"$bash" by "Andrew Mager" on Flickr

One to read: Put your bash code in functions

I’ve got a few mildly ropey bash scripts which I could do with making a bit more resilient, and perhaps even operating faster ;)

As such, I found this page really interesting: https://ricardoanderegg.com/posts/bash_wrap_functions/

In it, Ricardo introduces me to two things which are interesting.

  1. Using the wait command literally waits for all the backgrounded tasks to finish.
  2. Running bash commands like this: function1 & function2 & function3 should run all three processes in parallel. To be honest, I’d always usually do it like this:
    function1 &
    function2 &
    function3 &

The other thing which Ricardo links to is a page suggesting that if you’re downloading a bash script and executing it (which, you know, probably isn’t a good idea at the best of times), then wrapping it in a function, like this:

#!/bin/bash

function main() {
  echo "Some function"
}

main

This means that the bash scripting engine needs to download and parse all the functions before it can run the script. As a result, you’re less likely to get a broken run of your script, because imagine it only got as far as:

#!/bin/bash
echo "Some fun

Then it wouldn’t have terminated the echo command (as an example)…

Anyway, some great tricks here! Love it!

Featured image is “$bash” by “Andrew Mager” on Flickr and is released under a CC-BY-SA license.

“New shoes” by “Morgaine” from Flickr

Making Windows Cloud-Init Scripts run after a reboot (Using Terraform)

I’m currently building a Proof Of Value (POV) environment for a product, and one of the things I needed in my environment was an Active Directory domain.

To do this in AWS, I had to do the following steps:

  1. Build my Domain Controller
    1. Install Windows
    2. Set the hostname (Reboot)
    3. Promote the machine to being a Domain Controller (Reboot)
    4. Create a domain user
  2. Build my Member Server
    1. Install Windows
    2. Set the hostname (Reboot)
    3. Set the DNS client to point to the Domain Controller
    4. Join the server to the domain (Reboot)

To make this work, I had to find a way to trigger build steps after each reboot. I was working with Windows 2012R2, Windows 2016 and Windows 2019, so the solution had to be cross-version. Fortunately I found this script online! That version was great for Windows 2012R2, but didn’t cover Windows 2016 or later… So let’s break down what I’ve done!

In your userdata field, you need to have two sets of XML strings, as follows:

<persist>true</persist>
<powershell>
$some = "powershell code"
</powershell>

The first block says to Windows 2016+ “keep trying to run this script on each boot” (note that you need to stop it from doing non-relevant stuff on each boot – we’ll get to that in a second!), and the second bit is the PowerShell commands you want it to run. The rest of this now will focus just on the PowerShell block.

  $path= 'HKLM:\Software\UserData'
  
  if(!(Get-Item $Path -ErrorAction SilentlyContinue)) {
    New-Item $Path
    New-ItemProperty -Path $Path -Name RunCount -Value 0 -PropertyType dword
  }
  
  $runCount = Get-ItemProperty -Path $path -Name Runcount -ErrorAction SilentlyContinue | Select-Object -ExpandProperty RunCount
  
  if($runCount -ge 0) {
    switch($runCount) {
      0 {
        $runCount = 1 + [int]$runCount
        Set-ItemProperty -Path $Path -Name RunCount -Value $runCount
        if ($ver -match 2012) {
          #Enable user data
          $EC2SettingsFile = "$env:ProgramFiles\Amazon\Ec2ConfigService\Settings\Config.xml"
          $xml = [xml](Get-Content $EC2SettingsFile)
          $xmlElement = $xml.get_DocumentElement()
          $xmlElementToModify = $xmlElement.Plugins
          
          foreach ($element in $xmlElementToModify.Plugin)
          {
            if ($element.name -eq "Ec2HandleUserData") {
              $element.State="Enabled"
            }
          }
          $xml.Save($EC2SettingsFile)
        }
        $some = "PowerShell Script"
      }
    }
  }

Whew, what a block! Well, again, we can split this up into a couple of bits.

In the first few lines, we build a pointer, a note which says “We got up to here on our previous boots”. We then read that into a variable and find that number and execute any steps in the block with that number. That’s this block:

  $path= 'HKLM:\Software\UserData'
  
  if(!(Get-Item $Path -ErrorAction SilentlyContinue)) {
    New-Item $Path
    New-ItemProperty -Path $Path -Name RunCount -Value 0 -PropertyType dword
  }
  
  $runCount = Get-ItemProperty -Path $path -Name Runcount -ErrorAction SilentlyContinue | Select-Object -ExpandProperty RunCount
  
  if($runCount -ge 0) {
    switch($runCount) {

    }
  }

The next part (and you’ll repeat it for each “number” of reboot steps you need to perform) says “increment the number” then “If this is Windows 2012, remind the userdata handler that the script needs to be run again next boot”. That’s this block:

      0 {
        $runCount = 1 + [int]$runCount
        Set-ItemProperty -Path $Path -Name RunCount -Value $runCount
        if ($ver -match 2012) {
          #Enable user data
          $EC2SettingsFile = "$env:ProgramFiles\Amazon\Ec2ConfigService\Settings\Config.xml"
          $xml = [xml](Get-Content $EC2SettingsFile)
          $xmlElement = $xml.get_DocumentElement()
          $xmlElementToModify = $xmlElement.Plugins
          
          foreach ($element in $xmlElementToModify.Plugin)
          {
            if ($element.name -eq "Ec2HandleUserData") {
              $element.State="Enabled"
            }
          }
          $xml.Save($EC2SettingsFile)
        }
        
      }

In fact, it’s fair to say that in my userdata script, this looks like this:

  $path= 'HKLM:\Software\UserData'
  
  if(!(Get-Item $Path -ErrorAction SilentlyContinue)) {
    New-Item $Path
    New-ItemProperty -Path $Path -Name RunCount -Value 0 -PropertyType dword
  }
  
  $runCount = Get-ItemProperty -Path $path -Name Runcount -ErrorAction SilentlyContinue | Select-Object -ExpandProperty RunCount
  
  if($runCount -ge 0) {
    switch($runCount) {
      0 {
        ${file("templates/step.tmpl")}

        ${templatefile(
          "templates/rename_windows.tmpl",
          {
            hostname = "SomeMachine"
          }
        )}
      }
      1 {
        ${file("templates/step.tmpl")}

        ${templatefile(
          "templates/join_ad.tmpl",
          {
            dns_ipv4 = "192.0.2.1",
            domain_suffix = "ad.mycorp",
            join_account = "ad\someuser",
            join_password = "SomePassw0rd!"
          }
        )}
      }
    }
  }

Then, after each reboot, you need a new block. I have a block to change the computer name, a block to join the machine to the domain, and a block to install an software that I need.

Featured image is “New shoes” by “Morgaine” on Flickr and is released under a CC-BY-SA license.

"Fishing line and bobbin stuck on tree at Douthat State Park" by "Virginia State Parks" on Flickr

Note to self: Linux shell scripts don’t cope well with combined CRLF + LF files… Especially in User-Data / Custom Data / Cloud-Init scripts

This one is more a nudge to myself. On several occasions when building Infrastructure As Code (IAC), I split out a code sections into one or more files, for readability and reusability purposes. What I tended to do, and this was more apparent with the Linux builds than the Windows builds, was to forget to set the line terminator from CRLF to LF.

While this doesn’t really impact Windows builds too much (they’re kinda designed to support people being idiots with line endings now), Linux still really struggles with CRLF endings, and you’ll only see when you’ve broken this because you’ll completely fail to run any of the user-data script.

How do you determine this is your problem? Well, actually it’s a bit tricky, as neither cat, less, more or nano spot this issue. The only two things I found that identified it were file and vi.

The first part of the combined file with mixed line endings. This part has LF termination.
The second part of the combined file with mixed line endings. This part has CRLF termination.
What happens when we cat these two parts into one file? A file with CRLF, LF line terminators obviously!
What the combined file looks like in Vi. Note the blue ^M at the ends of the lines.

So, how to fix this? Assuming you’re using Visual Studio Code;

A failed line-ending clue in Visual Studio Code

You’ll notice this line showing “CRLF” in the status bar at the bottom of Code. Click on that, which brings up a discrete box near the top, as follows:

Oh no, it’s set to “CRLF”. That’s not what we want!

Selecting LF in that box changes the line feeds into LF for this file, but it’s not saved. Make sure you save this file before you re-run your terraform script!

Notice, we’re now using LF endings, but the file isn’t saved.

Fantastic! It’s all worked!

In Nano, I’ve opened the part with the invalid line endings.

Oh no! We have a “DOS Format” file. Quick, let’s fix it!

To fix this, we need to write the file out. Hit Ctrl+O. This tells us that we’re in DOS Format, and also gives us the keyboard combination to toggle “DOS Format” off – it’s Alt+D (In Unix/Linux world, the Alt key is referred to as the Meta key – hence M not A).

This is how we fix things

So, after hitting Alt+D, the “File Name to write” line changes, see below:

Yey, no pesky “DOS Format” warning here!

Using either editor (or any others, if you know how to solve line ending issues in other editors), you still need to combine your script back together before you can run it, so… do that, and your file will be fine to run! Good luck!

Featured image is “Fishing line and bobbin stuck on tree at Douthat State Park” by “Virginia State Parks” on Flickr and is released under a CC-BY license.

"Unnatural Love" by "Keith Garner" on Flickr

Configuring a Remote Desktop (Gnome Shell) for Ubuntu

I started thinking a couple of weeks ago, when my coding laptop broke, that it would be really useful to have a development machine somewhere else that I could use.

It wouldn’t need a lot of power (after all, I’m mostly developing web apps and not compiling stuff), but it does need to be a desktop OS, as I rather like being able to open code editors and suchlike, while I’ve got a web browser open.

I have an Android tablet, which while it’s great for being a tablet, it’s not much use as a desktop, and … yes, I’ve got a work laptop, but I don’t really want to install software on that (and I don’t think my admin team would be happy if I did).

Also, I quite like Linux.

Some time ago, I spotted that AWS has a “Virtual Desktop” environment, and I think that’s kinda what I’m after. Something I can spin up, run for a bit and then shut it down, so I thought I’d build something like that… but not pesky Windows, after all… who likes Windows, eh? ;)

So, I built a Virtual Desktop Environment (VDE) in AWS, using Terraform and a bit of shell script!

I start from an Ubuntu 18.04 server image, and, after the install is complete, I run this user-data script inside it. Yes, I know I could be doing this with Ansible, but… eh, I wanted it to be a quick deployment ;)

Oh, and there’s a couple of Terraform managed variables in here – ${aws_eip.vde.public_ip} is the AWS public IP address assigned to this host., ${var.firstuser} is the username we want to rename “ubuntu” (the stock server username) to. ${var.firstgecos} is the user’s “real name” which the machine identifies the user as (like “Log out Jon Spriggs” and so on). ${var.userpw} is either the password you want it to use, OR (by default) pwgen 12 which generates a 12 character long password. ${var.desktopenv} is the name of the desktop environment I want to install (Ubuntu by default) and … well, ${var.var_start} is a bit of a fudge, because I couldn’t, in a hurry, work out how to tell Terraform not to mangle the bash variable allocation of ${somevar} which is the format that Terraform also uses. D’oh.

#! /bin/bash
#################
# Set Hostname
#################
hostnamectl set-hostname vde.${aws_eip.vde.public_ip}.nip.io
#################
# Change User
#################
user=${var.firstuser}
if [ ! "$user" == 'ubuntu' ]
then
  until usermod -c "${var.firstgecos}" -l $user ubuntu ; do sleep 5 ; done
  until groupmod -n $user ubuntu ; do sleep 5 ; done
  until usermod  -d /home/$user -m $user ; do sleep 5 ; done
  if [ -f /etc/sudoers.d/90-cloudimg-ubuntu ]; then
    mv /etc/sudoers.d/90-cloudimg-ubuntu /etc/sudoers.d/90-cloud-init-users
  fi
  perl -pi -e "s/ubuntu/$user/g;" /etc/sudoers.d/90-cloud-init-users
fi
if [ '${var.userpw}' == '$(pwgen 12)' ]
then 
  apt update && apt install pwgen
fi
newpw="${var.userpw}"
echo "$newpw" > /var/log/userpw
fullpw="$newpw"
fullpw+="\n"
fullpw+="$newpw"
echo -e "$fullpw" | passwd $user
##########################
# Install Desktop and RDP
##########################
apt-get update
export DEBIAN_FRONTEND=noninteractive
apt-get full-upgrade -yq
apt-get autoremove -y
apt-get autoclean -y
apt-get install -y ${var.desktopenv}-desktop xrdp certbot
##########################
# Configure Certbot
##########################
echo "#!/bin/sh" > /etc/letsencrypt/merge_cert.sh
echo 'cat ${var.var_start}{RENEWED_LINEAGE}/privkey.pem ${var.var_start}{RENEWED_LINEAGE}/fullchain.pem > ${var.var_start}{RENEWED_LINEAGE}/merged.pem' >> /etc/letsencrypt/merge_cert.sh
echo 'chmod 640 ${var.var_start}{RENEWED_LINEAGE}/merged.pem' >> /etc/letsencrypt/merge_cert.sh
chmod 750 /etc/letsencrypt/merge_cert.sh
certbot certonly --standalone --deploy-hook /etc/letsencrypt/merge_cert.sh -n -d vde.${aws_eip.vde.public_ip}.nip.io -d ${aws_eip.vde.public_ip}.nip.io --register-unsafely-without-email --agree-tos
# Based on https://www.snel.com/support/xrdp-with-lets-encrypt-on-ubuntu-18-04/
sed -i 's~^certificate=$~certificate=/etc/letsencrypt/live/vde.${aws_eip.vde.public_ip}.nip.io/fullchain.pem~; s~^key_file=$~key_file=/etc/letsencrypt/live/vde.${aws_eip.vde.public_ip}.nip.io/privkey.pem' /etc/xrdp/xrdp.ini
##############################
# Fix colord remote user issue
##############################
# Derived from http://c-nergy.be/blog/?p=12043
echo "[Allow Colord all Users]
Identity=unix-user:*
Action=org.freedesktop.color-manager.create-device;org.freedesktop.color-manager.create-profile;org.freedesktop.color-manager.delete-device;org.freedesktop.color-manager.delete-profile;org.freedesktop.color-manager.modify-device;org.freedesktop.color-manager.modify-profile
ResultAny=no
ResultInactive=no
ResultActive=yes" > /etc/polkit-1/localauthority/50-local.d/45-allow.colord.pkla
##############################
# Configure Desktop
##############################
if [ '${var.desktopenv}' == 'ubuntu' ]
then 
  echo "#!/bin/bash" > /tmp/desktop_settings
  echo "gsettings set org.gnome.desktop.input-sources sources \"[('xkb', 'gb')]\"" >> /tmp/desktop_settings
  echo "gsettings set org.gnome.desktop.app-folders folder-children \"['Utilities', 'Sundry', 'YaST']\"" >> /tmp/desktop_settings
  echo "gsettings set org.gnome.desktop.privacy report-technical-problems false" >> /tmp/desktop_settings
  echo "gsettings set org.gnome.desktop.screensaver lock-enabled false" >> /tmp/desktop_settings
  echo "gsettings set org.gnome.desktop.session idle-delay 0" >> /tmp/desktop_settings
  echo "echo yes > /home/${var.firstuser}/.config/gnome-initial-setup-done" >> /tmp/desktop_settings
  sudo -H -u ${var.firstuser} dbus-launch --exit-with-session bash /tmp/desktop_settings
  rm -f /tmp/desktop_settings
fi
##########################
# Install VSCode
##########################
wget https://vscode-update.azurewebsites.net/latest/linux-deb-x64/stable -O /tmp/vscode.deb
apt install -y /tmp/vscode.deb
rm /var/crash/*
shutdown -r now

Ubuntu 18.04 has a “first login” wizard, that lets you pre-set up things like, what language will you be using. I bypassed this with the gsettings commands towards the end of the script, and writing the string “yes” to ~/.config/gnome-initial-setup-done.

Also, I wanted to be able to RDP to it. I’m a bit concerned by the use of VNC, especially where RDP is more than capable. It’s just an apt-install away, so… that’s what I do. But, because I’m RDP’ing into this box, I wanted to prevent the RDP session from locking, so I provide two commands to the session: gsettings set org.gnome.desktop.screensaver lock-enabled false which removes the screensaver’s ability to lock the screen, and gsettings set org.gnome.desktop.session idle-delay 0 which stops the screensaver from even starting in the first place.

Now all I need to do is to figure out where I’m going to store my code between boots ;)

So, in summary, I now have a Virtual Machine, which runs Ubuntu 18.04 Desktop, in AWS, with an RDP connection (powered by xRDP), and a disabled screensaver. Job done, I think!

Oh, and if I’m doing it “wrong”, let me know in the comments? :)

Featured image is “Unnatural Love” by “Keith Garner” on Flickr and is released under a CC-BY-SA license.

"Shipping Containers" by "asgw" on Flickr

Creating my first Docker containerized LEMP (Linux, nginx, MariaDB, PHP) application

Want to see what I built without reading the why’s and wherefore’s? The git repository with all the docker-compose goodness is here!

Late edit 2020-01-16: The fantastic Jerry Steel, my co-host on The Admin Admin podcast looked at what I wrote, and made a few suggestions. I’ve updated the code in the git repo, and I’ll try to annotate below when I’ve changed something. If I miss it, it’s right in the Git repo!

One of the challenges I set myself this Christmas was to learn enough about Docker to put an arbitrary PHP application, that I would previously have misused Vagrant to contain.

Just before I started down this rabbit hole, I spoke to my Aunt about some family tree research my father had left behind after he died, and how I wished I could easily share the old tree with her (I organised getting her a Chromebook a couple of years ago, after fighting with doing remote support for years on Linux and Windows laptops). In the end, I found a web application for genealogical research called HuMo-gen, that is a perfect match for both projects I wanted to look at.

HuMo-gen was first created in 1999, with a PHP version being released in 2005. It used MySQL or MariaDB as the Database engine. I was reasonably confident that I could have created a Vagrantfile to deliver this on my home server, but I wanted to try something new. I wanted to use the “standard” building blocks of Docker and Docker-Compose, and some common containers to make my way around learning Docker.

I started by looking for some resources on how to build a Docker container. Much of the guidance I’d found was to use Docker-Compose, as this allows you to stand several components up at the same time!

In contrast to how Vagrant works (which is basically a CLI wrapper to many virtual machine services), Docker isolates resources for a single process that runs on a machine. Where in Vagrant, you might run several processes on one machine (perhaps, in this instance, nginx, PHP-FPM and MariaDB), with Docker, you’re encouraged to run each “service” as their own containers, and link them together with an overlay network. It’s possible to also do the same with Vagrant, but you’ll end up with an awful lot of VM overhead to separate out each piece.

So, I first needed to select my services. My initial line-up was:

  • MariaDB
  • PHP-FPM
  • Apache’s httpd2 (replaced by nginx)

I was able to find official Docker images for PHP, MariaDB and httpd, but after extensive tweaking, I couldn’t make the httpd image talk the way I wanted it to with the PHP image. Bowing to what now seems to be conventional wisdom, I swapped out the httpd service for nginx.

One of the stumbling blocks for me, particularly early on, was how to build several different Dockerfiles (these are basically the instructions for the container you’re constructing). Here is the basic outline of how to do this:

version: '3'
services:
  yourservice:
    build:
      context: .
      dockerfile: relative/path/to/Dockerfile

In this docker-compose.yml file, I tell it that to create the yourservice service, it needs to build the docker container, using the file in ./relative/path/to/Dockerfile. This file in turn contains an instruction to import an image.

Each service stacks on top of each other in that docker-compose.yml file, like this:

version: '3'
services:
  service1:
    build:
      context: .
      dockerfile: service1/Dockerfile
    image: localhost:32000/service1
  service2:
    build:
      context: .
      dockerfile: service2/Dockerfile
    image: localhost:32000/service2

Late edit 2020-01-16: This previously listed Dockerfile/service1, however, much of the documentation suggested that Docker gets quite opinionated about the file being called Dockerfile. While docker-compose can work around this, it’s better to stick to tradition :) The docker-compose.yml files below have also been adjusted accordingly. I’ve also added an image: somehost:1234/image_name line to help with tagging the images for later use. It’s not critical to what’s going on here, but I found it useful with some later projects.

To allow containers to see ports between themselves, you add the expose: command in your docker-compose.yml, and to allow that port to be visible from the “outside” (i.e. to the host and upwards), use the ports: command listing the “host” port (the one on the host OS), then a colon and then the “target” port (the one in the container), like these:

version: '3'
services:
  service1:
    build:
      context: .
      dockerfile: service1/Dockerfile
    image: localhost:32000/service1
    expose:
    - 12345
  service2:
    build:
      context: .
      dockerfile: service2/Dockerfile
    image: localhost:32000/service2
    ports:
    - 8000:80

Now, let’s take a quick look into the Dockerfiles. Each “statement” in a Dockerfile adds a new “layer” to the image. For local operations, this probably isn’t a problem, but when you’re storing these images on a hosted provider, you want to keep these images as small as possible.

I built a Database Dockerfile, which is about as small as you can make it!

FROM mariadb:10.4.10

Yep, one line. How cool is that? In the docker-compose.yml file, I invoke this, like this:

version: '3'
services:
  db:
    build:
      context: .
      dockerfile: mariadb/Dockerfile
    image: localhost:32000/db
    restart: always
    environment:
      MYSQL_ROOT_PASSWORD: a_root_pw
      MYSQL_USER: a_user
      MYSQL_PASSWORD: a_password
      MYSQL_DATABASE: a_db
    expose:
      - 3306

OK, so this one is a bit more complex! I wanted it to build my Dockerfile, which is “mariadb/Dockerfile“. I wanted it to restart the container whenever it failed (which hopefully isn’t that often!), and I wanted to inject some specific environment variables into the file – the root and user passwords, a user account and a database name. Initially I was having some issues where it wasn’t building the database with these credentials, but I think that’s because I wasn’t “building” the new database, I was just using it. I also expose the MariaDB (MySQL) port, 3306 to the other containers in the docker-compose.yml file.

Let’s take a look at the next part! PHP-FPM. Here’s the Dockerfile:

FROM php:7.4-fpm
RUN docker-php-ext-install pdo pdo_mysql
ADD --chown=www-data:www-data public /var/www/html

There’s a bit more to this, but not loads. We build our image from a named version of PHP, and install two extensions to PHP, pdo and pdo_mysql. Lastly, we copy the content of the “public” directory into the /var/www/html path, and make sure it “belongs” to the right user (www-data).

I’d previously tried to do a lot more complicated things with this Dockerfile, but it wasn’t working, so instead I slimmed it right down to just this, and the docker-compose.yml is a lot simpler too.

  phpfpm:
    build:
      context: .
      dockerfile: phpfpm/Dockerfile
    image: localhost:32000/phpfpm

See! Loads simpler! Now we need the complicated bit! :) This is the Dockerfile for nginx.

FROM nginx:1.17.7
COPY nginx/default.conf /etc/nginx/conf.d/default.conf

COPY public /var/www/html

Weirdly, even though I’ve added version numbers for MariaDB and PHP, I’ve not done the same for nginx, perhaps I should! Late edit 2020-01-16: I’ve put a version number on there now, previously where it said nginx:1.17.7 it actually said nginx:latest.

I’ve created the configuration block for nginx in a single “RUN” line. Late edit 2020-01-16: This Dockerfile now doesn’t have a giant echo 'stuff' > file block either, following Jerry’s advice, and I’m using COPY instead of ADD on his advice too. I’ll show that config file below. There’s a couple of high points for me here!

server {
  index index.php index.html;
  server_name _;
  error_log /proc/self/fd/2;
  access_log /proc/self/fd/1;
  root /var/www/html;
  location ~ \.php$ {
    try_files $uri =404;
    fastcgi_split_path_info ^(.+\.php)(/.+)$;
    fastcgi_pass phpfpm:9000;
    fastcgi_index index.php;
    include fastcgi_params;
    fastcgi_param SCRIPT_FILENAME $document_root$fastcgi_script_name;
    fastcgi_param PATH_INFO $fastcgi_path_info;
  }
}
  • server_name _; means “use this block for all unnamed requests”.
  • access_log /proc/self/fd/1; and error_log /proc/self/fd/2;These are links to the “stdout” and “stderr” file descriptors (or pointers to other parts of the filesystem), and basically means that when you do docker-compose logs, you’ll see the HTTP logs for the server! These two files are guaranteed to be there, while /dev/stderr isn’t!

Because nginx is “just” caching the web content, and I know the content doesn’t need to be written to from nginx, I knew I didn’t need to do the chown action, like I did with the PHP-FPM block.

Lastly, I need to configure the docker-compose.yml file for nginx:

  nginx:
    build:
      context: .
      dockerfile: Dockerfile/nginx
    image: localhost:32000/nginx
    ports:
      - 127.0.0.1:1980:80

I’ve gone for a slightly unusual ports configuration when I deployed this to my web server… you see, I already have the HTTP port (TCP/80) configured for use on my home server – for running the rest of my web services. During development, on my home machine, the ports line instead showed “1980:80” because I was running this on Instead, I’m running this application bound to “localhost” (127.0.0.1) on a different port number (1980 selected because it could, conceivably, be a birthday of someone on this system), and then in my local web server configuration, I’m proxying connections to this service, with HTTPS encryption as well. That’s all outside the scope of this article (as I probably should be using something like Traefik, anyway) but it shows you how you could bind to a separate port too.

Anyway, that was my Docker journey over Christmas, and I look forward to using it more, going forward!

Featured image is “Shipping Containers” by “asgw” on Flickr and is released under a CC-BY license.

"Mesh Facade" by "Pedro Ângelo" on Flickr

Looking at the Nebula Overlay Meshed VPN Network from Slack

Around 2-3 years ago, Slack– the company who produces Slack the IM client, started working on a meshed overlay network product, called Nebula, to manage their environment. After two years of running their production network on the back of it, they decided to open source it. I found out about Nebula via a Medium Post that was mentioned in the HangOps Slack Group. I got interested in it, asked a few questions about Nebula in the Slack, and then in the Github Issues for it, and then recently raised a Pull Request to add more complete documentation than their single (heavily) commented config file.

So, let’s go into some details on why this is interesting to me.

1. Nebula uses a flat IPv4 network to identify all hosts in the network, no matter where in the network those hosts reside.

This means that I can address any host in my (self allocated) 198.18.0.0/16 network, and I don’t need to worry about routing tables, production/DR sites, network tromboneing and so on… it’s just… Flat.

[Note: Yes, that IP address “looks” like a public subnet – but it’s actually a testing network, allocated by IANA for network testing!]

2. Nebula has host-based firewalling built into the configuration file.

This means that once I know how my network should operate (yes, I know that’s a big ask), I can restrict my servers from being able to reach my laptops, or I can stop my web server from being able to talk to my database server, except for on the database ports. Lateral movement becomes a LOT harder.

This firewalling also looks a lot like (Network) Security Groups (for the AWS and Azure familiar), so you have a default “Deny” rule, and then layer “Allow” rules on top. You also have Inbound and Outbound rules, so if you want to stop your laptops from talking anything but DNS, SSH, HTTPS and ICMP over Nebula…. well, yep, you can do that :)

3. Nebula uses a PKI environment. Where you can have multiple Certificate Authorities.

This means that I have a central server running my certificate authority (CA), with a “backup” CA, stored offline – in case of dire disaster with my primary CA, and push both CA’s to all my nodes. If I suddenly need to replace all the certificates that my current CA signed, I can do that with minimal interruption to my nodes. Good stuff.

Nebula also created their own PKI attributes to identify the roles of each device in the Nebula environment. By signing that as part of the certificate on each node too, means your CA asserts that the role that certificate holds is valid for that node in the network.

Creating a node’s certificate is a simple command:

nebula-cert sign -name jon-laptop -ip 198.18.0.1/16 -groups admin,laptop,support

This certificate has the IP address of the node baked in (it’s 198.18.0.1) and the groups it’s part of (admin, laptop and support), as well as the host name (jon-laptop). I can use any of these three items in my firewall rules I mentioned above.

4. It creates a peer-to-peer, meshed VPN.

While it’s possible to create a peer-to-peer meshed VPN with commercial products, I’ve not seen any which are as light-weight to deploy as this. Each node finds all the other nodes in the network by using a collection of “Lighthouses” (similar to Torrent Seeds or Skype Super Nodes) which tells all the connecting nodes where all the other machines in the network are located. These then initiate UDP connections to the other nodes they want to talk to. If they are struggling (because of NAT or Double NAT), then there’s a NAT Punching process (called, humourously, “punchy”) which lets you signal via the Lighthouse that you’re trying to reach another node that can’t see your connection, and asks it to also connect out to you over UDP… thereby fixing the connection issue. All good.

5. Nebula has clients for Windows, Mac and Linux. Apparently there are clients for iOS in the works (meh, I’m not on Apple… but I know some are) and I’ve heard nothing about Android as yet, but as it’s on Linux, I’m sure some enterprising soul can take a look at it (the client is written in Go).

If you want to have a look at Nebula for your own testing, I’ve created a Terraform based environment on AWS and Azure to show how you’d manage it all using Ansible Tower, which builds:

2 VPCs (AWS) and 1 VNet (Azure)
6 subnets (3 public, 3 private)
1 public AWX (the upstream project from Ansible Tower) Server
1 private Nebula Certificate Authority
2 public Web Servers (one in AWS, one in Azure)
2 private Database Servers (one in AWS, one in Azure)
2 public Bastion Servers (one in AWS, one in Azure) – that lets AWX reach into the Private sections of the network, without exposing SSH from all the hosts.

If you don’t want to provision the Azure side, just remove load_web2_module.tf from the Terraform directory in that repo… Job’s a good’n!

I have plans to look at a couple of variables, like Nebula’s closest rival, ZeroTier, and to look at using SaltStack instead of Ansible, to reduce the need for the extra Bastion servers.

Featured image is “Mesh Facade” by “Pedro Ângelo” on Flickr and is released under a CC-BY-SA license.

“code crunching” by “Ruben Molina” on Flickr

Getting Started with Terraform on AWS

I recently wrote a blog post about Getting Started with Terraform on Azure. You might have read it (I know I did!).

Having got a VM stood up in Azure, I wanted to build a VM in AWS, after all, it’s more-or-less the same steps. Note, this is a work-in-progress, and shouldn’t be considered “Final” – this is just something to use as *your* starting block.

What do you need?

You need an AWS account for this. If you’ve not got one, signing up for one is easy, but bear in mind that while there are free resource on AWS (only for the first year!), it’s also quite easy to suddenly enable a load of features that cost you money.

Best practice suggests (or rather, INSISTS) you shouldn’t use your “root” account for AWS. It’s literally just there to let you define the rest of your admin accounts. Turn on MFA (Multi-Factor Authentication) on that account, give it an exceedingly complex password, write that on a sheet of paper, and lock it in a box. You should NEVER use it!

Create your admin account, log in to that account. Turn on MFA on *that* account too. Then, create an “Access Token” for your account. This is in IAM (Identity and Access Management). These are what we’ll use to let Terraform perform actions in AWS, without you needing to actually “log in”.

On my machine, I’ve put the credentials for this in /home/<MYUSER>/.aws/credentials and it looks like this:

[default]
aws_access_key_id = ABC123DEF456
aws_secret_access_key = AaBbCcDd1234EeFf56

This file should be chmod 600 and make sure it’s only your account that can access this file. With this token, Terraform can perform *ANY ACTION* as you, including anything that charges you money, or creating servers that can mine a “cryptocurrency” for someone malicious.

I’m using Windows Subsystem for Linux (WSL). I’m using the Ubuntu 18.04 distribution obtained from the Store. This post won’t explain how to get *that*. Also, you might want to run Terraform on Mac, in Windows or on Linux natively… so, yehr.

Next, we need to actually install Terraform. Excuse the long, unwrapped code block, but it gets what you need quickly (assuming the terraform webpage doesn’t change any time soon!)

mkdir -p ~/bin
cd ~/bin
sudo apt update && sudo apt install unzip
curl -sLO $(curl https://www.terraform.io/downloads.html | grep "linux_amd64.zip" | cut -d\" -f 2) && unzip terraform*.zip && rm terraform*.zip && chmod 755 terraform

Starting coding your infrastructure

Before you can build your first virtual machine on AWS, you need to stand up the supporting infrastructure. These are:

  • An SSH Keypair (no password logins here!)
  • A VPC (“Virtual Private Cloud”, roughly the same as a VNet on Azure, or somewhat like a L3 switch in the Physical Realm).
  • An Internet Gateway (if your VPC isn’t classed as “the default one”)
  • A Subnet.
  • A Security Group.

Once we’ve got these, we can build our Virtual Machine on EC2 (“Elastic Cloud Compute”), and associate a “Public IP” to it.

To quote my previous post:

One quirk with Terraform, versus other tools like Ansible, is that when you run one of the terraform commands (like terraform init, terraform plan or terraform apply), it reads the entire content of any file suffixed “tf” in that directory, so if you don’t want a file to be loaded, you need to either move it out of the directory, comment it out, or rename it so it doesn’t end .tf. By convention, you normally have three “standard” files in a terraform directory – main.tf, variables.tf and output.tf, but logically speaking, you could have everything in a single file, or each instruction in it’s own file.

Getting Started with Terraform on Azure – Building the file structure

For the sake of editing and annotating the files for this post, these code blocks are all separated, but on my machine, they’re all currently one big file called “main.tf“.

In that file, I start by telling it that I’m working with the Terraform AWS provider, and that it should target my nearest region.

If you want to risk financial ruin, you can put things like your access tokens in here, but I really wouldn’t chance this!

Next, we create our network infrastructure – VPC, Internet Gateway and Subnet. We also change the routing table.

I suspect, if I’d created the VPC as “The Default” VPC, then I wouldn’t have needed to amend the routing table, nor added an Internet Gateway. To help us make the routing table change, there’s a “data” block in this section of code. A data block is an instruction to Terraform to go and ask a resource for *something*, in this case, we need AWS to tell Terraform what the routing table is that it created for the VPC. Once we have that we can ask for the routing table change.

AWS doesn’t actually give “proper” names to any of it’s assets. To provide something with a “real” name, you need to tag that thing with the “Name” tag. These can be practically anything, but I’ve given semi-sensible names to everything. You might want to name everything “main” (like I nearly did)!

We’re getting close to being able to create the VM now. First of all, we’ll create the Security Groups. I want to separate out my “Allow Egress Traffic” rule from my “Inbound SSH” rule. This means that I can clearly see what hosts allow inbound SSH access. Like with my Azure post, I’m using a “data provider” to get my public IP address, but in a normal “live” network, you’d specify a collection of valid source address ranges.

Last steps before we create the Virtual Machine. We need to upload our SSH key, and we need to find the “AMI” (AWS Machine ID) of the image we’ll be using. To create the key, in this directory, along side the .tf files, I’ve put my SSH public key (called id_rsa.pub), and we load that key when we create the “my_key” resource. To find the AMI, we need to make another data call, this time asking the AMI index to find the VM with the name containing ubuntu-bionic-18.04 and some other stuff. AMIs are region specific, so the image I’m using in eu-west-2 will not be the same AMI in eu-west-1 or us-east-1 and so on. This filtering means that, as long as the image exists in that region, we can use “the right one”. So let’s take a look at this file.

So, now we have everything we need to create our VM. Let’s do that!

In here, we specify a “user_data” file to upload, in this case, the contents of a file – CloudDev.sh, but you can load anything you want in here. My CloudDev.sh is shown below, so you can see what I’m doing with this file :)

So, having created all this lot, you need to execute the terraform workload. Initially you do terraform init. This downloads all the provisioners and puts them into the same tree as these .tf files are stored in. It also resets the state of the terraform discovered or created datastore.

Next, you do terraform plan -out tfout. Technically, the tfout part can be any filename, but having something like tfout marks it as clearly part of Terraform. This creates the tfout file with the current state, and whatever needs to change in the Terraform state file on it’s next run. Typically, if you don’t use a tfout file within about 20 minutes, it’s probably worth removing it.

Finally, once you’ve run your plan stage, now you need to apply it. In this case you execute terraform apply tfout. This tfout is the same filename you specified in terraform plan. If you don’t include -out tfout on your plan (or even run a plan!) and tfout in your apply, then you can skip the terraform plan stage entirely.

Once you’re done with your environment, use terraform destroy to shut it all down… and enjoy :)

Featured image is “code crunching” by “Ruben Molina” on Flickr and is released under a CC-ND license.

"Key mess" by "Alper Çuğun" on Flickr

Making Ansible Keys more useful in complex and nested data structures

One of the things I miss about Jekyll when I’m working with Ansible is the ability to fragment my data across multiple files, but still have it as a structured *whole* at the end.

For example, given the following directory structure in Jekyll:

+ _data
|
+---+ members
|   +--- member1.yml
|   +--- member2.yml
|
+---+ groups
    +--- group1.yml
    +--- group2.yml

The content of member1.yml and member2.yml will be rendered into site.data.members.member1 and site.data.members.member2 and likewise, group1 and group2 are loaded into their respective variables.

This kind of structure isn’t possible in Ansible, because all the data files are compressed into one vars value that we can read. To work around this on a few different projects I’ve worked on, I’ve ended up doing the following:

- set_fact:
    my_members: |-
      [
        {%- for var in vars | dict2items -%}
          {%- if var.key | regex_search(my_regex) is not none -%}
            "{{ var.key | regex_replace(my_regex, '') }}": 
              {%- if var.value | string %}"{% endif -%}
              {{ var.value }}
              {%- if var.value | string %}"{% endif %},
          {%- endif -%}
        {%- endfor -%}
      ]
  vars:
    my_regex: '^member_'

So, what this does is to step over all the variables defined (for example, in host_vars\*, group_vars\*, from the gathered facts and from the role you’re in – following Ansible’s loading precedence), and then checks to see whether the key of that variable name (e.g. “member_i_am_a_member” or “member_1”) matches the regular expression (click here for more examples). If it does, the key (minus the regular expression matching piece [using regex_replace]) is added to a dictionary, and the value attached. If the value is actually a string, then it wraps it in quotes.

So, while this doesn’t give me my expressive data structure that Jekyll does (no site.data.members.member1.somevalue for me), I do at least get to have my_members.member1.somevalue if I put the right headers in! :)

I’ll leave extending this model for doing other sorts of building variables out (for example, something like if var.value['variable_place'] | default('') == 'my_members.member' + current_position) to the reader to work out how they could use something like this in their workflows!

Featured image is “Key mess” by “Alper Çuğun” on Flickr and is released under a CC-BY license.

"Seca" by "Olearys" on Flickr

Getting Started with Terraform on Azure

I’m strongly in the “Ansible is my tool, what needs fixing” camp, when it comes to Infrastructure as Code (IaC) but, I know there are other tools out there which are equally as good. I’ve been strongly advised to take a look at Terraform from HashiCorp. I’m most familiar at the moment with Azure, so this is going to be based around resources available on Azure.


Late edit: I want to credit my colleague, Pete, for his help getting started with this. While many of the code samples have been changed from what he provided me with, if it hadn’t been for these code samples in the first place, I’d never have got started!

Late edit 2: This post was initially based on Terraform 0.11, and I was prompted by another colleague, Jon, that the available documentation still follows the 0.11 layout. 0.12 was released in May, and changes how variables are reused in the code. This post now *should* follow the 0.12 conventions, but if you spot something where it doesn’t, check out this post from the Terraform team.


As with most things, there’s a learning curve, and I struggled to find a “simple” getting started guide for Terraform. I’m sure this is a failing on my part, but I thought it wouldn’t hurt to put something out there for others to pick up and see if it helps someone else (and, if that “someone else” is you, please let me know in the comments!)

Pre-requisites

You need an Azure account for this. This part is very far outside my spectrum of influence, but I’m assuming you’ve got one. If not, look at something like Digital Ocean, AWS or VMWare :) For my “controller”, I’m using Windows Subsystem for Linux (WSL), and wrote the following notes about getting my pre-requisites.

Building the file structure

One quirk with Terraform, versus other tools like Ansible, is that when you run one of the terraform commands (like terraform init, terraform plan or terraform apply), it reads the entire content of any file suffixed “tf” in that directory, so if you don’t want a file to be loaded, you need to either move it out of the directory, comment it out, or rename it so it doesn’t end .tf. By convention, you normally have three “standard” files in a terraform directory – main.tf, variables.tf and output.tf, but logically speaking, you could have everything in a single file, or each instruction in it’s own file. Because this is a relatively simple script, I’ll use this standard layout.

The actions I’ll be performing are the “standard” steps you’d perform in Azure to build a single Infrastructure as a Service (IAAS) server service:

  • Create your Resource Group (RG)
  • Create a Virtual Network (VNET)
  • Create a Subnet
  • Create a Security Group (SG) and rules
  • Create a Public IP address (PubIP) with a DNS name associated to that IP.
  • Create a Network Interface (NIC)
  • Create a Virtual Machine (VM), supplying a username and password, the size of disks and VM instance, and any post-provisioning instructions (yep, I’m using Ansible for that :) ).

I’m using Visual Studio Code, but almost any IDE will have integrations for Terraform. The main thing I’m using it for is auto-completion of resource, data and output types, also the fact that control+clicking resource types opens your browser to the documentation page on terraform.io.

So, creating my main.tf, I start by telling it that I’m working with the Terraform AzureRM Provider (the bit of code that can talk Azure API).

This simple statement is enough to get Terraform to load the AzureRM, but it still doesn’t tell Terraform how to get access to the Azure account. Use az login from a WSL shell session to authenticate.

Next, we create our basic resource, vnet and subnet resources.

But wait, I hear you cry, what are those var.something bits in there? I mentioned before that in the “standard” set of files is a “variables.tf” file. In here, you specify values for later consumption. I have recorded variables for the resource group name and location, as well as the VNet name and subnet name. Let’s add those into variables.tf.

When you’ve specified a resource, you can capture any of the results from that resource to use later – either in the main.tf or in the output.tf files. By creating the resource group (called “rg” here, but you can call it anything from “demo” to “myfirstresourcegroup”), we can consume the name or location with azurerm_resource_group.rg.name and azurerm_resource_group.rg.location, and so on. In the above code, we use the VNet name in the subnet, and so on.

After the subnet is created, we can start adding the VM specific parts – a security group (with rules), a public IP (with DNS name) and a network interface. I’ll create the VM itself later. So, let’s do this.

BUT WAIT, what’s that ${trimspace(data.http.icanhazip.body)}/32 bit there?? Any resources we want to load from the terraform state, but that we’ve not directly defined ourselves needs to come from somewhere. These items are classed as “data” – that is, we want to know what their values are, but we aren’t *changing* the service to get it. You can also use this to import other resource items, perhaps a virtual network that is created by another team, or perhaps your account doesn’t have the rights to create a resource group. I’ll include a commented out data block in the overall main.tf file for review that specifies a VNet if you want to see how that works.

In this case, I want to put the public IP address I’m coming from into the NSG Rule, so I can get access to the VM, without opening it up to *everyone*. I’m not that sure that my IP address won’t change between one run and the next, so I’m using the icanhazip.com service to determine my IP address. But I’ve not defined how to get that resource yet. Let’s add it to the main.tf for now.

So, we’re now ready to create our virtual machine. It’s quite a long block, but I’ll pull certain elements apart once I’ve pasted this block in.

So, this is broken into four main pieces.

  • Virtual Machine Details. This part is relatively sensible. Name RG, location, NIC, Size and what happens to the disks when the machine powers on. OK.
name                             = "iaas-vm"
location                         = azurerm_resource_group.rg.location
resource_group_name              = azurerm_resource_group.rg.name
network_interface_ids            = [azurerm_network_interface.iaasnic.id]
vm_size                          = "Standard_DS1_v2"
delete_os_disk_on_termination    = true
delete_data_disks_on_termination = true
  • Disk details.
storage_image_reference {
  publisher = "Canonical"
  offer     = "UbuntuServer"
  sku       = "18.04-LTS"
  version   = "latest"
}
storage_os_disk {
  name              = "iaas-os-disk"
  caching           = "ReadWrite"
  create_option     = "FromImage"
  managed_disk_type = "Standard_LRS"
}
  • OS basics: VM Hostname, username of the first user, and it’s password. Note, if you want to use an SSH key, this must be stored for Terraform to use without passphrase. If you mention an SSH key here, as well as a password, this can cause all sorts of connection issues, so pick one or the other.
os_profile {
  computer_name  = "iaas"
  admin_username = var.ssh_user
  admin_password = var.ssh_password
}
os_profile_linux_config {
  disable_password_authentication = false
}
  • And lastly, provisioning. I want to use Ansible for my provisioning. In this example, I have a basic playbook stored locally on my Terraform host, which I transfer to the VM, install Ansible via pip, and then execute ansible-playbook against the file I uploaded. This could just as easily be a git repo to clone or a shell script to copy in, but this is a “simple” example.
provisioner "remote-exec" {
  inline = ["mkdir /tmp/ansible"]

  connection {
    type     = "ssh"
    host     = azurerm_public_ip.iaaspubip.fqdn
    user     = var.ssh_user
    password = var.ssh_password
  }
}

provisioner "file" {
  source = "ansible/"
  destination = "/tmp/ansible"

  connection {
    type     = "ssh"
    host     = azurerm_public_ip.iaaspubip.fqdn
    user     = var.ssh_user
    password = var.ssh_password
  }
}

provisioner "remote-exec" {
  inline = [
    "sudo apt update > /tmp/apt_update || cat /tmp/apt_update",
    "sudo apt install -y python3-pip > /tmp/apt_install_python3_pip || cat /tmp/apt_install_python3_pip",
    "sudo -H pip3 install ansible > /tmp/pip_install_ansible || cat /tmp/pip_install_ansible",
    "ansible-playbook /tmp/ansible/main.yml"
  ]

  connection {
    type     = "ssh"
    host     = azurerm_public_ip.iaaspubip.fqdn
    user     = var.ssh_user
    password = var.ssh_password
  }
}

This part of code is done in three parts – create upload path, copy the files in, and then execute it. If you don’t create the upload path, it’ll upload just the first file it comes to into the path specified.

Each remote-exec and file provisioner statement must include the hostname, username and either the password, or SSH private key. In this example, I provide just the password.

So, having created all this lot, you need to execute the terraform workload. Initially you do terraform init. This downloads all the provisioners and puts them into the same tree as these .tf files are stored in. It also resets the state of the terraform discovered or created datastore.

Next, you do terraform plan -out tfout. Technically, the tfout part can be any filename, but having something like tfout marks it as clearly part of Terraform. This creates the tfout file with the current state, and whatever needs to change in the Terraform state file on it’s next run. Typically, if you don’t use a tfout file within about 20 minutes, it’s probably worth removing it.

Finally, once you’ve run your plan stage, now you need to apply it. In this case you execute terraform apply tfout. This tfout is the same filename you specified in terraform plan. If you don’t include -out tfout on your plan (or even run a plan!) and tfout in your apply, then you can skip the terraform plan stage entirely.

When I ran this, with a handful of changes to the variable files, I got this result:

Once you’re done with your environment, use terraform destroy to shut it all down… and enjoy :)

The full source is available in the associated Gist. Pull requests and constructive criticism are very welcome!

Featured image is “Seca” by “Olearys” on Flickr and is released under a CC-BY license.

"Tower" by " Yijun Chen" on Flickr

Building a Gitlab and Ansible Tower (AWX) Demo in Vagrant with Ansible

TL;DR – I created a repository on GitHub‌ containing a Vagrantfile and an Ansible Playbook to build a VM running Docker. That VM hosts AWX (Ansible Tower’s upstream open-source project) and Gitlab.

A couple of years ago, a colleague created (and I enhanced) a Vagrant and Ansible playbook called “Project X” which would run an AWX instance in a Virtual Machine. It’s a bit heavy, and did a lot of things to do with persistence that I really didn’t need, so I parked my changes and kept an eye on his playbook…

Fast-forward to a week-or-so ago. I needed to explain what a Git/Ansible Workflow would look like, and so I went back to look at ProjectX. Oh my, it looks very complex and consumed a lot of roles that, historically, I’ve not been that impressed with… I just needed the basics to run AWX. Oh, and I also needed a Gitlab environment.

I knew that Gitlab had a docker-based install, and so does AWX, so I trundled off to find some install guides. These are listed in the playbook I eventually created (hence not listing them here). Not all the choices I made were inspired by those guides – I wanted to make quite a bit of this stuff “build itself”… this meant I wanted users, groups and projects to be created in Gitlab, and users, projects, organisations, inventories and credentials to be created in AWX.

I knew that you can create Docker Containers in Ansible, so after I’d got my pre-requisites built (full upgrade, docker installed, pip libraries installed), I add the gitlab-ce:latest docker image, and expose some ports. Even now, I’m not getting the SSH port mapped that I was expecting, but … it’s no disaster.

I did notice that the Gitlab service takes ages to start once the container is marked as running, so I did some more digging, and found that the uri module can be used to poll a URL. It wasn’t documented well how you can make it keep polling until you get the response you want, so … I added a PR on the Ansible project’s github repo for that one (and I also wrote a blog post about that earlier too).

Once I had a working Gitlab service, I needed to customize it. There are a bunch of Gitlab modules in Ansible but since a few releases back of Gitlab, these don’t work any more, so I had to find a different way. That different way was to run an internal command called “gitlab-rails”. It’s not perfect (so it doesn’t create repos in your projects) but it’s pretty good at giving you just enough to build your demo environment. So that’s getting Gitlab up…

Now I need to build AWX. There’s lots of build guides for this, but actually I had most luck using the README in their repository (I know, who’d have thought it!??!) There are some “Secrets” that should be changed in production that I’m changing in my script, but on the whole, it’s pretty much a vanilla install.

Unlike the Gitlab modules, the Ansible Tower modules all work, so I use these to create the users, credentials and so-on. Like the gitlab-rails commands, however, the documentation for using the tower modules is pretty ropey, and I still don’t have things like “getting your users to have access to your organisation” working from the get-go, but for the bulk of the administration, it does “just work”.

Like all my playbooks, I use group_vars to define the stuff I don’t want to keep repeating. In this demo, I’ve set all the passwords to “Passw0rd”, and I’ve created 3 users in both AWX and Gitlab – csa, ops and release – indicative of the sorts of people this demo I ran was aimed at – Architects, Operations and Release Managers.

Maybe, one day, I’ll even be able to release the presentation that went with the demo ;)

On a more productive note, if you’re doing things with the tower_ modules and want to tell me what I need to fix up, or if you’re doing awesome things with the gitlab-rails tool, please visit the repo with this automation code in, and take a look at some of my “todo” items! Thanks!!

Featured image is “Tower” by “Yijun Chen” on Flickr and is released under a CC-BY-SA license.