Like a lot of developers, I’ve been using Azure DevOps to manage my CI/CD pipelines. I think (or I hope anyway) most developers now are using a continuous integration process – commit to a code repository like GitHub, which has a hook into a tool (like DevOps Build Pipelines) which checks every change to make sure it compiles, and doesn’t break any tests.

Just a note – this post doesn’t have any code, it’s more of an introduction to how I’ve found Azure Pipelines can make powerful improvements to a development process. It also looks at one of the new preview features, multi-stage build pipelines. I’ll expand on individual parts of this with more code in later posts.

Azure Pipelines have traditionally been split into two types – build and release pipelines.

oldpipelines

I like to think of the split in this way:

Build Pipelines

I’ve used these for Continuous Integration (CI) activities, like compiling code, running automated tests, creating and publishing build artifacts. These pipelines can be saved as YAML – a human readable serialisation language – and pushed to a source code repository. This means that one developer can pull another developer’s CI code and run exactly the same pipeline without having to write any code. One limitation is that Build Pipelines are a simple list of tasks/jobs – historically it’s not been possible to split these instructions into separate stages which can depend on one or more conditions (though later in this post, I’ll write more about a preview feature which introduces this capability).

Release Pipelines

I’ve used these for Continuous Deployment (CD) activities, like creating environments and deploying artifacts created during the Build Pipeline process to these environments. Azure Release Pipelines can be broken up into stages with lots of customized input and output conditions, e.g. you could choose to not allow artifacts to be deployed to an environment until a manual approval process is completed. At the time of writing this, Release Pipelines cannot be saved as YAML.

What does this mean in practice?

Let’s make up a simple scenario.

A client has defined a website that they would like my team to develop, and would like to see a demonstration of progress to a small group of stakeholders at the end of each two-week iteration. They’ve also said that they care about good practice – site stability and security are important aspects of system quality. If the demonstration validates their concept at the end of the initial phase, they’ll consider opening the application up to a wider audience on a environment designed to handle a lot more user load.

Environments

The pattern I often use for promoting code through environments is:

Development machines:

  • Individual developers work on their own machine (which can be either physical or virtual) and push code from here to branches in the source code repository.
  • These branches are peer reviewed and subject to passing this are merged into the master branch (which is my preference for this scenario – YMMV).

Integration environment (a.k.a. Development Environment, or Dev-Int):

  • If the Build Pipeline has completed successfully (e.g. code compiles and no tests break), then the Release Pipeline deploys the build artifacts to the Integration Environment (it’s critical that these artifacts are used all the way through the deployment process as these are the ones that have been tested).
  • This environment is pretty unstable – code is frequently pushed here. In my world, it’s most typically used by developers who want to check that their code works as expected somewhere other than their machine. It’s not really something that testers or demonstrators would want to use.
  • I also like to run vulnerability scanning software like OWASP ZAP regularly on this environment to highlight any security issues, or run a baseline accessibility check using something like Pa11y.

Test environment:

  • The same binary artifacts deployed to Integration (like a zipped up package of website files) are then deployed to the Test environment.
  • I’ve usually set up this environment for testers who use it for any manual testing processes. Obviously user journey testing is automated as much as possible, but sometimes manual testing is still required – e.g. for further accessibility testing.

Demonstration environment:

  • I only push to this environment when I’m pretty confident that the code does what I expect and I’m happy to present it to a room full of people.

And in this scenario, if the client wishes to open up to a wider audience, I’d usually recommend the following two environments:

Pre-production a.k.a. QA (Quality Assurance), or Staging

  • Traditionally security checks (e.g. penetration tests) are run on this environment first, as are final performance tests.
  • This is the last environment before Production, and the infrastructure should mirror the infrastructure on Production so that results of any tests here are indicative of behaviour on Production.

Production a.k.a. Live

  • This is the most stable environment, and where customers will spend most of their time when using the application.
  • Often there’ll also be a mirror of this environment for ‘Disaster Recovery’ (DR) purposes.

Obviously different teams will have different and more customized needs – for example, sometimes teams aren’t able to deploy more frequently than once every sprint. If an emergency bug fix is required it’s useful to have a separate environment to allow these bug fixes to be tested before production, without disrupting the development team’s deployment process.

Do we always need all of these environments?

The environments used depend on the user needs – there’s no strategy which works in all cases. For our simple fictitious case, I think we only need Integration, Testing and Demonstration.

Here’s a high level process using the Microsoft stack that I could use to help meet the client’s initial need (only deploying as far as a demonstration platform):

reference-architecture-diagram-2

  • Developers build a website matching client needs, often pushing new code and features to a source code environment (I usually go with either GitHub or Azure DevOps Repos).
  • Code is compiled and tested using Azure DevOps Pipelines, and then the tested artifacts are deployed to:
    • The Integration Environment, then
    • The Testing Environment, and then
    • The Demonstration Environment.
  • Each one of these environments lives in its own Azure Resource Group with identical infrastructure (e.g. web farm, web application, database and monitoring capabilities). These are built using Azure Resource Manager (ARM) templates.

Using Azure Pipelines, I can create a Release Pipeline to build artifacts and deploy to the three environments above in three separate stages, as shown in the image below.

stages8

But as mentioned previously, a limitation is that this Release Pipeline doesn’t exist as code in my source code repository.

Fancy All New Multi-Stage Pipelines

But recently Azure have introduced a preview feature, where Build Pipelines have been renamed as “Pipelines”, and added some new functions.

If you want to see these in your Azure DevOps instance, log into your DevOps instance on Azure, and head over to the menu in the top right – select “Preview features”:

pipelinemenu

In the dialog window that appears, turn on the “Multi-stage Pipelines” option, highlighted in the image below:

multistagepipelines

Now your DevOps pipeline menu will look like the one below – note how the “Builds” sub-menu item has been renamed to Pipelines:

newpipelines

Now I’m able to use YAML to not only capture individual build steps, but I can package them up into stages. The image below shows how I’ve started to mirror the Release Pipeline process above using YAML – I’ve build and deployed to integration and testing environments.

build stages

I’ve also shown a fork in the process where I can run my OWASP ZAP vulnerability scanning tool after the site has been deployed on integration, at the same time as the Testing environment is being built and having artefacts deployed to it. The image below shows the tests that have failed and how they’re reported – I can select individual tests and add them as Bugs to Azure DevOps Boards.

failing tests

Microsoft have supplied some example YAML to help developers get started:

  • A simple Build -> Test -> Staging -> Production scenario.
  • A scenario with a stage that on completion triggers two stages, which then are both required for the final stage.

It’s a huge process improvement to be able to have my website source code and tests as C#, my infrastructure code as ARM templates, and my pipeline code as YAML.

For example, if someone deleted the pipeline (either accidentally or deliberately), it’s not really a big deal – we can recreate everything again in a matter of minutes. Or if the pipeline was acting in an unexpected way, I could spin up a duplicate of the pipeline and debug it safely away from production.

Current Limitations

Multi-stage pipelines are a preview feature in Azure DevOps, and personally I wouldn’t risk this with every production application yet. One major missing feature is the ability to manually approve progression from one stage to another, though I understand this is on the team’s backlog.

Wrapping Up

I really like how everything can live in source code – my full build and release process, both CI and CD, are captured in human readable YAML. This is incredibly powerful – code, tests, infrastructure and the CI/CD pipeline can be created as a template and new projects can be spun up in minutes rather than days. Additionally, I’m able to create and tear down containers which cover some overall system quality testing aspects, for example using the OWASP ZAP container to scan for vulnerabilities on the integration environment website.

As I mentioned at the start of this post, I’ll be writing more over the coming weeks about the example scenario in this post – with topics such as:

  • writing a multi-stage pipeline in YAML to create resource groups to encapsulate resources for each environment;
  • how to deploy infrastructure using ARM templates in each of the stages;
  • how to deploy artifacts to the infrastructure created at each stage;
  • use the OWASP ZAP tool to scan for vulnerabilities in the integration website, and the YAML code to do this.

 

5 thoughts on “Everything as Code with Azure DevOps Pipelines: C#, ARM, and YAML: Part #1

  1. Thanks Jeremy. I like the fact that you lay it out without a heavy code which could put me off, but know I read it till the end 😉

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