A system statistics collection daemon that gathers metrics from various sources and provides mechanisms to store and monitor them.
collectd is a system statistics collection daemon that gathers performance metrics from various sources including CPU, memory, disk, network interfaces, applications, and hardware sensors. It solves the problem of centralized metric collection by providing a lightweight, extensible solution that can monitor diverse systems and forward data to various storage backends.
System administrators, DevOps engineers, and infrastructure teams who need to monitor server performance, collect time-series metrics, and integrate with monitoring stacks.
Developers choose collectd for its extensive plugin ecosystem, efficient resource usage, and flexibility in both data collection and output destinations. Its mature codebase, cross-platform support, and ability to integrate with virtually any monitoring system make it a reliable foundation for metric collection pipelines.
The system statistics collection daemon. Please send Pull Requests here!
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Over 100 built-in plugins collect metrics from system resources, applications, databases, and hardware sensors, as detailed in the extensive README list.
Supports writing data to RRD files, CSV, databases like PostgreSQL, and modern systems like Prometheus and Kafka, enabling seamless integration with various monitoring stacks.
Runs as a persistent, multi-threaded daemon designed for minimal overhead, avoiding startup costs and using caching to reduce system load, per the project philosophy.
Works on Linux, BSD, macOS, Solaris, and Windows via Cygwin, making it suitable for heterogeneous server environments without major modifications.
Requires manual editing of configuration files for each plugin and output, which can be error-prone and time-consuming, despite claims of easy setup.
Many plugins rely on external libraries (e.g., libcurl, libpq), complicating installation and maintenance, especially when compiling from source.
Focuses solely on data collection; users must integrate with separate tools like Graphite or Grafana for dashboards and graphing, adding to the setup burden.