Using ADOP and Docker to Learn Ansible

As I have written here, the DevOps Platform (aka ADOP) is an integration of open source tools that is designed to provide the tooling capability required for Continuous Delivery.  Through the concept of cartridges (plugins) ADOP also makes it very easy to re-use automation.

In this blog I will describe an ADOP Cartridge that I created as an easy way to experiment with Ansible.  Of course there are many other ways of experimenting with Ansible such as using Vagrant.  I chose to create an ADOP cartridge because ADOP is so easy to provision and predictable.  If you have an ADOP instance running you will be able to experience Ansible doing various interesting things in under 15 minutes.

To try this for yourself:

  1. Spin up and ADOP instance
  2. Load the Ansible 101 Cartridge (instructions)
  3. Run the jobs one-by-one and in each case read the console output.
  4. Re-run the jobs with different input parameters.

To anyone only loosely familiar with ADOP, Docker and Ansible, I recognise that this blog could be hard to follow so here is a quick diagram of what is going on.

docker-ansible

The Jenkins Jobs in the Cartridge

The jobs do the following things:

As the name suggests, this job just demonstrates how to install Ansible on Centos.  It installs Ansible in a Docker container in order to keep things simple and easy to clean up.  Having build a Docker image with Ansible installed, it tests the image just by running inside the container.

$ ansible --version

2_Run_Example_Adhoc_Commands

This job is a lot more interesting than the previous.  As the name suggests, the job is designed to run some adhoc Ansible commands (which is one of the first things you’ll do when learning Ansible).

Since the purpose of Ansible is infrastructure automation we first need to set up and environment to run commands against.  My idea was to set up an environment of Docker containers pretending to be servers.  In real life I don’t think we would ever want Ansible configuring running Docker containers (we normally want Docker containers to be immutable and certainly don’t want them to have ssh access enabled).  However I felt it a quick way to get started and create something repeatable and disposable.

The environment created resembles the diagram above.  As you can see we create two Docker containers (acting as servers) calling themselves web-node and one calling it’s self db-node.  The images already contain a public key (the same one vagrant uses actually) so that they can be ssh’d to (once again not good practice with Docker containers, but needed so that we can treat them like servers and use Ansible).  We then use an image which we refer to as the Ansible Control Container.  We create this image by installing Ansible installation and adding a Ansible hosts file that tells Ansible how to connect to the db and web “nodes” using the same key mentioned above.

With the environment in place the job runs the following ad hoc Ansible commands:

  1. ping all web nodes using the Ansible ping module: ansible web -m ping
  2. gather facts about the db node using the Ansible setup module: ansible db -m setup
  3. add a user to all web servers using the Ansible user module:  ansible web -b -m user -a “name=johnd comment=”John Doe” uid=1040″

By running the job and reading the console output you can see Ansible in action and then update the job to learn more.

3_Run_Your_Adhoc_Command

This job is identical to the job above in terms of setting up an environment to run Ansible.  However instead of having the hard-coded ad hoc Ansible commands listed above, it allows you to enter your own commands when running the job.  By default it pings all nodes:

ansible all -m ping

4_Run_A_Playbook

This job is identical to the job above in terms of setting up an environment to run Ansible.  However instead of passing in an ad hoc Ansible command, it lets you pass in an Ansible playbook to also run against the nodes.  By default the playbook that gets run installs Apache on the web nodes and PostgreSQL on the db node.  Of course you can change this to run any playbook you like so long as it is set to run on a host expression that matches: web-node-1, web-node-2, and/or db-node (or “all”).

How the jobs 2-4 work

To understand exactly how jobs 2-4 work, the code is reasonably well commented and should be fairly readable.  However, at a high-level the following steps are run:

  1. Create the Ansible inventory (hosts) file that our Ansible Control Container will need so that it can connect (ssh) to our db and web “nodes” to control them.
  2. Build the Docker image for our Ansible Control Container (install Ansible like the first Jenkins job, and then add the inventory file)
  3. Create a Docker network for our pretend server containers and our Ansible Control container to all run on.
  4. Create a docker-compose file for our pretend servers environment
  5. Use docker-compose to create our pretend servers environment
  6. Run the Ansible Control Container mounting in the Jenkins workspace if we want to run a local playbook file or if not just running the ad hoc Ansible command.

Conclusion

I hope this has been a useful read and has clarified a few things about Ansible, ADOP and Docker.  If you find this useful please star the GitHub repo and or share a pull request!

Bonus: here is an ADOP Platform Extension for Ansible Tower.

ADOP with Pivotal Cloud Foundry

As I have written here, the DevOps Platform (aka ADOP) is an integration of open source tools that is designed to provide the tooling capability required for Continuous Delivery.

In this blog I will describe integrating ADOP and the Cloud Foundry public PaaS from Pivotal.  Whilst it is of course technically possible to run all of the tools found in ADOP on Cloud Foundry, that wasn’t our intention.  Instead we wanted to combine the Continuous Delivery pipeline capabilities of ADOP with the industrial grade cloud first environments that Cloud Foundry offers.

Many ADOP cartridges for example the Java Petclinic one contain two Continuous Delivery pipelines:

  • The first to build and test the infrastructure code and build the Platform Application
  • The second to build and test the application code and deploy it to an environment built on the Platform Application.

The beauty of using a Public PaaS like Pivotal Cloud Foundry is that your platforms and environments are taken care of leaving you much more time to focus on the application code.  However you do of course still need to create an account and provision your environments.

  1. Register here
  2. Click Pivotal Web Services
  3. Create a free tier account
  4. Create and organisation
  5. Create one or more spaces

With this in place you are ready to:

  1. Spin up and ADOP instance
  2. Store your Cloud Foundry credentials in Jenkins’ Secure Store
  3. Load the Cloud Foundry Cartridge (instructions)
  4. Trigger the Continuous Delivery pipeline.

Having done all of this, the pipeline now does the following:

  1. Builds the code (which happens to be the JPetStore
  2. Runs the Unit Test and performs Static Code Analysis using SonarQube
  3. Deploys the code to an environment also known in Cloud Foundry as a Space
  4. Performs functional testing using Selenium and some security testing using OWASP ZAPP.
  5. Performs some performance testing using Gatling.
  6. Kills the running application in environment and waits to verify that Cloud Foundry automatically restores it.
  7. Deploys the application to a multi node Cloud Foundry environment.
  8. Kills one of the nodes in Cloud Foundry and validates that Cloud Foundry automatically avoids sending traffic to the killed node.

The beauty of ADOP is that all of this great Continuous Delivery automation is fully portable and can be loaded time and time again into any ADOP instance running on any cloud.

There is plenty more we could have done with the cartridge to really put the PaaS through its paces such as generating load and watching auto-scaling in action.  Everything is on Github, so pull requests will be warmly welcomed!  If you’ve tried to follow along but got stuck at all, please comment on this blog.

Abstraction is not Obsoletion – Abstraction is Survival

Successfully delivering Enterprise IT is a complicated, probably even complex problem.  What’s surprising, is that as an industry, many of us are still comfortable accepting so much of the problem as our own to manage.

Let’s consider an albeit very simplified and arguably imprecise view of The “full stack”:

  • Physical electrical characteristics of materials (e.g. copper / p-type silicon, …)
  • Electronic components (resistor, capacitor, transistor)
  • Integrated circuits
  • CPUs and storage
  • Hardware devices
  • Operating Systems
  • Assembly Language
  • Modern Software Languages
  • Middleware Software
  • Business Software Systems
  • Business Logic

When you examine this view, hopefully (irrespective of what you think about what’s included or missing and the order) it is clear that when we do “IT” we are already extremely comfortable being abstracted from detail. We are already fully ready to use things which we do not and may never understand. When we build an eCommerce Platform, an ERP, or CRM system, little thought it given to Electronic components for example.

My challenge to the industry as a whole is to recognise more openly the immense benefit of abstraction for which we are already entirely dependent and to embrace it even more urgently!

Here is my thinking:

  • Electrons are hard – we take them for granted
  • Integrated circuits are hard – so we take them for granted
  • Hardware devices (servers for example) are hard – so why are so many enterprises still buying and managing them?
  • The software that it takes to make servers useful for hosting an application is hard – so why are we still doing this by default?

For solutions that still involve writing code, the most extreme example of abstraction I’ve experienced so far is the Lambda service from AWS.  Some seem to have started calling such things ServerLess computing.

With Lambda you write your software functions and upload them ready for AWS to run for you. Then you configure the triggering event that would cause your function to run. Then you sit back and pay for the privilege whilst enjoying the benefits. Obviously if the benefits outweigh the cost for the service you are making money. (Or perhaps in the world of venture capital, if the benefits are generating lots of revenue or even just active users growth, for now you don’t care…)

Let’s take a mobile example. Anyone with enough time and dedication can sit at home on a laptop and start writing mobile applications. If they write it as a purely standalone, offline application, and charge a small fee for it, theoretically they can make enough money to retire-on without even knowing how to spell server.  But in practice most applications (even if they just rely on in app-adverts) require network enabled services. But for this our app developer still doesn’t need to spell server, they just need to use the API of the online add company e.g. Adwords and their app will start generating advertising revenue. Next perhaps the application relies on persisting data off the device or notifications to be pushed to it. The developer still only needs to use another API to do this, for example Parse can provide that to you all as a programming service.  You just use the software development kit and are completely abstracted from servers.

So why are so many enterprises still exposing themselves to so much of the “full stack” above?  I wonder how much inertia there was to integrated circuits in the 1950s and how many people argued against abstraction from transistors…

To survive is to embrace Abstraction!

 


[1] Abstraction in a general computer science sense not a mathematical one (as used by Joel Spolsky in his excellent Law of Leaky Abstractions blog.)

Join the DevOps Community Today!

As I’ve said in the past, if your organisation does not yet consider itself to be “doing DevOps” you should change that today.

If I was pushed to say the one thing I love most about the DevOps movement, it would be the sense of community and sharing.

I’ve never experienced anything like it previously in our industry.  It seems like everyone involved is united by being passionate about collaborating in as many ways as possible to improve:

  • the world through software
  • the rate at which we can do that
  • the lives of those working our industry.

The barrier to entry to this community is extremely low, for example you can:

You could also consider attending the DevOps Enterprise Summit London (DOES).  It’s the third DOES event and the first ever in Europe and is highly likely to be one of the most important professional development things you do this year.  Organised by Gene Kim (co-author of The Phoenix Project) and IT Revolution, the conference is highly focused on bringing together anyone interested in DevOps and providing them as much support as humanly possible in two days.  This involves presentations from some of the most advanced IT organisations in the world (aka unicorns), as well as many from those in traditional enterprises who may be on a very similar journey to you.   Already confirmed are talks from:

  • Rosalind Radcliffe talking about doing DevOps with Mainframe systems
  • Ron Van Kemenade CIO of ING Bank
  • Jason Cox about doing DevOps transformation at Disney
  • Scott Potter Head of New Engineering at News UK
  • And many more.

My recommendation is to get as many of your organisation along to the event as possible.  They won’t be disappointed.

Early bird tickets are available until 11th May 2016.

(Full disclosure – I’m a volunteer on the DOES London committee.)

London Banner logo_770x330

Running the DevOps Platform on Microsoft Azure

As per my last post about GCE sometimes knowing something is possible just isn’t good enough.  So here is how I spun up the DevOps Platform on the Microsoft Azure cloud.  Warning thanks to Docker Machine, this post is very similar to this earlier one.

1. I needed an Azure account.

2. I logged into my Azure account and didn’t click “view the new Portal”.

3. On the left hand menu, I scrolled down to the bottom (it didn’t look immediately to me like it will scroll so hover) and clicked settings.  Here I was able to see my subscription ID and copy it.

4. (Having previously installed Docker Toolbox, see here) I opened Git Bash (as an Administrator) and ran this command:

$ docker-machine create --driver azure --azure-size Standard_A3 --azure-subscription-id <the ID I just copied> markos01

I was prompted to open a url in my brower, enter a confirmation code, and then login with my Azure credentials.  Credit to Microsoft, this was easier than GCE for which I needed to install the gcloud commandline utility!

You will notice that this is fairly standard.  I picked an Standard_A3 machine type which is roughly equivalent to what we use for AWS and GCP.

5. I waited while a machine was created in Azure containing Docker

6. I cloned the ADOP Docker Compose repository from GitHub:

$ git clone https://github.com/Accenture/adop-docker-compose
$ cd adop-docker-compose

7. I ran the normal startup.sh command as follows:

$ ./startup.sh -m markos01 -c NA

And entered a user name (thanks to this recent enhancement), hey presto

...
SUCCESS, your new ADOP instance is ready!
Run these commands in your shell:
eval \"$(docker-machine env $MACHINE_NAME)\"
source env.config.sh
Navigate to http://52.160.97.159 in your browser to use your new DevOps Platform!

And just to prove it:

$ whois 52.160.97.159 | grep Org
Organization: Microsoft Corporation (MSFT)
OrgName: Microsoft Corporation
OrgId: MSFT

8. I had to go to All resources > markos01-firewall > Inbound security rules and added a rule to allow HTTP to my server on port 80.

9. I viewed my new ADOP on Azure hosted instance in (of course…) Chrome! 😉

More lovely stuff!

 

Running the DevOps Platform on Google Compute Engine

Sometimes knowing something is possible just isn’t good enough.  So here is how I spun up the DevOps Platform on Google Compute Engine (GCE).

1. I needed a Google Compute Engine account.

2. I enabled the Google Compute APIs for my GCE account

3. I installed the Google Cloud commandline API

4. I opened the Google Cloud SDK Shell link that had appeared in my Windows Start menu and ran:

C:\> gcloud auth login

This popped open a Chrome window and asked me to authenticate against my GCE account.

5. (Having previously installed Docker Toolbox, see here) I opened Git Bash (as an Administrator) and ran this command:

$ docker-machine create --driver google \
                 --google-project <a project in my GCE account> \
                 --google-machine-type n1-standard-2 \
                 markosadop01

You will notice that this is fairly standard.  I picked an n1-standard-2 machine type which is roughly equivalent to what we use for AWS.

6. I waited while a machine was created in Google containing Docker

7. I cloned the ADOP Docker Compose repository from GitHub:

$ git clone https://github.com/Accenture/adop-docker-compose
$ cd adop-docker-compose

8. I ran the normal startup.sh command as follows:

$ git clone https://github.com/Accenture/adop-docker-compose
$ ./startup.sh -m markosadop01 -c NA

And hey presto:

...
SUCCESS, your new ADOP instance is ready!
Run these commands in your shell:
eval "$(docker-machine env $MACHINE_NAME)"
source env.config.sh
Navigate to http://104.197.235.64 in your browser to use your new DevOps Platform!

And just to prove it:

$ whois 104.197.235.64 | grep Org
Registrant Organization: Google Inc.
Admin Organization: Google Inc.
Tech Organization: Google Inc.

9. I had to go to Networks > Firewall rules and added a rule to allow HTTP to my server.

10. I viewed my new ADOP on Google instance in (of course…) Chrome!

Lovely stuff!

Start Infrastructure Coding Today!

* Warning this post contains mildly anti-Windows sentiments *

It has never been easier to get ‘hands-on’ with Infrastructure Coding and Containers (yes including Docker), even if your daily life is spent using a Windows work laptop.  My friend Kumar and I proved this the other Saturday night in just one hour in a bar in Chennai.  Here are the steps we performed on his laptop.  I encourage you to do the same (with an optional side order of Kingfisher Ultra).

 

  1. We installed Docker Toolbox.
    It turns out this is an extremely fruitful first step as it gives you:

    1. Git (and in particular GitBash). This allows you to use the world’s best Software Configuration Management tool Git and welcomes you into the world of being able to use and contribute to Open Source software on Git Hub.  Plus it has the added bonus of turning  your laptop into something which understands good wholesome Linux commands.
    2. Virtual Box. This is a hypervisor that turns your laptop from being one machine running one Operating System (Windoze) into something capable of running multiple virtual machines with almost any Operating System you want (even UniKernels!).  Suddenly you can run (and develop) local copies of servers that from a software perspective match Production.
    3. Docker Machine. This is a command line utility that will create virtual machines for running Docker on.  It can do this either locally on your shiny new Virtual Box instance or remotely in the cloud (even the Azure cloud – Linux machines of course)
    4. Docker command line. This is the main command line utility of Docker.  This will enable you to download and build Docker images, and turn them into running Docker containers.  The beauty of the Docker command line is that you can run it locally (ideally in GitBash) on your local machine and have it control Docker running on a Linux machine.  See diagram below.
    5. Docker Compose. This is a utility that gives you the ability to run and associate multiple Docker containers by reading what is required from a text file.DockerVB
  2. Having completed step 1, we opened up the Docker Quickstart Terminal by clicking the entry that had appeared in the Windows start menu. This runs a shell script via GitBash that performs the following:
    1. Creates a virtual box machine (called ‘default’) and starts it
    2. Installs Docker on the new virtual machine
    3. Leaves you with a GitBash window open that has the necessary environment variables set to instruct point Docker command line utility to point at your new virtual machine.
  3. We wanted to test things out, so we ran:
    $ docker ps –a
    CONTAINER ID  IMAGE   COMMAND   CREATED   STATUS   PORTS  NAMES

     

    This showed us that our Docker command line tool was successfully talking to the Docker daemon (process) running on the ‘default’ virtual machine. And it showed us that no containers were either running or stopped on there.

  4. We wanted to testing things a little further so ran:
    $ docker run hello-world
     
    Hello from Docker.
    
    This message shows that your installation appears to be working correctly.
     
    
    To generate this message, Docker took the following steps:
    
    The Docker client contacted the Docker daemon.
    The Docker daemon pulled the "hello-world" image from the Docker Hub.
    The Docker daemon created a new container from that image which runs the
    executable that produces the output you are currently reading.
    
    The Docker daemon streamed that output to the Docker client, which sent it
    to your terminal.
    
     
    
    To try something more ambitious, you can run an Ubuntu container with:
    
    $ docker run -it ubuntu bash
    
     
    
    Share images, automate workflows, and more with a free Docker Hub account:
    
    https://hub.docker.com
    
     
    
    For more examples and ideas, visit:
    
    https://docs.docker.com/userguide

     

    The output is very self-explanatory.  So I recommend reading it now.

  5. We followed the instructions above to run a container from the Ubuntu image.  This started for us a container running Ubuntu and we ran a command to satisfy ourselves that we were running Ubuntu.  Note one slight modification, we had to prefix the command with ‘winpty’ to work around a tty-related issue in GitBash
    $ winpty docker run -it ubuntu bash
    
    root@2af72758e8a9:/# apt-get -v | head -1
    
    apt 1.0.1ubuntu2 for amd64 compiled on Aug  1 2015 19:20:48
    
    root@2af72758e8a9:/# exit
    
    $ exit

     

  6. We wanted to run something else, so we ran:
    $ docker run -d -P nginx:latest

     

  7. This caused the Docker command line to do more or less what is stated in the previous step with a few exceptions.
    • The –d flag caused the container to run in the background (we didn’t need –it).
    • The –P flag caused docker to expose the ports of Nginx back to our Windows machine.
    • The Image was Nginx rather than Ubuntu.  We didn’t need to specify a command for the container to run after starting (leaving it to run its default command).
  8. We then ran the following to establish how to connect to our Nginx:
    $ docker-machine ip default
    192.168.99.100
    
     $ docker ps
    
    CONTAINER ID        IMAGE               COMMAND                  CREATED             STATUS              PORTS                                           NAMES
    
    826827727fbf        nginx:latest        "nginx -g 'daemon off"   14 minutes ago      Up 14 minutes       0.0.0.0:32769->80/tcp, 0.0.0.0:32768->443/tcp   ecstatic_einstein
    
    

     

  9. We opened a proper web brower (Chrome) and navigated to: http://192.168.99.100:32769/ using the information above (your IP address may differ). Pleasingly we were presented with the: ‘Welcome to nginx!’ default page.
  10. We decided to clean up some of what we’re created locally on the virtual machine, so we ran the following to:
    1. Stop the Nginx container
    2. Delete the stopped containers
    3. Demonstrate that we still had the Docker ‘images’ downloaded

 

$ docker kill `docker ps -q`

8d003ca14410
$ docker rm `docker ps -aq`

8d003ca14410

2af72758e8a9

…

$ docker ps -a

CONTAINER ID        IMAGE               COMMAND             CREATED             STATUS              PORTS               NAMES

$ docker images

REPOSITORY                     TAG                 IMAGE ID            CREATED             VIRTUAL SIZE

nginx                          latest              sha256:99e9a        4 weeks ago         134.5 MB

ubuntu                         latest              sha256:3876b        5 weeks ago         187.9 MB

hello-world                    latest              sha256:690ed        4 months ago        960 B

 

 

  1. We went back to Chrome and hit refresh. As expected Nginx was gone.
  2. We opened Oracle VM Virtual box from the Windows start machine so that we could observe our ‘default’ machine listed as running.
  3. We ran the following to stop our ‘default’ machine and also observed it then stopping Virtual Box:
    $ docker-machine stop default

     

  4. Finally we installed Vagrant. This is essentially a much more generic version of Docker-Machine that is capable of creating not just virtual machines in Virtual Box for Docker, but for many other purposes.  For example from an Infrastructure Coding perspective, you might run a virtual machine for developing Chef code.

 

Not bad for one hour on hotel wifi!

Kumar keenly agreed he would complete the following next steps.  I hope you’ll join him on the journey and Start Infrastructure Coding Today!

  1. Learn Git. It really only takes 10 minutes with this tutorial LINK to learn the basics.
  2. Docker – continue the journey here
  3. Vagrant
  4. Chef
  5. Ansible

 

Please share any issues following this and I’ll improve the instructions.  Please share  any other useful tutorials and I will add those also.