promql
promql
Using PromQL to Analyze CPU, Memory, and Network Metrics Effectively
If you’ve ever stared at a Grafana dashboard wondering why your server’s CPU is spiking like a caffeinated squirrel, you’re not alone. Prometheus and PromQL are my go-to tools for making sense of infrastructure metrics—once you get the hang of them, they’re like having X-ray vision for your systems. In this guide, I’ll walk you through writing effective PromQL queries to monitor CPU, memory, and network performance. Whether you’re debugging a mysterious latency issue or just keeping an eye on resource usage, these tips will save you hours of head-scratching.
Prometheus Anomaly detection: Z-Score in PromQL
Monitoring HTTP request rates is one of the most basic yet essential tasks in observability. A sudden spike might indicate a traffic surge or even a DDoS attack, while a sudden drop could signal a backend failure. Static thresholds work, but they often miss subtle patterns or raise too many false alarms. A better way is to use statistical anomaly detection—specifically Z-score based alerts in Prometheus. In this post, we’ll walk through how to set up a Z-score PromQL alert to detect anomalies in HTTP request rates using only Prometheus and native PromQL.