Steve Fenton

The Fundamental DevOps Principles

Over the course of the past couple of months, I have been writing a great deal about DevOps and Continuous Delivery. This isn’t my first rodeo, as they say, as I’ve worked on teams that have been using Continuous Delivery (and before that, Lean, Agile, and adaptive techniques such as Extreme Programming).

I’ll post links to some blue papers and articles here shortly if you want to read more. You’ll notice that the quality of writing on these items is significantly better than my casual “capture my thoughts” style of writing you find here. That’s because I’m working with some incredibly talented writers and editors at Octopus Deploy who are helping me improve my word-chops. I’m not sure they’d want me to say “word-chops”, for example.

(The links will drop in here once I have URLs!)

So, bear in mind that this little thought has come not just from two decades of practical experience as a software developer, manager, and director in charge of product, data, and software development; it is also based on a good deal of research.

My research encompassed stacks of work done by others. DORA (obviously), but also the CD Foundation, Puppet, The Association for Computer Machinery, and many others.

Fundamental principle: “At the current time”

Continuous Delivery is the best way that we know of to deliver software at the current time. The State of DevOps Report has the most extensive research into software delivery that we have at the current time.

When we get new information, this will change. As a result of the research, the Structural Equation Model gets updated to reflect the best knowledge we have so far. As you read this version, it may already be out of date.

For example, it seems like five minutes ago we were talking about the four DORA metrics, but now it’s five. And that’s before we introduce the broader SPACE framework into the conversation.

It’s worth keeping up to date with changes to get insights that might sharpen your competitive edge further…

DevOps Structural Equation Model (SEM)

So, the first fundamental principle is that our new era of software delivery is about doing the best thing that we know of at the current time.

Fundamental principle: Types of work

To keep things simple, I’ll say there’s two types of work. What I mean is that work can be plotted along a scale that is either more like one type, or more like the other.

These two classifications have become more clear over time as we’ve learned more about software delivery and if you are interested, Dave Farley’s Modern Software Engineering is a great read that has a lot more detail on this point (and many others).

Software engineering can be divided into two types of tasks (remember my point about there being a scale):

  1. Complex and unpredictable work
  2. Predictable work

The fundamental principle of DevOps is that we reduce as much of the predictable work as possible to give us more time and energy to handle the complex and unpredictable stuff.

We can reduce it by automation (such as automated builds and deployment automation) and we can reduce it – either by removing work that isn’t needed or by never letting things get too big (like small batches and continuous integration of changes into trunk).

The specific ways we reduce that routine work might change. We might identify more things that we can reduce. Advancements in tools might make some things that are hard to reduce far easier, shifting the economics of things like automation.

Automation

If this sounds familiar, it’s because it connects back to my automation philosophy. I used the terms “routine” and “eccentric” to describe the work and said that all the routine stuff should be automated. Anything truly “routine” can be automated, but there is an economic U-curve to surf. As technology advances, the economics will shift in favour of automation for even the most difficult routine stuff we handle right now.

The goal isn’t to just automate everything. Some things don’t need to be done at all, so don’t automated them, just stop doing them. In other cases, doing something more often makes it vastly less work.

Continuous integration is a great example of this. If you merge every change into your mainline, you never create a giant merge with complex conflicts. This is reducing the problem, rather than attempting to automate it.

We are also targeting routine or predictable tasks in particular. There may be some future point where the definition of routine extends further into what we thought was eccentric, diminishing the amount of complex unpredictable work we handle. Who knows, there might be some incredible technological advancement that means whole classes of complexity can be solved with a tool.

The point is, at the current time and with the currently available tools, we should reduce and automate all the tasks that are predictable so the humans can get to work on the tough stuff.

Summary

The summary is short and sharp…

  1. Use the best way to stuff that we know at the current time
  2. Identify, reduce, and automate routine work

Further reading

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