observability
observability
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 vs VictoriaMetrics: Performance, Storage, and Query Language Compared
I’ve spent years tinkering with monitoring tools—first as a hobbyist automating my smart home, then professionally while scaling observability pipelines. Two names kept popping up: Prometheus (the de facto standard) and VictoriaMetrics (the “faster, cheaper” alternative). But which one should you use? Let’s break it down with real-world benchmarks, storage deep-dives, and query language quirks. Why Compare Prometheus and VictoriaMetrics? Prometheus is the Kubernetes-monitoring darling, but VictoriaMetrics promises better performance with lower resource usage.
Building a Monitoring Stack with Prometheus, VictoriaMetrics, and Grafana: A Full Setup Guide
Ever found yourself drowning in server logs, wondering why your application crashed again at 3 AM? I’ve been there—more times than I’d like to admit. That’s why I built a robust monitoring stack with Prometheus, VictoriaMetrics, and Grafana to keep an eye on my systems. In this guide, I’ll walk you through setting up this powerful trio to collect, store, and visualize metrics at scale—without losing your sanity. Why This Stack?
Log Aggregation Using Grafana Loki: A Beginner’s Guide
Ever stared at a mountain of logs, desperately searching for that one error that crashed your service? I’ve been there—more times than I’d like to admit. That’s why I fell in love with Grafana Loki, a lightweight log aggregation system that pairs perfectly with Grafana for seamless troubleshooting. In this guide, I’ll walk you through setting up Loki to collect logs and correlate them with metrics in Grafana. By the end, you’ll have a powerful observability stack that makes debugging feel less like detective work and more like a well-guided tour.
How to Use Recording Rules in Prometheus to Reduce Load and Speed Up Queries
I remember the first time my Prometheus server groaned under the weight of a complex Grafana dashboard—it felt like asking a toddler to solve calculus. Dashboards loaded slower than my morning coffee brewed, and my CPU metrics looked like a stress test. That’s when I discovered recording rules, Prometheus’s secret weapon for taming expensive queries. Here’s how to use them effectively. Why Recording Rules Matter Recording rules let you precompute frequently used or resource-intensive queries and save the results as new time series.
How to Monitor API Health with Blackbox Exporter and Prometheus
Ever had an API go down silently, only to realize it after users started complaining? I’ve been there—more times than I’d like to admit. That’s why I now rely on Prometheus and Blackbox Exporter to proactively monitor API health. In this guide, I’ll walk you through setting up Blackbox Exporter to probe endpoints, track latency, and alert you the moment something goes sideways. Why Blackbox Exporter? Blackbox Exporter is like having a dedicated API watchdog.
High-Cardinality Metrics: Detection and Optimization in Prometheus and VictoriaMetrics
I remember the first time my Prometheus instance crashed spectacularly after I added a new exporter. The logs screamed about “out of memory” errors, and my Grafana dashboards turned into ghost towns. After some frantic debugging, I discovered the culprit: high-cardinality metrics. In this guide, I’ll share practical techniques I’ve learned for identifying and optimizing these metric monsters in both Prometheus and VictoriaMetrics. 💡 Pro Tip: High-cardinality metrics are like uninvited guests at a party - they consume all your resources and leave you with a mess to clean up.
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.
A Full Guide to Monitoring Strategies for Enterprises
Monitoring, stop being blind! After 3 years working in the monitoring team at the one of the biggest videogames company, I will unveil some secrets and strategies of monitoring for enterprises. These strategies can be applied to small or big companies depending on their context and uses. But what is monitoring? Monitoring is the fact of collecting information about an entity (application, machine, URL…) on a frequent basis. In general, this raw data is passed through visualization apps to give a visibility on the enterprise entities.
Vmalert: Revamp your Prometheus alert with microservices
Vmalert: the prometheus alerting microservice While collecting metrics is important for monitoring, the real end goal is not visualizing the data. The most critical goal of metrics is to create alerts in case of bad behaviors or incidents. In the Prometheus world, prometheus itself generates alerts based on the metrics collected. Let’s discover another product of VictoriaMetrics called Vmalert. To be honest, Vmalert is not a revolutionary product, exactly like Vmagent, but its strength is the ability to do 1 role: handling alerts with the minimum resources.