Whiteboard Confessional: The 15-Person Startup with 700 Microservices: A Cautionary Tale

Episode Summary

Join me as I continue a new series called Whiteboard Confessional with a look at the rise of microservices and some of the reasons why people started breaking apart monoliths in the first place, why microservices can be a great approach to software development, how Hacker News took the wrong lessons from microservices and encouraged devs from 15-person startups to embrace them at their own peril, why Google would argue that its own internal systems shouldn’t look like Google’s internal systems, how microservices make platforms much harder to scale, why you shouldn’t necessarily hop on the latest software development trends just because, and more.

Episode Show Notes & Transcript

About Corey Quinn
Over the course of my career, I’ve worn many different hats in the tech world: systems administrator, systems engineer, director of technical operations, and director of DevOps, to name a few. Today, I’m a cloud economist at The Duckbill Group, the author of the weekly Last Week in AWS newsletter, and the host of two podcasts: Screaming in the Cloud and, you guessed it, AWS Morning Brief, which you’re about to listen to.



Corey: Welcome to AWS Morning Brief: Whiteboard Confessional. I’m Cloud Economist Corey Quinn. This weekly show exposes the semi-polite lie that is whiteboard architecture diagrams. You see, a child can draw a whiteboard architecture, but the real world is a mess. We discuss the hilariously bad decisions that make it into shipping products, the unfortunate hacks the real-world forces us to build, and that the best to call your staging environment is “theory”. Because invariably whatever you’ve built works in the theory, but not in production. Let’s get to it.

On this show, I talk an awful lot about architectural patterns that are horrifying. Let’s instead talk for a moment about something that isn’t horrifying. CHAOSSEARCH. Architecturally, they do things right. They provide a log analytics solution that separates out your storage from your compute. The data lives inside of your S3 buckets, and you can access it using APIs you’ve come to know and tolerate, through a series of containers that live next to that S3 storage. Rather than replicating massive clusters that you have to care and feed for yourself, instead, you now get to focus on just storing data, treating it like you normally would other S3 data and not replicating it, storing it on expensive disks in triplicate, and fundamentally not having to deal with the pains of running other log analytics infrastructure. Check them out today at CHAOSSEARCH.io.

Today, I want to rant about microservices. What are microservices you may very well ask? the broken answer to a misunderstood question. Let’s Talk about what gave rise to microservices: specifically monoliths. that is generally what predated microservices as a design pattern. And by monoliths, I mean one giant codebase, the way that grandpa used to build things. back then Git wasn’t a thing. Subversion and Perforce ruled the day, and everyone wore a pair of fighting trousers to work in the morning. The problem with monoliths was that it’s challenging in the extreme, culturally, to have a whole host of developers working on the same codebase. one person’s change can inadvertently break the build for the other 5000 engineers all working on that same codebase, and with the various version control systems that were heavily in use before Git became usable by mere mortals. There weren’t a lot of workflows that made it easy to have multiple people collaborate on the same system. 

So microservices, for that and a few other reasons, became suggested as a way of solving for this problem. breaking apart those ancient monoliths into functional microservices where each item does one thing began to make a lot of sense. And it solves for a political problem super neatly. You want each team responsible for a given microservice, And that promise is compelling. Because in theory, if you think about this, if you build a microservice and you publish what data that service takes in and in what format it needs to be, and what it will return in response to having that data sent to it, then what your microservice does to achieve its goal and how it works doesn’t matter at all to anyone else. You can replace the database, you can move to serverless, or containers, or punch cards. It doesn’t really matter how it does the work, just so long as the work gets done in the way that is published. And whatever decisions you make, only ever impact your own team as far as how those things get done. So the sky’s the limit, you don’t have to really focus on collaborating with folks the way that you once had. As long as that API remains stable, the sky remains the limit. So suddenly, your 5000 developers are now able to not be tightly coupled to one another and can do all kinds of things and move way faster. It’s a great model. But it’s also a problem. Why?

In the late 19th and early 20th centuries, democracy flourished around the world. This was good for most folks, but terrible for the log analytics industry because there was now a severe shortage of princesses to kidnap for ransom to pay for their ridiculous implementations. It doesn’t have to be that way. Consider CHAOSSEARCH. The data lives in your S3 buckets in your AWS accounts, and we know what that costs. You don’t have to deal with running massive piles of infrastructure to be able to query that log data with APIs you’ve come to know and tolerate, and they’re just good people to work with. Reach out to CHAOSSEARCH.io. And my thanks to them for sponsoring this incredibly depressing podcast. 

The problem is that this works super well for places with thousands of engineers all working on one product. But then Hacker News got a hold of it, And because it’s Hacker News, it took exactly the wrong lessons from this approach and instead started embracing this pattern where it was patently ridiculous, rather than helpful. Now, as a result of this, you see startups out there with 700 microservices, but somehow only 15 employees. Where’s the sense in that? As a result, you have massive sprawl, an awful lot of duplicate work, and no real internal standards in many cases. And as a result, this misguided belief that everything should look architecturally like Google’s internal systems, Despite the fact that your entire application could run on a single computer from 2012 without breaking a sweat. Even Google would argue that its internal systems should not look like Google’s internal systems, but technical debt is a thing and the choices we make constrain what we’re able to do. The problem here is that microservices fundamentally are tied to solving a political problem by a technical means. It’s about how to make people work more effectively. And this somehow instead became a technical best practice. It’s not, not for everyone. It introduces complexity. It leads to scenarios where no one, absolutely no one has all of the various moving parts in their head. It’s prime material for here on the whiteboard confessional Because when you truly embrace microservices, your whiteboards are always full of things that nobody understands. It ties into the larger problem of building things in service to your own resume, or to your engineering department or to the world practice of engineering as some abstract ideal, rather than in service to The business needs that your company exists entirely to cater to. Plus, when you have oodles of microservices hanging around everywhere, First you have a library problem of which service does what. no one knows, And if you ever track it down, great, each one of those services also needs to be monitored. Some monitoring vendor somewhere just wet themselves at the idea of the new boat they’re going to be able to afford now because, with that many things to monitor, it gets really expensive really quickly. You also have testing problems, because testing is critical, And in most shops, the testing quality between different team’s solutions is woefully uneven. There are library problems where people have to not just solve hard problems, but solve them the same way globally. An easy gimme example of that is time zones. How you determine time zones has got to match how other services you deal with, interact with time zones, or you’re gonna wind up with a giant obnoxious problem that is very difficult to track down as time zones change and get updated on one microservice, but not the other. Anyone who’s been through that will absolutely scream at you because you’re never supposed to do that. It leaves scars that last a lifetime. 

And the real problem that I don’t see addressed very much is that embracing the microservices model means that you’re committing to an incredibly complicated architecture, regardless of the reasons. And simplicity is always easier to scale, understand and improve. I’m not saying that you should never use microservices. In fact, if you’re a fan of this show, you’ll notice that I virtually never say anything definitive here at all, other than use Route 53 as a database. And the reason that I’m not definitive about what you should or should not do in absolutes is because context is key. It always is. Without context, you go round and round and round attempting to solve for problems you will likely never encounter, all in the name of doing what the thought leaders tell you to do. You are not Google. You are not Twitter for pets. You’re something else. Go do what works for you and what makes sense for your business. 

I’m Corey Quinn, cloud economist. This has been the Whiteboard Confessional on the AWS Morning Brief, and I’ve enjoyed irritating positively everyone with my thoughts on microservices. 

Thank you for joining us on Whiteboard Confessional. If you have terrifying ideas, please reach out to me on twitter at @quinnypig and let me know what I should talk about next time.

Announcer: This has been a HumblePod production. Stay humble.

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