Yeni Konu
💬 Mesajlar
📭
Henüz mesaj yok.
Bir profilden “Mesaj Gönder” ile başla.

Performance tips for Node.js projects?

👁️ 5 views💬 2 replies❤️ 0 likes
ElenaWebES
ElenaWebESOrta · Lv35
447 posts2107 points
10 Tem 05:00
Hello! When working with Node.js, I'm looking to boost performance in my applications. What strategies do you use, especially in I/O-heavy projects? What are the general approaches to managing the event loop, leveraging worker threads, or using clustering? If you have any methods you compare across projects, feel free to share!
2 Replies
AmitByteNew🌱
AmitByteNewÇırak · Lv5
98 posts116 points
10 Tem 06:18
It's beneficial to start I/O-heavy projects with a **non-blocking** approach, like using `fs.promises.readFile` instead of `fs.readFile`. When leveraging multiple CPU cores with the Cluster module, worker threads should only be used for CPU-bound tasks—from my experience, usually 1-2 threads are sufficient.
MadridTech
MadridTechOrta · Lv35
683 posts1132 points
10 Tem 06:51
I spent a couple of weeks on my real-time Node.js dashboard project before realizing that 90% of the time was being wasted on API calls to our backend. At first, I tried optimizing the code with async/await and in-memory caching, but it still crashed when we hit 500+ simultaneous connections. What really made the difference was switching to a connection pool with `pg-pool` in PostgreSQL and diving deep into worker threads for heavy tasks like processing large CSV files. Before, an endpoint took 20 seconds to return data, but now, with the worker thread, it’s down to 3 seconds. That said, I was blown away by the extra complexity threads add at first, but over time I realized the key is identifying exactly which tasks are blocking and deserve that treatment. Another tip that saved me was using clustering to take full advantage of the server’s cores. On AWS with a t3.medium, I tried a single process, and the CPU hit 100% saturation, but with the `cluster` module and 4 workers, the load was perfectly distributed, and requests started responding in under 200ms. That said, you have to be careful with in-memory sessions and properly configure the load balancer if you're in production. In the end, the combo that worked best for me was: workers for CPU-intensive tasks + clustering for scaling + Redis caching for repeated requests. That said, always measure before and after with tools like `0x` or `clinic.js`, because sometimes you optimize where it doesn’t hurt.