I disagree with this so much. I have personally worked with Rails application monoliths and Node.js microservices and I can tell you that making changes on the monolith is muliple times easier mostly depending on the code structure. I would take a properly structured monolith any day. This not only includes code/features but also deployments. Adding more services introduces more complexity in the deployment architecture as well.

A good example of this is just by looking at the GitLab codebase https://gitlab.com/gitlab-org/gitlab, it's a monolith but has good abstractions/structure vs say the Google Microservices Demo app https://github.com/GoogleCloudPlatform/microservices-demo which is not tightly coupled but introduces more complexity from implementation to deployment.

I'd really wager that you should be building your code to last the full life of the product from the start, with adaptability to changing scope and requirements being delivered by that modularity.

You of course don't always know the full scope of the project from day one, so you don't typically build software like you would build a bridge, with a fully holistic plan. But you can totally account for the future when you initial build a monolith.

This is true, but having multiple services or even instances that can horizontally scale gives you more leeway as far as resiliency against errors goes. For example, the issues in the monolith at my current day job could have a way lower priority/impact, if they didn't risk bringing down the entire application should the application server/instance fail - instead, if there were N instances, the load could just be handled by the other instances.

Actually, one of my first blog posts talks about how scalable and modular monoliths might be a way to leverage the best of both worlds: https://blog.kronis.dev/articles/modulith-because-we-need-to...

Of course, if you actually have the operational capacity to support microservices, that may be a good investment in some particular systems - if a part of the whole system would start to misbehave, its impact could be far more limited, for example, due to resource constraints or rate limits that should be in place. Monitoring could also become somewhat easier and you could figure out where any problem lies, except for the most complex Byzantine failures.

> I have personally worked with Rails application monoliths and Node.js microservices and I can tell you that making changes on the monolith is muliple times easier mostly depending on the code structure.

However, where monoliths fail in my eyes, is the strong coupling. For example, i currently need to migrate the very same monolith to far newer frameworks and runtimes, which is essentially impossible to ship, because a large number of edge cases and specific functionality all break when this is done.

So instead of being able to ship the parts that'd work (say, the RESTful API and the front end), i'm blocked by the things that don't work (report functionality, PDF functionality, file handling functionality, database migration functionality), so for approx. the past month it has probably looked like i'm not generating much business value, due to struggling with all of this. I wrote more about the problems with this here on HN: https://news.ycombinator.com/item?id=29204841

Their argument is that if you have a monolith and want to say move from Ruby to TypeScript, you basically have to rewrite the whole thing and migrate in one go, which is a massive pain. If you have microservices and want to do the same, you can move one service at a time.

You can even move from one monolith to the other. Eg. a single DB store, and you just move a set of APIs (this set of APIs live in Ruby monolith, and it can talk to TypeScript monolith). Just put a reverse proxy in front (which you're likely to already run), and route requests to the proper service. Sure, some things will be trickier, but they'd be tricky anyway (eg. if you want to move user management APIs which tie into privilege handling, it's going to be finicky no matter how you slice it).

The main driver for SOA (and microservices by extension) is to allow independent iteration of components by independent teams. On small teams, it's usually not a win at all because you introduce a lot of overhead and you need to really be on point regarding backwards compatibility (or statelessness) and API specs.

Sure, monoliths do allow unrestricted use of (a particular set of) suboptimal patterns, but I've seen one too many "microservices" which have "custom" protocols that are only ever changed in sync on both sides of the microservice (server and clients).

But that was exactly my point: how hard it is depends on the code structure that has no relationship to whether you are in a SOA or a monolith architecture.

Sure, some (bad) patterns are hard in SOA which are trivial in a monolith, but they are bad patterns regardless. What matters is that you replace bad patterns with good patterns.

People just code like everything works all the time. Then when they want to integrate an external service, they have to rewrite everything.

If you understand that network calls have to made and electrical signals have to pass down a bus when you write either a monolith or a microservice, going back and swapping services is super easy.

The only extra work is updating the monolith to call the extracted service, which isn't that hard.

Note what they are talking about. They are right: of course if you want to change your Rails monolith to a Spring Boot java project it will be extremely difficult. The point is, how often do you want to do that ? Maybe in google it happens often, but for most smaller companies that is "very rarely"

This is also applicable to microkernel model, where everything is almost processes intercommunicating either locally or remotely, but their performance is so bad they are either academically significant only or are simply abandoned.

There are also critical services that bottlenecks the entire service plane. When one of them critical services (such as authentication service, which brought down Google recently) dies, it still have the cascading failures that monolithic services usually have.

And sometimes it is impossible to properly make microservices when data consistency is highly important. These services (such as database and transaction based services) are critical by nature and is almost impossible to scale, and have to either using leader-election, which in a nutshell, distributed locks where the losers are merely standbys wasting resources on whether they can obtain the lock again, and will be highly susceptible to deadlock unless a reliable transactional memory source is involved (such as etcd leader lock in Kubernetes). Or replicate only. They are also what bugger up microservices to have degenerated into its monolithic equivalent or have to pay a great toll to get it scalable to survive in the microservice world.

Well, to be honest, microservices in practice are just multiple monolithic services strapped together; We need to redesign all our current infrastructure to remove these critical bottlenecks. That's how you do microservices right.

This isn't a fair comparison. Microkernels can still be performant if done correctly. Syscalls have become slower over time as Spectre/Meltdown mitigations are added.

And from a quick google, sel4 did indeed add such mitigations: https://research.csiro.au/tsblog/crisis-security-vs-performa...

> The question of whether or not a userspace attacker can attack another userspace thread running on seL4 is an unequivocal yes.

> We are currently in the middle of deploying the branch-prediction barriers (x86) and BTB flushing mechanisms (ARMv6, ARMv7) that are necessary to prevent attackers from attacking other userspace threads on seL4.

> [....]

> seL4 will allow userspace processes to specify whether or not they want to take the performance hit that is incurred by effectively flushing the branch predictor when switching between processes on x86 (which is currently the only way to mitigate this variant of Spectre). The performance penalty of flushing the branch predictor on x86 processors is, unfortunately, very high.

And later regarding performance, although I'm not 100% sure on which parts this exactly applies:

> Initial tentative estimations of the impact are that they will likely be higher than those experienced by a monolithic kernel for the simple reason that microkernels switch address spaces more due to their essential function as an IPC engine, and the SKIM window patch alone increases the number of address space switches that the kernel must do.

That's not the only case where privilege barriers can be crossed. In x86, that is the most common way for calling into kernel code, but it is not the only way. See io_uring.

> Also, IPC does often involve calling to kernel space to execute the IPC?

Often. Not always.

The real advantages of microservices (I would say the only advantage but YMMV) is that it greatly simplifies development work, CI etc. when your organisation reaches a certain point.

Even at 200 devs, working on just one monolith has a lot of friction. Open a pull request for something trivial and you'll have to wait hours for the CI to finish.

Of course, in time, this might spiral into an uncontrollable mess without discipline, but it feels good at the beginning.

(If you're interviewing somewhere and they say they have microservices, ask how they deal with poison messages. The answer can be telling).

[0](https://cloud.google.com/pubsub/docs/ordering#receiving_mess...)

[1](https://cloud.google.com/pubsub/docs/handling-failures#setti...)

Don't use microservices.

Short answer:

It's extremely difficult and, there isn't always a good solution. Different approaches are used on a _per consumer_ basis. That's the gist of what you want to hear.

Long answer:

In some cases ordering and lost messages don't matter so much, so any failed messages can just be thrown out.

For some queues lost messages matters, but not ordering. So you can put the message at the back of the queue (possibly delayed if that's support), so you don't block other messages.

Partitioning helps isolated poison messages to a vertical (e.g., customer_id).

Always adding id and timestamp to each message can let consumers behave more smartly (e.g. throwing away out of order or already processed messages).

But when none of that helps and ordering and delivery matter (which I think will make up most cases for any non-trivial system)...I'd love to know too. Good alerts to wake up developers?

Then there's how to scale this and making it reliable What's the ordering guarantee across N consumers? What if a consumer nack's a message. What if there's prefetching?

However, what these have in common is that using them properly can more or less become a full time job for a few months. It's never simple or easy. I've been on multiple teams where somebody was doing that stuff full time for months on end. I don't have that kind of spare time. Either I do product development or I do devops but I can't do both and I certainly can't take two months out of my schedule for this stuff. So, I tend to look for solutions that minimize the amount of devops I need to do to the absolute bare minimum. Additionally, we are a bootstrapped setup, which means I also look for cost savings.

Google cloud run is awesome for this. Last year I wanted to setup CI/CD for a simple service and have it run in Google cloud so we could point our web app at it. This took around fifteen minutes from start to finish. My starting point was a git repository that already had a Docker file in it. It took me a few mouse clicks to get cloud run to generate a cloud build and deploy the first version of that in an autoscaling cloud run environment. Awesome. Love it. The best part is that this setup cost us close to 0$/month while we were developing for the next six months. It had so few requests that we stayed below the freemium tier. It even does websockets now. We actually applied to get access to the beta of that.

Later we ran into some limitations with background threads with cloud run. It aggressively throttles running containers when they are not serving a request. So, I decided to spin up a vm with the same docker container. Unlike AWS, spinning up a vm that runs a docker container is stupidly easy. I just grabbed the same docker container that we used for cloud run and passed that to the vm configuration (in the UI) and it launched in one go with a container optimized OS and our container came up after a few seconds. Every UI screen in Google cloud has a "copy this as a gcloud command line" as a well; so that became the basis for our CD (Github actions). We simply defined a github action that updates the vm. Prototype what you want in a UI and then copy and adapt the command for automation. Great stuff.

Fast forward a year and I had a need to finally make this a bit more proper production environment. So, I bought a wildcard certificate, created a load balancer with it, defined an instance group with an instance template similar to the vm I prototyped earlier and I ended up with a nice auto-scaling service that we can deploy with zero down time. The hardest part was a bit of trial and error to figure out what I needed to do exactly. Took me about a day to get this right. Again, we have a github action that updates this; so full CI/CD.

If you run monoliths, this stuff goes a long way. There's more to our setup of course but mostly I manage to not spend most of my days on devops topics. We will at some point out grow our current setup and hire a full time devops person to scale our setup a bit more responsibly. But actually, this setup already ticks most of my boxes. It's simple enough that I don't actually care about automating how it is created. It's flexible enough that it is easy to tweak. And it has things like logging, health checks, monitoring, alerting, etc. that is relatively easy to manage as well although maybe a little bare-bones.

If/when we move to kubernetes, we'll end up roughly quadrupling our cost. Right now that would not really solve a problem I have. But it's probably a valid next step to take. Until then, monoliths and KISS are what we do.