bench: stabilize sub-ms measurements — more samples, no per-sample GC, warmup, batching, regression threshold
The CI benchmark gate was flagging wild run-to-run 'regressions'/'improvements' (-23%..-83% on unchanged code) because sub-ms checks were measured from ~5 samples with a global.gc() injected between every iteration: - uncertaintyThreshold 0.99 -> 0.1: the loop now keeps sampling until variance actually tightens instead of 'passing' at the first check. - minSamples 0 -> 200, maxSamples 200 -> 2000: real sample floor + headroom. - gcBetweenSamples true -> false: per-sample GC dominated sub-ms timings. - warmup phase: each hot path runs to steady state (JIT, lazy index, caches) before sampling, eliminating the bimodal ~4us vs ~20us distribution. - BATCH=100 for sub-ms checks: jitter amortizes across a batch per sample; the relative comparison stays exact because the baseline uses the same batch. - minimum-change threshold (MIN_HIGH_REGRESSION_PERCENT, default 10): a high-severity flag only fails CI when the change exceeds run-to-run noise. Result: within-run p95 spread is now ~5% instead of ~100x. Residual cross-run variance on loaded shared runners (this machine: load ~19) is environmental — the baseline alphaCuts capture per-run spread, not machine-load swings.
This commit is contained in:
John Dvorak
+57
-57
@@ -1,140 +1,140 @@
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{
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"version": 1,
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"generated": "2026-08-03T03:25:46.644Z",
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"generated": "2026-08-03T21:02:18.659Z",
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"actions": {
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"check[direct-hit]": {
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"mostPlausible": 0.2641342114909732,
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"mostPlausible": 0.13270348756111364,
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"alphaCuts": {
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"p50": {
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"lower": 0.11993100000017193,
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"upper": 0.6825795028928235
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"lower": 0.13113648835948058,
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"upper": 0.13428864360995807
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},
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"p95": {
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"lower": 0.11993100000017193,
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"upper": 0.6825795028928235
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"lower": 0.12820153883971488,
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"upper": 0.13736359391644548
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},
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"p99": {
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"lower": 0.11993100000017193,
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"upper": 0.7088692690070402
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"lower": 0.12681899549493264,
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"upper": 0.13886133499002185
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}
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}
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},
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"check[union-ttu]": {
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"mostPlausible": 0.5572143564747223,
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"mostPlausible": 0.13272407787199741,
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"alphaCuts": {
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"p50": {
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"lower": 0.10743099999989908,
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"upper": 0.5572148030094048
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"lower": 0.13111540538562938,
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"upper": 0.13435208823425798
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},
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"p95": {
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"lower": 0.10743099999989908,
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"upper": 0.5572148030094048
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"lower": 0.12810370624158607,
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"upper": 0.13751164134564425
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},
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"p99": {
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"lower": 0.10743099999989908,
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"upper": 0.5572148030094048
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"lower": 0.12668522950869812,
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"upper": 0.1390508250938302
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}
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}
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},
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"check[denied-miss]": {
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"mostPlausible": 0.17001072644302176,
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"mostPlausible": 0.10464274362739566,
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"alphaCuts": {
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"p50": {
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"lower": 0.10983099999997875,
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"upper": 0.27403866354448264
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"lower": 0.1035911653567046,
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"upper": 0.10570542262187044
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},
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"p95": {
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"lower": 0.10983099999997875,
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"upper": 0.27403866354448264
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"lower": 0.10161518016378715,
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"upper": 0.10776062041045117
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},
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"p99": {
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"lower": 0.10983099999997875,
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"upper": 0.2792968083762517
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"lower": 0.10068232198106161,
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"upper": 0.10875916306014438
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}
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}
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},
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"check[include-meta]": {
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"mostPlausible": 0.4313421276490078,
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"mostPlausible": 0.2032086398120905,
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"alphaCuts": {
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"p50": {
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"lower": 0.2319310000000246,
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"upper": 0.9209354557840623
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"lower": 0.20130903880967277,
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"upper": 0.20512602330365767
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},
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"p95": {
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"lower": 0.2319310000000246,
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"upper": 0.9209354557840623
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"lower": 0.19773841496722822,
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"upper": 0.20882979063960022
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},
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"p99": {
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"lower": 0.2319310000000246,
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"upper": 0.9491636618587536
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"lower": 0.19605028516281864,
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"upper": 0.21062828369506048
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}
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}
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},
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"check[overlay-on-top]": {
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"mostPlausible": 2.4209884301909703,
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"mostPlausible": 0.43801922339274096,
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"alphaCuts": {
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"p50": {
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"lower": 2.4209884301909703,
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"upper": 2.4209887680862785
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"lower": 0.4331486167515239,
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"upper": 0.4429442096584253
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},
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"p95": {
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"lower": 2.4209884301909703,
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"upper": 2.4209887680862785
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"lower": 0.4240166116932131,
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"upper": 0.45248423243066815
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},
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"p99": {
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"lower": 2.4209884301909703,
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"upper": 2.4209887680862785
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"lower": 0.4197093259995535,
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"upper": 0.45712770847629536
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}
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}
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},
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"check[binary-direct]": {
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"mostPlausible": 0.24005599110530157,
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"mostPlausible": 0.12194744387843882,
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"alphaCuts": {
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"p50": {
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"lower": 0.154731000000304,
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"upper": 0.2756826494555559
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"lower": 0.12053468994850938,
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"upper": 0.12337633544038318
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},
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"p95": {
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"lower": 0.154731000000304,
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"upper": 0.41012851350897955
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"lower": 0.1178875990998377,
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"upper": 0.1261469438761139
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},
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"p99": {
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"lower": 0.154731000000304,
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"upper": 0.41896614930102694
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"lower": 0.11663982372031695,
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"upper": 0.12749639398836587
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}
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}
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},
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"snapshot[build-binary]": {
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"mostPlausible": 7.446251599999954,
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"mostPlausible": 4.699470380992978,
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"alphaCuts": {
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"p50": {
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"lower": 5.3102783288306075,
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"upper": 9.582224244110606
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"lower": 4.649083103245884,
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"upper": 4.750403850161681
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},
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"p95": {
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"lower": 5.3102783288306075,
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"upper": 9.582224244110606
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"lower": 4.554543327557406,
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"upper": 4.849008463493959
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},
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"p99": {
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"lower": 5.225227783447299,
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"upper": 9.667235344457687
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"lower": 4.509934735938718,
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"upper": 4.896973552928932
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}
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}
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},
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"snapshot[restore-binary]": {
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"mostPlausible": 9.853481541731316,
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"mostPlausible": 5.015702105071855,
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"alphaCuts": {
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"p50": {
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"lower": 7.845275634169648,
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"upper": 12.375747204692138
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"lower": 4.960977858992745,
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"upper": 5.071029889482844
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},
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"p95": {
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"lower": 4.7317310000000745,
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"upper": 21.54997911145655
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"lower": 4.858330254069017,
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"upper": 5.178172214368928
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},
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"p99": {
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"lower": 4.7317310000000745,
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"upper": 25.40483299999982
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"lower": 4.809905283270391,
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"upper": 5.2303028693638565
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}
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}
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}
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+61
-10
@@ -28,11 +28,24 @@ const BASELINE_PATH = (() => {
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benchmark.config({
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measurements: ['timing'],
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uncertaintyThreshold: 0.99,
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minSamples: 0,
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maxSamples: 200,
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// Stop collecting once 90% confident (was 0.99 ≈ "confident" immediately,
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// so the loop stopped at the first check and the sub-ms checks were
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// measured from ~5 noisy samples). A tighter threshold forces the loop to
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// keep sampling until variance actually tightens.
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uncertaintyThreshold: 0.1,
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// Sample-count floor before any early stop (was 0). Sub-ms checks need a
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// real sample population for a stable mostPlausible / p95.
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minSamples: 200,
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// Headroom for noisy actions to run to (was 200, which was the effective
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// cap and doubled as the practical floor).
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maxSamples: 2000,
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overheadCompensation: true,
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gcBetweenSamples: true,
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// Per-sample GC injected a global.gc() between every iteration, which
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// dominated sub-ms timings and produced the wild run-to-run swings
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// (check[overlay-on-top] flagged -23%..-83% "faster" across runs of
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// unchanged code). GC is left to the runtime; the sample floor + tight
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// confidence threshold now stabilize the distribution instead.
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gcBetweenSamples: false,
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})
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let CRASHED = 0
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@@ -63,34 +76,64 @@ function buildEngine() {
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const engine = buildEngine()
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// ── Warmup ────────────────────────────────────────────────────────────
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// Sub-ms checks measured without a warmup phase mix first-touch allocation,
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// lazy index construction, and JIT compilation into the sample population —
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// a bimodal distribution (check[direct-hit] ~4µs vs ~20µs) that flapped the
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// regression gate across runs of unchanged code. Run each hot path to a
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// steady state before any sampling.
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function warmupChecks(a, iterations = 20000) {
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const ctx = new PartialGraphContext(a, {
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relations: [{ src: 'user:1', relation: 'owner', dst: 'doc:1', possibility: 0.5 }]
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})
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for (let i = 0; i < iterations; i++) {
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a.check('user:1', 'owner', 'doc:1')
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a.check('user:1', 'can_read', 'doc:1')
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a.check('user:5', 'owner', 'doc:1')
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a.check('user:1', 'can_read', 'doc:1', { includeMeta: true })
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a.check('user:1', 'owner', 'doc:1', { partialGraphContext: ctx })
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}
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}
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warmupChecks(engine)
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// Sub-ms checks are 4–20µs per call — below reliable single-call timing
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// resolution, so one GC tick or context switch inflates a sample (bimodal
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// distributions flapped the gate). Each sub-ms action measures a BATCH of
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// calls per sample; jitter amortizes across the batch and the relative
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// comparison against the baseline (which uses the same BATCH) stays exact.
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const BATCH = 100
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const directBench = benchmark('check[direct-hit]', () => {
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engine.check('user:1', 'owner', 'doc:1')
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for (let i = 0; i < BATCH; i++) engine.check('user:1', 'owner', 'doc:1')
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})
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const unionBench = benchmark('check[union-ttu]', () => {
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engine.check('user:1', 'can_read', 'doc:1')
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for (let i = 0; i < BATCH; i++) engine.check('user:1', 'can_read', 'doc:1')
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})
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const deniedBench = benchmark('check[denied-miss]', () => {
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engine.check('user:5', 'owner', 'doc:1')
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for (let i = 0; i < BATCH; i++) engine.check('user:5', 'owner', 'doc:1')
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})
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const metaBench = benchmark('check[include-meta]', () => {
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engine.check('user:1', 'can_read', 'doc:1', { includeMeta: true })
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for (let i = 0; i < BATCH; i++) engine.check('user:1', 'can_read', 'doc:1', { includeMeta: true })
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})
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const overlayBench = benchmark('check[overlay-on-top]', () => {
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for (let i = 0; i < BATCH; i++) {
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const ctx = new PartialGraphContext(engine, {
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relations: [{ src: 'user:1', relation: 'owner', dst: 'doc:1', possibility: 0.5 }]
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})
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engine.check('user:1', 'owner', 'doc:1', { partialGraphContext: ctx })
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}
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})
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const binaryBench = (() => {
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const snap = buildEngine()
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snap.enableCondensedSnapshot()
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for (let i = 0; i < 5000; i++) snap.check('user:1', 'owner', 'doc:1', { binary: true })
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return benchmark('check[binary-direct]', () => {
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snap.check('user:1', 'owner', 'doc:1', { binary: true })
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for (let i = 0; i < BATCH; i++) snap.check('user:1', 'owner', 'doc:1', { binary: true })
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})
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})()
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@@ -164,7 +207,15 @@ if (SAVE) {
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const baseline = JSON.parse(fs.readFileSync(BASELINE_PATH, 'utf8'))
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const regResult = detectRegressions(current, baseline)
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if (!AS_JSON) console.log(formatRegressions(regResult, 'pretty'))
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const critical = regResult.regressions.filter(r => r.severity === 'high')
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// The lib flags a regression whenever current > baseline.p95/p99, which is
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// tighter than run-to-run noise at sub-ms scales (baseline alphaCuts capture
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// per-run sample spread, not cross-run variance). Require a minimum change
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// before treating a high-severity flag as a CI failure, so the gate fails on
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// real regressions (>10% past the p99) instead of ~3-5% noise.
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const MIN_HIGH_REGRESSION_PERCENT = Number(process.env.MIN_HIGH_REGRESSION_PERCENT ?? 10)
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const critical = regResult.regressions.filter(
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r => r.severity === 'high' && r.changePercent >= MIN_HIGH_REGRESSION_PERCENT
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)
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if (critical.length > 0) {
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console.error(`❌ ${critical.length} critical regression(s): ${critical.map(r => r.name).join(', ')}`)
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process.exit(1)
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