File size: 2,700 Bytes
2b2ba56 | 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 | // Shared numeric helpers for perf scenarios. Every measure-*/profile-* script
// used to carry its own copy of these.
/** Nearest-rank percentile over an UNSORTED array. p in [0,1]. */
export function percentile(values, p) {
if (!values.length) {
return 0
}
const sorted = [...values].sort((a, b) => a - b)
const idx = Math.min(sorted.length - 1, Math.floor(sorted.length * p))
return sorted[idx]
}
/** min/p50/p90/p95/p99/max/mean over a sample array (rounded to 2dp). */
export function summarize(values) {
const round = n => Math.round(n * 100) / 100
if (!values.length) {
return { n: 0, min: 0, p50: 0, p90: 0, p95: 0, p99: 0, max: 0, mean: 0 }
}
const sorted = [...values].sort((a, b) => a - b)
const mean = values.reduce((a, b) => a + b, 0) / values.length
return {
n: values.length,
min: round(sorted[0]),
p50: round(percentile(sorted, 0.5)),
p90: round(percentile(sorted, 0.9)),
p95: round(percentile(sorted, 0.95)),
p99: round(percentile(sorted, 0.99)),
max: round(sorted[sorted.length - 1]),
mean: round(mean)
}
}
/** Median of a numeric array (used to reduce N repeated runs to one number). */
export function median(values) {
return percentile(values, 0.5)
}
/** Frame-interval histogram matching the buckets the stream scripts reported. */
export function frameHistogram(frames) {
const buckets = { '<=16.7': 0, '16.7-33': 0, '33-50': 0, '50-100': 0, '100-200': 0, '>200': 0 }
for (const f of frames) {
if (f <= 16.7) buckets['<=16.7']++
else if (f <= 33) buckets['16.7-33']++
else if (f <= 50) buckets['33-50']++
else if (f <= 100) buckets['50-100']++
else if (f <= 200) buckets['100-200']++
else buckets['>200']++
}
return buckets
}
/**
* Rank functions by self-time from a V8 CPU profile (Profiler.stop output).
* Returns the top `limit` entries as { ms, name, url, line }.
*/
export function cpuProfileTopSelf(profile, limit = 30) {
const samples = profile.samples || []
const timeDeltas = profile.timeDeltas || []
const nodes = new Map(profile.nodes.map(n => [n.id, n]))
const selfUs = new Map()
for (let i = 0; i < samples.length; i++) {
const id = samples[i]
selfUs.set(id, (selfUs.get(id) || 0) + (timeDeltas[i] ?? 0))
}
return [...selfUs.entries()]
.map(([id, us]) => {
const cf = nodes.get(id)?.callFrame || {}
return {
ms: us / 1000,
name: cf.functionName || '(anonymous)',
url: String(cf.url || '').slice(-70),
line: cf.lineNumber
}
})
.filter(x => !/\(root\)|\(idle\)|\(garbage collector\)|\(program\)/.test(x.name))
.sort((a, b) => b.ms - a.ms)
.slice(0, limit)
}
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