api-chaos-runner

Overview

Most API tests run against perfect networks: <1ms latency, no packet loss, infinite bandwidth, deterministic ordering. Real production isn't like that. Network chaos testing drives the existing tests under controlled network impairment - the team discovers which retry / circuit-breaker / timeout patterns actually hold up before real customers find out.

The canonical open-source primitive is Toxiproxy - Shopify's "TCP proxy to simulate network and system conditions for chaos and resiliency testing" (toxiproxy-readme). The pattern: sit Toxiproxy between client and upstream; manipulate toxics (latency, timeout, bandwidth, etc.) during test execution.

This skill is build-an-X - the workflow chains the team's existing API tests (Postman / Karate / RestAssured / Tavern / Schemathesis) through a Toxiproxy-managed connection and orchestrates a per-scenario chaos matrix.

When to use

• The API has documented resilience requirements (retry on 5xx, circuit-break on 3 consecutive failures, timeout at 5s, etc.) and the team needs to verify them, not just document them.
• A new external dependency just got added; the team wants to pressure-test fallback behavior.
• An incident postmortem identified a "we should have caught this in testing" item; chaos coverage is the prevention mechanism for that class.
• The team has integration tests already and wants to multiply their signal value via fault injection.

If the team is just starting API testing and has no resilience patterns to verify, this skill is overkill - start with happy-path coverage via postman-collections or the language-native equivalents first.

How to use

1. Confirm documented resilience requirements exist (retry, circuit-breaker, timeout, fallback). If none, start with happy-path coverage instead of chaos.
2. Pick the chaos primitive - Toxiproxy for per-API chaos in CI (Step 1).
3. Build the chaos matrix: for each existing test scenario, list the toxics to inject and the expected behavior per condition (Step 2).
4. Wire Toxiproxy between the app and its upstream and register a proxy per dependency (references/toxiproxy-wiring.md).
5. Run the matrix, always including a no-toxic control row; capture one JUnit XML per scenario (Step 3).
6. Aggregate results into a resilience matrix and report which assertions break under which toxic (Step 4).
7. Inject only toxics that map to a documented expectation; run the full matrix nightly, the control row per PR.

Step 1 - Pick the chaos primitive

Tool	Layer	Best for
Toxiproxy	TCP proxy	Per-connection latency / bandwidth / timeout / drop. Most precise.
Pumba	Docker container	Container-level chaos (kill, pause, network).
Gremlin (commercial)	Multi-platform	Production-grade chaos with audit / approval flow.
LitmusChaos	Kubernetes operator	Cloud-native; experiments declared as CRDs.
tc qdisc (Linux native)	Network interface	Lowest level; most setup; CI-friendly only with --cap-add NET_ADMIN.

Default recommendation: Toxiproxy for per-API chaos in CI. The others fit when the team is already in those ecosystems (Docker-Compose-heavy projects, Kubernetes-first projects).

Step 2 - Define the chaos matrix

For each existing API test scenario, define a matrix of conditions to run it under:

Scenario	Toxic	Expected behavior
Order create (POST /orders)	None (control)	201 in <500ms
Order create	latency=1000ms	201 in <2s (within timeout budget)
Order create	latency=10000ms	504 with retry-after, OR client gives up
Order create	bandwidth=10kbps	201 (eventual) OR 408 timeout
Order create	reset_peer	502 with retry attempted
Order create	timeout	504; circuit-breaker opens after 3rd

Per toxiproxy-readme, the canonical toxic types include latency, down (forced failure), bandwidth, slow_close, timeout, slicer, limit_data, reset_peer.

The matrix is the load-bearing artifact: what the team expects under each condition is what differentiates resilience verification from "did the test pass?" The Expected column drives the assertions.

Step 3 - Wire Toxiproxy and run the matrix

Sit Toxiproxy between the app and its upstream, register a proxy per dependency, and add/remove toxics around each test run. Point the app at Toxiproxy's listen ports; it forwards to the real upstream when no toxic is active. A matrix runner loops the scenarios, adding one toxic per row and producing one JUnit XML per scenario to aggregate in the report stage.

Full docker-compose setup, control-API proxy registration, toxiproxy-cli toxic commands, and the runner shell script: references/toxiproxy-wiring.md.

Step 4 - Report what broke under what

A successful chaos run produces a resilience matrix report:

## API Chaos Matrix - verdict: REVIEW

| Scenario         | Control | Latency 1s | Bandwidth 10k | Timeout 5s | Reset peer |
|------------------|:-------:|:----------:|:-------------:|:----------:|:----------:|
| POST /orders     |    ✅   |     ✅     |       ✅      |     ✅     |     ❌     |
| GET /orders/:id  |    ✅   |     ✅     |       ❌      |     ✅     |     ✅     |
| DELETE /orders/:id |  ✅   |     ✅     |       ✅      |     ✅     |     ✅     |

### Failures

| Test | Toxic       | Expected                                          | Actual |
|------|-------------|---------------------------------------------------|--------|
| POST /orders | reset_peer | 502 + retry attempted; second attempt succeeds | 502; no retry observed in client logs |
| GET /orders/:id | bandwidth=10k | 200 in <30s | 408 timeout at 10s |

A green matrix isn't the goal - finding where resilience is missing is the goal. A failure under a chaos scenario is a feature request, not a bug in the test.

Choosing what to inject

Match toxics to documented resilience requirements:

Resilience pattern documented	Toxic to inject
Retry on 5xx	down (forced 5xx) or reset_peer
Timeout after Nms	latency=N+500 (force the timeout)
Circuit-breaker after 3 failures	down for ≥3 requests
Fallback to cache when upstream unreachable	down indefinitely
Bulkhead under load	bandwidth=very-low
Slow-loris client	slow_close on response

Run only the toxics that map to a documented expectation; running every toxic against every endpoint is noise.

Worked example

The orders service documents "retry once on connection reset." The team already has a Postman collection covering POST /orders.

1. Wire Toxiproxy in front of orders-api-real and register the orders-api proxy (Step 3).
2. Matrix rows: control (no toxic) and reset_peer. Expected for reset_peer: 502, retry attempted, second attempt succeeds.
3. Run the matrix. Control passes - 201 in <500ms.
4. Under reset_peer the run returns 502 with no retry in the client logs, so POST /orders reset_peer is marked failing in the matrix.

Verdict: REVIEW. The documented retry policy is not actually implemented. The output is a fix request against the client (add the retry), not a bug in the test - exactly the resilience gap the matrix exists to surface.

Anti-patterns

Anti-pattern	Why it fails	Fix
Chaos in production	Real users observe; oncall pages.	CI / staging only. Production chaos requires the team's full chaos engineering practice (Gremlin / Litmus + approval flow).
Per-PR chaos matrix	Adds 10+ minutes; team disables.	Nightly chaos runs; PR runs only the control row.
Asserting "chaos must not break anything"	Every system has a breaking point; the test trivially fails.	Assert specific resilience behavior under specific conditions; document the breaking point as accepted.
Using down for everything	down forces 5xx; doesn't model real-world latency / bandwidth.	Mix latency, bandwidth, timeout, reset_peer for realistic mixes.
Skipping the control row	Without control, the matrix can't distinguish chaos failures from test bugs.	Always run a no-toxic scenario as the baseline.

Limitations

• Toxiproxy is TCP-level. UDP flaws (DNS resolver weirdness, QUIC) need different tooling.
• Doesn't model partial failures within a single connection. Toxiproxy treats each connection uniformly; for "retry on the 3rd request after 2 successes" patterns, layer a counting middleware.
• Per-test-suite isolation. When tests run in parallel against the same Toxiproxy instance, they fight over the same toxic state; serialize chaos scenarios or use one Toxiproxy instance per worker.

References

• toxiproxy - main repo: install, control API, toxic types, language-native clients.
• references/toxiproxy-wiring.md - docker-compose setup, proxy registration, toxic commands, matrix runner.
• Pumba - https://github.com/alexei-led/pumba
• LitmusChaos - https://litmuschaos.io/
• Principles of Chaos Engineering - https://principlesofchaos.org/
• postman-collections, tavern-testing, karate-testing, restassured-testing - example-based test suites that this skill drives through chaos.