Effective infrastructure for efficient development workflows

anchorThe machine that builds the machine

Elon Musk famously talks about the real challenge that Tesla faced/s isn't building cars but building and running the factories for building cars at scale. He refers to the factories as "the machine that builds the machine" and while the factories aren't still fully autonomous and can't build cars without human labor, those humans' productivity is multiplied manyfold through highly automated and integrated production processes. The same applies to software development – the code isn't going to write itself anytime soon (although we're getting closer to that situation step-by-step), but with the help of integrated and automated infrastructure, developers are enabled to write code more efficiently, faster, and with more certainty.

At the core of every development team's developer workflow, there is git (of course other version control systems exist as well, but in reality, almost everyone is using git these days). And while git's cheap and simple (admittedly not everyone agrees about that) branching model makes it easy for developers to code away in their own branch as well as rebase and merge their branches back together, the real challenge that teams are facing is orchestrating just that so that one person's changes propagate to the codebases others are working on and merged code ends up in the production system fast, predictably and with certainty.

Most teams rely on one of two techniques for managing branches and getting merged code deployed to production:

  • main branch plus feature branches: once a pull request is merged, the new version of the main branch is deployed to production automatically.
  • main or production branch, development branch and feature branches branched off of that: pull requests are merged into development, and that branch is merged into production in intervals following some sort of schedule or process.

anchorMulti-branch setups

While in reality there are thousands of variations of the latter approach, they are essentially all the same. The goal of those multi-branch setups is to add a safety net between a pull request being merged and the code being deployed to the production system. Whenever "enough" changes have accumulated in the development branch (or a scheduled release date has been reached), those changes would typically go through some kind of testing process where they would be checked for correctness (and potentially fixed) before eventually making their way to the production system(s).

That results in all kinds of inefficiencies though:

  • When bugs come up in the development branch it's often unclear where they originate; all kinds of unrelated changes have been merged together in the branch so it's unclear whether a bug originates in the pull request that implemented the respective feature or whether it is only caused by the combination of those changes with others.
  • Once bugs or unmet requirements are identified, the developers responsible for the changes will have switched to a different task already since there's a delay between the merge of their PR to the develop branch and the testing/validation being performed on that branch. They now have to get back to something they considered done already, get all the context in their minds again while at the same time leaving whatever they are working on now behind, potentially causing problems for others that might be dependant on that work so that in the worst case whole cascades of focus and context switches are triggered.
  • Changes can only be released to production with a delay. Something might be done in one week but could potentially only be released the next week or even later when the next release is scheduled or the testing cycle completes.
  • Sometimes these branching models are so complex that developers don't always understand where to branch from, where to merge back and how to resolve the conflicts that might occur along the way (after all, rebasing git branches on top of each other is a key technique to master with git but in reality not something that all developers are comfortable doing).

In fact, these branching models and the inefficient workflows they force developer teams into are almost always only necessary due to a lack of powerful infrastructure. If that infrastructure is in place with proper automation and integration, teams are enabled to adopt a much simpler model and workflow:

anchorSingle main branch with auto-deployment

A branching model with a single main branch and feature branches that are branched off of that and merged back right into it, is conceptually much simpler obviously. Furthermore, deploying all changes that are merged back into the main branch immediately and automatically, dramatically improves the workflow:

  • Changes can be tested in isolation so it's clear what causes bugs that are found in the process (unless the bug exists in production already, it has to be the changes in the respective branch since everything else is just like in production).
  • Developers will not have progressed to a different task yet. Their pull request is still open and a short feedback loop gives them all the feedback they need when they need it, thus reducing the need for context switches later on.
  • Changes can be released to production fast without the need to wait for a release date to arrive or enough other changes to have accumulated to "justify" a release.
  • There's never any uncertainty about what base branch to branch off from, where to merge something into, what branch to rebase on what base etc.
Single  branch workflow

Of course, the challenge is to do all the testing (and QA in the wider sense) that happens based on some sort of schedule or process in multi-branch models, for every single pull request – and ideally for multiple pull requests in parallel to achieve high throughput. This is where infrastructure and automation come in.

anchorTesting (sub)systems in isolation

The main building block of any effective developer infrastructure is of course a good Continuous Integration (CI) system. The most basic task of which being to run automated checks on a set of code changes to establish baseline correctness.

Typically the foundation of those checks are some sort of unit tests (or whatever concept the language/framework of choice uses) to ensure the code in fact does what it is supposed to. They also help to catch regressions early on in cases where a change to one feature causes another, seemingly unrelated one to break. Good test coverage and a fast and stable CI system that runs tests are an absolute requirement for any development team to be successful. While that's something that's not really new or controversial in our industry, there's more than just unit tests that can be leveraged to ensure a set of changes is correct and doesn't lead to regressions

  • Visual regression testing can be used to ensure the code doesn't just work correctly but the UI it generates also looks right (and doesn't change in unexpected ways). Visual testing tools like Percy will report any visual deviation from a baseline caused by a set of code changes and developers will manually approve (or revert) every single one of those. That makes all UI changes intentional and avoids accidental changes. Once the visual changes are approved and the respective code is merged back, they become part of the visual baseline the next PR is compared against, etc.
  • Linting and static analysis, in general, can be a powerful tool to find more errors and inconsistencies than just particular lines that go against an agreed-upon coding style. You could lint translation files to ensure all language files have the same set of keys to prevent missing translations or prevent the use of document.cookie in case you don't want your web app to be required to render a cookie banner – there are countless opportunities and I personally believe there's still a lot to do in that area that could have huge positive impacts on developer teams' efficiency.
  • For server systems with databases, migrations can be run against (anonymized) snapshots of the production database(s) to ensure they in fact modify the data as expected and won't run into unforeseen errors during deployment. It's also advisable to test that the server system's currently deployed code runs correctly with a migration applied and without it since that's usually what will happen when a deployment is rolled back – while it's easy to roll back code changes, rolling back migrations is often not an option or the migration has to run and complete before the respective code changes can be deployed at all.
  • For server systems it's also essential to test the deployment of the code itself as well as rolling back that deployment. Like with testing migrations, this should be done with an (anonymized) snapshot of the production database and all tests should be run on the system after the rollback to ensure it still operates correctly.

This list isn't even nearly complete. Carefully analyzing any system and its history of issues usually reveals countless opportunities to automate checks that would have prevented those issues or can help prevent other issues in the future.

All of these techniques test one (sub)system in isolation. However, many systems today aren't built as monoliths but as networks of multiple, distributed systems – e.g. single page apps (SPAs) with their respective server backends or microservice architectures. In order to be able to auto-deploy any of the individual subsystems of such architectures, it's critical to validate they operate correctly in combination with all other subsystems.

anchorTesting all subsystems together

The key technique for testing a multitude of subsystems together of course is end-to-end testing (sometimes also referred to as "integration" or "acceptance" testing – the terminology is a bit unclear in practice, asking four different people would typically result in five different opinions on the exact meaning of each of these terms). For a proper end-to-end test, a pull request that changes the code of one subsystem is tested together with the respective deployed revisions of all other subsystems. That allows catching situations where changes to the one subsystem, while completely consistent and correct within that subsystem, cause problems when interfacing with other subsystems. Typical examples for such situations would be backward-incompatible API changes that would cause errors for any client of the API that hasn't been updated yet.

Running such tests requires the ability to spin up a complete system including all of the subsystems on demand. Typically that is achieved by containerizing all of the systems so that a set of interconnected containers could be started up on the CI server. In the case of a web app that would mean serving the frontend app as well as running the API server in two containers and then running a headless browser to send requests to the frontend app (which would make requests against the backend) and asserting on the response.

Besides the simple ability to run these containers any time, another aspect of this is to maintain an example data set to load into those containers so that that data can be used in the end-to-end tests. Such datasets are typically maintained in the form of seed scripts that generate a number of well-defined resources. If such a setup isn't considered early in the project, this is particularly hard to build later on when there is a plethora of different resource types and data stores already – maintaining and evolving that data set along with the code is much easier and efficient.

End-to-end tests aren't the only valuable thing that is enabled by the ability to spin up instances of the system on demand though:

  • Providing fully functional preview systems for every pull request allows getting stakeholder approval. If there's a preview link in every pull request that points to what's essentially the same system that the end-to-end tests run against, allows product managers, designers, and other stakeholders to see a new feature in action before it is deployed to production. That way they can give feedback while the developer is still actively working on the task which again shortens the feedback loop.
  • That same system can also be used to do manual QA on a feature. Not everything can be automatically tested all the time – sometimes it's just necessary for a human to check for example whether an animation "feels" good.
  • To some extent, such an on-demand system could also be used for testing the performance characteristics of a change. While a containerized system that's spun up for testing purposes only will never use the same resources or experience the same load as the real production system, of course, it might be sufficient to get an idea for the performance characteristics of a feature and help to identify problems earlier rather than later.

With all this infrastructure in place, it's possible to move all of the testing and validation that's done en-block after a whole bunch of pull requests have been merged in a multi-branch model to the point before every individual pull request is merged. Once it passes all these checks, it can be merged into the main branch and auto-deployed to production with confidence. In fact, this process can even lead to increased confidence in comparison to scheduled big releases since every single deployment is now also much smaller in scope which already reduces risk.

anchorPost deployment

With all that testing and automation in place, it is still possible for things to blow up in production of course. Besides having error tracking with tools like Bugsnag or others in place, the ideal infrastructure also includes a process for running automated smoke tests against the production system after every single deployment.

These are quite similar in nature to the end-to-end tests with the main difference that they run against the production system. Typically those tests would focus on the main flows of an application that also have the highest relevance for the business:

  • Can new users still sign up?
  • Does the payment flow still work?
  • Are the features that are critical to users still functional?

One concern when running anything automated against the production system – potentially many times per day – is the amount of test data that is being produced in the process and that could potentially interfere with analytics or show up for real users. One way to address that (in the case of web applications) is to set a custom header that identifies the client as a test client so that the server can schedule the generated data to be deleted later on or otherwise be filtered from anything real users can see.

anchorEfficient development workflows based on effective infrastructure

An efficient workflow based on effective infrastructure as described in this post undoubtedly raises teams to new levels of productivity. Admittedly, it takes time and effort to set it all up but the productivity gains easily outweigh the cost. In particular, when considered early on in a project, that cost isn't even as substantial as it might seem. The cost however of not having infrastructure like this once it's absolutely needed, which is when trying to scale a team up without scaling down its relative velocity at the same time, is certainly much higher.

Mainmatter is a digital product development consultancy that helps teams ship better software faster, more predictably, and with higher quality. If you're interested in how we could help improve your infrastructure and workflow, schedule a call with us.

Stay up to date!

Subscribe to our newsletter and stay up to date about the latest events, workshops, and other news around our work.

Grow your business with us

Our experts are ready to guide you through your next big move. Let us know how we can help.
Get in touch