A New Node.js library When you scale a Node.js backend…
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A New Node.js library
When you scale a Node.js backend using `node:cluster`, you run into a surprisingly annoying problem:
**How do you rate-limit external APIs like Stripe or OpenAI while making sure all workers share the same rate limit?**
The first solution that comes to mind is usually: *"Just use Redis."*
But does it really make sense to introduce additional infrastructure complexity, network overhead, and an entire external dependency just to coordinate a few API calls coming from the same server?
That's exactly why I built **cluster-rate-limiter**.
A Node.js library that gives you coordinated rate limiting across workers, powered entirely by Node.js's native IPC messaging, with no Redis and absolutely zero external dependencies.
Rather than diving into the internals here, here's what you get out of the box:
* **Zero Setup:** Install it and start using it. No additional infrastructure required.
* **Developer-Friendly API:** The `run()` method accepts your `fetch` call or any function directly, along with all its arguments. No complicated callback patterns.
* **Two Types of Limits:** Supports both Semaphore-based concurrency limits and Duration-based rate limits for enforcing strict time windows, such as those imposed by Stripe APIs.
* **Typed Errors:** If a request times out waiting for permission, the library throws a clear, specific error that you can easily translate into an HTTP `429 Too Many Requests` response.
And when it comes to performance, the core data structure for tracking request timestamps uses a **zero-allocation approach** built around a `Float64Array` ring buffer.
This eliminates the need for repeated array reallocations and significantly reduces garbage collection pressure under heavy load.
If you're interested in the low-level details, I've explained the ring buffer implementation and how it reduces memory overhead in the README.
If you're running a clustered Node.js backend and want to avoid the overhead of introducing Redis just for rate limiting, check out the links below, explore the architecture, and let me know what you think!
#js #programming #dev #technology
When you scale a Node.js backend using `node:cluster`, you run into a surprisingly annoying problem:
**How do you rate-limit external APIs like Stripe or OpenAI while making sure all workers share the same rate limit?**
The first solution that comes to mind is usually: *"Just use Redis."*
But does it really make sense to introduce additional infrastructure complexity, network overhead, and an entire external dependency just to coordinate a few API calls coming from the same server?
That's exactly why I built **cluster-rate-limiter**.
A Node.js library that gives you coordinated rate limiting across workers, powered entirely by Node.js's native IPC messaging, with no Redis and absolutely zero external dependencies.
Rather than diving into the internals here, here's what you get out of the box:
* **Zero Setup:** Install it and start using it. No additional infrastructure required.
* **Developer-Friendly API:** The `run()` method accepts your `fetch` call or any function directly, along with all its arguments. No complicated callback patterns.
* **Two Types of Limits:** Supports both Semaphore-based concurrency limits and Duration-based rate limits for enforcing strict time windows, such as those imposed by Stripe APIs.
* **Typed Errors:** If a request times out waiting for permission, the library throws a clear, specific error that you can easily translate into an HTTP `429 Too Many Requests` response.
And when it comes to performance, the core data structure for tracking request timestamps uses a **zero-allocation approach** built around a `Float64Array` ring buffer.
This eliminates the need for repeated array reallocations and significantly reduces garbage collection pressure under heavy load.
If you're interested in the low-level details, I've explained the ring buffer implementation and how it reduces memory overhead in the README.
If you're running a clustered Node.js backend and want to avoid the overhead of introducing Redis just for rate limiting, check out the links below, explore the architecture, and let me know what you think!
#js #programming #dev #technology
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