Microservices: From One Big Pile to Many Small Piles

Level 1: Many Bins, Same Odor

If one trash can in your kitchen is making a terrible smell, you wouldn't fix it by dividing that garbage into five small trash cans and putting one in every room, right? That would just make all the rooms smelly instead of one. It's a pretty silly idea: now the stink is everywhere! In the picture, that's basically what happened – someone took one big smelly problem and split it into lots of little smelly problems. Clearly, that doesn't solve anything; it just spreads the bad smell around. We find it funny because anyone can see that's not how you fix a mess, and yet people still try it expecting a better result.

Level 2: Small Pieces, Same Smell

In simple terms, a monolithic architecture means your whole application is built as one single unit or package. Think of it as one big program that contains all the code for all the features. For example, a monolithic web app might have code for user login, product listings, payments, and everything else all in one codebase, running together. In contrast, a microservices architecture breaks that big application into a bunch of smaller, separate services (called microservices). Each microservice focuses on a specific portion of the overall functionality. So you might have one service just for user accounts, another service just for the product catalog, another for processing payments, and so on. These small services run independently (often in their own process or even on different servers) and communicate with each other over a network. For instance, one service might make an HTTP request or send a message to another service to get some data or trigger an action. The goal of splitting things up this way is to make each piece of the system simpler to understand and manage, and to allow different teams to work on different services without stepping on each other's toes. It can also help with scaling: if one part of your system (say the payments part) starts getting a ton of traffic, you can scale up just that microservice (add more instances of it) without having to scale the entire application.

Now, the meme is joking about a scenario where this split to microservices doesn’t actually deliver the expected benefits. Normally, the hope is that by breaking a big app into smaller pieces, you can make each piece cleaner and easier to maintain. But here, the original monolith was "smelly" to start with – meaning the code was badly structured or full of problems (we often call such problems technical debt or code smell). If you turn one messy application into several smaller applications without fixing the mess, you just end up with many messy applications. It's like copying and pasting bad code into five different places – now you have the same bugs and sloppy logic in five spots instead of one. The meme uses the poop emoji to represent messy, smelly code. On the left, there's one big stinky pile (one large, messy app). On the right, there are six smaller stinky piles (six small apps), but each one still stinks. The smell (i.e. the underlying issues) didn't go away; it just got spread to every service. So, the joke is that the so-called "advantages" of rewriting the monolith into microservices are non-existent in this case – you didn't improve the code at all, you merely multiplied the number of applications that have to be managed, each carrying the same old problems.

You've maybe heard the term spaghetti code. That's a nickname for code that's all tangled up and convoluted, like a bowl of spaghetti where every strand is crossing over another. It's hard to follow what's going on in spaghetti code because there's no clear structure; one part jumps to another in unpredictable ways. If a monolithic application is written in a spaghetti code style (everything intertwined with everything else), simply splitting it into microservices is like ladling that spaghetti into several bowls. Each bowl still contains a knotted tangle of noodles; nothing fundamentally changed about the spaghetti itself. In software terms, each microservice might still have a confusing internal structure and, even worse, now those services have to talk to each other across a network. So instead of one big confusing codebase, you have several confusing codebases plus the complexity of remote communication. For example, where before you might call a function in the same code to get something done, now Service A might have to call Service B over the network to accomplish a task. If the design is bad, Service A might call Service B, which calls Service C, and so on, creating a chain of dependencies for even simple operations. This can actually slow down development and make debugging harder because now when something goes wrong, you have to figure out which service (or which interaction between services) caused it.

Microservices, when done right, do have a lot of trade-offs that can be positive: you can deploy updates to one service without redeploying everything, you can choose different technologies for different services if needed, and you can isolate failures (one broken service ideally doesn't crash the whole system). But they also introduce trade-offs in terms of complexity: more things to deploy, more things to monitor, and more places where an error can occur. The meme highlights a common anti-pattern (which means a typical bad solution to a problem) where a team jumps on the microservices bandwagon without addressing the root cause of their problems. It's like trying to solve a dirty kitchen by moving the dirt into different corners of the house. To really benefit from microservices, you have to refactor (rework) the code and establish clear boundaries: decide what each service is responsible for and clean up that service's internals. In the bad scenario shown by the meme, they skipped the cleanup part. They just chopped the system into pieces and hoped for the best. The result is an architecture that is just as smelly as before, only now it's spread out and harder to manage. It's a well-known gotcha in software design: changing the architecture won't help if you don't also improve the design and code quality.

Think of it this way from a beginner's perspective: say you have a really messy room (clothes everywhere, toys on the floor, old sandwiches under the bed – it's bad!). Now, someone suggests splitting the stuff in that room into five different rooms to make it "better": clothes in one room, toys in another, food in another, etc., without actually cleaning anything. You end up with five messy rooms instead of one. Clearly, that didn't solve the original problem of messiness; it just relocated it. In a coding context, the "messy room" is the monolith with messy code. Splitting into microservices is like spreading the mess into multiple rooms. The funny (and painful) part is recognizing that this approach is obviously flawed. The meme is a light-hearted warning: before you jump into a fancy new architecture, make sure you're not just scattering your problems around. Clean up the code, untangle that spaghetti, and define solid module boundaries. Otherwise, microservices will just give you a distributed mess – and dealing with a distributed mess can be even harder than dealing with a centralized one. Lesson for juniors: use microservices thoughtfully, and remember that technical debt must be paid off with actual effort, not simply hidden in more places.

Level 3: Microservices, Macro Problems

This meme elicits a knowing groan from seasoned engineers because it nails a classic blunder in system design. We often hear the rallying cry, "Let's break the monolith into microservices!", as if it’s a cure-all for a messy codebase. But as the image humorously shows, if that monolith was one big pile of messy code (a big ball of mud architecture, as it's famously called), then splitting it up without cleaning anything just gives you a distributed big ball of mud. In plainer terms: you end up with many little mud balls instead of one big mud ball. The left side's single poop emoji is the monolithic architecture – one deployable application encompassing all the features (and thus all the problems in one place). The right side's cluster of six poops represents a microservices architecture – multiple independent services. The punchline is that each microservice is as smelly as the original monolith because nothing was done to improve the code quality or design; the pieces just got smaller and multiplied. So the "advantage" being poked fun at in the post message ("Advantages of rewriting monolithic code into micro services") is purely sarcastic – the only "advantage" shown is having more places for the bad smells to come from. In other words, the mess didn't vanish; it was replicated.

For anyone who's been through a monolith-to-microservices project gone wrong, this scenario is painfully familiar. Imagine a legacy monolith (maybe a huge all-in-one application written in something like Java or PHP) that's grown unwieldy over the years – tangled logic everywhere, modules tightly coupled, a lot of hidden interdependencies. Management or a zealous architect comes along and says, "Microservices will fix everything! It's the modern approach – if it works for Netflix, it’ll work for us." Fast-forward a few months: the team carves the monolith into, say, a dozen services. They split the codebase into multiple smaller repositories or modules, perhaps even copy-pasting chunks of code into different services. Now you have a microservices architecture... on paper. In reality, all those new services still depend on each other in complex ways – like that spaghetti code just got spread across multiple bowls. Need to implement a simple new feature? Now it requires touching five different services and orchestrating their releases. Did the original code have a sneaky bug or a memory leak? Congrats, now that bug might exist in three or four services (since the original functionality was duplicated), and you get to patch them all. Instead of one big application to deploy and debug, you have to juggle many. You've essentially traded a single big headache for many smaller headaches. It's a textbook case of over-engineering: adopting a complicated solution (microservices) without addressing the fundamental design problems. The end result is often worse than where you started.

The humor here lands especially well with backend developers and distributed systems engineers who have lived through this kind of fiasco. We half-jokingly call it "microservice hell" or label the result a distributed monolith. That's an architectural anti-pattern where the system is theoretically broken into services, but they're so tightly coupled (and often share the same data or logic quirks) that you haven't actually gained the benefits of separation. It’s all the downsides of microservices (extra complexity, network calls, duplication of effort) with none of the upsides (true independence, easier scaling of parts). There's a cynical saying: "You can't polish a turd." This meme basically updates that to: "You can't just break a turd into smaller pieces and call it clean." If the code was rotten with technical debt, then splitting it up without refactoring is just multiplying the technical debt. In practice, what you often get is cascading failures: Service A crashes and brings down Service B because B was depending on A's response. Or a tiny change in Service C unexpectedly breaks something in Service D because, whoops, they were sharing some data assumptions or an API contract that nobody realized. Debugging these issues is a nightmare – where you used to grep through one log file, now you have to sift through logs from multiple services (if you even had the foresight to implement centralized logging). It’s not uncommon to end up introducing a whole new infrastructure for observability – like setting up an ELK stack for logs or using a distributed tracing tool (e.g. Jaeger) – just to figure out what the heck is happening across all these interconnected microservices. So instead of one big flame to put out, you have a bunch of little fires and need a map to even find them. At 3 AM on call, this is exactly as fun as it sounds.

Another aspect that makes experienced devs smirk is the herd mentality behind such architecture decisions. Often, higher-ups push for microservices because "that's what the cool tech companies do." "Amazon does it, so should we!" – ignoring that Amazon also invested in uber-sophisticated tooling and had very specific organizational needs. I've seen teams break apart a perfectly serviceable monolith just to tick the microservices checkbox, only to end up with a fragile system where no one person understands how the pieces fit together anymore. Each service might be simpler in isolation, sure, but the overall system becomes like a Rube Goldberg machine. The meme’s poop imagery is spot-on: it's like having one big smelly garbage heap versus several smaller garbage cans scattered around. The total smell is at best the same, if not worse, but now you need multiple trips (or processes) to manage the trash instead of one. Seasoned engineers have a term for this kind of outcome: architecture trade-offs gone wrong. You traded the devil you knew (one big complicated app) for a legion of smaller devils (lots of interdependent apps). Without careful design, microservices just amplify the problems: more deployments to manage, more points of failure, and more complexity in testing and versioning. In fact, many of us have witnessed projects where, after struggling with a botched microservice implementation, the team either regretted the move entirely or had to invest major effort in re-architecting again to untangle the services properly. It's Equal Opportunity Pain: everyone from developers to ops to QA feels the burn. This darkly comic outcome is exactly what the meme is highlighting.

In summary, the meme resonates because it delivers an "I told you so" lesson with a dash of humor. It underscores an industry lesson learned the hard way: architecture alone doesn't automatically fix technical debt or poor design. You can break a giant, smelly app into six smaller apps, but if each of those is poorly structured, you just end up with six smelly problems. The odor (be it bugs, ugly hacks, or performance issues) is now spread out. So instead of one big fire to fight, you're dealing with multiple little flare-ups. Many of us carry the scars from being on-call in systems like this – where microservices turned an occasional big outage into a constant game of whack-a-mole with failing services. The meme is funny in a "laugh so you don't cry" kind of way. It's a truth wrapped in a poop joke: new tech won't save you from bad practices. You still have to do the hard work of cleaning up the code. That jab hits close to home for any senior developer who’s seen silver-bullet solutions come and go. We chuckle because, yeah, we've seen this exact nonsense play out in real life, and at least when it's drawn as a cartoon poop, we can smile about it.

Level 4: Conservation of Stink

In any software system, there's a bit of an unwritten law akin to conservation of mass: you can't just make technical debt disappear by chopping up the system. This meme is essentially illustrating a Law of Conservation of Stink in architecture. The single smelly monolith on the left represents one big codebase rife with code smell (bad design, messy code). Now, migrating to a microservice architecture means turning that one codebase into many smaller services. But according to this conservation principle, the total "smell" (i.e. underlying complexity and flaws) remains constant or even increases across the system. It's distributed now: each microservice carries a piece of the original stench. So instead of a singular messy codebase, you get a cluster of messy codebases, each raising its own little stink lines. In formal terms, your monolith's entropy (disorder) has not decreased; it's just been partitioned into multiple modules. Without an actual cleanup or redesign, splitting simply propagates the entropy. In other words, you haven't eliminated the complexity — you've just spread it out over a network.

From a distributed systems theory perspective, splitting a monolith introduces all the classic challenges spelled out by the Fallacies of Distributed Computing. For example, in a monolith, one function calls another in the same process with minimal fuss. In microservices, that same interaction becomes a network call – meaning you've invited latency, network reliability issues, and potential partial failures to the party. The meme's arrow might as well be labeled "introduce network partitioning here." If one service needs to call another over the network, you now have to worry about what happens if that call fails or times out. This is where the CAP theorem (Consistency, Availability, Partition Tolerance) starts to loom. A monolith calling a local function didn't have to choose between consistency and availability in the face of a network partition – it was all in one consistent process. But slice that logic into microservices and suddenly, if Service A can't reach Service B (a tiny "partition" in the system), you're forced into tough choices: do you make the user wait (reduce availability) or risk some data being out-of-sync (reduce consistency)? By making the system distributed, you've stepped into the arena of CAP trade-offs, where you can't have it all. The total smell now includes not just the original bad code, but also the stink of network complexity and uncertainty.

We can also look at it in terms of data consistency and transactions. In the original monolith, you might have the luxury of ACID transactions – say a single relational database ensuring a consistent commit for a multi-step operation (e.g. creating an order and billing it all in one go). Break that into microservices – say an Order Service and a Billing Service, each with its own database – and a formerly simple operation becomes a distributed coordination problem. Implementing a traditional two-phase commit across services is notoriously tricky (and often considered an anti-pattern in microservices due to its fragility and performance cost). The common alternative is embracing eventual consistency via asynchronous messaging or saga patterns, which basically means accepting that the data in these separate services might take time to catch up and become consistent. That can work, but if your original monolith's design was a tangled web of shared state, splitting it without untangling can create a nightmare of out-of-sync data or duplicate logic. The bounded context that each service should own becomes blurry. The code smell that was once contained in one place now wafts through message queues and across REST calls. Essentially, if the monolith's design was flawed, a naive microservice version will reproduce those flaws in each service and add new failure modes. You haven't eliminated the inconsistency or complexity; you've just hidden it behind a fleet of APIs and background workers that now require careful orchestration.

Let's talk reliability and failure for a moment. In a monolith, there's one deployable unit – if it's up, most features work; if it's down, everything is down (one big failure domain). Microservices promise better fault isolation: one small service crashing should only affect that part of the system. However, when people create a distributed monolith, each new microservice is often not truly independent – it synchronously depends on several others to fulfill a single user request. It's like a chain: if any link breaks, the whole chain fails. So imagine originally one big app had 99% uptime. Now you have six services that each also have, say, 99% uptime. If a single user action requires all six to be working, the combined availability of that action is roughly 0.99^6 ≈ 94%. In trying to improve things, you might accidentally reduce overall reliability because there are more points of failure that can go wrong. And debugging those failures is harder: the error could be in Service A, or in how A talks to B, or maybe B is fine but the network between A and B is flaky. We've essentially traded a simple failure mode (monolith goes down) for a complex grid of potential failures. This is a known anti-pattern often jokingly referred to as a distributed big ball of mud or simply a distributed monolith. The humor of the meme comes from this insight: by distributing a mess, you don't get rid of the mess – you just create many messy, interdependent parts. It's the difference between one big dumpster fire and six little trash can fires spread around; either way, you’re still dealing with fire, but now you need a fleet of fire trucks instead of one.

A truly successful migration from monolith to microservices requires more than just slicing things up – it needs a fundamental redesign with clear separations of concern. Often teams use Domain-Driven Design principles to define bounded contexts for each service (so that each microservice has a well-defined responsibility and minimal overlap with others). The meme highlights what happens when you skip that crucial step. There's also a nod here to Conway's Law: systems end up shaped like the communication structure of the organizations that build them. If the organization or team structure that produced the messy monolith is unchanged, they'll likely produce a similarly messy set of microservices. In essence, the meme is a humorous reminder of a core architectural trade-off mistake: treating microservices as a drop-in solution for bad design. Complexity and technical debt are not magically solved by chopping a system into pieces. In fact, blindly slicing a smelly monolith can increase overall complexity. It's a bit like trying to improve a bad soup by pouring it into six bowls – you still have the same soup. The fundamental issues must be addressed through refactoring and design improvements, not just through architectural buzzwords. The deep irony underlying the joke is that some problems are intrinsic to the content of the system, not its form. No amount of microservice pixie dust will save a rotten core; you have to clean up the muck, not just spread it out.