Simulador de Testes de Carga
Construa um cenário de load test profissional em menos de um minuto: VUs, ramp-up, plateau, distribuição de tráfego e conselhos experts de performance e 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 do projeto
Quanto mais preciso o contexto, mais afiados os conselhos
Everything flows through a single deployable. Typical bottlenecks: shared DB, locks, transactions too wide.
Parâmetros do teste
Defina a carga alvo e a duração
Cenários de utilizador
A soma das percentagens deve ser 100% (atual: 100%)
Volume total estimado
~52 500
em 20 min (ramp-up + plateau)
Carga alvo
500
Throughput
3 000/min
Duração total
20 min
Coerência %
100%
Total a 100%
Divisão por cenário
Passe à versão pro na app Timint
Analise os resultados (P95/P99, erros, score), receba conselhos experts de remediação e gere um relatório de auditoria PDF.
✅ Score de performance auto-calculado
✅ Conselhos experts de performance e cloud
✅ Relatório PDF com gráficos + guardar no 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
RiskAutenticação
Auth saturation = session blocks and refresh-token loops. Watch: users-table DB pool, JWT cache, /login throttling.
RiskPesquisa
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).
RiskPagamento
PSP error = lost payments or double charges. Idempotency keys are mandatory, PSP A → B failover in < 60s.
Expert recommendations — Monólito
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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Um load test bem planeado custa 10x menos que uma falha em produção. Planeie aqui, execute com a sua ferramenta (k6, JMeter, Locust), analise na app Timint.
Timint Smart Tips
AIServiço de pagamento marcado: use o sandbox do PSP e prepare retries 3DS / idempotency keys.
E-commerce com pesquisa: indexe as facets populares e ative uma cache curta nas queries frequentes.
Faça download da app Timint para analisar resultados e gerar um relatório PDF.
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