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feat: deterministic sampling with adaptive sample size #1849
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ff88d90
feat: deterministic sampling with adaptive sample size
7002392
fix: remove unused computeEffectiveSampleSize import, clarify SpanId …
aa2102f
Merge branch 'main' into event-deltas/sampling
alex-fedotyev 980ab65
refactor: use source's spanIdExpression instead of hardcoded SpanId
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,5 @@ | ||
| --- | ||
| "@hyperdx/app": patch | ||
| --- | ||
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| feat: deterministic sampling with adaptive sample size for Event Deltas |
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57 changes: 57 additions & 0 deletions
57
packages/app/src/components/__tests__/deltaChartSampling.test.ts
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,57 @@ | ||
| import { | ||
| computeEffectiveSampleSize, | ||
| getStableSampleExpression, | ||
| MAX_SAMPLE_SIZE, | ||
| MIN_SAMPLE_SIZE, | ||
| SAMPLE_RATIO, | ||
| SAMPLE_SIZE, | ||
| } from '../deltaChartUtils'; | ||
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| describe('getStableSampleExpression', () => { | ||
| it('returns cityHash64 of spanIdExpression when provided', () => { | ||
| expect(getStableSampleExpression('SpanId')).toBe('cityHash64(SpanId)'); | ||
| }); | ||
|
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| it('uses custom spanId column name', () => { | ||
| expect(getStableSampleExpression('my_span_id')).toBe( | ||
| 'cityHash64(my_span_id)', | ||
| ); | ||
| }); | ||
|
|
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| it('falls back to rand() when spanIdExpression is undefined', () => { | ||
| expect(getStableSampleExpression(undefined)).toBe('rand()'); | ||
| }); | ||
|
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| it('falls back to rand() when spanIdExpression is empty', () => { | ||
| expect(getStableSampleExpression('')).toBe('rand()'); | ||
| }); | ||
| }); | ||
|
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| describe('computeEffectiveSampleSize', () => { | ||
| it('returns SAMPLE_SIZE when totalCount is 0 (fallback)', () => { | ||
| expect(computeEffectiveSampleSize(0)).toBe(SAMPLE_SIZE); | ||
| }); | ||
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| it('returns SAMPLE_SIZE when totalCount is negative', () => { | ||
| expect(computeEffectiveSampleSize(-1)).toBe(SAMPLE_SIZE); | ||
| }); | ||
|
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| it('returns MIN_SAMPLE_SIZE for small datasets', () => { | ||
| expect(computeEffectiveSampleSize(100)).toBe(MIN_SAMPLE_SIZE); | ||
| }); | ||
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| it('returns SAMPLE_RATIO * totalCount for mid-size datasets', () => { | ||
| const result = computeEffectiveSampleSize(200_000); | ||
| expect(result).toBe(Math.ceil(200_000 * SAMPLE_RATIO)); | ||
| expect(result).toBeGreaterThan(MIN_SAMPLE_SIZE); | ||
| expect(result).toBeLessThan(MAX_SAMPLE_SIZE); | ||
| }); | ||
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| it('caps at MAX_SAMPLE_SIZE for very large datasets', () => { | ||
| expect(computeEffectiveSampleSize(10_000_000)).toBe(MAX_SAMPLE_SIZE); | ||
| }); | ||
|
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| it('returns exact 1% for datasets where 1% falls in the valid range', () => { | ||
| expect(computeEffectiveSampleSize(100_000)).toBe(1000); | ||
| }); | ||
| }); |
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