randarium
Network

Chaos Scenario Generator

Create realistic API errors, HTTP failures, retry backoff scenarios, latency patterns, and timeout events. Useful for testing error handling, circuit breakers, and resilience patterns.

Also known as: failure scenario · error simulation · network chaos

seeded · synthetic data

Presets

Output

No output yet — set your options and hit .
About this tool, tips & examples

What it does

The Chaos Scenario Generator produces realistic failure data for resilience testing: API error payloads, HTTP failure codes, retry/backoff sequences, latency spike patterns, and timeout events. Pick a scenario type, generate up to 1,000 events per run, and reuse a seed so a failing test case can be reproduced exactly.

Common use cases

  • Chaos engineering — feed synthetic failure streams into consumers to observe how they degrade.
  • Error-handling tests — table-driven tests over a varied set of API errors and HTTP failures instead of the same hand-written 500.
  • Circuit breakers and retries — validate backoff logic against realistic retry timing sequences and mixed success/failure runs.
  • Timeout and latency budgets — latency patterns with spikes and tail-heavy values for testing SLO alerts and client-side timeouts.

Settings

  • Scenario type — API errors, HTTP failures, retry scenarios, or latency patterns; presets jump straight to each.
  • How many — 1 to 1,000 events, exportable as text, JSON, or CSV for replay in your test harness.
  • Seed — the same seed and settings reproduce the identical scenario — essential when a generated case exposes a real bug.

Privacy note

Scenarios are generated locally in your browser and never uploaded. All of it is synthetic test content — fabricated errors and timings, not real telemetry from any system.

FAQ

Why generate failures instead of writing them by hand? Hand-written fixtures encode your assumptions; generated ones surprise you. Variety in codes, messages, and timing is exactly what shakes out brittle error handling.

Can I reproduce the scenario that broke my test? Yes — that’s why the generator is seeded. Pin the seed in the failing test and the same event sequence comes back every run.

Does this actually inject faults into my system? No — it produces the data (payloads, codes, timings). Injection is up to your harness: serve the payloads from a mock, or drive delays from the latency values.