Simulador de Pruebas de Carga
Diseña un escenario de prueba de carga profesional en menos de un minuto: VU, ramp-up, plateau, distribución del tráfico y consejos expertos de performance y cloud.
Quick glossary
VU (Virtual User)
Virtual user simulated in parallel by k6/JMeter/Gatling. Load metric #1.
Ramp-up
Progressive climb from 0 → target load. Gives autoscalers time to react.
Plateau
Load held at target. Where you measure stability and SLOs.
P95 (latency)
95% of requests answer faster than this. Better than average for UX.
P99 (latency)
Catches the slowest requests (cold-starts, GC pauses, locks). Reveals anomalies.
RPM / RPS
Requests per minute (or per second). Effective throughput absorbed by the system.
Throughput
Volume of requests handled over time. Measure on plateau, not peak.
SLO
Service Level Objective. Numeric target to honor (e.g. P95 ≤ 500ms, errors ≤ 1%).
Contexto del proyecto
Cuanto más preciso el contexto, más afilados los consejos
Everything flows through a single deployable. Typical bottlenecks: shared DB, locks, transactions too wide.
Parámetros de la prueba
Define la carga objetivo y la duración
Escenarios de usuario
La suma de porcentajes debe ser 100 % (actual: 100 %)
Volumen total estimado
~52 500
sobre 20 min (ramp-up + plateau)
Carga objetivo
500
Throughput
3 000/min
Duración total
20 min
Coherencia %
100%
Total al 100 %
Desglose por escenario
Pasa a la versión pro en la app Timint
Analiza los resultados (P95/P99, errores, score), recibe consejos expertos de remediación y genera un informe de auditoría PDF estilizado.
✅ Score de performance auto-calculado
✅ Consejos expertos de performance y cloud
✅ Informe PDF con gráficos + guardado en perfil
Detailed shot plan
Total and per-scenario load at each ramp-up step, then on plateau
| Phase | Timing | Total VU | Total req/min | search-cart (75%) | checkout (20%) | signup (5%) |
|---|---|---|---|---|---|---|
| Ramp-up step 1/4 (25%) | T+1.3 min | 125 | 750 | 94 ·563 | 25 ·150 | 6 ·38 |
| Ramp-up step 2/4 (50%) | T+2.5 min | 250 | 1 500 | 188 ·1 125 | 50 ·300 | 13 ·75 |
| Ramp-up step 3/4 (75%) | T+3.8 min | 375 | 2 250 | 281 ·1 688 | 75 ·450 | 19 ·113 |
| Ramp-up step 4/4 (100%) | T+5.0 min | 500 | 3 000 | 375 ·2 250 | 100 ·600 | 25 ·150 |
| Sustained plateau | T+5→20 min | 500 | 3 000 | 375 ·2 250 | 100 ·600 | 25 ·150 |
| Estimated volume: ~7 500 actions during ramp-up + ~45 000 actions during plateau = ~52 500 requests total. | ||||||
Risks per critical service
4 service(s) checked — each exposed service has its own risk profile
RiskAutenticación
Auth saturation = session blocks and refresh-token loops. Watch: users-table DB pool, JWT cache, /login throttling.
RiskBúsqueda
Search latency → instant cart abandonment. Bottlenecks: Elasticsearch / Algolia indexes, facet queries, full-text scans.
RiskCheckout
Checkout failure = direct revenue loss. Measure the full funnel (cart → shipping → payment → confirmation).
RiskPago
PSP error = lost payments or double charges. Idempotency keys are mandatory, PSP A → B failover in < 60s.
Expert recommendations — Monolito
DevOps, SRE and Solution Architect tips tailored to your architecture
🛠️ DevOps & Cloud scale
- •Profile the 10 most expensive endpoints before the test (APM: Datadog, NewRelic, Sentry Performance)
- •Enable DB slow query log and archive it during the test for post-mortem analysis
- •Provision +30% CPU/RAM during the test if stateful — otherwise scale horizontally with round-robin LB
- •Freeze deployments and disable cron jobs during the test window
⚙️ SRE / Platform
- •Define the 4 golden signals BEFORE the test: latency, traffic, errors, saturation
- •Alert on SLOs (P95, error rate), not raw CPU thresholds
- •Prepare a read-only degraded DB mode in case of unexpected saturation
- •Test rollback of the previous deployment in under 5 minutes
🏛️ Solution architect
- •Identify bounded contexts (DDD) to prepare a potential split into modular monolith
- •Progressively move heavy jobs (PDFs, emails, exports) to a queue + worker
- •Externalize sessions to Redis before scaling horizontally (stateless first)
- •Measure operational cost per feature to target the next split
Expert advice tailored to the project — E-commerce
Recommendations specific to the business constraints of your project type
Ready-to-copy k6 code
Script auto-generated with your parameters + scenarios + SLO thresholds
import http from 'k6/http';
import { sleep, group } from 'k6';
export const options = {
stages: [
{ duration: '1.3m', target: 125 },
{ duration: '1.3m', target: 250 },
{ duration: '1.3m', target: 375 },
{ duration: '1.3m', target: 500 },
{ duration: '15m', target: 500 },
],
thresholds: {
http_req_duration: ['p(95)<500', 'p(99)<1500'],
http_req_failed: ['rate<0.01'],
},
};
export default function () {
group('search-cart', () => {
// 75% du trafic — 375 VU cible · 2250 req/min
// TODO: implement requests for the search-cart scenario
});
group('checkout', () => {
// 20% du trafic — 100 VU cible · 600 req/min
// TODO: implement requests for the checkout scenario
});
group('signup', () => {
// 5% du trafic — 25 VU cible · 150 req/min
// TODO: implement requests for the signup scenario
});
sleep(10.0);
}Pre-shot checklist
Tick before launching to avoid false alarms
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Una prueba de carga bien planificada cuesta 10x menos que una caída en producción. Diséñala aquí, ejecútala con tu herramienta (k6, JMeter, Locust) y analiza los resultados en la app Timint.
Timint Smart Tips
AIServicio de pago marcado: usa el sandbox del PSP y prepara plan de retries 3DS / idempotency keys.
E-commerce con búsqueda: indexa las facetas populares y activa una caché corta en consultas frecuentes.
Descarga la app Timint para analizar resultados y generar un informe PDF estilizado.
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