what is prometheus fr?

Prometheus is a monitoring system that helps you keep track of how your applications and infrastructure are performing. Instead of waiting for something to break, it continuously collects metrics, like CPU usage, memory, request counts, or error rates, from your services at regular intervals. If something goes wrong, like a server running out of disk space or error rates spiking, it can trigger alerts so your team finds out before users do. At a high level, it works by periodically reaching out to your applications over HTTP and asking them for their current metrics. Your applications just need to expose a simple endpoint that reports numbers, and it takes care of storing and querying that data over time. It stores everything as a time series database, meaning every data point is recorded with a timestamp and optional labels, so you can slice and filter the data later. It also includes a query language that lets you ask questions like "what's the 95th percentile response time for our checkout service over the past hour?" and graph the results. This is built for engineering teams running services that need to stay healthy, think a SaaS company with dozens of microservices, a team managing a fleet of servers, or anyone who needs to know in real time whether their systems are up and performing well. If you're a founder or PM, this is the tool your engineers might reach for when they need to set up dashboards showing "requests per second" or "average latency," and configure alerts that page someone on call when error rates cross a threshold. One notable design choice is that each server operates independently rather than relying on a shared distributed storage layer. This keeps things simpler to run but means scaling to very large environments typically involves a "federation" approach, where one server collects summarized data from many others. It also primarily pulls data from targets rather than waiting for them to push it in, which gives it central control over what gets monitored and how often.