Grafana & Prometheus - Practical Guide

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In DevOps, monitoring is essential for keeping systems healthy and identifying issues early. Grafana and Prometheus are two powerful tools that help DevOps teams visualize and track key metrics, ensuring smooth application performance. This guide will introduce you to these tools and show how to set up a basic monitoring stack using Grafana and Prometheus.

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Prometheus is an open-source tool designed to collect and store metrics over time. Known for its flexibility and powerful alerting capabilities, Prometheus is a favorite for monitoring various systems, from microservices to databases.

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• Data Collection and Storage: Prometheus collects data from “exporters” that monitor specific applications, systems, or databases, storing metrics over time.
• Real-Time Alerts: By integrating with Alertmanager, Prometheus sends alerts based on rules you set, helping you catch issues early.
• Broad Compatibility: Use Prometheus with exporters for a wide range of services, including Node, Redis, MySQL, and custom applications.

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1. Install Prometheus: Set it up in your environment (Docker, Kubernetes, or a standalone server).
2. Define Exporters: Configure exporters to gather metrics from your systems, such as CPU usage or memory.
3. Create Alert Rules: Set up rules to notify your team when key metrics, like CPU usage or disk space, cross specific thresholds.

For further guidance, visit the Prometheus documentation.

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Grafana is a visualization tool that works seamlessly with Prometheus, allowing you to create interactive dashboards and turn raw data into easy-to-read charts. Grafana dashboards help DevOps teams see trends and potential issues at a glance.

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• Customizable Dashboards: Create and organize dashboards to display key metrics in real-time.
• Multiple Data Sources: Grafana supports various data sources, allowing you to pull in metrics from multiple systems if needed.
• Alerts on Dashboards: Set alerts on individual dashboard panels to get notified when certain thresholds are reached.

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1. Connect Prometheus as a Data Source: Add Prometheus as a data source in Grafana to access your collected metrics.
2. Build a Dashboard: Create visualizations (like line graphs and gauges) for essential metrics, including CPU, memory, and disk usage.
3. Set Alerts: Configure alerts on dashboards to receive notifications if key metrics exceed thresholds.

For further information, see the Grafana documentation.

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Combining Grafana and Prometheus creates a powerful monitoring stack that’s easy to set up and maintain. Prometheus handles data collection and alerting, while Grafana provides real-time visualization and alerting on the frontend.

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1. Install Prometheus on your preferred environment and configure exporters for each system you want to monitor.
2. Set Up Grafana and connect it to Prometheus as the data source.
3. Create Custom Dashboards: Build dashboards for system health metrics, application-specific metrics, and any custom metrics relevant to your operations.
4. Configure Alerts: Use Prometheus for primary alerts and add Grafana alerts on specific dashboards for additional visibility.

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Grafana and Prometheus together provide a robust solution for monitoring in DevOps. With Prometheus collecting real-time metrics and Grafana displaying them in clear dashboards, you can track performance, troubleshoot issues, and stay proactive in maintaining system health.

Below, you’ll find a list of key Prometheus queries to set up on your Grafana dashboard, which will help you monitor essential metrics easily.

If you have any questions or tips on setting up Grafana and Prometheus? Share your insights in the comments!

Additional Resources

• Grafana Documentation: Learn more about Grafana’s features and setup options.
• Prometheus Documentation: Explore Prometheus configurations, exporters, and alerting.

Grafana Dashboard Metrics Cheat Sheet

Save this article as a quick reference for setting up essential metrics on your Grafana dashboard using Prometheus queries:

• CPU Usage:

100 - (avg(irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)        

• Memory Usage:

(node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes) / node_memory_MemTotal_bytes * 100        

• Disk Usage:

(node_filesystem_size_bytes{fstype!="tmpfs",fstype!="rootfs"} - node_filesystem_avail_bytes{fstype!="tmpfs",fstype!="rootfs"}) / node_filesystem_size_bytes{fstype!="tmpfs",fstype!="rootfs"} * 100        

• Pod Restarts (Kubernetes):

increase(kube_pod_container_status_restarts_total[5m])        

• CPU Usage by Application:

sum(rate(container_cpu_usage_seconds_total{container_name="your_app_container"}[5m]))        

• Memory Usage by Application:

container_memory_usage_bytes{container_name="your_app_container"}        

• Network Traffic (Incoming):

rate(node_network_receive_bytes_total{device!="lo"}[5m])        

• Network Traffic (Outgoing):

rate(node_network_transmit_bytes_total{device!="lo"}[5m])        

• Disk Read Rate:

rate(node_disk_read_bytes_total[5m])        

• Disk Write Rate:

rate(node_disk_written_bytes_total[5m])        

• Load Average (1 Minute):

node_load1        

• Uptime:

time() - node_boot_time_seconds        

This layout above provides a structured view for quick reference, enabling teams to monitor and respond to system health at a glance. Adding these dashboard numbers will make navigation smoother.

Take a look below to explore examples of Grafana dashboards that can elevate your DevOps monitoring capabilities.