Now, any system I’ve seen designed around exactly once is complete garbage.
Not entirely true, DBOS can guarantee exactly once execution if your API calls are idempotent.
I thought that was what 'idempotent' meant.
The concept originates in maths, where it's functions that can be idempotent. The canonical example is projection operators: if you project a vector onto a subspace and then apply that same projection operator again you get the same vector again. In computing the term is sometimes used fairly loosely/analogistically like in the light switch example above. Sometimes, though, there is a mathematical function involved that is idempotent in the mathematical sense.
A form of idempotence is implied in "retries ... can't produce duplicate work" in the quote, but it isn't the whole story. Atomicity, for example, is also implied by the whole quote: the idea that an operation always either completes in its entirety or doesn't happen at all. That's independent of idempotence.
You don't have idempotent crashes.
Transactional outboxes specifically are one of my favorite patterns: they’re not too hard to add and don’t require changing many core invariants of your system. If you already use some sort of message bus or queue, making publishes to it transactional under a given RDBMS is often as simple as adding some client side code and making sure that logical message deduplication and is present where appropriate: https://microservices.io/patterns/data/transactional-outbox....
If you use a separate message broker (Kafka, SQS, RabbitMQ) with this pattern, you’ll also need a sweeper cron job to re-dispatch failed publishes from the outbox table(s) as well.
Bonus points if this can be implemented on top of existing trigger-based audit table functionality.
send a() send b()
And know both will be sent at least once, without having to introduce an outbox and re-architect your code to use a message relay. We can nitpick the details, but being able to "just write normal code" and get strong guarantees is, imo, real progress.
I get that it is particularly valuable in that scenario by treating other services as "external API", but monolith also do call "external API" and delegate work to async tasks. The principles discussed here API are interesting beyond just micro-services while being lighter and simpler than Durable Execution.
Didn't we get to the point where we realized that microservices cause too much trouble down the road?
That's a largely ignorant opinion to have. Like any architecture, microservices have clear advantages and tradeoffs. It makes no sense to throw vague blanket statements at an architure style because you assume it "causes trouble", particularly when you know nothing about requirements or constraints and all architectures are far from bullet proof.
Being prepared for these things to happen and having code in place to automatically prevent, recognize and resolve these errors will keep you, the customers and everyone in between sane and happy.
I am particularly not a fan of doing unnecessary work/over engineering, e.g. see https://charemza.name/blog/posts/agile/over-engineering/not-..., but even I think that sometimes things _are_ worth it
Strong disagree. Addressing expectable failure modes is not over engineering. It's engineering. How do you put together a system without actually thinking through how it fails, and how to prevent those failure scenarios from taking down your whole operation?
I have bad news for everyone. Nothing in computing is synchronous. Every instance we pretend it’s not and call it something else you have a potential failure under the right circumstances.
The more your design admits this the safer it will be. There are practical limits to this which you have to determine for yourself.
I think you need to sit this one out. This sort of vacuous pedantry does no one any good, and ignores that it's perfectly fine to model and treat some calls are synchronous, including plain old HTTP ones. Just because everything is a state machine this does not mean you buy yourself anything of value by modeling everything as a state machine.
TCP connect(3) is synchronous. Making a directory on a local filesystem is synchronous. fsync(2) is synchronous. Committing an RDBMS transaction is synchronous.
