Page 3 of 8~112 min topic

Canary deploy lab

Implement one traceable happy path

One clean transaction through **POST /answer** must match the oracle: 5% canary healthy 20m → promote to 25%; guardrail breach → weight 0 in < 2m.

~14 min this pageVertical slice

1Try it yourself

Decision drill

Canary deploy desk

Split traffic, watch metrics, rollback or promote — never big-bang without a safety net.

Release safety72%

1/3You routed 10% of traffic to v2. Smoke looks fine so far.

2Learn the idea

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Order the successful transaction

Code the narrow path that serves release commander watching revision-sliced error rate: accept → authorize/normalize → call dependency → validate → record. Keep stages named so a trace can show which boundary passed. Success must emit evidence useful to canary_weight, error_rate_by_revision, grounded_rate_by_revision, not only a 200 with prose. Predict the observable for POST /answer before running: 5% canary healthy 20m → promote to 25%; guardrail breach → weight 0 in < 2m.

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Run with fakes first

Drive the path with recording fakes or local stubs. Assert call order and arguments. Idempotency keys or stable ids should keep retries from duplicating costly work where the product requires it. Product under test remains containerized answer API releasing image v2 at 5%→25%→100% weights — resist adding unrelated features mid-path.

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Implementation artifact

kubectl set image deploy/answer-canary api=answer:v2
./scripts/set_weight.sh --revision v2 --percent 5

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Compare prediction to result

For Canary deploy lab, paste the CLI/HTTP transcript beside your prediction for POST /answer. If the oracle is unmet (5% canary healthy 20m → promote to 25%; guardrail breach → weight 0 in < 2m), stop and debug this page; do not compensate with prompt folktales. Re-run once after a clean process start to catch hidden global state that would invalidate CANARY-PROMOTE-BLIND-6.

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Stage depth

Performance sketch: measure local p95 for the fake-backed path so later regressions are obvious. Keep concurrency modest until failure-handling proves limits. Log a single structured event per success with request id, revision, and the evidence field behind canary_weight, error_rate_by_revision, grounded_rate_by_revision. Avoid hidden global caches in the happy path unless the lab is about caching — and even then key by tenant. If the path calls a model, pin model id in config and echo it in the response for auditability. Remember release commander watching revision-sliced error rate experiences wall-clock time, not your debugger’s single-step comfort.

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Field notes for `canary-deploy-lab` / `happy-path`

Prefer explicit function names over a single god-object handleRequest. Thread a correlation id from ingress to the last log line. When streaming, define what partial failure means before coding. Snapshot one successful response body in fixtures after redaction. If the path writes to a queue, assert message attributes in the fake. Stop adding retries on this page; that is the next concern. In this chapter the product is containerized answer API releasing image v2 at 5%→25%→100% weights, the human stakeholder is release commander watching revision-sliced error rate, and the incident id you design against is CANARY-PROMOTE-BLIND-6. Re-state the oracle in your notes — 5% canary healthy 20m → promote to 25%; guardrail breach → weight 0 in < 2m — and keep the invariant visible: promote only if canary error_rate ≤ baseline+0.5pp and groundedness ≥ baseline−1pp for two 10m windows. Track canary_weight, error_rate_by_revision, grounded_rate_by_revision as the scoreboard. Surface under change control: POST /answer. If you only have forty minutes, finish the fixture for automation promotes while scrape_up{revision=v2}==0 before polishing UI. Promotion language stays ternary: promote, hold, or roll back based on evidence, not hope.

Go deeper

Before you start

Why this matters

Without calling production, order the steps a single success takes for release commander watching revision-sliced error rate. Circle the first irreversible side effect. Your prediction should mention POST /answer and the evidence field that proves 5% canary healthy 20m → promote to 25%; guardrail breach → weight 0 in < 2m.

In the wild

See how this idea shows up as a product and a company — then come back to the lesson. Skills transfer across vendors.

Check your understanding

Page assessment

Answer from memory. Completion is saved from this evidence, not from opening the next page.

1. Is call order asserted, not assumed?
2. Does success evidence support canary_weight, error_rate_by_revision, grounded_rate_by_revision?
3. Did you compare prediction vs transcript?

All responses are required.