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Netdata

netdata/netdata

Netdata is an observability and monitoring system for near real-time metrics from servers, services, and applications.

Forks 6,448
Author netdata
Language C
License GPL-3.0
Synced 2026-06-11

What it is

Netdata is an observability tool for servers, services, and applications. Teams use it when they need to quickly see what is happening on a machine or in a cluster: where load increased, why disk latency changed, which service slowed down, and which alerts have fired.

The netdata/netdata repository has been on GitHub since 2013. The project is written mostly in C, is distributed under GPL-3.0, and is part of the CNCF ecosystem. It includes an agent, collectors, alerting rules, node-parenting modes, and an optional cloud component.

What is inside the repository

Inside are the Netdata agent, collectors for Linux and popular services, metric storage, a web interface, alerting rules, and documentation. The public setup path is agent installation, collector configuration, alerts, optional parent nodes, and optional Netdata Cloud connection.

Checking a local agent

This example does not install Netdata. It shows common commands after installation: check the service and open the local agent dashboard. In production, installation should follow the official instructions for the target system.

Language: Bash
systemctl status netdata
# The local agent dashboard is usually opened at:
# http://localhost:19999

Where it helps

Netdata is useful for fast investigations: whether a host is overloaded, latency is growing, memory is being consumed by one process, a collector failed, or disk response time changed. Smaller teams benefit from getting many metrics immediately after installing the agent.

In larger systems Netdata is often one observability layer. It can sit next to Prometheus, Grafana, logs, and traces: Netdata gives a live operational slice, while other tools handle long history, reporting, and company-wide context.

Strengths and tradeoffs

The strength is speed of insight. A team does not need to design hundreds of dashboards before seeing useful CPU, memory, network, disk, and service metrics. That makes it helpful before a perfect observability platform exists.

The tradeoff is that monitoring does not fix architecture by itself. Alerts need to match real risks, retention must fit team requirements, and dashboard access must follow security rules. Otherwise any monitoring system becomes attractive noise.