Factorish and the 12Fakter App

Written by: Paul Czarkowski
10 min read
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This article was originally published on Paul Czarkowski's blog, and we are sharing it here for Codeship readers.

Unless you’ve been living under a rock (in which case I envy you), you’ve heard a fair bit about the Twelve-Factor App. It's a wonderful stateless application that's completely disposable and can run anywhere from your own physical servers to Deis, Cloud Foundry, or Heroku.

Chances are you’re stuck writing and running an application that is decidedly not twelve-factor, nor will it ever be. In a perfect world, you’d scrap it and rewrite it as a dozen microservices that are loosely coupled but run and work independently of each other. The reality however is that you could never get the okay to do that.

Fortunately with the rise of Docker and its ecosystem, it has become easier to not only write a twelve-factor app but also to fake it by producing a Docker container that acts like a twelve-factor app but contains something that is decidedly not. I call this the 12Fakter app.

I’ve been playing with this concept for a while. Over Christmas, I spent a bunch of time trying to figure out the best ways to fake out the twelve factors, and I feel that I’ve come up with something that works pretty well. In the process, I've created a Vagrant-based development sandbox called Factorish, which I used to create 12fakter-wordpress and elk_confd.

Fakter I. Codebase: one codebase tracked in revision control, many deploys

The goal here is to have both your app and deployment tooling in the same codebase, which is stored in source control. This means adding a Dockerfile and a Vagrantfile and other pieces of tooling into your codebase. If however you have a monolithic codebase that contains more than just your app, you can create a separate codebase (use Git!) containing this tooling and have that tooling collect the application from its existing codebase.

You should be able to achieve this by either merging Factorish into your existing Git repo or fork it and use the Dockerfile in it to pull the actual application code in as part of the build process.

Fakter II. Dependencies: explicitly declare and isolate dependencies

This is a really easy win with Docker. The very nature of Docker both explicitly declares your dependencies in the form of the Dockerfile and isolates them in the form of the built Docker image.



FROM python:2
# Base deps layer
  apt-get update && apt-get install -yq \
  make \
  ca-certificates \
  net-tools \
  sudo \
  wget \
  vim \
  strace \
  lsof \
  netcat \
  lsb-release \
  locales \
  socat \
  supervisor \
  --no-install-recommends && \
  locale-gen en_US.UTF-8
# etcdctl and confd layer
  curl -sSL -o /usr/local/bin/etcdctl https://s3-us-west-2.amazonaws.com/opdemand/etcdctl-v0.4.6 \
  && chmod +x /usr/local/bin/etcdctl \
  && curl -sSL -o /usr/local/bin/confd https://github.com/kelseyhightower/confd/releases/download/v0.7.1/confd-0.7.1-linux-amd64 \
  && chmod +x /usr/local/bin/confd
ADD . /app
# app layer
  useradd -d /app -c 'application' -s '/bin/false' app && \
  chmod +x /app/bin/* && \
  pip install -r /app/example/requirements.txt
# Define default command.
CMD ["/app/bin/boot"]
# Expose ports.

You might notice I have sets of commands joined together with && in my Dockerfile. I do this to better control the Docker layers in order to try and end up with fewer, more meaningful layers.


$ docker build -t factorish/example example
Sending build context to Docker daemon 20.99 kB
Sending build context to Docker daemon
Step 0 : FROM python:2
  ---> 96e13ecb4dba
Step 8 : EXPOSE 8080
  ---> Running in 8dc9a04eaf78
  ---> 374cb835239c
Removing intermediate container 8dc9a04eaf78
Successfully built 374cb835239c

Fakter III. Configuration: store config in the environment

Another easy win with Docker. You can pass in environment variables in the Dockerfile as well as when running the Docker container using the -e option like this:

$ docker run -d -e TEXT=bacon factorish/example

However, chances are that your app reads from a config file rather than environment variables. There are (at least) two fairly simple ways to achieve this.

sed inline replacement

Use a startup script to edit your config file and replace values in it with the values of the environment variables, using sed before running your app:


# !/bin/bash
sed -i "s/xxxTEXTxxx/${TEXT}" /app/example/example.conf
python /app/example/app.py

confd templating

confd is a tool written specifically for templating config files from data sources such as environment variables. This is a much better option because it also opens up the ability to use service discovery tooling like etcd (also supported in Factorish), rather than environment variables.


src = "example.conf"
dest = "/app/example/example.conf"
keys = ["/services/example"]


text: {{ getv "/services/example/text" }}

The {{ }} syntax above is the golang/confd macros used to perform tasks like fetching variables from etcd or environment.


confd -onetime
python /app/example/app.py

Fakter IV. Backing Services: treat backing services as attached resources

Anything that's needed to store persistent data should be treated as an external dependency to your application. As far as your app is concerned, there should be no difference between a local MySQL server or Amazon’s RDS.

This is easier for some backing services than others. For example, if your app requires a MySQL database, it's relatively straight forward. Whereas a local filesystem for storing images is harder but can be solved:

  • Docker: volume mounts, data containers

  • Remote Storage: netapp, nfs, fuse-s3fs

  • Clustered FS: drdb, gluster

  • Ghetto: rsync + concerned

The Docker volume mounts actually work really well in a Vagrant-based development environment. You can pass your code all the way into the container from your workstation. However, there are definitely some security considerations to think about if you want to do volume mounts in production.


A fictional PHP-based blog about bacon requires a database and a filestore:


define('DB_NAME', '{{ getv "/db/name" }}');
define('DB_USER', '{{ getv "/db/user" }}');
define('DB_PASSWORD', '{{ getv "/db/pass" }}');
define('DB_HOST', '{{ getv "/db/host" }}');

Docker Run command

$ docker run -d -e DB_NAME=bacon -e DB_USER=bacon \
  -e DB_PASSWORD=bacon $DB_HOST=my.database.com \
  -v /mnt/nfs/bacon:/app/bacon factorish/bacon-blog

confd will use the environment variables passed in via the docker run command to fill out the variables called in the {{ }} macros. Note that confd transforms the environment variables so that the environment variable DB_USER will be read by {{ getv "/db/user" }}. This is done to normalize the macro across the various data source options.

Fakter V. Build, Release, Run: strictly separate build and run stages

Build. Converts a code repo into an executable bundle. Sound familiar? Yup, we’ve already solved this with our Dockerfile.

Release. Takes the build and combines it with the current configuration. In a purely Docker-based system, this can be split between the Build (versioning and defaults) and Run (current config) stages. However, systems like Heroku and Deis have a separate step for this, which they handle internally.

Run. Runs the application by launching a set of the app’s processes against a selected release. In a Docker-based system, this is simply the $ docker run command, which can be called via a deploy script or an init script (systemd/runit) or a scheduler like fleet or Mesos.

Fakter VI. Processes: execute the app as one or more stateless processes

Your application inside the Docker container should behave like a standard Linux process running in the foreground and be stateless and share-nothing. Being inside a Docker container means that this is hidden. Therefore, we can fairly easily fake this, but you do need to think about process management and logging which are discussed later and is further explored here.

Fakter VII. Port binding: export services via port binding

Your application should appear to be completely self-contained and not require runtime injection of a webserver. Thankfully this is pretty easy to fake in a Docker container. Any extra processes are isolated in the container and effectively invisible to the outside.

It is still preferable to use a native language-based web library such as Jetty (Java) or Flask (Python), but for languages like PHP, using Apache or nginx is ok.

Docker itself takes care of the port binding by use of the -p option on the command line. It’s useful to register the port and host IP to somewhere (etcd) to allow for load balancers and other services to easily locate your application.

Fakter VIII. Concurrency: scale out via the process model

We should be able to scale up or down simply by creating or destroying Docker containers containing the application. Any upstream load balancers as external dependencies would need to be notified of the container starting (usually a fairly easy API call) and stopping. But these are external dependencies and should be solved outside of your application itself.

Inside the container, your application should not daemonize or write pid files (if unavoidable, it's not too difficult to script around) and use tooling like upstart or supervisord if there is more than one process that needs to be run.

Fakter IX. Disposability: maximize robustness with fast startup and graceful shutdown

Docker helps a lot with this. We want to ensure that we’re optimized for fast yet reliable startup as well as graceful shutdown. Your app should be able to be shut down gracefully when docker kill is called. Just as importantly, there should be minimal if any external effect if the application crashes or stops ungracefully.

The container should kill itself if the app inside it stops working right. If your app is running behind a supervisor, this can be a achieved with a really lightweight healthcheck script like this.


# !/bin/bash
while [[ ! -z $(netstat -lnt | awk "\$6 == \"LISTEN\" && \$4 ~ \".$PORT\" && \$1 ~ \"tcp.?\"") ]] ;
  do [[ -n $ETCD_HOST ]] && etcdctl set /service/web/hosts/$HOST $PORT --ttl 10 >/dev/null
  sleep 5
done kill `cat /var/run/supervisord.pid`

You’ll note that I’m also publishing host and port values to etcd if $ETCD_HOST is set. This can then be used to notify load balancers and the like when services start or stop.

Fakter X. Dev/prod parity: keep development, staging, and production as similar as possible

By following the previous fackters, we’ve done most of the work to make this possible. We use Vagrant in development to deploy your app (and any backing services), using the appropriate provisioning methodology (the same ones we’d use for production).

By wrapping the application in a Docker container, it's portable across just about any system capable of running Docker.

By provisioning with the same tooling to both dev and prod (and any other envs), any deployment of development (should happen frequently) is also a test of most of the tooling used to deploy to production.

Fakter XI. Logs: treat logs as event streams

Your application (even inside the container) should always log to stdout. By writing to stdout of your process, we can utilize the Docker logging subsystem. When combined with tooling like logspout, this makes it very easy to push all logs to a central system.

If your app has to write to a logfile, you should be able to configure that log file to be /dev/stdout, which should cause it to write to stdout of the process. If your app only writes to syslog, then configure it to write to a remote syslog. Basically, do whatever you can to ensure you don’t log to the local filesystem.


This example shows running Supervisord as your primary process in the Docker container and nginx writing logs to stdout, which in turn are written to the container's stdout. A more thorough writeup on using Supervisor inside Docker containers can be found here.




worker_processes 1;
daemon off;
error_log /dev/stdout;
http {
  access_log /dev/stdout;
  server {
    listen *:8080;
    root /app/bacon-blog;
    index index.php;

For a more detailed post on using logspout to produce consumable logs, check out @behemphi’s blog post: "Docker Logs – Aggregating with Ease."

Fakter XII. Admin processes: run admin/management tasks as one-off processes

This one is pretty easy. Tasks such as database migrates should be run in one-off, throw-away containers.

$ docker run -t -e DB_SERVER=user@pass:db.server.com myapp:1.3.2 rake db:migrate


Most of the fakters above are relatively straight forward to utilize and can be built upon slowly. No need to perfect things before working on them. They can also be utilized with any existing provisioning/config management tooling that you already have.

If you’re already using Chef for deploying your application, you can use the Docker Cookbook to start running Docker containers instead. You can also write out confd templates rather than the final config file which confd will then use to do the final configuration of your app from the environment variables you pass through to the docker_run< resource in the cookbook.

Making your application act like a twelve-factor app may not be enough to run it on a purely hosted PAAS like Heroku, but chances are you’ll be able to run it on a Docker-based PAAS like Deis. You can go full stack with Mesos or CoreOS+fleet+ETCD, or you can stick to Ubuntu servers running Docker.

The flexibility that the 12Fakter application gives you means that you can move to a more modern infrastructure at your own pace. Move when it makes sense, without having to abandon or completely rewrite your existing applications.

Please check out Factorish and some of the example 12Fakter apps like 12fakter-wordpress and elk_confd to see how easy it can be to start making your applications act like twelve-factor apps.

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