Microservices Of Madness

The Great Disintegration began, as all great technical disasters do, with a PowerPoint presentation. It was a Thursday. Thursdays were important—everyone knew that—because they were adjacent to Friday, which meant that by the time anyone realized the true implications of what was happening, it would already be Happy Hour, and their existential panic could be drowned in the company-approved two-drink minimum.

A bright-eyed engineer named Kyle—who had read exactly one blog post on distributed architectures and had since declared himself a Cloud Prophet—stood before the CTO, VP of Engineering, and a smattering of middle managers whose primary job was to write Slack messages that ended in “Just circling back on this.” His slides were sleek, minimal, and full of bullet points that meant absolutely nothing but sounded impressive when read aloud.

“THE FUTURE,” Kyle announced, his voice thick with confidence that came from never having been held accountable for anything. “Is Microservices.”

The old guard, a few weary-eyed engineers who still remembered when deploying required physical media, shifted uncomfortably in their chairs. They had seen this before. They knew what came next. They had been around for Service-Oriented Architecture, for the Great Kubernetes Migration of ‘17, for the ill-fated adoption of GraphQL when REST was working just fine. But Kyle was young, and worse, he had the favor of The Visionaries—the executives who attended conferences in expensive blazers and came back with buzzwords they did not understand but demanded be implemented immediately.

Kyle continued, flipping to a slide with an absurdly over-engineered diagram. “Our monolith,” he said, pausing for dramatic effect, “is holding us back. It’s slow. It’s unscalable. It’s preventing us from achieving true agility.”

He let that last word hang in the air, as if agility were a tangible thing, like a small exotic animal that could be captured and monetized. A hush fell over the room. The VP of Engineering, who hadn’t written a line of code since the Bush administration but still insisted on calling himself “technical,” leaned forward. “Go on.”

Kyle grinned. The trap had been sprung.

“What if,” he continued, “instead of one big, bloated, outdated monolith, we broke it apart? Into smaller, more manageable services? Independent. Isolated. Self-contained. Each team owning their own domain. Decoupled.”

He was using all the right words. The executives began to nod. The CTO, who had spent the last six months struggling to define “technical debt” in a way that didn’t make it sound like the company was moments from collapse, saw an opportunity. “Microservices,” he muttered, rolling the word around in his mouth like an aged whiskey. It tasted of promotions.

By the end of the meeting, it was decided. The monolith would be dismantled. Broken apart. Separated into dozens—no, hundreds—of tiny, efficient, independent services, each running in its own container, speaking to each other over a robust and well-documented API layer. It would be beautiful. It would be efficient. It would make for an excellent case study on Medium.

It took precisely two days before the first signs of trouble emerged.

The Login Service, once a humble function that authenticated users, was now a dedicated microservice deployed in a separate cluster, speaking via gRPC to the User Service, which in turn relied on the Profile Service to fetch user metadata, which itself had to request permissions data from the Authentication Service, which—due to a last-minute architectural decision that made sense at 2 AM but not in the cold light of day—had been split into five separate services, each responsible for precisely one field in the authentication process.

Logging in now required 78 network calls, three retries, and a PhD in distributed systems to debug.

But Kyle was undeterred. “It’s fine,” he insisted, waving a printout of a performance report like it was a sacred text. “We just need a service mesh.”

The words sent a ripple of fear through the engineering team. The Service Mesh Initiative (SMI) was declared. Within a week, the company had onboarded Istio, Linkerd, and Consul, each managing a separate subset of services because no one could agree on which one was best. Half the engineers now spent their days trying to figure out why simple API requests were vanishing into the void.

The platform team—once a scrappy group of developers who had prided themselves on keeping the infrastructure lean—had ballooned into an army, their days consumed by YAML configurations and mysterious 500 errors that only appeared in production. No one was entirely sure what they did anymore, but their Slack channel was now the most active in the company, second only to #random, where everyone posted memes about quitting.

Meanwhile, the business team was growing restless. “The app is slow,” they complained. “It takes forever to do anything.”

Kyle, who now wore AirPods at all times and responded to emails exclusively with the phrase “Let’s sync on this,” had an answer. “Caching,” he declared. “We’ll just add caching.”

A week later, there were six different caching layers, each implemented by a different team, each unaware of the others. One was Redis, another was Memcached, a third was a homegrown solution written in Go for absolutely no reason other than the fact that someone had just learned Go and wanted to put it on their resume. Data inconsistencies spread like a plague. A customer could log in and see five different versions of their own profile, each fetched from a different cache layer, each equally incorrect.

By month three, there were over a thousand microservices, each communicating asynchronously via Kafka topics that no one could trace. The simple act of updating a user’s email now required coordinating changes across seventeen teams, two of which had been reassigned to different projects and no longer had any idea what their services even did.

At the all-hands meeting, the CEO—who had been suspiciously absent for most of the ordeal—stood before the company and, with a forced smile, delivered the news everyone had feared.

“We’re pivoting back to a monolith.”

A single engineer in the back, whose soul had long since left his body, let out a hollow, joyless laugh.

Kyle, ever the visionary, was already preparing his next presentation. The title? “Why You Should Break Up Your Monolith… Again.”

The official announcement came in an all-company email, written in the passive voice, which was the corporate way of admitting catastrophic failure without assigning blame.

"After careful consideration, leadership has decided to pursue a streamlined approach to our architecture, reducing inter-service complexity to improve operational efficiency. This change will enable teams to move faster, reduce toil, and deliver value with greater impact. More details will be provided in the coming weeks. Thank you for your continued adaptability and innovation."

It took precisely six minutes for the #engineering Slack channel to explode into a wildfire of speculation, chaos, and defeat.

"They’re rolling it back."

"No, no, no, it’s not a rollback, it’s a realignment."

"I JUST spent six months rewriting the Auth Service into fifteen different microservices. You’re telling me we’re going back?"

"How many times do we have to teach you this lesson, old man?" (posted alongside a meme of the elderly fish from SpongeBob.)

"If anyone needs me, I’ll be at the bar."

Kyle, curiously absent from the initial fallout, reappeared hours later with an official-sounding take.

"This isn’t a rollback. It’s a natural evolution of our architecture based on our learnings. We’re moving toward a domain-driven, modular monolith."

Someone immediately googled "modular monolith" and found a blog post written by an engineer at a different company that had gone through the exact same cycle of microservice-induced misery five years prior. The article concluded with a haunting sentence:

"If I could do it all over again, I would have stayed with the monolith and spent my time fixing what actually mattered."

The damage, however, was already irreversible. The microservices initiative had run unchecked for nearly a year, and what once was a single, reasonably complex codebase had been dismembered, fragmented, and scattered across an incomprehensible web of cloud instances, networked databases, and containerized chaos.

By the time leadership had realized the problem, it was too late.

The system was no longer one thousand microservices. It was one thousand tiny monoliths, each controlled by a different team, each built in total isolation, each with its own database, caching layer, and proprietary API contract that adhered to precisely zero internal standards.

Kyle had won.

The company had been so terrified of the original monolith that they had inadvertently created a hydra of self-contained, non-communicating nightmares. The real problem was never about "monolith versus microservices"—it was about understanding the trade-offs, maintaining discipline, and making decisions that didn’t hinge on whatever was trending on Tech Twitter that week.

Now, whenever a feature request came in, the project managers had to consult a map that looked less like a software architecture diagram and more like a conspiracy board in a detective drama.

"You want to update the user's profile picture? Okay, so the request first has to go through the User Service, but that only holds IDs, so you have to call the Profile Service. But the Profile Service doesn’t actually store images; it offloads that to the Media Processing Service, which operates asynchronously. But wait—the Media Processing Service doesn’t store the files either; that’s actually in the Storage Service, which uses a proprietary file format because someone thought it would be cool to reinvent object storage."

"So how long will this take?"

"Best case? Three weeks."

"Worst case?"

"We shut down the company."

The developers were trapped in a hell of their own making. Every engineer who had championed microservices had since moved on, leaving behind an indecipherable landscape of half-built frameworks, abandoned CI/CD pipelines, and brittle network dependencies.

It was time for a reckoning.

The war effort—dubbed "The Great Re-Monolithing"—was launched with all the solemnity of a doomed military campaign. The senior engineers, their spirits long since broken, gathered in a conference room with whiteboards, index cards, and an industrial-sized box of dry-erase markers.

"Alright," one of them sighed, cracking open a beer at 10 AM. "How do we put this thing back together?"

Silence.

Kyle, now sporting the smug self-assurance of someone who was about to get a promotion for fixing a problem he had created, cleared his throat.

"I actually think we should take this opportunity to explore serverless."

There was a moment of stunned silence before a chair was thrown.

The chair, a cheap ergonomic knockoff ordered in bulk from a procurement department that prided itself on cutting costs everywhere except executive retreats, flew across the room and smacked into the whiteboard with the force of an entire engineering team’s pent-up frustration. It was the first honest display of emotion the company had seen in years.

Kyle ducked instinctively, though no one had actually aimed at him. He had developed a survival instinct for moments like these—an innate ability to sense when his enthusiasm for "industry best practices" was about to result in workplace violence.

But it was too late.

The team had reached the breaking point. The infrastructure was unsustainable, the documentation was a work of fiction, and the last remaining DevOps engineer had gone into hiding, refusing to deploy anything unless someone could present a detailed list of which services would break as a result. No one could.

The CTO, who had been conspicuously absent for most of the company’s descent into distributed madness, finally emerged from his office. It was never a good sign when the CTO personally attended a working session—it meant that either the board had taken an interest, or the investors had started asking questions that could no longer be dodged with phrases like “velocity-driven paradigm shifts.”

He surveyed the room. A battlefield. Empty coffee cups. Bloodshot eyes. Engineers who had aged a decade in six months. The whiteboard, now covered in an incoherent mess of arrows, boxes, and hastily erased regrets.

"We’re in deep shit, aren’t we?"

There was no response. Only the weary silence of people who had known this for months.

The CTO sighed, rubbing his temples. “Alright,” he said, “walk me through it.”

A senior engineer, once bright-eyed and full of hope, now a husk of his former self, stepped forward. He had stopped speaking in complete sentences weeks ago.

"We tried to re-aggregate services. Consolidate where we could. Problem is, dependencies." He pointed at the board. "User Service can’t talk to Profile Service unless it goes through API Gateway. API Gateway needs Auth Service to verify tokens. But Auth Service doesn’t hold permissions anymore. That’s the Permissions Service. Which has its own database, which syncs to the Cache Service. Which is inconsistent because no one actually knows who owns it. Also, Messaging Service goes down every two days for reasons no one understands."

The CTO squinted at the diagram. “Why is there a service called ‘Notification Orchestrator Coordinator Dispatcher Service’?”

A different engineer, whose soul had long since fled his body, spoke. “We needed notifications.”

“You could have just built a function for that.”

“We did. But then someone said notifications should be a separate service.”

“Who?”

“Gone. He left for Google last month.”

The CTO massaged his temples. “So we now have a dedicated microservice… to tell other microservices that they should notify the user?”

“Yes.”

“…And where does this sit in the call chain?”

A pause.

“…Upstream of everything.”

Silence.

The CTO exhaled, long and slow, the sound of a man realizing that he may never know peace again. “So what happens if this service goes down?”

Another pause.

“…The entire system stops.”

Another silence. A long one. The kind of silence that should have happened a year ago, before the first PR was merged. The kind of silence that would have prevented this entire mess if someone had just stopped to think, for even a second, about what they were doing.

Finally, the CTO spoke.

“So what’s the plan?”

Another silence. But this time, it wasn’t the silence of hopelessness. It was the silence of engineers realizing that, for the first time in months, they were being asked to fix something instead of just chasing the next architectural trend.

A junior engineer, who had been too afraid to speak until now, cleared his throat. “We could… just put it back in a monolith.”

The room turned to him. The old guard nodded solemnly. The engineers, once beaten down, now sat up in their chairs. The idea—the forbidden idea—was spoken aloud.

The CTO hesitated.

“…Would that work?”

The senior engineer grabbed a marker, wiped the board clean, and began to draw.

A single box.

Inside it, a few simple components.

One codebase. One deployment. One database.

One monolith.

The room stared.

It was so stupid.

So simple.

So horrifyingly elegant.

“…Wow,” someone whispered.

The team sprang into action.

Services were merged. Dependencies were cut. Kafka topics—hundreds of them—were deleted without remorse. The API Gateway was ripped out and replaced with a simple routing layer. The Notification Orchestrator Coordinator Dispatcher Service was set on fire. The Platform Team, long held hostage by the tyranny of YAML, finally reclaimed their dignity.

It took months.

But by the end of it, something incredible happened.

Deployments went from four hours to ten minutes.

New features could be built without coordinating across sixteen teams.

The engineers, once dead inside, began to smile again.

Even the DevOps engineers, once believed lost to the wilderness, returned, emerging from the shadows like mythical creatures from a forgotten age. They took one look at the new system, nodded approvingly, and simply said:

“…Nice.”

The company, once teetering on the edge of chaos, found stability once more. The great microservices experiment was over. The monolith—majestic, reliable, and thoroughly unexciting—stood triumphant.

Kyle, however, was not deterred.

As the engineers celebrated, he stood in the corner, typing furiously on his laptop.

He was drafting his next proposal.

"The Future is Web3: Why Every API Call Should Be an NFT."

Author's Afterword

Ah, dear reader—particularly you, my esteemed assembly of architects, backend philosophers, and DevOps warlocks—before you ignite your torches and form a distributed, highly scalable mob to hunt me down, allow me a moment of indulgence. I know exactly what you’re thinking. I can hear the rebuttals forming, the counterarguments brewing, the indignant clacking of mechanical keyboards as you prepare a 3,000-word Medium post explaining why microservices are actually the future (if only everyone would just do them correctly, unlike all these other foolish companies that somehow got it wrong).

Yes, I see the irony. Yes, I recognize the paradox of mocking both the reckless adoption of microservices and their chaotic dismantling. And yes, I am fully aware that some poor soul is probably reading this in the middle of their fourth consecutive on-call shift, trapped in a Kafka-induced nightmare, muttering, It’s not funny when it’s your life.

But let me tell you this—this is a tale that had to be told.

Because we’ve all seen it. We’ve all lived it. We’ve all been in the meeting where someone, drunk on their third read-through of a trendy blog post, declared that “monoliths don’t scale” with the confidence of a man who has never scaled anything larger than a personal side project. We’ve all been there when an eager engineering team split a perfectly fine system into 237 independently deployed, infinitely more fragile services, only to spend the next two years reintroducing API gateways, service meshes, and orchestration layers until they accidentally reinvented the monolith, but worse.

We’ve watched as the complexity spiraled out of control, as deployments became a gauntlet of broken dependencies, as outages became weekly fires to be extinguished by a team now fluent in the arcane rituals of tracing distributed logs through a fog of event-driven despair. And, if we’ve been lucky, we’ve seen the cycle come full circle—the realization that maybe, just maybe, not every problem is best solved by breaking it into a thousand smaller problems that now need to talk to each other over a network.

But let’s be honest—this isn’t just about microservices. It’s about the cycle.

Every era of software engineering is marked by grand proclamations that this time, we have discovered the one true way to build systems.

That this paradigm, this framework, this architectural pattern will solve all our woes—until it doesn’t, and we find ourselves scrambling to undo the mess we made in pursuit of supposed enlightenment.

And that, dear reader, is why this story exists.

Not to declare that monoliths are better or that microservices are wrong—but to remind us that every decision we make comes with trade-offs. That buzzwords are not blueprints. That trends are not solutions. That blindly following the industry’s latest gospel is just as dangerous as stubbornly refusing to evolve.

And, most importantly, that no matter how many times we go through this, someone—somewhere—is already writing the next blog post, ready to convince an entire company that they absolutely must rebuild everything in Rust on a blockchain with AI-powered smart contracts.

God help us all.

Now go forth, my distributed, loosely coupled, highly scalable friends. May your deployments be swift, your outages be rare, and your architecture be decided by something more substantial than a conference talk and a dream.

- M

Also available on ChairTheory.com