In basically every engineering organization I’ve ever regarded as particularly high functioning, I’ve sat through one specific recurring conversation which is not – a conversation about “complexity”. Things are good or bad because they are or aren’t complex, architectures needs to be redone because it’s too complex – some refactor of whatever it is won’t work because it’s too complex. You may have even been a part of some of these conversations – or even been the one advocating for simple light-weight solutions. I’ve done it. Many times.

Rarely, if ever, do we talk about complexity within its rightful context – complexity for whom. Is a solution complex because it’s complex for the end user? Is it complex if it’s complex for an API consumer? Is it complex if it’s complex for the person maintaining the API service? Is it complex if it’s complex for someone outside the team maintaining it to understand? Complexity within a problem domain I’ve come to believe, is fairly zero-sum – there’s a fixed amount of complexity in the problem to be solved, and you can choose to either solve it, or leave it for those downstream of you to solve that problem on their own.

That being said, while I believe there is a lower bound in complexity to contend with for a problem, I do not believe there is an upper bound to the complexity of solutions possible. It is always possible, and in fact, very likely that teams create problems for themselves while trying to solve a problem. The rest of this post is talking to the lower bound. When getting feedback on an early draft of this blog post, I’ve been informed that Fred Brooks coined a term for what I call “lower bound complexity” – “Essential Complexity”, in the paper “No Silver Bullet—Essence and Accident in Software Engineering”, which is a better term and can be used interchangeably.

Complexity Culture

In a large enough organization, where the team is high functioning enough to have and maintain trust amongst peers, members of the team will specialize. People will begin to engage with subsets of the work to be done, and begin to have their efficacy measured against that part of the organization’s problems. Incentives shift, and over time it becomes increasingly likely that two engineers may have two very different priorities when working on the same system together. Someone accountable for uptime and tasked with responding to outages will begin to resist changes. Someone accountable for rapidly delivering features will resist gates between them and their users. Companies (either wittingly or unwittingly) will deal with this by tasking engineers with both production (feature development) and operational tasks (maintenance), so the difference in incentives isn’t usually as bad as it could be.

When we get a bunch of folks from far-flung corners of an organization in a room, fire up a slide deck and throw up some aspirational to-be architecture diagram in order to get a sign-off to solve some problem (be it someone needs a credible promotion packet, new feature needs to get delivered, or the system has begun to fail and needs fixing), the initial reaction will, more often than I’d like, start to devolve into a discussion of how this is going to introduce a bunch of complexity, going to be hard to maintain, why can’t you make it less complex?

Right around here is when I start to try and contextualize the conversation happening around me – understand what complexity is that being discussed, and understand who is taking on that burden. Think about who should be owning that problem, and work through the tradeoffs involved. Is it best solved here, or left to consumers (be them other systems, developers, or users). Should something become an API call’s optional param, taking on all the edge-cases and on, or should users have to implement the logic using the data you return (leaving everyone else to take on all the edge-cases and maintenance)? Should you process the data, or require the user to preprocess it for you?

Frequently it’s right to make an active and explicit decision to simplify and leave problems to be solved downstream, since they may not actually need to be solved – or perhaps you expect consumers will want to own the specifics of how the problem is solved, in which case you leave lots of documentation and examples. Many other times, especially when it’s something downstream consumers are likely to hit, it’s best solved internal to the system, since the only thing that can come of leaving it unsolved are bugs, frustration and half-correct solutions. This is a grey-space of tradeoffs, not a clear decision tree. No one wants the software manifestation of a katamari ball or a junk drawer, nor does anyone want a half-baked service unable to handle the simplest use-case.

Head-in-sand as a Service

Popoffs about how complex something is, are, to a first approximation, best understood as meaning “complicated for the person making comments”. A lot of the #thoughtleadership believe that an AWS hosted EKS k8s cluster running images built by CI talking to an AWS hosted PostgreSQL RDS is not complex. They’re right. Mostly right. This is less complex – less complex for them. It’s not, however, without complexity and its own tradeoffs – it’s just complexity that they do not have to deal with. Now they don’t have to maintain machines that have pesky operating systems or hard drive failures. They don’t have to deal with updating the version of k8s, nor ensuring the backups work. No one has to push some artifact to prod manually. Deployments happen unattended. You click a button and get a cluster.

On the other hand, developers outside the ops function need to deal with troubleshooting CI, debugging access control rules encoded in turing complete YAML, permissions issues inside the cluster due to whatever the fuck a service mesh is, everyone needs to learn how to use some k8s tools they only actually use during a bad day, likely while doing some x.509 troubleshooting to connect to the cluster (an internal only endpoint; just port forward it) – not to mention all sorts of rules to route packets to their project (a single repo’s binary being run in 3 containers on a single vm host).

Beyond that, there’s the invisible complexity – complexity on the interior of a service you depend on. I think about the dozens of teams maintaining the EKS service (which is either run on EC2 instances, or alternately, EC2 instances in a trench coat, moustache and even more shell scripts), the RDS service (also EC2 and shell scripts, but this time accounting for redundancy, backups, availability zones), scores of hypervisors pulled off the shelf (xen, kvm) smashed together with the ones built in-house (firecracker, nitro, etc) running on hardware that has to be refreshed and maintained continuously. Every request processed by network ACL rules, AWS IAM rules, security group rules, using IP space announced to the internet wired through IXPs directly into ISPs. I don’t even want to begin to think about the complexity inherent in how those switches are designed. Shitloads of complexity to solve problems you may or may not have, or even know you had.

What’s more complex? An app running in an in-house 4u server racked in the office’s telco closet in the back running off the office Verizon line, or an app running four hypervisors deep in an AWS datacenter? Which is more complex to you? What about to your organization? In total? Which is more prone to failure? Which is more secure? Is the complexity good or bad? What type of Complexity can you manage effectively? Which threaten the system? Which threaten your users?

COMPLEXIVIBES

This extends beyond Engineering. Decisions regarding “what tools are we able to use” – be them existing contracts with cloud providers, CIO mandated SaaS products, a list of the only permissible open source projects – will incur costs in terms of expressed “complexity”. Pinning open source projects to a fixed set makes SBOM production “less complex”. Using only one SaaS provider’s product suite (even if its terrible, because it has all the types of tools you need) makes accreditation “less complex”. If all you have is a contract with Pauly T’s lowest price technically acceptable artisinal cloudary and haberdashery, the way you pay for your compute is “less complex” for the CIO shop, though you will find yourself building your own hosted database template, mechanism to spin up a k8s cluster, and all the operational and technical burden that comes with it. Or you won’t and make it everyone else’s problem in the organization. Nothing you can do will solve for the fact that you must now deal with this problem somewhere because it was less complicated for the business to put the workloads on the existing contract with a cut-rate vendor.

Suddenly, the decision to “reduce complexity” because of an existing contract vehicle has resulted in a huge amount of technical risk and maintenance burden being onboarded. Complexity you would otherwise externalize has now been taken on internally. With a large enough organizations (specifically, in this case, i’m talking about you, bureaucracies), this is largely ignored or accepted as normal since the personnel cost is understood to be free to everyone involved. Doing it this way is more expensive, more work, less reliable and less maintainable, and yet, somehow, is, in a lot of ways, “less complex” to the organization. It’s particularly bad with bureaucracies, since screwing up a contract will get you into much more trouble than delivering a broken product, leaving basically no reason for anyone to care to fix this.

I can’t shake the feeling that for every story of technical mandates gone awry, somewhere just out of sight there’s a decisionmaker optimizing for what they believe to be the least amount of complexity – least hassle, fewest unique cases, most consistency – as they can. They freely offload complexity from their accreditation and risk acceptance functions through mandates. They will never have to deal with it. That does not change the fact that someone does.

TC;DR (TOO COMPLEX; DIDN’T REVIEW)

We wish to rid ourselves of systemic Complexity – after all, complexity is bad, simplicity is good. Removing upper-bound own-goal complexity (“accidental complexity” in Brooks’s terms) is important, but once you hit the lower bound complexity, the tradeoffs become zero-sum. Removing complexity from one part of the system means that somewhere else – maybe outside your organization or in a non-engineering function must grow it back. Sometimes, the opposite is the case, such as when a previously manual business processes is automated. Maybe that’s a good idea. Maybe it’s not. All I know is that what doesn’t help the situation is conflating complexity with everything we don’t like – legacy code, maintenance burden or toil, cost, delivery velocity.

• Complexity is not the same as proclivity to failure. The most reliable systems I’ve interacted with are unimaginably complex, with layers of internal protection to prevent complete failure. This has its own set of costs which other people have written about extensively.
• Complexity is not cost. Sometimes the cost of taking all the complexity in-house is less, for whatever value of cost you choose to use.
• Complexity is not absolute. Something simple from one perspective may be wildly complex from another. The impulse to burn down complex sections of code is helpful to have generally, but sometimes things are complicated for a reason, even if that reason exists outside your codebase or organization.
• Complexity is not something you can remove without introducing complexity elsewhere. Just as not making a decision is a decision itself; choosing to require someone else to deal with a problem rather than dealing with it internally is a choice that needs to be considered in its full context.

Next time you’re sitting through a discussion and someone starts to talk about all the complexity about to be introduced, I want to pop up in the back of your head, politely asking what does complex mean in this context? Is it lower bound complexity? Is this complexity desirable? Is what they’re saying mean something along the lines of I don’t understand the problems being solved, or does it mean something along the lines of this problem should be solved elsewhere? Do they believe this will result in more work for them in a way that you don’t see? Should this not solved at all by changing the bounds of what we should accept or redefine the understood limits of this system? Is the perceived complexity a result of a decision elsewhere? Who’s taking this complexity on, or more to the point, is failing to address complexity required by the problem leaving it to others? Does it impact others? How specifically? What are you not seeing?

What can change?

What should change?