running-clair.md

Running Clair

The following document outlines possible ways to deploy Clair both on your local machine and to a cluster of machines.

Official Container Repositories

Clair is officially packaged and released as a container.

• quay.io/coreos/clair - Stable releases
• quay.io/coreos/clair-jwt - Stable releases with an embedded instance of jwtproxy
• quay.io/coreos/clair-git - Development releases

Common Architecture

Registry Integration

Clair can be integrated directly into a container registry such that the registry is responsible for interacting with Clair on behalf of the user. This type of setup avoids the manual scanning of images and creates a sensible location to which Clair's vulnerability notifications can be propagated. The registry can also be used for authorization to avoid sharing vulnerability information about images to which one might not have access.

CI/CD Integration

Clair can be integrated into a CI/CD pipeline such that when a container image is produced, the step after pushing the image to a registry is to compose a request for Clair to scan that particular image. This type of integration is more flexible, but relies on additional components to be setup in order to secure.

Deployment Strategies

NOTE: These instructions demonstrate running HEAD and not stable versions.

The following are community supported instructions to run Clair in a variety of ways. A PostgreSQL 9.4+ database instance is required for all instructions.

Cluster

Kubernetes (Helm)

If you don't have a local Kubernetes cluster already, check out minikube. This assumes you've already ran helm init, you have access to a currently running instance of Tiller and that you are running the latest version of helm.

git clone https://github.com/coreos/clair
cd clair/contrib/helm
cp clair/values.yaml ~/my_custom_values.yaml
vi ~/my_custom_values.yaml
helm dependency update clair
helm install clair -f ~/my_custom_values.yaml

Local

Docker Compose

$ curl -L https://raw.githubusercontent.com/coreos/clair/master/contrib/compose/docker-compose.yml -o $HOME/docker-compose.yml
$ mkdir $HOME/clair_config
$ curl -L https://raw.githubusercontent.com/coreos/clair/master/config.yaml.sample -o $HOME/clair_config/config.yaml
$ $EDITOR $HOME/clair_config/config.yaml # Edit database source to be postgresql://postgres:password@postgres:5432?sslmode=disable
$ docker-compose -f $HOME/docker-compose.yml up -d

Docker Compose may start Clair before Postgres which will raise an error. If this error is raised, manually execute docker-compose start clair.

Docker

$ mkdir $PWD/clair_config
$ curl -L https://raw.githubusercontent.com/coreos/clair/master/config.yaml.sample -o $PWD/clair_config/config.yaml
$ docker run -d -e POSTGRES_PASSWORD="" -p 5432:5432 postgres:9.6
$ docker run --net=host -d -p 6060-6061:6060-6061 -v $PWD/clair_config:/config quay.io/coreos/clair-git:latest -config=/config/config.yaml

Source

To build Clair, you need to latest stable version of Go and a working Go environment. In addition, Clair requires some additional binaries be installed on the system $PATH as runtime dependencies:

• git
• bzr
• rpm
• xz

$ go get github.com/coreos/clair
$ go install github.com/coreos/clair/cmd/clair
$ $EDITOR config.yaml # Add the URI for your postgres database
$ ./$GOPATH/bin/clair -config=config.yaml

Troubleshooting

I just started up Clair and nothing appears to be working, what's the deal?

During the first run, Clair will bootstrap its database with vulnerability data from the configured data sources. It can take several minutes before the database has been fully populated, but once this data is stored in the database, subsequent updates will take far less time.

I'm seeing Linux kernel vulnerabilities in my image, that doesn't make any sense since containers share the host kernel!

Many container base images using Linux distributions as a foundation will install dummy kernel packages that do nothing but satisfy their package manager's dependency requirements. The Clair developers have taken the stance that Clair should not filter results, providing the most accurate data as possible to user interfaces that can then apply filters that make sense for their users.