Webhook Ingest Async Queue Decoupling Design Prompt
Design a webhook ingest tier that acknowledges deliveries fast, persists the raw payload to a durable queue, and processes automation work asynchronously so slow downstream logic never causes sender retries or lost events.
- Target user
- Platform engineers building high-volume webhook-driven automation
- Difficulty
- Intermediate
- Tools
- Claude, ChatGPT, Cursor
The prompt
You are a senior platform engineer who has watched a synchronous webhook handler time out under load, causing the sender to retry and re-fire the same automation repeatedly while events were silently dropped. I will provide: - The webhook source(s) and their delivery-retry and timeout behavior - The current handler and the automation work it performs inline - The queue/broker options available (SQS, Kafka, Redis Streams, Pub/Sub) - Peak burst rate, average processing time, and the acknowledgement SLA of the sender Your job: 1. **Fast-ack ingest** — design a thin receiver that verifies the signature, writes the RAW payload plus delivery metadata to [QUEUE], and returns 2xx within the sender's timeout. No business logic runs on the request path. 2. **Durability boundary** — specify exactly where the event becomes durable (queue write committed) and how the ack is gated on that write, so an accepted request is never lost. 3. **Async processing** — define the consumer that pulls from [QUEUE], performs the automation idempotently (keyed on the delivery ID), and handles partial failure. 4. **Backpressure** — describe what happens when the queue backs up: bounded concurrency, visibility/lease timeouts, and when to shed vs. buffer. 5. **Poison handling** — route repeatedly failing messages to a DLQ with enough context to replay after a fix, without blocking the main flow. 6. **Observability** — enumerate the metrics (ingest latency, queue depth, consumer lag, DLQ rate) and the alert thresholds that reveal decoupling failures early. Output as: an ingest/consumer sequence diagram description, the queue message schema, the idempotency and retry policy, and a rollout plan that shadow-runs the async path before cutting the synchronous handler over. Confirm the sender's exact timeout and retry semantics from its official docs before choosing the ack SLA; an ack that is too slow re-triggers duplicate deliveries.
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Why this prompt works
A webhook handler that does real work inline couples the sender’s timeout to your slowest downstream. The moment processing takes longer than the sender’s timeout, the sender assumes failure and retries — so a slow database, a rate-limited API, or a GC pause turns one event into a storm of duplicate deliveries, each firing the automation again. The prompt fixes the shape of the system rather than the symptom: split ingest from processing, make the ingest tier do almost nothing, and move all the work behind a durable queue where it can take as long as it needs.
The critical design point the prompt forces is the durability boundary. Engineers often ack fast by returning 2xx and then enqueueing, but if the process dies between the ack and the enqueue, the sender believes the event was accepted and never resends it — a silent loss that is nearly impossible to detect later. By requiring the ack to be gated on a committed queue write, the prompt makes “accepted” mean “durable,” which is the only version of fast-ack that is actually safe. It then pairs that at-least-once queue with idempotency on the delivery ID, because decoupling trades one failure mode (timeout-driven retries) for another (duplicate consumption) and only idempotency closes it.
Finally, the prompt refuses to let decoupling become a way to hide overload. Backpressure, DLQ routing, and consumer-lag metrics are required outputs because an async consumer that drains a backed-up queue at full concurrency simply moves the stampede downstream. The model can draft the receiver, schema, and consumer quickly, but you validate the ack SLA against the sender’s real retry semantics and shadow-run the async path first, since a decoupling bug fails quietly — events sit in a queue nobody is watching.
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