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Frontend Architecture and Performance ​

Frontend Architecture and Performance Graphics Coverage ​

Primary chapter graphic: Frontend Performance Control Map, Large Frontend Monorepo Topology. Accepted graphics: 2. Reviewed non-signal pages: 1. Open graphics in review: 0. QA status lives in graphics audit and visual review ledger.

Corpus pages: p. 28-29, p. 91-92, p. 442 Coverage: 5 pages

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.

Frontend Performance Control Map ​

Frontend Performance Control Map

Large Frontend Monorepo Topology ​

Large Frontend Monorepo Topology

Open Review Queue ​

  • none

Reviewed Non-Signal Pages ​

  • Frontend Architecture And Performance: Cache + Port Map: source p. 92; batch 35; status non-signal/reviewed; ledger reason in visual-review-ledger.json

Use When ​

  • Browser experience, bundle size, dependency shape, or frontend delivery affects users.

Avoid When ​

  • The bottleneck is a backend query or external API call.

Core Model ​

  • Frontend architecture controls what code, data, and assets the browser must fetch before the user can act.
  • 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 ​

  • Measure route bundles, split by user journey, cache stable assets, and keep dependencies visible.
  • 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 ​

  • Splitting reduces initial load but can create waterfall fetches if boundaries are poorly chosen.
  • 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 shared dependency change silently inflates every route bundle.
  • 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 ​

  • Track first load size, critical path requests, cache headers, hydration work, and route-level errors.
  • 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 ​

  • An admin dashboard lazy-loads heavy chart code while keeping navigation and filters immediately interactive.
  • 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.