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Example for masterless Puppet with versioned recipes and an ENC

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Octocat-spinner-32 manifests
Octocat-spinner-32 modules
Octocat-spinner-32 .gitignore
Octocat-spinner-32 LICENSE
Octocat-spinner-32 README.rst
Octocat-spinner-32 enc.sh
Octocat-spinner-32 input.yaml
Octocat-spinner-32 localpuppet.py
Octocat-spinner-32 node.yaml
Octocat-spinner-32 puppet.conf
README.rst

Masterless Puppet, versioned recipes, and an ENC

Puppet should be treated like code, and it should have a sensible release process. Some people keep development, staging, and production branches and merge code between them. I prefer the model of tagging releases, and then pointing a node at a specific release of modules. All dependencies should be tagged also, so a tagged release should be immutable. This means you have the very desirable property of idempotent configuration logic (ie. given the same inputs, it should produce exactly the same output today or two years from now)

Interfaces may change across releases, so every release also requires a way to adapt the way you use puppet classes and definitions. For this, I prefer to use Puppet's External Node Classifier, or ENC. I think of the relationship between the ENC and Puppet as the ENC making an API call to the classes. With this in mind, good programming practice applies - functions with the same inputs should work the same way every time. It's for this reason I prefer not to rely on global parameters, facts, and using external data sources during the puppet run (eg. Stored configs, Hiera, or mcollective queries).

As this wrapper script is just an example, I've only implemented a simple node-specific pass-through ENC. A more complicated version would probably include release-specific classes which are inherited, and some kind of variable lookup mechanisms to customise it to the datacenter, etc. However, for a node in an untrusted environment like the cloud, it's probably better to pre-compute the ENC output and ship it to a node by itself.

As for why this is masterless and on-demand, it's mostly because I work with regular application deployment, and I think that when you intend to make a change to an application it should be explicitly on a node-by-node basis, and you should know immediately whether it succeeded or failed, and then it should get out of the way to let the application do its work. This is in contrast to standard operating system configuration (ie. for mailservers etc.) which isn't likely to change very often, or need any coordination with other systems.

While it's possible to do versioned modules using a central puppetmaster and changing the puppet environment, I generally prefer to split the steps when it comes to orchestration - so I can have one step to ensure that all the code for a release is available on the node itself, and another step to do the release.

The only trick is getting the puppet recipes to the machine in the first place. I've purposefully used the subversion-typical layout of trunk and tags to show a version control system as a possibility.

For the purposes of this example, I assume that I store an ENC YAML file in /etc/puppet/input.yaml, which is, except for an extra top-level key, valid input to Puppet's ENC. You could of course get the ENC information from somewhere else, such as an EC2 Instance's metadata or a web service, or it can be passed in by your orchestration scripts.

The extra top-level key in the ENC input is called 'app' and is used to lookup the root for the app's modules, which should contain a file called manifest.yaml manifest.yaml file can contain a modulepath key with a list of directories as its value, and these will be appended to the modulepath in addition to the 'app' path. Both the 'app' value and the 'modulepath' value are relative to the APP_MODULE_DIR.

Example:

root@cloud001:/etc/puppet# tree modules/
modules/
|-- dist
|   `-- trunk
|       `-- platform
|           `-- manifests
|               `-- init.pp
`-- foo
    |-- tags
    |   `-- r1
    |       |-- foo
    |       |   |-- files
    |       |   |-- manifests
    |       |   |   `-- init.pp
    |       |   `-- templates
    |       `-- manifest.yaml
    `-- trunk
        |-- foo
        |   |-- files
        |   |-- manifests
        |   |   `-- init.pp
        |   `-- templates
        `-- manifest.yaml

16 directories, 5 files

root@cloud001:/etc/puppet# cat input.yaml
app: app/foo/trunk
classes:
  foo:
  platform:

root@cloud001:/etc/puppet# cat modules/foo/trunk/manifest.yaml
modulepath:
  - dist/trunk

root@cloud001:/etc/puppet# ./localpuppet.py
# /usr/bin/puppet apply --node_terminus exec --external_nodes /etc/puppet/enc.sh --modulepath /etc/puppet/modules/foo/trunk:/etc/puppet/modules/dist/trunk /etc/puppet/manifests/default.pp
notice: I notify you!
notice: /Stage[main]/Foo/Notify[I notify you!]/message: defined 'message' as 'I notify you!'
notice: platform class
notice: /Stage[main]/Platform/Notify[platform class]/message: defined 'message' as 'platform class'
notice: Finished catalog run in 0.01 seconds

Similar Work

While I'm not a huge fan of capistrano (as I'd rather not have anyone or anything SSH'ing into production servers), this blogpost outlines an approach to deploying and executing standalone Puppet recipes using it:

Another Idea

I can imagine a solution where Puppet recipes are compiled to .deb or .rpm packages (along with dependencies) and are distributed via whichever package distribution method you use along with your software (eg. yum repo in S3, https://github.com/rmela/yum-s3-plugin). You could then use a tool like mcollective to trigger a call to your_version_of_localpuppet.py with the ENC data & app release, which would first install the appropriate recipe package before triggering a Puppet run against the supplied ENC data. If you use mcollective or another tool that might timeout, or otherwise background the run, you should have the script update status and log files, which can be later queried for success or failure.

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