Queues and Webhooks
An update that lands once
A payment, an order, or a status change is recorded once. If the system goes down, the update is not lost. A call that did not come from the real sender is refused.
Examples
The engineering problem, in a few shapes. A commit, a signature, and a crash that must not drop the message.
- High-throughput flows. Kafka consumers commit the offset only after the write succeeds, so a crash does not drop the message.
- Third-party webhooks. The call is signed. A forged call does not land. The event arrives in seconds, instead of aging until an overnight job.
- Lighter pub/sub inside one system. Redis Streams or Redis Pub/Sub when the load does not need Kafka, and the queue already shares the rest of the app.
Technologies
The queue is a durability and throughput decision. The signature is how a webhook proves it came from the sender.
- Message queues and streaming. Kafka, SQS, RabbitMQ, and Redis Streams, picked based on the durability, retention, and throughput the workload needs.
- Offset commit. Kafka consumers commit the offset only after successful processing, so a crash does not drop messages.
- Webhook signing. HMAC on the call. A request that does not verify is rejected.
How we'd work on this
A technical situation
The offset is committed before the write. A crash drops the message. Or a webhook arrives unsigned, and the call still lands.
How we'd approach it
We commit the offset after the write succeeds, and we reject a webhook that does not verify. Kafka, SQS, RabbitMQ, or Redis Streams, picked for durability and throughput. The mix comes out of the diagnostic based on load, latency, and retention needs.
What you'd get
You leave with that path running on your events, the code in a repo you own, and you decide whether to continue.
Questions about queues and webhooks
For high-throughput real-time flows we use Kafka, with consumers that commit offsets only after successful processing so a crash does not drop messages. For lighter pub/sub inside a single system, Redis Streams and Redis Pub/Sub are cheaper and easier to operate. For third-party systems, we prefer webhooks with HMAC signing over polling. The mix comes out of the diagnostic based on load, latency, and retention needs.
For anything an end user notices (notifications, order status, account balance), yes. Webhooks deliver in seconds instead of letting data age until the 3am job. For accounting reconciliation and closed reports, batch still has its place.
It depends on durability and throughput. SQS for AWS workloads where you want minimal ops. RabbitMQ when you need topic routing or per-message acknowledgments. Redis Streams when the queue already shares cache with the rest of the app. The choice goes into the technical plan based on the actual load.
What you get
- A working fix on your data
- You keep the code
- 2 to 5 weeks when the owner and the files are available
Let's talk about your case
Talk to the Lab
Tell us the problem in a few lines. What the team does today, and where it breaks. We reply the same business day.
What happens next
- We reply the same business day
- Diagnostic in 3-5 days
- Working fix in 1-3 weeks. Technical plan in the last days, with the work done.
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