Draw the architecture of almost any production system on a whiteboard – a food delivery app, a bank’s API, a video platform – and somewhere near the left edge, between the users and everything else, you’ll draw the same box. It’s rarely the interesting part of the design.
Imagine you run an online store.
It’s Monday morning, and your inbox is filling up. One customer wants to know where their order is. Another says they’ve been charged twice. Someone else cannot log in to their account, and a fourth customer is asking for a refund.
A database that is down gets fixed fast. A database that is merely slow quietly takes your whole system with it. Here is how that happens, and how to stop it.
Arjun’s traces from last week capture every raw prompt and tool observation flowing through the system. This week, Priya finds out why that matters for more than debugging.
A monolith doesn’t need to ask where its own functions live – they’re all compiled into the same process, at the same address, forever.
“Our database is struggling, so let’s add read replicas” and “Our database is struggling, so let’s shard it” get thrown around in design interviews and architecture reviews almost interchangeably – as if they were two flavors of the same fix. They aren’t.
Something has changed in the way we talk to AI.
The data structure server hiding behind your caching layer – and where it quietly does its best work
Kubernetes 1.37 represents a significant convergence of improvements built on real production experience. Learn what changed from 1.35 through 1.36 to reach this release, understand the actual breaking changes, and discover why this release matters for your infrastructure.
If you are a DevOps engineer, there is a good chance that you already know more about system design than you think.
You have probably configured a load balancer at some point.