From LXC to docker-machine and cloudery

Attention: this post provides a very quick and simplistic (but functional) vision of the promised title.


In the beginning

Linux is a fantastic OS, it has more than we imagine and it still manages to get better. There is a feature called cgroups:

which provides a mechanism for easily managing and monitoring system resources, by partitioning things like cpu time, system memory, disk and network bandwidth, into groups, then assigning tasks to those groups

Let’s say we created a cgroup with: 50% of cpu, 20% memory, 2% of disk and a virtual network with 100% of bandwidth, now we can run our application under that cgroups restrictions.

Another cool feature of Linux is LXC (linux-containers):

which combines kernel’s cgroups and support for isolated namespaces to provide an isolated environment for applications

Now we’re able to provide a Linux machine capable of running multiple applications that run in isolation (like if there was an isolated OS for each application). This sounds like something we achieved with virtualization (app-level, os-level, cpu-level and so on) but faster and cheaper and without the overhead of running multiple kernels.



Docker is:

an open-source project that automates the deployment of applications inside software containers, by providing an additional layer of abstraction and automation of operating-system-level virtualization on Linux. This is what Docker is but remember, it is not perfect.

The highlighted part is very interesting, docker will provide you a layer of abstraction that allows you to create and deploy your application within a container (an isolated, resource managed place to run processes) in a standardized way.

Docker machine, compose and so on

Life almost always get easier with abstractions, we (developers) don’t worry about how disks works (drivers) or even how a package left your pc and hit another one (we should know how this works :P). Our productivity had increased a lot since we relied on these abstractions.

And this is the same for the docker ecosystem, as we start to use it more often. We create best practices, solve issues with workarounds and etc, some of these will become part of the docker solution.

  • docker-machine: An application needs a machine to run regardless if it’s local, physical, virtual or in the cloud.
  • docker-compose: An application needs a way to declare its dependencies, either packages or distinct services like datastore.

Step 0: get ready

  1. If you’re on MacOS/Windows you’ll need to install VirtualBox or VMWare
  2. If you’re on MacOS/Windows install docker toolbox otherwise apt-get them all

Step 1: create the app

Let’s say we’ll create a rails 4 application with mongo.

rails new myapp –skip-active-record

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Step 2: declare the app and its dependencies

We declare our dependencies by using two files: docker-compose.yaml and Dockerfile. In the Dockerfile we’ll describe how our machine should be (aka: all need packages and stuffs).

# we'll use an existent image which already have ruby installed
FROM ruby:2.3.0
# creating our app folder
RUN mkdir /myapp
# move to this folder
WORKDIR /myapp
# move the Gemfile to our app's root folder
ADD Gemfile /myapp/Gemfile
# move the Gemfile.lock too
ADD Gemfile.lock /myapp/Gemfile.lock
# updates the system gem
RUN gem update –system
# run bundle install for production
RUN bundle install –without development test
# copy my code's folder to myapp
ADD . /myapp

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Then we can move to its broad services dependencies, like database or even web server. We’ll use mongo as datastore and nginx as the web server.

# we'll call our web app of web
# it'll build this container based the docker file ./Dockerfile
build: .
# it'll run this command to star the server
command: bash -c "rm -f tmp/pids/ || true && bundle exec rails s -p 3000 -b ''"
# it'll export the port 3000
# it links to db container (on mongoid.yaml at rail's config we say host: db:27017 it's linked)
# we called our db container of db 🙂
# instead of building it from zero we'll use an existent image (see docker hub)
image: mongo
# it needs to persist data so we keep it (even if we "kill" the container)
# we export the port 27017 :B
# we'll use nginx as web server
# it'll restart when the container does too
restart: always
# we're going to build this container from docker file ./docker/nginx/Dockerfile
build: ./docker/nginx/
# we're gonna expose 80
# since we'll be a proxy we need to be linked to our web app (upstream= web:3000)

# we'll build our container from an existent image 🙂
FROM nginx
# for some reason we need to create this folder
RUN mkdir -p /var/lib/nginx/proxy
# copy our app config to nginx
COPY sites-enabled/myapp.conf /etc/nginx/nginx.conf

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# we'll use 2 processors/core
worker_processes 2;
# we set a new limit for open files for our workers
worker_rlimit_nofile 100000;
# we define how we're going to work
events {
# for each worker we'll handle 4000 requests (enquee them)
worker_connections 4000;
# we'll accept multiple
multi_accept on;
# we'll use epoll as a IO event notification
use epoll;
# our server
http {
server_tokens off;
include /etc/nginx/mime.types;
default_type application/octet-stream;
access_log off;
open_file_cache max=200000 inactive=20s;
open_file_cache_valid 30s;
open_file_cache_min_uses 2;
open_file_cache_errors on;
sendfile on;
keepalive_timeout 30;
reset_timedout_connection on;
gzip on;
gzip_http_version 1.0;
gzip_proxied any;
gzip_min_length 500;
gzip_disable "MSIE [1-6]\.";
# we created that folder because we save our cache in there
proxy_cache_path /var/lib/nginx/proxy levels=1:2 keys_zone=backcache:8m max_size=50m;
proxy_cache_key "$scheme$request_method$host$request_uri$is_args$args";
proxy_cache_valid 404 1m;
# we'll forward request to our web app at 3000
upstream app_server {
server web:3000 fail_timeout=0;
server {
# listening at 80
listen 80;
# compreess it
gzip_static on;
gzip_http_version 1.1;
gzip_proxied expired no-cache no-store private auth;
gzip_disable "MSIE [1-6]\.";
gzip_vary on;
# some security precautions
client_body_buffer_size 8K;
client_max_body_size 20m;
client_body_timeout 10s;
client_header_buffer_size 1k;
large_client_header_buffers 2 16k;
client_header_timeout 5s;
keepalive_timeout 40;
# let's get rid of simple attackers GET /admin/setup.php ….
location ~ \.(aspx|php|jsp|cgi)$ {
return 404;
# let's try to serve static files otherwise forward to app
try_files $uri $uri/index.html $uri.html @app;
# app is a proxy to our web app
location @app {
proxy_set_header X-Url-Scheme $scheme;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header Host $host;
proxy_redirect off;
proxy_pass http://app_server;
# let's serve error pages
error_page 500 502 503 504 /500.html;
location = /500.html {
root /myapp/public;

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Step 3: deploy it locally

We need to create a machine for it and then we need to run it.

# we'll create a "machine" called dev on virtualbox
docker-machine create –driver virtualbox dev
# let's use this machine
eval "$(docker-machine env dev)"
# let's take note of our docker-ip
docker-machine ip dev
# let's run this app
docker-compose up
# now go to your browser and type ip 🙂 it should show something

Step 4: deploy in the cloud

The same way we created a machine to run our app locally ,we can create any number of machines to run this application, even in cloud environment such as digitalocean, aws, azure, google and etc.

That’s it 🙂 for a more explained rails app docker workflow read this great post or yet a fresh new example of docker-compose.yaml.

# creating an amazon machine
docker-machine create –driver amazonec2 –amazonec2-access-key XXX –amazonec2-secret-key "xxxxx" –amazonec2-vpc-id vpc-xxx –amazonec2-zone Y amazon
# creating a digital ocean machine
docker-machine create –driver digitalocean –digitalocean-access-token=XXX do
# let's take note of our ip
docker-machine ip amazon
# let's deploy our application
docker-machine up

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// TODO: some things

Let’s suppose we just created a staging environment and another developer come to help us, it seems that there is no an official way to share our created machine (amazon, google app engine, azure, digital ocean…) with team members. There are some workarounds but it’ll be nice to see this becoming a feature.


  • Useful commands to troubleshooting, exploration and debug:
    • To enter on a machine: $ docker-machine ssh staging (either local or cloud)
    • To enter on a container: $ docker-compose run db bash (either local or cloud)
    • To list files within a container: $ docker-compose run db ls -lah data/db
    • To edit/add/remove data on mongo: $ mongo –host DOCKER_IP
  • If you face any error like E: Failed to fetch … during the docker-compose build try it again
  • If you face any error like “Error creating machine: Error running provisioning: Unable to verify the Docker daemon is listening: Maximum number of retries (10) exceeded” during any deployment, try to download docker-toolbox again and install it.

Google is your friend.

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