technology – Thinking Toasters

Ever seen a whale pretending to be a grass field? Or a sheep swimming in the ocean?
Of course not.
Some things just don’t fit.

But the software world is different. Here the four-eyed sheep can fly in space and no one will care. Until the moment it hits the ground.

“Oh my – this guy is talking about sheep and whales again…”

Relax. No whales this time. Instead, let me show you two architecture failure modes —
and one solution they both quietly ignore.

When execution models impersonate each other, complexity leaks. The fix is a real boundary.

For our examples we will use Modbus an ancient way of exchanging data between machines—and one that still refuses to be replaced. Each device exposes a set of registers, read and written in a fixed, periodic loop.

Simple. Brutal. Effective.

Scenario 1

We start clean. A Modbus system runs in a single deterministic loop:

read state -> process -> write state -> repeat

One day, a new requirement appears: the Modbus data must be sent elsewhere using a modern RPC protocol.

Without much thinking we start adding the communication logic into the main control loop. Suddenly alien constructions start to appear – retry counters, timestamps, acknowledge signals. Before we know we create a full-fledged message broker inside our simple loop.

Complexity grows.

Scenario 2

Now the opposite.

We start with a clean, event-driven environment. Requests, responses, handlers, queues. Perfect.

We add Modbus handling. “Easy,” we think.
“We’ll poll registers and emit events on change.”

It works… until signals start changing faster than the event system can digest.
Events pile up, updates get dropped or reordered, information is lost

And the more we try to solve it the more complex system becomes.

What happened?

In both cases we made the same fundamental mistake – we tried to bend the problem we were solving so it fits architecture that was already in place. We ignored the quiet signal saying:
“This does not belong here.”

There’s a simple rule—very much in the spirit of model-driven design:

Software should model the domain and its execution semantics.

For each domain, we must choose abstractions that fit naturally—without distortion.

The solution: a boundary with translation

The solution isn’t a smarter loop or a better event system.
It’s a boundary.

Keep each concern in its native execution model—and translate only at the edge.

On one side, a deterministic polling loop:

Read registers
Process state
Write registers
Repeat at a fixed rate

On the other side, an event-driven system:

Requests
Handlers
Queues
Backpressure

The boundary translates stable state from the deterministic world into meaningful change for the event-driven world.

No retries in the loop.
No event queues pretending to be registers.
No execution model impersonating another.

Each side runs the way it was designed to run.

Getting there isn’t a technical trick—it’s a change in how you think about the problem.

Not:

“What’s the fastest way to implement this feature?”

But:

“What is the domain—and how does it naturally execute?”

Follow that, and things fall into place.

Sheep stay on grass.
Whales stay in the ocean.

And systems quietly become what they’re supposed to be.

Has Anyone Seen My Architecture?

There are countless definitions of software architecture.
Some emphasize decisions, others structures, others “the important stuff,” or whatever is hardest to change. Read enough of them and architecture begins to feel like something that slips through every classification—a creature everyone describes differently, yet no one seems to have seen.

And yet, this creature clearly exists. No one doubts that.
We recognize it by its effects: slow delivery, bugs that refuse to die, changes that feel far riskier than they should, systems that push back against even the smallest improvement.

The Mysterious Creature

One might try to exercise the imagination—to picture something that lives partly in code and partly in our heads. A multidimensional entity, not bound to a single moment in time, but stretched across the full span of its existence. Shaped by past decisions and external forces, while simultaneously guiding—and constraining—what changes are possible next. With enough effort, one might even convince oneself of having seen it.

But that is not the point.

We are software developers. Our job is not to chase mystical creatures, but to solve problems. We have deadlines. Features. Things that must work. We have bugs that reliably appear at 3 a.m.

What actually matters are the long-term consequences of change:

Whether, given what we have today, we can meet business requirements tomorrow.
Where to look when things begin to break apart.
Whether deleting a piece of code is safe—or the first step toward disaster.

Chop It!

To reason about architecture, we do what physicists do with spacetime—a similarly ungraspable monstrosity. If you are still holding on to some animal-like mental picture of architecture, now is the time to let it go. Things are about to get drastic.

We are going to slice it.

The axis we choose depends on what we want to understand, and which trade-offs we want to bring into the light.

Boundary axis (Context diagram)
What is inside the system, what is outside, and who depends on whom.

Time axis (Architecture Decision Records)
How the system arrived at its current shape.
Which decisions were made under which constraints—and which alternatives were rejected.

Runtime behavior axis (Sequence diagram)
How work flows through the system while it is running.
Who calls whom, in what order, and where latency or failure can occur.

Infrastructure axis (Deployment diagram)
How the system maps onto physical or virtual resources.
What runs where, what can be deployed independently—and what cannot.

Change axis (Module or service diagram)
How the system tends to evolve over time.
What changes together, what should not, and where change is expensive.

There are many more possible slices.

But the important thing is this: none of these projections is the architecture.
They are views—showing relationships, revealing trade-offs, and giving your brain something it can actually navigate.

The End Game

The goal of the architecture game is not to catch the mysterious whale.
Those who try usually end up with piles of documents that age faster than the code—and quickly become useless.

The goal is to deliver. To know which axes to use at any given moment.
To move comfortably across different projections, and to predict the consequences of change—whether we introduce it deliberately or it is forced upon us. To prepare for disasters and to minimize the impact radius when they arrive.

One who knows how to play the game can deliberately evolve the system.
One who does not will eventually be eaten by code-degradation crabs.

Thinking Toasters

coding, thinking and toasters

Tag: technology

Failure Mode 1 — Sheep in the Ocean