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🔍 Introduction
After understanding the fundamentals of Prometheus and
Grafana, it's time to set them up in your environment.
In this chapter, you’ll learn:
By the end, you’ll have a fully functional monitoring stack
ready to collect and visualize metrics!
🛠️ Part 1: Installing
Prometheus
Prometheus is lightweight and easy to install across various
platforms.
🔹 Installation Methods
Method |
Suitable for |
Binary download |
Quick setups, Linux
servers |
Docker container |
Testing,
containerized environments |
Kubernetes (Helm
chart) |
Cloud-native
environments |
Package Managers (apt/yum) |
Linux
distributions (limited versions) |
🔥 Installing Prometheus
via Binary (Linux)
bash
wget
https://github.com/prometheus/prometheus/releases/download/v2.48.0/prometheus-2.48.0.linux-amd64.tar.gz
bash
tar
xvfz prometheus-2.48.0.linux-amd64.tar.gz
cd
prometheus-2.48.0.linux-amd64
bash
./prometheus
--config.file=prometheus.yml
Prometheus starts by default at:
➡️ http://localhost:9090
✅ You now have Prometheus
running!
🔥 Installing Prometheus
via Docker
bash
docker run -p 9090:9090 prom/prometheus
➡️ Easy, fast deployment for
local testing or containerized environments.
📋 Default Prometheus
Directory Structure
Directory/File |
Purpose |
prometheus.yml |
Main configuration
file |
data/ |
Storage for
time-series metrics |
console_libraries/ |
Templates for UI |
consoles/ |
Web console
pages |
📚 Part 2: Configuring
Prometheus
The heart of Prometheus is its configuration file:
prometheus.yml.
🔹 Basic prometheus.yml
Example
yaml
global:
scrape_interval: 15s
scrape_configs:
- job_name: 'prometheus'
static_configs:
- targets: ['localhost:9090']
Field |
Meaning |
scrape_interval |
How often metrics are
scraped (default: 15s) |
job_name |
Logical group
name for a set of targets |
targets |
List of endpoints
exposing /metrics |
✅ Out of the box, Prometheus
monitors itself!
🔥 Adding Your Own
Applications to Prometheus
Suppose your app exposes metrics at http://myapp:8000/metrics.
Add this to scrape_configs:
yaml
-
job_name: 'myapp'
static_configs:
- targets: ['myapp:8000']
✅ Prometheus will now scrape your
app's metrics automatically.
🏗️ Part 3: Installing
Grafana
Grafana can be installed just as easily as
Prometheus.
🔹 Installation Methods
Method |
Suitable for |
Binary download |
Linux servers |
Docker container |
Local
dev/test |
Helm chart
(Kubernetes) |
Cloud-native clusters |
Package Manager |
Linux
distributions |
🔥 Installing Grafana via
Package Manager (Ubuntu/Debian)
bash
sudo
apt-get install -y software-properties-common
sudo
add-apt-repository "deb https://packages.grafana.com/oss/deb stable
main"
bash
sudo
apt-get update
sudo
apt-get install grafana
bash
sudo
systemctl start grafana-server
sudo
systemctl enable grafana-server
➡️ Access Grafana:
http://localhost:3000
Default credentials:
✅ You can (and should) change the
password on first login!
🔥 Installing Grafana via
Docker
bash
docker
run -d -p 3000:3000 grafana/grafana
📦 Part 4: Connecting
Grafana to Prometheus
After Grafana is running:
✅ Grafana now pulls data from
Prometheus!
📋 Quick Troubleshooting
Table
Issue |
Solution |
Grafana cannot
connect to Prometheus |
Check Prometheus is
running on port 9090 |
Prometheus UI not loading |
Ensure no
firewall blocks port 9090 |
Metrics not
appearing |
Check target /metrics
endpoint accessibility |
📈 Part 5: Setting Up Your
First Dashboard
Now let’s visualize some real data!
🔹 Steps to Create a
Dashboard
promql
rate(http_requests_total[5m])
✅ You have your first real-time
dashboard!
📋 Best Practices for
Dashboards
Tip |
Why It Matters |
Use Variables |
Make dashboards
dynamic for different environments |
Group related metrics |
Easier
readability |
Set alerts on
important panels |
Immediate notification
on issues |
Keep dashboards clean |
Avoid clutter
and overload |
🚀 Quick Commands Summary
Purpose |
Command |
Start Prometheus
binary |
./prometheus
--config.file=prometheus.yml |
Start Prometheus Docker |
docker run -p
9090:9090 prom/prometheus |
Start Grafana
binary |
systemctl start
grafana-server |
Start Grafana Docker |
docker run -d
-p 3000:3000 grafana/grafana |
🎯 Conclusion
You have now installed and configured Prometheus and
Grafana on your local machine or server.
From here, you can expand by:
In the next chapter, we'll dive into collecting custom
metrics, mastering PromQL, and advanced visualization techniques!
Your monitoring journey has officially begun. 🚀
Answer:
Prometheus is used to collect, store, and query time-series metrics from
applications, servers, databases, and services. It scrapes metrics endpoints at
regular intervals, stores the data locally, and allows you to query and trigger
alerts based on conditions like performance degradation or system failures.
Answer:
Grafana is used to visualize and analyze the metrics collected by
Prometheus. It allows users to build interactive, real-time dashboards
and graphs, making it easier to monitor system health, detect anomalies, and
troubleshoot issues effectively.
Answer:
Prometheus scrapes and stores metrics → Grafana queries Prometheus via APIs →
Grafana visualizes the metrics through dashboards and sends alerts if
conditions are met.
Answer:
You can monitor web applications, microservices, databases, APIs, Kubernetes
clusters, Docker containers, infrastructure resources (CPU, memory, disk),
and virtually anything that exposes metrics in Prometheus format (/metrics
endpoint).
Answer:
Prometheus has a built-in Alertmanager component that manages alert
rules, deduplicates similar alerts, groups them, and routes notifications (via
email, Slack, PagerDuty, etc.). Grafana also supports alerting from dashboards
when thresholds are crossed.
Answer:
PromQL (Prometheus Query Language) is a powerful query language used to
retrieve and manipulate time-series data stored in Prometheus. It supports
aggregation, filtering, math operations, and advanced slicing over time
windows.
Answer:
By default, Prometheus is optimized for short-to-medium term storage
(weeks/months). For long-term storage, it can integrate with systems
like Thanos, Cortex, or remote storage solutions to scale and retain
historical data for years.
Answer:
Yes! Prometheus and Grafana are commonly used together to monitor Kubernetes
clusters, capturing node metrics, pod statuses, resource usage, networking,
and service health. Tools like kube-prometheus-stack simplify this
setup.
Answer:
Grafana supports time-series graphs, gauges, bar charts, heatmaps, pie
charts, histograms, and tables. It also allows users to create dynamic
dashboards using variables and templating for richer interaction.
Answer:
Yes, both Prometheus and Grafana are open-source and free to use.
Grafana also offers paid enterprise editions with additional features
like authentication integration (LDAP, SSO), enhanced security, and advanced
reporting for larger organizations.
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