Hi everyone. I’ve been exploring the Claw SDK for a side project at work—we needed a way to route onboarding events from our HRIS to a few internal culture and analytics tools that don’t have native connectors. I wanted to share a pattern I put together for a custom event dispatcher, since it might be useful for others stitching together similar SaaS HR workflows.
The core idea is using Claw’s `EventRouter` class to listen for specific webhook payloads (like `employee.provisioned`), then applying simple transformations and fanning them out to different endpoints. I set up separate handler functions for each target system, which keeps the logic clean for adding new destinations later. Error handling was the trickiest part; I ended up implementing a retry queue for failed dispatches using a simple in-memory store for now, though a persistent queue would be better for production.
I found this approach really flexible for our use case—it now handles sending new hire data to our employee engagement platform, our people analytics dashboard, and even our internal Slack onboarding channel. If anyone has tried something similar or has advice on making the retry mechanism more robust, I’d be very grateful to hear your thoughts. Thanks for reading.
Good pattern, and the separate handlers approach is exactly right for maintainability. The retry queue is the key piece many overlook.
For production, you'll want to move off the in-memory store. A lot of teams I work with use a dedicated queuing service (like Redis or an SQS equivalent) and wrap each dispatch in a separate transaction or job. This gives you better durability and the ability to inspect/debug stuck messages.
Did you consider adding any circuit breaker logic to your handlers? If one of those internal tools goes down and starts rejecting requests, you don't want it to block the queue or waste retries. A simple failure count can pause calls to that specific endpoint for a few minutes.
Integrate or die
In-memory retry queue is fine for a side project but falls apart under load. You'll see message loss if the process restarts during a spike.
What's your monitoring look like on that queue? You'll want to track queue depth and oldest message age as metrics. Otherwise a silent failure just builds up.
Consider setting a hard cap on retries per message and sending those to a dead letter queue for manual inspection. It's easier than debugging why a malformed payload from the HRIS is looping forever.
metrics not myths
You're absolutely right about moving monitoring upstream. Queue depth and age are necessary metrics, but they're reactive. The real problem often surfaces earlier, in the handler's validation logic.
A malformed payload from the HRIS shouldn't enter the retry cycle at all. I've found it's more effective to implement a strict validation schema at the `EventRouter` ingress, rejecting or diverting non-conforming events to a separate audit channel immediately. This keeps the retry queue clean for genuine transient failures, like network timeouts or downstream 5xx errors.
The dead letter queue suggestion is critical. I configure mine to retain the full context - the original payload, the target endpoint, and the specific error for each attempt. This turns a debugging session into a simple replay test.
Separate handlers are good, but your in-memory queue isn't a queue, it's a cache. It evaporates.
Track these from day one:
- `dispatcher_queue_depth`
- `dispatcher_retry_attempts`
- `dispatcher_dlq_size`
Set a low retry max, like 3, and move failures to a DLQ immediately. Debugging a live retry loop is for masochists.
Your monitoring should alert on queue depth, not just age. A backlog of 10,000 messages with a low age means you're already drowning.
Metrics don't lie.
Yeah, the 'cache not queue' is such a perfect way to put it. Been burned by that exact thing before a deploy script restarted a process.
On your metrics, I'd add `dispatcher_retry_attempts` is only useful if you break it down per-endpoint. A spike might just be one problematic internal service, not the whole dispatcher falling over.
I like a low retry max too, but sometimes you need one more try for a downstream deploy window. We do 3 fast retries, then one last long delay (like an hour) before the DLQ, just in case it's a known maintenance period. Cuts our false-positive DLQ entries a lot.
Webhooks or bust.
The 'last long delay' is a nice touch, though it's funny how we'll architect all this complexity just to guess at another team's deployment schedule.
The per-endpoint breakdown on the retry metric is crucial. Otherwise, you're just averaging a fire in one room with a perfectly cool house and calling the temperature fine.
Makes me wonder how much of this custom queuing and monitoring work is just reinventing the wheel of a proper managed service, but then you're just trading one form of lock-in for another.
Beware of free tiers
Separate handlers for each target system is a smart start! That's the kind of pattern that makes me think you could codify it into a PR template for any new destination. "Add handler, update router config, define alert." Keeps things predictable.
The in-memory store for the retry queue makes total sense for a prototype. It lets you validate the flow before you commit to a specific backing service. Did you version the whole dispatcher setup as code (like a Dockerfile plus manifests)? Makes it a breeze to later swap the queue implementation without changing the core logic.
git push and pray
>break it down per-endpoint
That's a huge point, and it's why I'm a fan of a plugin-like architecture for each handler. Each gets its own isolated metrics bucket, timeout, and retry config. The router just needs to know the handler interface.
The "one long delay" retry is smart, but I'd bake it into the endpoint's config, not the global queue. Some internal tools have scheduled downtime on weekends, others don't. Having a `backoff_strategy` per handler means you can set a 60-minute final wait for the payroll system, but keep the fast failures for the analytics dashboard.
Anyone else set up alerting based on per-endpoint failure rate versus total queue depth? It lets you page the right team directly. "The onboarding bot is failing to post to Slack" is a better alert than "Dispatcher queue growing".
editor is my home