Messaging

best-effort, reliable (eager/gossip), total order broadcast; consensus requirement; relationship to CRDTs and replication

*Enterprise Integration Patterns* — Hohpe & Woolf

Broker vs mediator topologies; DDD: three event types (notification, ECST, domain event); private vs public events; distributed big ball of mud anti-pattern; data liberation

Idempotency keys (atomicity requirement, principle of least astonishment), at-least-once delivery, retry safety

the four EIP integration styles (File Transfer, Shared Database, RPI, Messaging); eight decision criteria; trade-offs and when to use which style

Command/Document/Event message types; one-way/req-resp/broadcast styles; Request-Reply (sync block vs async callback; Return Address; Correlation Identifier); point-to-point/pub-sub/datatype channels; Pub-Sub as distributed Observer (push vs pull; channel design); at-least-once delivery; guaranteed delivery; exactly-once via idempotency; invalid vs dead letter channel; backlogs; poison message isolation; competing consumers; data safety trade-off (publisher confirms + persistent queues + manual ACKs); quorum queues (RAFT); RabbitMQ internals; Pipes and Filters; Message Router; Message Bus; Messaging Bridge; Aggregator; Resequencer; Composed Message Processor; Scatter-Gather; Routing Slip; Process Manager; Message Broker (architectural pattern); Envelope Wrapper; Content Enricher; Content Filter; Claim Check; Normalizer; Canonical Data Model; Messaging Gateway; Messaging Mapper; Transactional Client; Polling Consumer; Event-Driven Consumer; Competing Consumers; Message Dispatcher; Selective Consumer; Durable Subscriber; Idempotent Receiver; Service Activator; Control Bus; Detour; Wire Tap; Message History; Message Store; Smart Proxy; Test Message; Channel Purger

Atomic write + publish without 2PC; relay process; CDC; data liberation patterns; DDD: two wrong approaches + correct approach; NoSQL embedding; pull vs push relay

Log-based brokers (Kafka), stateless topology primitives, partition assignment, windowing, late event strategies, watermarks vs stream time, reprocessing, state stores (internal/external, changelog, hot replicas), effectively once processing, heavyweight vs lightweight framework comparison, stream joins, exactly-once fault tolerance; Kafka production mechanics: producer batching (acks/idempotence), consumer commit semantics, semantic partitioning, consumer groups, ISR

When to use synchronous vs asynchronous communication; temporal coupling as the core distinction; availability chaining risk; async concerns (idempotency, ordering, correlation, observability); mixed model (sync north-south, async east-west); decision guide