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Backend Service Architecture
Backend Service Architecture Graphics Coverage
Primary chapter graphic: Distributed Counter Service Flow, Secure Delivery Integration Flow, Social App Backend Core Flow. Accepted graphics: 3. Reviewed non-signal pages: 1. Open graphics in review: 0. QA status lives in graphics audit and visual review ledger.
Corpus pages: p. 35, p. 58, p. 89, p. 93, p. 108, p. 263, p. 318, p. 323, p. 353-354 Coverage: 10 pages; low-confidence extraction ranges: p. 318, p. 323, p. 353-354
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.
Distributed Counter Service Flow
- source-page: p. 191
- batch: 06
- status: accepted
- reviewer-status: reviewed
- fidelity-score: 0.9
- spec: bbg-p0191-api-gateways-and-contracts-api.json
- svg: bbg-p0191-api-gateways-and-contracts-api.svg

Secure Delivery Integration Flow
- source-page: p. 182
- batch: 08
- status: accepted
- reviewer-status: reviewed
- fidelity-score: 0.9
- spec: bbg-p0182-architecture-source-map-architecture-source.json
- svg: bbg-p0182-architecture-source-map-architecture-source.svg

Social App Backend Core Flow
- source-page: p. 89
- batch: 16
- status: accepted
- reviewer-status: reviewed
- fidelity-score: 0.9
- spec: bbg-p0089-backend-service-architecture-backend-service.json
- svg: bbg-p0089-backend-service-architecture-backend-service.svg

Open Review Queue
- none
Reviewed Non-Signal Pages
- Backend Service Architecture: DNS + Topic Map: source p. 108; batch 10; status non-signal/reviewed; ledger reason in visual-review-ledger.json
Use When
- Business capabilities need process boundaries, ownership, and runtime shape.
Avoid When
- The domain is too fluid to split into independent services.
Core Model
- Start with cohesive modules. Split services only when ownership, scale, or reliability needs justify the boundary.
- 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
- Name capabilities, data ownership, contracts, deployment independence, and operational load for each boundary.
- 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
- A modular monolith keeps coordination cheap; microservices isolate change at the cost of distributed failure.
- 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
- A service boundary splits one transaction into fragile network choreography.
- 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
- Write the reason for every process boundary and the data each service owns.
- 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 billing capability stays one module until traffic and ownership require an independently deployed service.
- 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.