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Messaging, Events, and Streaming ​

Messaging, Events, and Streaming Graphics Coverage ​

Primary chapter graphic: Cloud Messaging Choice Map, RabbitMQ Exchange Routing Flow, Kafka Log and Consumer Group Flow, Kafka Log vs RabbitMQ Broker, CRUD State vs Event Sourcing, Kafka Cluster At A High Level. Accepted graphics: 6. Reviewed non-signal pages: 1. Open graphics in review: 0. QA status lives in graphics audit and visual review ledger.

Corpus pages: p. 69-70, p. 206-207, p. 310-311, p. 330-331, p. 419-420 Coverage: 10 pages; low-confidence extraction ranges: p. 310-311

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.

Cloud Messaging Choice Map ​

Cloud Messaging Choice Map

RabbitMQ Exchange Routing Flow ​

RabbitMQ Exchange Routing Flow

Kafka Log and Consumer Group Flow ​

Kafka Log and Consumer Group Flow

Kafka Log vs RabbitMQ Broker ​

Kafka Log vs RabbitMQ Broker

CRUD State vs Event Sourcing ​

CRUD State vs Event Sourcing

Kafka Cluster At A High Level ​

Kafka Cluster At A High Level

Open Review Queue ​

  • none

Reviewed Non-Signal Pages ​

  • Messaging, Events, And Streaming: Queue + Topic Map: source p. 420; batch 36; status non-signal/reviewed; ledger reason in visual-review-ledger.json

Use When ​

  • Work is slow, bursty, retryable, scheduled, or consumed by several downstream systems.

Avoid When ​

  • The team cannot observe or repair asynchronous failures.

Core Model ​

  • Queues control work. Events record facts. Streams preserve ordered change for replay and continuous processing.
  • 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 schema, owner, idempotency key, retry policy, ordering assumptions, and dead-letter repair path.
  • 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 ​

  • Async boundaries protect user requests but make progress less visible.
  • 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 ​

  • Handlers retry side effects without idempotency and duplicate customer-visible actions.
  • 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 ​

  • Monitor depth, age, lag, retries, poison messages, and replay outcomes.
  • 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 document import queues extraction jobs and emits a completion event for indexing and notification consumers.
  • 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.