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magnus919_agent-skills/release-engineering/assets/deployment-strategy-matrix.md
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usernameandfactory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> 6730015312 feat(release-engineering): add release engineering skill bundle
Dense release engineering methodology bundle: 15 references (role/career, process models,
CD pipelines, progressive delivery, change governance/compliance, readiness gates, rollback
by system type, versioning/artifacts, feature flags, monorepo/polyrepo, toolchain,
supply-chain security, DORA metrics, release operations), 6 templates, 4 assets, 5 stdlib-only
Python CLIs (version_bump, semver_check, changelog_check, dora_metrics, release_plan_scaffold)
with colocated tests, schema-v1 evals (8 cases), README, and catalog regeneration.

Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
2026-08-01 22:54:13 -04:00

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Deployment Strategy Matrix

Choose a strategy per release based on risk, reversibility, and audience. Strategies combine: feature flags gate a feature's visibility while a canary gates the binary. Rollback time assumes an immutable, previously healthy artifact is available.

Comparison

Strategy Speed to full rollout Safety (defect exposure) Rollback time Complexity When to use
Rolling Fast — minutes Moderate — all instances briefly run the new version as it spreads Minutes (redeploy previous artifact) Low Default for stateless services with good monitoring
Blue-green Fast cutover; swap in seconds High — the full old environment stays live Seconds (router change) Medium; 2× resources Risky changes needing instant rollback; infrastructure you can afford to double
Canary Medium — staged % over minuteshours High — defects hit a small slice first Minutes (redeploy / auto-rollback of the canary) Medium Risky changes; comparing against a control population
Ring Slow by design — hoursdays across cohorts Very high — exposure grows by ring Slow (rings are users/devices, hard to recall) High Large user bases, clients/devices, compliance-driven exposure limits
Feature flag Instant on/off — no redeploy Depends on rollout %; off = zero exposure Seconds (toggle off) Medium (flag debt) Decoupling deploy from release; dark launch; kill switch
Shadow Immediate (traffic mirrored) No user impact (nothing live is served) N/A (nothing served) High Validating performance/behavior without exposing users

Decision Guidance

Situation Pick
Stateless service, good monitoring Rolling or canary
Change can break irreversibly (schema, data) Canary + expand/contract migration; never rely on rollback past finalization
Instant rollback is a hard requirement Blue-green (or canary with auto-rollback)
Mobile / desktop / IoT users Ring (phased release) + feature flags / kill switch — true rollback is impossible
Defect can be gated behind a flag Flag off is the fastest lever — reach for it before any redeploy
Need to compare behavior vs. control Canary with canary-vs-control metrics
Regulatory exposure limits Ring with approved exposure caps per cohort

Gotchas

  • Teeing / shadowing limits: synthetic or mirrored traffic does not validate stateful systems (e.g., billing) — a shadow run can look perfect and still fail on real writes.
  • Before/after comparisons lie: compare canary vs. control populations, not the same metric over time.
  • Flags are not free: every flag left behind is rollback speed today and operational debt tomorrow — plan removal in the flag's lifecycle.
  • Canary size × error budget: a 5% canary at 20% error costs ~1% of the overall budget — size the canary so a bad release stays inside the budget.

Sources and Further Reading