While this repository has been inactive for some time, this formal notice, issued on December 10, 2024, serves as the official declaration to clarify the situation. Consequently, this repository and all associated resources (including related projects, code, documentation, and distributed packages such as Docker images, PyPI packages, etc.) are now explicitly declared unmaintained and abandoned.
I would like to remind everyone that this project’s free license has always been based on the principle that the software is provided "AS-IS", without any warranty or expectation of liability or maintenance from the maintainer. As such, it is used solely at the user's own risk, with no warranty or liability from the maintainer, including but not limited to any damages arising from its use.
Due to the enactment of the Cyber Resilience Act (EU Regulation 2024/2847), which significantly alters the regulatory framework, including penalties of up to €15M, combined with its demands for unpaid and indefinite liability, it has become untenable for me to continue maintaining all my Open Source Projects as a natural person. The new regulations impose personal liability risks and create an unacceptable burden, regardless of my personal situation now or in the future, particularly when the work is done voluntarily and without compensation.
No further technical support, updates (including security patches), or maintenance, of any kind, will be provided.
These resources may remain online, but solely for public archiving, documentation, and educational purposes.
Users are strongly advised not to use these resources in any active or production-related projects, and to seek alternative solutions that comply with the new legal requirements (EU CRA).
Using these resources outside of these contexts is strictly prohibited and is done at your own risk.
Regarding the potential transfer of the project to another entity, discussions are ongoing, but no final decision has been made yet. As a last resort, if the project and its associated resources are not transferred, I may begin removing any published resources related to this project (e.g., from PyPI, Docker Hub, GitHub, etc.) starting March 15, 2025, especially if the CRA’s risks remain disproportionate.
cAdvisor (Container Advisor) provides container users an understanding of the resource usage and performance characteristics of their running containers. It is a running daemon that collects, aggregates, processes, and exports information about running containers. Specifically, for each container it keeps resource isolation parameters, historical resource usage, histograms of complete historical resource usage and network statistics. This data is exported by container and machine-wide.
cAdvisor has native support for Docker containers and should support just about any other container type out of the box. We strive for support across the board so feel free to open an issue if that is not the case. cAdvisor's container abstraction is based on lmctfy's so containers are inherently nested hierarchically.
To quickly tryout cAdvisor on your machine with Docker, we have a Docker image that includes everything you need to get started. You can run a single cAdvisor to monitor the whole machine. Simply run:
VERSION=v0.36.0 # use the latest release version from https://github.com/google/cadvisor/releases
sudo docker run \
--volume=/:/rootfs:ro \
--volume=/var/run:/var/run:ro \
--volume=/sys:/sys:ro \
--volume=/var/lib/docker/:/var/lib/docker:ro \
--volume=/dev/disk/:/dev/disk:ro \
--publish=8080:8080 \
--detach=true \
--name=cadvisor \
--privileged \
--device=/dev/kmsg \
gcr.io/cadvisor/cadvisor:$VERSION
cAdvisor is now running (in the background) on http://localhost:8080. The setup includes directories with Docker state cAdvisor needs to observe.
Note: If you're running on CentOS, Fedora, or RHEL (or are using LXC), take a look at our running instructions.
We have detailed instructions on running cAdvisor standalone outside of Docker. cAdvisor running options may also be interesting for advanced usecases. If you want to build your own cAdvisor Docker image, see our deployment page.
For Kubernetes users, cAdvisor can be run as a daemonset. See the instructions for how to get started, and for how to kustomize it to fit your needs.
See the more detailed instructions in the build page. This includes instructions for building and deploying the cAdvisor Docker image.
cAdvisor supports exporting stats to various storage plugins. See the documentation for more details and examples.
cAdvisor exposes a web UI at its port:
http://<hostname>:<port>/
See the documentation for more details.
cAdvisor exposes its raw and processed stats via a versioned remote REST API. See the API's documentation for more information.
There is also an official Go client implementation in the client directory. See the documentation for more information.
cAdvisor aims to improve the resource usage and performance characteristics of running containers. Today, we gather and expose this information to users. In our roadmap:
- Advise on the performance of a container (e.g.: when it is being negatively affected by another, when it is not receiving the resources it requires, etc).
- Auto-tune the performance of the container based on previous advise.
- Provide usage prediction to cluster schedulers and orchestration layers.
Contributions, questions, and comments are all welcomed and encouraged! cAdvisor developers hang out on Slack in the #sig-node channel (get an invitation here). We also have discuss.kubernetes.io.
Please reach out and get involved in the project, we're actively looking for more contributors to bring on board!