Simplifying DevOps and Infrastructure-as-Code (IaC)

Let’s face it, tools for infrastructure-as-code (IaC) configuration management may be vital, but they’re difficult to use. But what if it’s not so much the tools themselves as it is our understanding of them? What if we looked at provisioning and configuration systems differently and did so using vocabulary that was less confusing and more common? 

Simply put, we’d gain a greater understanding of these tools—and, therefore, they would be simpler to use—if the terminology was simpler. Reframing these complex tools would not only allow us to make greater sense of this realm, but we’d be more effective in addressing the DevOps and software issues we all experience. 

Coming to Terms With Infrastructure-as-Code (IaC)

Software engineering encompasses all the actions developers take to create software and technology products. This typically entails writing code, testing it and making sure it satisfies the original requirements for users. Operations is the practice of ensuring those software systems are ready to go and will be able to serve users on a day-to-day basis. Operations engineers aren’t as focused on writing new code as developers are; instead, they are focused on how existing software (mis-)behaves in existing systems.

Now let’s cover some other key phrases we use and hear all the time in DevOps.

Forward engineering means creating a system based on requirements that exist for that system. It entails first building a model, implementing it in software, then configuring and deploying it into operation. While the goal is for the new system to closely match the original requirements, the modeling process can be ambiguous, causing a system to perform in unanticipated ways. Furthermore, over time, the system itself may differ from the original model due to interaction with its environment. Consider that some parts can change the configuration of other parts; procedures performed by engineers shift the system state or configuration, too.

Unfortunately, forward engineering offers no feedback with regard to the eventual success or failure of the product during the model creation process. These models and the subsequent software are created first and then deployed to test for success. Such a system can only provide feedback later based on its behavior, which can then be used to improve and build new models.

Reverse engineering calls for understanding existing systems, usually by first creating a partial mental model by inspecting such a system and then using it to plan any modifications. Often, changes are made directly to the system without always fully understanding how it works. Feedback for changes is fast because putting the system through its paces and inspecting the results is relatively simple, easy and cheap to do. A good example of reverse engineering is software security professionals who scan network services or memory to identify anomalies and exploitable gaps and then try to manipulate their content for instant feedback.

Then there’s infrastructure-as-code (IaC). This modeling technique began with declarations about how server operating system content should appear and was later adapted for the purpose of laying out and configuring cloud resources using API calls. While many vendors and products have come and gone, most tools have been declarative—with good reason.

Anything to Declare?

With declarative tools, you specify how things should appear without explicitly showing how you’ll get there. Why? It’s the easiest way to create models, templates and maps because engineers only have to declare how things should look, not get them into the correct form or place to actually do so. The emphasis is on evolving from iterative improvements that take place over time.

Another feature of declarative IaC tools is idempotence, which means repeating an action multiple times with consistent results; the system only changes when it detects deviations from the model. This can be compared with the imperative approach, which requires developers to first find all the differences between a model and system and then write each step that’s necessary for the system to match the model.

Declarative IaC tools eliminate the need for developers to write an implementation for these changes manually. These tools save thousands of labor hours by publishing templates that can be used repeatedly to build identical systems with less time and effort. The problem is that declarative tools rely on a forward engineering mindset—meaning there’s no way to get feedback from a live system—leading to many complaints from users.

The Rift With Drift

Configuration drift is what happens when a declarative forward engineering model no longer matches the state of a system. It can happen when a developer changes a model’s code without updating the systems built using that model. It could also be the result of an engineer that does exploratory ad-hoc operations and changes a system but fails to go back into the template and update its code. 

Is it realistic to ban operators from ad-hoc exploration? Actually, some companies have policies forbidding any operator or developer to touch a live production environment. Ironically, when a production system breaks, it’s the first rule overridden: Ad-hoc exploration is welcomed by anyone able to get to the bottom of the issue. 

Engineers who adopt IaC usually don’t like the work that comes with remodeling an existing system. Still, because of high demand fueled by user frustration, there’s a tool for every configuration management language—they just fail to live up to engineer expectations. The best-case scenario is that an engineer can use a reverse-engineered template to copy and paste segments into one but will need to manually write it elsewhere.

Bridging Gaps

What is needed is a new type of IaC tool. These must integrate reverse engineering at their core and allow feedback from living systems to update the very models that created them. In doing so, operators can adopt an ad-hoc change or simply revert back to the system as it was declared in the original model.

Yet, today’s standard tools fall short of this goal, and aren’t much good for helping engineers solve system operations issues. True, there are those capable of auditing templates and singling out misconfigurations and mistakes. Still, while helpful, the greater value would be the ability to review a living system, not just the templates that engineers initially used to create it. 

We have monitoring and observability tools and IaC tools, but in between them is a wide chasm. For effective DevOps, what’s required is a new breed of IaC tool, one that integrates reverse engineering practices that supports operations engineers and enables companies to bridge any development gaps.