Message Queues

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name: message-queues description: Deep message queue workflow—queue vs topic, ordering and partitions, retries and DLQ, idempotency, backpressure, observability, and failure design. Use when integrating workers, event buses, or debugging poison messages and lag.

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Message Queues (Deep Workflow)

Queues decouple producers and consumers—and introduce duplicates, ordering surprises, poison messages, and lag. Make delivery semantics and failure handling explicit.

When to Offer This Workflow

Trigger conditions:

Choosing Kafka vs RabbitMQ vs SQS vs Pub/Sub

Consumer lag, DLQ growth, reprocessing needs

“Exactly-once” expectations—need alignment on reality

Initial offer:

Use six stages: (1) delivery semantics, (2) topology & partitions, (3) message contract, (4) consumers & retries, (5) ops & scaling, (6) failure drills). Confirm cloud and ordering requirements.

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Stage 1: Delivery Semantics

Goal: Choose at-most-once, at-least-once, or effective-once via idempotency.

Questions

1. Can duplicate processing break invariants? 2. Is ordering global, per-entity, or unnecessary? 3. Latency vs durability trade-offs

Exit condition: One paragraph per pipeline stating semantics.

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Stage 2: Topology & Partitions

Goal: Throughput and ordering align—ordering only within partition when using Kafka-style partitions.

Practices

Partition key often equals business key (e.g., user id)

Watch hot partitions

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Stage 3: Message Contract

Goal: Versioned events or commands with schema registry.

Practices

Envelope: id, type, version, timestamp

Payload size limits; reference blobs by id

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Stage 4: Consumers & Retries

Goal: Exponential backoff + jitter; DLQ with reason; replay tooling owned.

Pitfalls

Retries can reorder unless single-threaded per partition

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Stage 5: Ops & Scaling

Goal: Lag metrics, consumer offset health, rebalance awareness (Kafka).

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Stage 6: Failure Drills

Goal: Kill consumer mid-batch; duplicate publish intentionally; validate idempotency.

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Final Review Checklist

[ ] Delivery semantics and idempotency explicit

[ ] Partitioning/ordering strategy documented

[ ] Versioned message contract

[ ] Retry, DLQ, replay documented

[ ] Lag metrics and alerts; capacity plan

Tips for Effective Guidance

Exactly-once end-to-end is rare—design for at-least-once + idempotent handlers.

Challenge global ordering requirements—they cost scale.

Visibility timeouts (SQS) differ by product—read the vendor docs.

Handling Deviations

Transactional outbox when you need DB + queue consistency without dual writes.