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Microservices Pros and Cons

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I Wrecked the Interview

The interview was going well. Questions were asked and answered and we were all smiling. But the interviewers kept talking about their company’s transformation into “microservices” but were awfully vague about it. So I asked them how they defined microservices.

The interview was no longer going well. The company had issued a directive that they were going to break up the monolith into microservices, but no one agreed on what microservices were. I got the contract.

This was not the first time this happened to me.

Defining Microservices

Microservices seems to be the “buzzword du jour” (after AI). When I mention microservices online, almost invariably I have people chime in that “the architecture is too complex,” “it’s just a fad,” “they don’t solve the real business problems,” and so on.

Those complaints are often right, but that doesn’t mean microservices don’t provide value. But to get to that value, we need a working definition. I’ll provide one that is short, sweet, and will be objectionable to some. We’ll also skip the differences between microservices and SOA because the latter also has plenty of contradictory descriptions, some of whom curiously resemble microservices.

A microservice is a small, self-contained process which exposes an API to solve a single business need.

OK, not helpful. Too vague. In actual practice, here are a few more things to consider.

The processes can be on the same or different servers. The API is often exposed via OpenAPI and typically uses REST, EDA (event-driven architectures), or WebSockets, depending on the need. GraphQL is also sometimes in the mix. Different services are loosely coupled (in theory) and communicate by sending data back and forth. There is no directly shared memory, though state is often shared via data stores.

OK, so that’s far too brief and entire books can be written on the topic, but this is a blog post. Are they a good idea or not?

Should You Choose Microservices?

Enter Shahina Abadi, the brand-new CTO of Mechanical Tendencies, an online machine parts supplier. She’s been brought in because their legacy platform is struggling and their SAFe® agile project failed, turning out to be neither safe nor agile . Now she has people arguing over cloud-first, containerization, and microservices as some of the approaches that can save the company. How is she going to decide?

There’s much Abadi can do and one of those things is to isolate the underlying problem(s), determine what can be fixed, and how various approaches are going to fix them. What are the risks and benefits to the customers, company, vendors, and employees? Are the various approaches complementary or not? Can they be done in isolation, prioritizing greater benefits first? We’ll just look at microservices.

Benefits of Microservices

Advocates of microservices tout their benefits, but often downplay the drawbacks. Let’s talk about the benefits first.


One of the most cited benefits is scalability. If you’re running a monolith and one part of your code is bogging down the system, scaling that often means scaling everything, regardless of whether or not it needs it. That can quickly get expensive. With microservices, if you find that your reservation service is slow, you can often just scale up that service. Move it to a beefier server or move its data store to a beefier server. There’s no need to spend the money on faster hardware for everything.

Learning the Code Base

For one project I was on, with a monolithic code base of tens of millions of lines of code, I found that the authentication system we were building was sometimes throwing a “method not found” exception. Lots of digging through logs and exploring code paths later, I found that, far, far away from my code, someone had created a custom “user” object which only provided the two methods they needed. It was being passed to my code. I later found tons of user objects, lovingly hand-crafted for different parts of the system, all of which presented different behaviors and different bugs. It was a mess, but given the size of the codebase, there was no reasonable way our team could have anticipated this up front. You become more productive by learning the entire code base, but that’s not possible.

With microservices, you only need to learn the code for your service, and the contracts provided by the services you interact with. All of a sudden, a huge, intractable problem becomes manageable.

Gradual Migration

In the year 2000, Joel Spolsky wrote a famous blog post entitled, Things You Should Never Do, Part 1 . Two decades later, we’re still waiting for part 2, but part 1 says you should never rewrite an existing application. Today we know that’s not true, but only because we have new patterns which allow us to do this and a microservice is one of those patterns. Yes, you might call it “refactoring” instead.

I won’t begrudge the correction, but microservices allow you to tear chunks out of your monolith, piece by piece. You can even stop and leave the remainder of the monolith in place if your approach is wrong, or if the remainder is “good enough.” With this strategy, you always have working code and you can take small steps forward. One approach, the unfortunately-named strangler pattern is great when you’re first dipping your toe in the microservice waters.

It’s All Data

When Alan Kay talks about OO programming, he thinks we got things wrong about objects. For him the key benefits are messaging, isolation, and extreme late-binding. While microservices can provide all of these benefits, for this point, we’ll talk about messaging. You send a message to a service (GET /products/{productId}) and the service sends back a message, often in the form of a JSON data structure. It might be another service. In a monolith, the analogous code might get a reference to data back and if it’s mutable, you can wind up with strange action at a distance. Having mutable data is a constant source of bugs in your code.

When you’re sending a stream of data and not using shared memory, whole classes of bugs go away. You don’t need to worry as much about mutating an incoming data structure because you know the sender won’t be affected. Because we’re only sending data back and forth, you can minimize action at a distance.

Asynchronous Code

In legacy monolith systems, calls to other parts of the code tend to be blocking. It’s not always a big deal because with everything running in the same process, you can do deep work to resolve performance bottlenecks. However, this comes at the cost of making the code more complex.

With microservices, calls are often asynchronous, minimizing blockage. You can fire off requests to several services at once, requesting a response or a change, and aggregate the results when they complete. Asynchronous requests, when possible, can do a fantastic job of taking a load off a server.

New Business Products

When you can start isolating different business concerns into microservices, those services might become stand-alone products you can expose to customers. Some of those customers might be internal. For one client, I rewrote how they generated search results to return a JSON data structure instead of an HTML document. Afterwards, one of their mobile developers thanked me. For years they provided limited search results because the HTML document was useless to them. By being able to request JSON, they said it took them 15 minutes to have a brand new, much more useful search engine on mobile. Well-designed services give flexibility.

Partial Failures

In a monolith, an unexpected exception can often result in the dreaded 500 - Internal Server Error being sent to a browser. With microservices, you often see a degradation in services instead of a complete failure. Maybe your recommendation system doesn’t show a list of related products for a while. The software itself still runs!

The Darkest Secret

It’s one that not everyone is happy with, but sometimes your beloved programming language isn’t the best choice for a given task. Maybe you need your authentication system to be lightning fast and you can’t risk bugs. When it’s safely encapsulated behind an OpenAPI front end, you can consider rewriting the implementation in a faster, more secure language. This one shouldn’t be controversial, but many developers are proud of the languages they prefer and suggesting that it’s not the best tool for the job rankles them, but our job isn’t to provide a particular programming language, it’s to provide solutions. A solid microservice system with services isolated behind well-defined APIs gives us possibilities to transform the system in ways we never thought possible. It’s a huge benefit that should not be overlooked.

Drawbacks of Microservices

Given the benefits of microservices, you might think it insane to not switch to them. But there’s a cost to everything and those costs need to be taken into account.


So you’ve decided that the first microservice you want to create is for authentication. You’ve already got feature switches in place so your authenticate( user, pass ) method looks like this:

def authenticate (self, username, password):
    if feature_running('auth_service'):
        # use authentication service
        # use monolithic code

Now, if something goes wrong, just switch off the feature and fix it.

But how do you get the authentication service running in the first place? How do you dispatch to it? How do you deploy it? Using the strangler pattern can allow you to take a small, isolated bit of code and transform it into a service, but there has to be something for it to run on. So now you’re looking at something like the gateway pattern to dispatch to your new service and you have to deal with the complexity that introduces. There are many yaks you’ll need to shave before you can solve this problem.

Monitoring and Debugging

Oops. Something failed. Hard. Everything is humming along nicely, but nobody can buy your products. Maybe they get a confirmation of purchase. Maybe they’ve been charged for the purchase. But no inventory was deducted or scheduled to be sent. So it’s the purchase service, right? Maybe not. Maybe there was an unexpected failure in your authentication service. Maybe your product service went down. Maybe there was something else going on.

Now you realized you needed better monitoring and nobody thought about log aggregation. The logs are there, but you can’t connect an individual service’s logs to the logs of the services which called it! If your monitoring systems weren’t great before, they need to be now, to surface issues quickly. Log aggregation is also critical. When you have a failed request, you want to have a full log reply of everything for that request show up on a single page/console/file/whatever. There are COTS tools to let you do this. You want them.

One the upside, one benefit of microservices is that you can deploy your service without waiting for the “weekly” release (or whatever the time frame is). As a result, when something goes wrong, you can often spot when it went wrong and while you might not know what caused it, you can check what was deployed and you’ll often see a much smaller set of code deployed, making many bugs easier to track down.

Communication Overhead

This one’s a huge issue. Now, instead of me passing a user object and a purchase request into a method, I have to send a message with the relevant data to a service. A service, which in turn, is probably sending messages to other services, which might be sending messages to other services, and so on. You might worry about cycles in the data calls, but in reality, they’re no worse than this possibility in a monolithic code base. No, the problem is that you’re now generating much more internal network traffic than you did previously. This is a trade-off that is often acceptable, but it needs to be known up front and dealt with. You might need to buy new hardware to deal with this.

Data Consistency

Many developers might be nodding along at many of these points, until they come to the hill they’re willing to die on. The following has been my hill and it’s probably the most challenging aspect of microservices (for me).

I teach developers how to design databases.

It’s a thankless task because developers get this wrong so often and I’ve noticed an interesting pattern. When I go into clients using PostgreSQL, their database is usually in decent shape. That’s because developers who choose PostgreSQL often understand and even obsess over data quality. MySQL fans, however, often obsess about ease of use. When I go into a client who uses MySQL, I often find it doesn’t use strict mode, or if it does, they’re often sloppy about NULL values or foreign key (FK) constraints. When I find a database not using FK constraints, I look for dangling records and I have never found a large database skipping FK constraints which doesn’t have dangling data, unconnected to anything else, or ids pointing to records which no longer exist.

I have to admit that this is helpful for me. It means I sometimes get paid more to fix these bugs, until the client points out that yeah, it’s a problem, but not a serious one. Despite data integrity issues, in many cases, clients consider ease of use to be a fair trade off. I’ll go cry into my beer, but I need to rethink my absolutist position about data integrity. Maybe I’ll eventually come around to eventual consistency.

If you’re breaking up a monolithic codebase, but you decide to stick with a monolithic database, you have a potential bottleneck. The code will be easier to maintain, but the scaling promises aren’t kept. Large databases can be hard to scale. Large transactions can guarantee ACID compliance at the cost of potentially blocking, something you’re trying to avoid with microservices.

So many microservice strategies involve creating micro data services, each with their tiny, backend data store. That’s a problem if you’re buying licences for proprietary systems such as Oracle. It’s also a problem when people object to eventual consistency, even though they don’t need perfectly up-to-date information, such as for a product search.

However, if you’re doing financial transactions, there are plenty of places where eventual consistency is not acceptable, so you’ll have to feel your own way here.

Other Drawbacks

Microservices create issues with testing complexity, deployments, dependency management, security management, permanent operational overheard, and the initial development effort. They’re not a step to take lightly, though if my past experience is anything to go on, many companies look at the benefits, ignore the costs, and don’t have a clear plan of attack.


For the drawbacks of microservices, once you read through them, many of them go away or are diminished after the first microservices are successfully launched. Unfortunately, the added complexity of the system will always be there and this is where the critical argument lies.

When I first learned to program, back in the 80s, I wrote something like this:

20 GOTO 10

Needless to say, as our business demands grow, the complexity of our software grows, too. CGI, which would have been unthinkably complex to me in the 80s, is unthinkably simplistic in dealing with modern requirements. We have greater needs and with that comes greater complexity. So let’s take a diversion.

The BBC “Atlas” Project

I worked for the BBC for several years. Were it not for the fact that my wife and I had decided we didn’t want to raise our child in London, I might still be there. I loved working for the BBC.

One day they announced the Atlas project. As a rule of thumb, when a company announces a grand project named “Atlas,” “Leviathon,” “V2,” and so on, looking for a new job becomes a serious option. Atlas was no different (today, I don’t know, but then, it was a mess).

The Atlas project stemmed out of a simple problem: the BBC used tons of different programming languages and it was getting harder to find developers for them, or to train developers for those systems. So the BBC decreed that all backend development would be in Java and all front-end development would be in PHP.

On the Atlas internal mailing list, an experienced PHP developer complained that he couldn’t figure out how to run “Hello, World!” on the new framework that had been built. No matter how many times he read the instructions, he couldn’t get it working. The admins held his hand and walked him through the process, but it was a mess. When I pointed out that running “Hello, World!” shouldn’t be so difficult that a senior developer can’t figure it out, one of the admins privately sent me an email telling me that obviously I didn’t know how to write code that scaled.

This is part of the issue with emerging complexity. When it first arrives, we’re not sure of the best way to manage it. Microservices, in the grand scheme of things, are still relatively new. We’re going to find new patterns to manage them (a future blog post) and new ways of hiding the complexity behind abstractions. This fits with the history of programming. Subroutines and pushing their arguments to the stack were a “design pattern” until they became first class constructs. Memory management was manual and painful until mark and sweep, reference counting, and other imperfect tools came along to make it automatic. Rust is now blowing the doors off of them.

Microservices will undergo the same process. Many common patterns will become first class constructs. It will take many years, perhaps a generation or more, until we better understand how to manage the new complexities.

But are they worth it? Alan Kay states that the internet is the only large scale OOP system which matches his expectations. If a server goes down, your browser doesn’t spontaneously crash; you find another server. Kay was thinking about the robustness of biological systems. You have billions of cells in your body die every day, but you don’t. We evolved over billions of years to a robustness we can only dream of for software. Microservices are yet another attempt to get closer to that robustness.

At the end of the day, a key takeaway is not that the “everything” is more complex. It’s that it’s a trade-off. In exchange for architectural complexity, you have software that’s easier to maintain and services that are easier to scale.


All systems become more complex over time. Whether or not the benefits of microservices override the drawbacks of their added complexity is not something I can answer for you, but I hope this has given you a starting point for understanding the trade offs.

I don’t think suggesting microservices is a great fit until the company truly needs this kind of scalability (and perhaps breaking down of a monolith) and is 100% committed to it. The actual development costs aren’t the important argument here. Of course, if microservices are the course a company decides, they had damned well be able to tell the team what they mean by them. I’ll be able to avoid future interview fiascos that way.

If you’d like to see a deeper dive into handling microservices at scale, here’s a great video about how Netflix handles them.

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