Monoliths imo are better for smaller projects with a few devs, but otherwise within a few years most of the time you'll regret building a monolith.
I also disagree with the duplicated code point. I don't understand why that would be a significant problem assuming you're using the same language and sharing packages between projects. This isn't a problem I've ever had while working on microservices anyway. I'd also debate whether they're anymore more complex than monoliths on average. My favourite thing about microservice architecture is how simple individual microservices are to understand and contribute to. The architecture and provisioning of microservices can be more complicated, but from the perspective of a developer working on a microservice it should be much simpler to work on compared to a monolith.
If anyone doubts that, this very browser used to read and write is built all the way up with dozens of libraries from compression, network, image encoding, decoding, video encoding decoding, encryption, graphics, sound and what not where each library is totally separate and sometimes was never intended to be used to build web browsers by the original authors.
Rest assured, most of the business (or web 2.0 systems, search, curate, recommend, surface etc kind of) systems are a lot more simpler then an A class browser.
Unlike in Microservices where each process is possibly a totally different language, runtime and framework spun up individually that too possibly in totally different ways.
2. microservices living in monorepos is common
Scale is just one. There is also fault tolerance, security, organizational separation (which can be, up to a point, also be realized with libraries as you suggest), bigger ecosystem to choose from, …
The graph of dependencies between components makes for explicit separation of concerns just like you would have a graph of dependencies between different network services.
I also find monoliths a lot easier to debug. You have the whole call stack, and you get rid of a lot of potential sources of latency problems. You don't have RPC calls that might sometimes get forgotten.
Given the choice, I'd choose monolith every time. Unless, of course, microservices are needed for various other reasons. (Scale, the ability to distribute, etc.)
In a technical sense it is a complexity, but IME pales in comparison with the alternative of having to manage multiple services.
I actually really like this model, it's pretty flexible. You can still have specialized server instances dedicated for certain tasks, but you have one codebase. What's very sweet is that for local development, you just run a single application which (with all enabled profiles) can fulfill all the roles at the same time.
Seperate processes, yes.
One could say "In my experience, the earth is flat", but there's not much a conversation to be had there.
One could say, "In my experience, the earth is flat - I got in my car, went for a drive in one direction, and eventually hit a cliff at the ocean instead of finding myself back where I started". Now there's something to talk about.
(To be clear: this is the internet, and a limited communication medium: I'd assume OP could relate details about their experience, and it's totally reasonable that instead of taking the time to do that, they went outside and touched grass)
From a technical POV they are good for horizontally scaling different workloads which have a different resource footprint
From my experience, when a company decides to go the microservice route, it's more for the sake of solving an organizational problem (e.g. making team responsibilities and oncall escalation more clear cut) than it is to solve a technical problem. Sometimes they will retroactively cite the technical benefit as the reason for using them, but it feels like more of an afterthought
But in all honesty: microservices are very good at solving this organizational problem. If microservice x breaks, ping line manager y who manages it. Very straightforward
I see people trying to apply micro services architectures to a web app with a single developer.
As in literally taking a working monolith written by one person and having that one person split it up into tiny services.
It’s madness.
If you cannot make a clean monolith, I have never seen any evidence that the same team can make good microservices. It is just the same crap, but distributed.
The last 2 years I see more and more seasoned devs who think the opposite: monoliths are better for most projects.
Yes, but also - more difficult to refactor, more difficult to debug (good luck tracking a business transaction over multiple async services), slower with network overhead, lack of ACID transactions... Microservices solve problems which few projects have, but adds a huge amount of complexity.
Whether this is good depends on the type of changes you need to make. Just as you mentioned maintaining modularity in a monolith can be difficult with entropy tending to push the code to spaghetti, there is an equivalent risk in microservices where developers duplicate code or hack around things locally versus making the effort to change the interfaces between microservices where it would make sense.
Ultimately microservices add structure that may be useful for large enough teams, but is still overhead that has to earn its keep.
This is both true and false in a way. Sure, the same business logic is distributed across microservices, but a method call in a monolith can only fail in a couple of ways, while network calls are much more finicky - handling it in every case is pure added complexity in case of a microservice architecture.
Also, don’t forget the observability part - a mainstream language will likely have a sane debugger, profiler, a single log stream, etc. I can easily find bugs, race conditions, slow code paths in a monolith. It’s much more difficult if you have to do it in a whole environment communicating with potentially multiple instances of a single, or multiple microservices.
Lastly, we have programming languages to help us write correct, maintainable code! A monolith != spaghetti code. We have language tools to enforce boundaries, we have static analysis, etc. A refactor will work correctly across the whole codebase. We have nothing of this sort for microservices. You might understand a given microservice better, but does anyone understand the whole graph of them? Sure, monoliths might become spaghettis, but microservices can become spaghettis that are tangled with other plates of spaghettis.
You can achieve exactly the same with simple individual libraries.
I generally agree with your stance and I would add that I find the whole simplistic „microservices suck“ talking point as nonsensical as viewing them as a panacea. They do solve a few specific (for most companies, mostly organizational/human factors because scale and redundancy don’t matter that much) problems that are harder to solve with monoliths.
I still think this point is a bit misleading, because yes, the components become simpler, but their interaction becomes more complex and that complexity is now less apparent. See:
>The architecture and provisioning of microservices can be more complicated, but from the perspective of a developer working on a microservice it should be much simpler to work on compared to a monolith.
I think that perspective often doesn’t match reality. Maybe most microservice domains I‘ve seen had poorly separated domains, but changes small enough to ignore the interactions with other services have been rare in my experience.
For example, you need some ETL to happen every day. Instead of having your pipeline logic inside an airflow task, you put your logic in a library, where you can test and establish boundaries for this behavior in isolation, and compose this logic portably into any system that can accept your library code. When you need to orchestrate, you just call this function inside an airflow task.
This has a few benefits. You now decouple, to a significant extent, your logic and state transfer from your orchestration. That means if you want to debug your DAG, you don’t need to do it in Airflow. You can take the same series of function calls and run them, for example, sequentially in a notebook and you would achieve the same effect. This also can reveal just how little logic you really need in orchestration.
There are some other tricks to making this work really well, such as reducing dependency injection to primatives only where possible, and focusing on decoupling logic from configuration. Some of this is pretty standard, but I’ve seen teams not have a strong philosophy on this and then struggle with maintaining clean orchestration interfaces.
The way you use Airflow, what advantage does it have over crontab? Or to put it another way, once you remove the pipeline logic, what's left?
With cron you have to be more thoughtful about failover especially when convincing others to write failure safe cron in invoked code. With airflow you shouldn’t be running code locally so you can have a mini framework for failure handling.
Cron doesn’t natively provide singleton locking so if the system bogs down you can end up running N of the same jobs at the same time which slows things down further. Airflow isn’t immune to this by default but it’s easier to setup centralized libraries that everything uses so more junior people avoid this when writing quick one off jobs.
I'm looking at a green field implementation of a task system, for human tasks - people need to do a thing, and then mark that they've done it, and that "unlocks" subsequent human tasks, and near as I can tell the overall task flow is a DAG.
I'm currently considering how (if?) to allow for complex logic about things like which tasks are present in the overall DAG - things like skipping a node based on some criteria (which, it occurs to me in typing this up, can benefit from your above advice, as that can just be a configured function call that returns skip/no-skip) - and, well... thoughts? (:
If nothing else, it makes it easy to understand what actually happened and when - just look at job logs.
Closed - Passed
Closed - Failed
Closed - Waived
When you hit that Waived state, it should include a note explaining why it was waived. This could be “parent transaction dropped below threshold amount, so we don’t need this control” or “Executive X signed off on it”.
I’m not sure about the auto-skip thing you propose, just from a UX perspective. I don’t want my task list cluttered up with unnecessary things. Still, I am struggling with precisely where to store the business logic about which tasks are needed when. I’m leaning towards implementing that in a reporting layer. Validation would happen in the background and raise warnings, rather than hard stopping people.
The theory there is that the people doing the work generally know what’s needed better than the system does. Thus the system just provides gentle reminders about the typical case, which users can make the choice to suppress.
I find embedding logic into DSLs usually quite painful and less portable than having a static job graph and all the logic firmly in my own code.
Someone suggested Temporal below and that’s a good suggestion too if you’re fine with a managed service.
However, if each node is more of a data check/wait (e.g. we’re on this step until you tell me you completed some task in the real world), then it would seem rather than your DAG orchestrating nodes of logic, the DAG itself is the logic. In this case, i think you have a few options, though Airflow itself is probably not something I would recommend for such a system.
In the case of the latter, there are a lot of specifics to consider in how it’s used. Is this a universal task list, where there is exactly one run of this DAG (e.g. tracking tasks at a company level), or would you have many independent runs of this (e.g. many users use it), are runs of it regularly scheduled (e.g. users run it daily, or as needed).
Without knowing a ton about your specifics, a pattern I might consider could be isolating your logic from your state, such that you have your logical DAG code, baked into a library of reusable components (a la the above), and then allowing those to accept configuration/state inputs that allow them to route logic appropriately. As a task is completed, update your database with the state as it relates to the world, not its place in the DAG. This will keep your state isolated from the logic of the DAG itself, which may or may not be desirable, depending on your objectives and design parameters.
Great description of good orchestration design. Airflow is fairly open ended in how you can construct dags, leading to some interesting results.
A while ago I moved from microservices to monolith because they were too complicated and had a lot of duplicated code. Without microservices there's less need for a message queue.
For async stuff, I used RabbitMQ for one project, but it just felt...old and over-architected? And a lot of the tooling around it (celery) just wasn't as good as the modern stuff built around redis (bullmq).
For multi-step, DAG-style processes, I prefer to KISS and just do that all in a single, large job if I can, or break it into a small number of jobs.
If I REALLY needed a DAG thing, there are tools out there that are specifically built for that (Airflow). But I hear they're difficult to debug issues in, so would avoid at most costs.
I have run into scaling issues with redis, because their multi-node architectures are just ridiculously over-complicated, and so I stick with single-node. But sharding by hand is fine for me, and works well.