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Deployment, Release, and Versioning ​

Deployment, Release, and Versioning Graphics Coverage ​

Primary chapter graphic: Service Deployment Strategies, Microservice Delivery Practices, Simplified Git Workflow, CI/CD Pipeline, Git Command Flow Cheatsheet, Code to Production Flow, Git vs GitHub, CI/CD Pipeline, Gitflow Branching, Semantic Version Numbers, Git Reset Modes. Accepted graphics: 11. Reviewed non-signal pages: 1. Open graphics in review: 0. QA status lives in graphics audit and visual review ledger.

Corpus pages: p. 24, p. 44-45, p. 110-111, p. 128-129, p. 137-138, p. 160-161, p. 184, p. 290, p. 293, p. 308-309, p. 316-317, p. 328-329, p. 441 Coverage: 21 pages; low-confidence extraction ranges: p. 24, p. 308-309, p. 316-317

This chapter is part of Marius's owned architecture build corpus. The text routes decisions; durable implementation signal is carried by accepted graphics, reviewed non-signal decisions, and the linked QA audit.

Chapter Visuals ​

Accepted graphics carry the canonical design signal for this chapter. Each selected source page is either accepted as a graphic or explicitly marked non-signal in the source-faithful ledger. Review and QA state live in visual inventory, visual review ledger, and graphics audit.

Service Deployment Strategies ​

Service Deployment Strategies

Microservice Delivery Practices ​

Microservice Delivery Practices

Simplified Git Workflow ​

Simplified Git Workflow

CI/CD Pipeline ​

CI/CD Pipeline

Git Command Flow Cheatsheet ​

Git Command Flow Cheatsheet

Code to Production Flow ​

Code to Production Flow

Git vs GitHub ​

Git vs GitHub

CI/CD Pipeline ​

CI/CD Pipeline

Gitflow Branching ​

Gitflow Branching

Semantic Version Numbers ​

Semantic Version Numbers

Git Reset Modes ​

Git Reset Modes

Open Review Queue ​

  • none

Reviewed Non-Signal Pages ​

  • Deployment, Release, And Versioning: Workflow + Step Map: source p. 111; batch 32; status non-signal/reviewed; ledger reason in visual-review-ledger.json

Use When ​

  • Code changes must move from development to production with traceability and rollback.

Avoid When ​

  • The artifact is an unpublished experiment without user impact.

Core Model ​

  • Release flow is a control system: build, test, approve, deploy, observe, and recover.
  • Prefer explicit ownership over accidental coupling. Every boundary should say who owns correctness, cost, data, recovery, and change.
  • Use corpus page pointers for inspection, and keep the chapter notes focused on reusable design decisions.

Implementation Guidance ​

  • Automate repeatable checks, separate deploy from release when risk is high, and keep version semantics clear.
  • Write the smallest useful design note: purpose, inputs, outputs, state, failure behavior, observability, and rollback.
  • Choose the first implementation that can be tested against the real workflow without hiding a known production risk.

Tradeoffs ​

  • More gates reduce accidental breakage but can slow small safe changes.
  • Centralization reduces duplicated work but can become a bottleneck when every team needs exceptions.
  • Specialized infrastructure helps at scale, but it must earn its operational cost.

Failure Modes ​

  • Rollback exists in theory but depends on hidden manual steps.
  • The diagram shows boxes but not ownership, retry behavior, data freshness, or user-visible failure.
  • The system has no proof path for the highest-risk assumption.

Decision Checklist ​

  • Record commit, artifact, environment, migration, owner, smoke test, and rollback command.
  • Name the owner, source of truth, timeout, retry policy, and evidence that the path works.
  • Add one regression check for the failure mode most likely to recur.

Neutral Automation Examples ​

  • A service ships behind a disabled feature flag, then enables traffic gradually after health checks pass.
  • A neutral internal automation starts with fixtures, then adds credentials, permissions, and production scheduling only after the boundary is tested.
  • A customer-facing workflow keeps irreversible actions behind explicit approval until metrics show it is safe to automate further.