Performance y load testing para apps Educabot (Go/TS/React/RN): k6 scripts, smoke/load/stress/soak/spike, thresholds por SLO, CI integration, reporte.
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Run score Not tested
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Framework basado en k6 (Grafana Labs) — scripting JS, ejecución Go, integración Prometheus/Grafana.
| Tipo | Objetivo | Duración | VUs |
|---|---|---|---|
| Smoke | Verificar sin error grueso | 1-2min | 1-5 |
| Load | Carga esperada sostenida | 15-30min | N esperado |
| Stress | Encontrar breaking point | 30-60min | Ramp hasta romper |
| Soak | Degradación/leaks sostenido | 2-8h | N esperado |
| Spike | Resiliencia picos súbitos | 5-15min | 0→10x→0 |
perf/k6/
config/envs.js, thresholds.js
lib/auth.js, data.js
scenarios/smoke.js, load-api.js, stress-login.js, soak-dashboard.js
results/ (gitignored)
SLO availability 99.9% → http_req_failed: rate<0.001. SLO p95 <500ms → http_req_duration: p(95)<500. Test falla (exit 1) si no se cumple → gate en CI.
k6 run k6/scenarios/smoke.js
BASE_URL=https://staging.educabot.com k6 run k6/scenarios/load-api.js
k6 run -o experimental-prometheus-rw k6/scenarios/load-api.js
workflow_dispatch manualresults/summary.json como artifact| Métrica | Qué significa |
|---|---|
http_req_duration | Latencia total (DNS+TCP+TLS+server) |
http_req_waiting | TTFB — latencia server |
http_req_failed | Error rate |
iterations | Completaciones del flow |
| Señal | Probable causa |
|---|---|
| p95 alto + error rate bajo | Server lento → profiler (pprof/clinic.js) |
| Error rate sube con VUs | Saturación → pool DB, file descriptors, CPU |
| p99 >> p95 | Long-tail → GC, cold cache, head-of-line blocking |
| Throughput plateau | Bottleneck identificado (DB/CPU/red) |
Import template ID 19665 (k6 Prometheus): RPS por endpoint, p50/p95/p99, error rate por status, VUs vs throughput.
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