Performance-Lasttest-Simulator
Erstelle in unter einer Minute ein professionelles Lasttest-Szenario: VUs, Ramp-up, Plateau, Traffic-Verteilung und Expertentipps zu Performance & 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%).
Projektkontext
Je präziser der Kontext, desto schärfer die Empfehlungen
Everything flows through a single deployable. Typical bottlenecks: shared DB, locks, transactions too wide.
Lastparameter
Lege Ziellast und Dauer fest
User-Szenarien
Summe der Prozente muss 100 % sein (aktuell: 100 %)
Geschätztes Gesamtvolumen
~52 500
über 20 Min (Ramp-up + Plateau)
Ziellast
500
Durchsatz
3 000/min
Gesamtdauer
20 Min
%-Konsistenz
100%
Summe = 100 %
Aufschlüsselung pro Szenario
Werde Pro in der Timint-App
Analysiere Ergebnisse (P95/P99, Fehler, Score), erhalte Experten-Empfehlungen und generiere einen stylischen PDF-Audit-Bericht.
✅ Automatisch berechneter Performance-Score
✅ Experten-Tipps Performance & Cloud
✅ PDF-Bericht mit Charts + Profil-Speicherung
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
RiskAuthentifizierung
Auth saturation = session blocks and refresh-token loops. Watch: users-table DB pool, JWT cache, /login throttling.
RiskSuche
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).
RiskZahlung
PSP error = lost payments or double charges. Idempotency keys are mandatory, PSP A → B failover in < 60s.
Expert recommendations — Monolith
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
Share this simulation
Generate a public link to send to your team, client or board
The link will be public. Only the values entered here will be published. No personal data beyond the optional name.
Ein gut geplanter Lasttest kostet 10x weniger als ein Produktionsausfall. Plane hier, führe mit deinem Tool aus (k6, JMeter, Locust) und analysiere in der Timint-App für einen teilbaren Audit-Bericht.
Timint Smart Tips
AIPayment ausgewählt: PSP-Sandbox nutzen und 3DS-Retries / Idempotency-Keys einplanen.
E-Commerce mit Suche: beliebte Facetten indexieren und einen kurzen Cache für häufige Queries aktivieren.
Lade die Timint-App, um Ergebnisse zu analysieren und einen PDF-Audit-Bericht zu generieren.
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