	250_925 ($\textcolor{red}{34.57\%}$)	648_731 ($\textcolor{red}{33.15\%}$)	3_293_282_643 ($\textcolor{red}{24.01\%}$)
heap_rs	463_292 ($\textcolor{green}{-0.12\%}$)	138_669_631 ($\textcolor{red}{14.04\%}$)	18_284_544	57_362 ($\textcolor{red}{11.04\%}$)	23_196 ($\textcolor{red}{27.14\%}$)	57_496 ($\textcolor{red}{10.99\%}$)	505_960_753 ($\textcolor{red}{14.80\%}$)

Growable array

	binary_size	generate 5k	max mem	batch_get 500	batch_put 500	batch_remove 500	upgrade
buffer	150_978 ($\textcolor{green}{-0.02\%}$)	2_718_410 ($\textcolor{red}{30.53\%}$)	65_644	104_997 ($\textcolor{red}{43.65\%}$)	858_376 ($\textcolor{red}{27.83\%}$)	172_497 ($\textcolor{red}{35.19\%}$)	3_141_451 ($\textcolor{red}{26.95\%}$)
vector	152_598 ($\textcolor{red}{0.03\%}$)	2_131_660 ($\textcolor{red}{34.21\%}$)	24_580	141_137 ($\textcolor{red}{34.17\%}$)	203_207 ($\textcolor{red}{35.53\%}$)	195_769 ($\textcolor{red}{32.19\%}$)	4_756_048 ($\textcolor{red}{23.71\%}$)
vec_rs	459_107 ($\textcolor{green}{-0.12\%}$)	288_898 ($\textcolor{red}{8.74\%}$)	1_310_720	17_148 ($\textcolor{red}{31.77\%}$)	30_473 ($\textcolor{red}{20.15\%}$)	25_609 ($\textcolor{red}{20.53\%}$)	3_141_188 ($\textcolor{red}{14.48\%}$)

Warning
Skip table 3 ## Stable structures from _out/collections/README.md, due to table shape mismatches from main branch.

Statistics

• binary_size: -0.08% [-0.11%, -0.05%]
• max_mem: no change
• cycles: 24.15% [22.42%, 25.89%]

Overall Statistics

• binary_size: -0.08% [-0.11%, -0.05%]
• max_mem: no change
• cycles: 24.15% [22.42%, 25.89%]

[github-actions]

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 * 64Kb; For stable benchmarks, it reports the region size of the stable memory storing the map.
• 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 only needs to initialize the metadata, 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.
• btree_stable comes from github.com/sardariuss.
• 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	160_146	8_415_853_637	61_987_852	344_668	6_554_314_393	372_027	11_086_889_804
triemap	163_317	14_814_581_233	74_216_172	291_744	714_312	700_163	16_814_045_622
rbtree	157_922	7_517_002_376	57_996_060	113_955	339_364	362_212	7_125_606_793
splay	159_759	15_011_765_466	53_995_996	719_957	758_215	1_051_199	4_732_822_678
btree	187_666	10_705_101_825	31_104_012	365_187	503_327	559_436	3_120_410_218
zhenya_hashmap	160_516	2_776_814_274	22_773_100	64_709	82_032	94_090	3_319_787_035
btreemap_rs	477_037	1_793_694_183	27_590_656	75_427	125_068	86_003	2_927_540_279
imrc_hashmap_rs	479_206	2_580_421_602	244_973_568	38_236	178_903	114_251	5_754_488_547
hashmap_rs	467_420	432_765_549	73_138_176	21_704	26_758	25_073	1_280_477_959

Priority queue

	binary_size	heapify 1m	max mem	pop_min 50	put 50	pop_min 50	upgrade
heap	147_633	6_206_169_472	29_995_956	680_841	250_925	648_731	3_293_282_643
heap_rs	463_292	138_669_631	18_284_544	57_362	23_196	57_496	505_960_753

Growable array

	binary_size	generate 5k	max mem	batch_get 500	batch_put 500	batch_remove 500	upgrade
buffer	150_978	2_718_410	65_644	104_997	858_376	172_497	3_141_451
vector	152_598	2_131_660	24_580	141_137	203_207	195_769	4_756_048
vec_rs	459_107	288_898	1_310_720	17_148	30_473	25_609	3_141_188

Stable structures

	binary_size	generate 50k	max mem	batch_get 50	batch_put 50	batch_remove 50	upgrade
btree	187_666	457_750_863	1_554_152	289_410	442_003	479_799	155_920_331
btree_stable	205_452	17_160_920_654	2_621_440	13_928_890	18_985_993	37_332_002	40_832
btreemap_rs	477_037	76_218_029	2_555_904	64_985	97_003	84_994	125_790_335
btreemap_stable_rs	478_103	4_751_143_101	2_621_440	2_844_949	5_181_254	8_809_593	731_069
heap_rs	463_292	7_001_559	2_293_760	49_871	23_444	49_845	26_519_303
heap_stable_rs	450_480	317_932_953	458_752	2_667_588	276_814	2_647_531	731_182
vec_rs	459_107	3_079_223	2_228_224	17_148	18_323	17_850	24_471_685
vec_stable_rs	445_471	74_445_158	458_752	69_489	90_685	92_681	731_195

Environment

• dfx 0.15.1
• Motoko compiler 0.10.0 (source a3ywvw0a-p5a03qy6-vscbl9j8-qxszbxa6)
• rustc 1.73.0 (cc66ad468 2023-10-03)
• ic-repl 0.5.1
• ic-wasm 0.6.0