Containers and Orchestration

Containers and Orchestration Graphics Coverage

Primary chapter graphic: Virtualization Type Comparison, Kubernetes Learning Map, Virtualization vs Containerization, Docker Packaging vs Kubernetes Orchestration, Docker Packaging vs Kubernetes Orchestration, Kubernetes Scaling Strategies, Virtual Machine Stack vs Container Stack, Kubernetes Control Plane Cheatsheet, Kubernetes Control Plane And Workers. Accepted graphics: 9. Reviewed non-signal pages: 0. Open graphics in review: 0. QA status lives in graphics audit and visual review ledger.

Corpus pages: p. 14-15, p. 61-62, p. 112, p. 172-173, p. 178, p. 210-211, p. 288-289, p. 296-298, p. 349-350, p. 401-402 Coverage: 19 pages; low-confidence extraction ranges: p. 14-15

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.

Open Review Queue

none

Reviewed Non-Signal Pages

none

Use When

Services need portable runtime packaging, scaling, rollout control, or workload isolation.

Avoid When

A single managed runtime can host the app with less operational surface.

Core Model

Containers package processes; orchestration schedules, connects, scales, and recovers them.
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

Define image build, runtime config, health checks, resource limits, networking, persistence, and rollout strategy.
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

Orchestration gives control, but it shifts complexity into cluster operations.
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

The image works locally but lacks production health checks or resource limits.
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

Set readiness checks, liveness checks, CPU and memory limits, secret injection, and rollback behavior.
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 worker pool scales on queue depth while the public API keeps a smaller stable replica count.
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.

Containers and Orchestration ​