While experimenting with Fedora Atomic, I was looking for an elegant way to automatically deploy Atomic into an OpenStack environment and then automatically schedule some Docker containers on the Atomic host. This post describes my solution.

Like many other cloud-targeted distributions, Fedora Atomic runs cloud-init when the system boots. We can take advantage of this to configure the system at first boot by providing a user-data blob to Nova when we boot the instance. A user-data blob can be as simple as a shell script, and while we could arguably mash everything into a single script it wouldn’t be particularly maintainable or flexible in the face of different pod/service/etc descriptions.

In order to build a more flexible solution, we’re going to take advantage of the following features:

  • Support for multipart MIME archives.

    Cloud-init allows you to pass in multiple files via user-data by encoding them as a multipart MIME archive.

  • Support for a custom part handler.

    Cloud-init recognizes a number of specific MIME types (such as text/cloud-config or text/x-shellscript). We can provide a custom part handler that will be used to handle MIME types not intrinsincally supported by cloud-init.

A custom part handler for Kubernetes configurations

I have written a custom part handler that knows about the following MIME types:

  • text/x-kube-pod
  • text/x-kube-service
  • text/x-kube-replica

When the part handler is first initialized it will ensure the Kubernetes is started. If it is provided with a document matching one of the above MIME types, it will pass it to the appropriate kubecfg command to create the objects in Kubernetes.

Creating multipart MIME archives

I have also created a modified version of the standard write-multipart-mime.py Python script. This script will inspect the first lines of files to determine their content type; in addition to the standard cloud-init types (like #cloud-config for a text/cloud-config type file), this script recognizes:

  • #kube-pod for text/x-kube-pod
  • #kube-service for text/x-kube-service
  • #kube-replica for text/x-kube-replca

That is, a simple pod description might look something like:

id: dbserver
    version: v1beta1
    id: dbserver
    - image: mysql
      name: dbserver
        - name: MYSQL_ROOT_PASSWORD
          value: secret

Putting it all together

Assuming that the pod description presented in the previous section is stored in a file named dbserver.yaml, we can bundle that file up with our custom part handler like this:

$ write-mime-multipart.py \
  kube-part-handler.py dbserver.yaml > userdata

We would then launch a Nova instance using the nova boot command, providing the generated userdata file as an argument to the user-data command:

$ nova boot --image fedora-atomic --key-name mykey \
  --flavor m1.small --user-data userdata my-atomic-server

You would obviously need to substitute values for --image and --key-name that are appropriate for your environment.

Details, details

If you are experimenting with Fedora Atomic 21, you may find out that the above example doesn’t work – the official mysql image generates an selinux error. We can switch selinux to permissive mode by putting the following into a file called disable-selinux.sh:


setenforce 0
sed -i '/^SELINUX=/ s/=.*/=permissive/' /etc/selinux/config

And then including that in our MIME archive:

$ write-mime-multipart.py \
  kube-part-handler.py disable-selinux.sh dbserver.yaml > userdata

A brief demonstration

If we launch an instance as described in the previous section and then log in, we should find that the pod has already been scheduled:

# kubecfg list pods
ID                  Image(s)            Host                Labels              Status
----------          ----------          ----------          ----------          ----------
dbserver            mysql               /                                       Waiting

At this point, docker needs to pull the mysql image locally, so this step can take a bit depending on the state of your local internet connection.

Running docker ps at this point will yield:

# docker ps
CONTAINER ID        IMAGE                     COMMAND             CREATED             STATUS              PORTS               NAMES
3561e39f198c        kubernetes/pause:latest   "/pause"            46 seconds ago      Up 43 seconds                           k8s--net.d96a64a9--dbserver.etcd--3d30eac0_-_745c_-_11e4_-_b32a_-_fa163e6e92ce--d872be51   

The pause image here is a Kubernetes detail that is used to configure the networking for a pod (in the Kubernetes world, a pod is a group of linked containers that share a common network namespace).

After a few minutes, you should eventually see:

# docker ps
CONTAINER ID        IMAGE                     COMMAND                CREATED             STATUS              PORTS               NAMES
644c8fc5a79c        mysql:latest              "/entrypoint.sh mysq   3 minutes ago       Up 3 minutes                            k8s--dbserver.fd48803d--dbserver.etcd--3d30eac0_-_745c_-_11e4_-_b32a_-_fa163e6e92ce--58794467   
3561e39f198c        kubernetes/pause:latest   "/pause"               5 minutes ago       Up 5 minutes                            k8s--net.d96a64a9--dbserver.etcd--3d30eac0_-_745c_-_11e4_-_b32a_-_fa163e6e92ce--d872be51        

And kubecfg should show the pod as running:

# kubecfg list pods
ID                  Image(s)            Host                Labels              Status
----------          ----------          ----------          ----------          ----------
dbserver            mysql                                   Running

Problems, problems

This works and is I think a relatively elegant solution. However, there are some drawbacks. In particular, the custom part handler runs fairly early in the cloud-init process, which means that it cannot depend on changes implemented by user-data scripts (because these run much later).

A better solution might be to have the custom part handler simply write the Kubernetes configs into a directory somewhere, and then install a service that launches after Kubernetes and (a) watches that directory for files, then (b) passes the configuration to Kubernetes and deletes (or relocates) the file.