Most payment APIs work fine at low traffic. Then you throw a few thousand concurrent requests at them and everything gets interesting. Client retries turn into duplicate payments. Postgres starts cont
blog.raghavdev.in10 min read
Really enjoyed this post. Event-driven systems always look elegant on paper, but in practice the interesting part is how they behave under load, especially when multiple services, queues, and async steps all start interacting at once. I like that you focused on the load testing angle instead of only talking about the architecture itself, because that’s where real confidence in a system actually comes from.
In my own DevOps and cloud work, I’ve seen how important it is to test not just the happy path but also throughput, backpressure, retries, and failure handling in distributed systems. Once you move into event-driven designs, observability and timing become just as important as the code itself. A setup can look clean in the diagram, but the real story shows up when you push it under realistic traffic.
This was a solid write-up and a good reminder that performance testing is not just about finding bottlenecks — it’s about understanding the behavior of the whole system under stress.
Hayrullah Kar
Founder & Automation Strategist @MageSheet | Google Apps Script & AI Specialist.
running into that pgbouncer transaction pooling issue with hibernate prepared statements is a total rite of passage when scaling postgres. glad you called out the ordered uuid locking to dodge deadlocks. most people just throw async at it and pray, but decoupling the accept path while engineering for raw db-level limits is how you actually survive a million hits.