Prometheus is one of the most popular open-source tools for monitoring. Teams use it to track the health and usage of servers, applications, and databases. In this guide, let us break down what Prometheus is, what problems it solves, and how you can set it up in your environment.
What is Prometheus?
Prometheus is a free tool that collects and stores numerical data from computers, apps, or systems. It keeps records over time. You can use these records to spot problems, plan for growth, and know when a service is not working well.
Why Use Prometheus?
Reliable: Prometheus keeps working even if other tools go down.
Scalable: You can use it for a single server or across thousands of machines.
Flexible: Track almost anything, from CPU use to web requests, by adding labels and custom metrics.
Open source: Use it and share it freely.
How Does Prometheus Work?
Prometheus works using a simple process , lets break it down below:
Collects Data: Prometheus asks target systems for new data every so often (usually every 15 seconds).
Stores Data: It saves this data in a time-series database. Every value is tied to a moment in time and labeled for easy searching.
Lets You Query: You can search the time-series database with a special language called PromQL.
Helps Trigger Alerts: When numbers go out of normal range, Prometheus can let you know.
Prometheus usually pulls ("scrapes") metrics from systems over HTTP, looking for endpoints that format data in a way it understands.
Key Components of Prometheus
[ Exporter / App ] → Prometheus → [ Rules / Alerts ] → Alertmanager
↓
Grafana
1. Prometheus Server
This is the heart of the system. It pulls data from target endpoints and stores it.
2. Exporters
Most software doesn’t send Prometheus-style data on its own. Exporters gather data from apps, systems, or databases and present it in a way Prometheus can read.
Example: The Node Exporter gets stats from Linux systems.
3. Service Discovery
Prometheus can automatically find new services to monitor by integrating with tools like Kubernetes, Consul, or by using simple static lists.
4. Alertmanager
Handles alerts from the Prometheus server. You decide who gets notified (email, Slack, PagerDuty) and when.
5. Visualization Tools
Tools like Grafana connect to Prometheus and turn the data into dashboards and charts.
Data Model and Queries
Prometheus stores each metric as a time-stamped value. Every metric has a name and a set of labels.
Labels add context, like:
Host name
App name
Environment (test, dev, prod)
You can pull up data on any label using PromQL. Example:
http_requests_total{instance="web-1", method="GET"}
(This query fetches the total number of HTTP GET requests from a specific web server.)
Sample Prometheus Configuration (YAML)
Below is a simple config file for Prometheus. It tells Prometheus to scrape its own metrics and also those from a "node-exporter."
global:
scrape_interval: 15s
scrape_configs:
- job_name: 'prometheus'
static_configs:
- targets: ['localhost:9090']
- job_name: 'node-exporter'
static_configs:
- targets: ['nodehost:9100']
The scrape_interval says how often to gather data.
Each job_name is a group of targets (endpoints) to collect from.
Example Data Flow
Node Exporter on a server exposes metrics at http://nodehost:9100/metrics.
Prometheus server scrapes this endpoint every 15 seconds.
The data is saved with timestamps.
You use PromQL or Grafana to make charts and alerts.
What Can You Monitor with Prometheus?
- CPU, memory, and disk usage
- Server and app uptime
- Number of users or web requests
- Database query speed
- Custom app statistics
- Almost anything you can count or measure over time is trackable.
Common Use Cases
Keeping an eye on microservices in Kubernetes
Tracking website traffic
Monitoring cloud servers
Alerting when resources are low
Finding patterns in app performance
Quick Start: Running Prometheus
You can run Prometheus directly on your machine, or inside a container.
Docker Example:
docker run -p 9090:9090 -v /path/to/prometheus.yml:/etc/prometheus/prometheus.yml prom/prometheus
Replace /path/to/prometheus.yml with where you put your config file.
Visit http://localhost:9090 in your browser to use the web UI.
Prometheus Best Practices
Use clear, simple metric names and labels.
Keep scrape intervals consistent across environments.
Always set up alerting, even if you don’t use it much at first.
Don’t overload Prometheus: avoid scraping thousands of metrics you don’t need.
Clean up old data or set up remote storage for big, long-term jobs.
Example Queries
Find average CPU use over time:
avg(rate(cpu_usage_seconds_total[5m]))
Get total web errors today:
sum(increase(http_errors_total[1d]))
Limitations
- Prometheus isn’t ideal for storing logs or large blobs of data.
- It focuses on numeric, time-series data.
- For very large or long-term storage, combine with remote storage or other monitoring solutions.
In Summary
Prometheus makes it simple to keep watch over your infrastructure. With core features like easy data scraping, flexible queries, and alerting, it’s a solid choice for anyone wanting to spot problems before they grow. Combine it with tools like Grafana for a visual view of your system’s health.
Explore, experiment, and after a bit of practice, Prometheus will feel like a natural part of your system-watching toolkit.
https://prometheus.io/docs/introduction/overview/
https://prometheus.io/docs/prometheus/latest/getting_started/
