So my understanding is that they're aligning the workflow progression unit and the database commit unit on a one-to-one basis. In other words, each step in the workflow becomes a database commit unit. That's why the outbox pattern gets simplified. But in exchange, the database itself becomes tightly coupled to the workflow, which will make it architecturally difficult to separate later on. Although, to be fair, I almost never actually need to separate the database anyway.
In most services, I often swap out the message broker or the workflow engine, but the database almost always stays the same.
I don't think it's true that distributed and decentralized mean the same thing. A hub and spoke rail system is centralized, but it's still a distributed system, if it has multiple trains running concurrently.* A distributed system has to coordinate somehow, and a single central DB is one way of doing it.
*: edit, maybe a better example here is a rail system with a single central dispatcher is centralized but may still be distributed
In fact - if you're building a very large distributed system the goal is usually to shrink that centralized component to the smallest and most robust surface you can. If the system is well designed it is amazing just how much consistency power you can get from a tiny component of centralization.
There are always tradeoffs of course, but building a truly decentralized system requires some really difficult compromises to correctness. The two general's problem is a great piece of reading on this topic - distribution always requires compromises in general, but to fully remove an authority on truth gets quite tricky.
I think Ducklake[1] is a terrific example of this. They said "look, let's build a lake house over S3, but for the bit that needs strong consistency (the manifest of which S3 blobs are in play), let's use Postgres". Postgres as a metadata catalog or control plane is brilliant for this, since you get strong consistency and the scaling story around a metadata catalog is far different than the volume of data you need to store. Use S3 for volume, Postgres for consistent metadata.
A similar pattern has spilled out of projects like Warpstream[2], which I suspect is using Postgres behind the scenes of their control plane.
Exactly! It's a distributed system, with many processes performing work in parallel, with a central database as a coordination point, used as little as possible. A mutex wouldn't get quite the same performance :)
Can you use postgres as a state store for a distributed application?
It seems this article is trending toward that view: If you can maintain transactional consistency along with application workflow state, then would this generalize to maintaining distributed application state in general?
The follow-up would be: Would this be preferable to Valkey/Redis?
Yes you can - usually I think it's advisable to wrap postgres in a shim application to provide a consistently defined surface you can control but postgres can absolutely serve as the authority node on data correctness.
As to which technical solution would be optimal there are a bunch of factors to consider and I think preferences around features could lead you to a variety of options. Postgres is excellent as long as you're minimizing the amount of data piping directly through it or operating at a reasonable scale.
i don't understand the last point of UDF.
Either you need the state to be updated atomically across different systems or you don't.
But writing a row in a system in order to update the second one at any random time in the future isn't really much different from enqueuing a job in queue.
The key is that the UDF's enqueue is transactional with the database update. Let's say the database update is inserting a new order. This provides the guarantee that if a new order is inserted, a job to process the order is also enqueued. It's impossible for a new order to be inserted without its processing job also being enqueued. Then the durable workflow/queue system is responsible for making sure the processing job, once enqueued, actually executes.
So my understanding is that they're aligning the workflow progression unit and the database commit unit on a one-to-one basis. In other words, each step in the workflow becomes a database commit unit. That's why the outbox pattern gets simplified. But in exchange, the database itself becomes tightly coupled to the workflow, which will make it architecturally difficult to separate later on. Although, to be fair, I almost never actually need to separate the database anyway.
In most services, I often swap out the message broker or the workflow engine, but the database almost always stays the same.
I'm not sure if I've understood this correctly.
Congratulations, you discovered a mutex.
Is it really a distributed system or just a bunch of services with a central database?
I don't think it's true that distributed and decentralized mean the same thing. A hub and spoke rail system is centralized, but it's still a distributed system, if it has multiple trains running concurrently.* A distributed system has to coordinate somehow, and a single central DB is one way of doing it.
*: edit, maybe a better example here is a rail system with a single central dispatcher is centralized but may still be distributed
In fact - if you're building a very large distributed system the goal is usually to shrink that centralized component to the smallest and most robust surface you can. If the system is well designed it is amazing just how much consistency power you can get from a tiny component of centralization.
There are always tradeoffs of course, but building a truly decentralized system requires some really difficult compromises to correctness. The two general's problem is a great piece of reading on this topic - distribution always requires compromises in general, but to fully remove an authority on truth gets quite tricky.
I think Ducklake[1] is a terrific example of this. They said "look, let's build a lake house over S3, but for the bit that needs strong consistency (the manifest of which S3 blobs are in play), let's use Postgres". Postgres as a metadata catalog or control plane is brilliant for this, since you get strong consistency and the scaling story around a metadata catalog is far different than the volume of data you need to store. Use S3 for volume, Postgres for consistent metadata.
A similar pattern has spilled out of projects like Warpstream[2], which I suspect is using Postgres behind the scenes of their control plane.
[1]: https://ducklake.select
[2]: https://www.warpstream.com/
Exactly! It's a distributed system, with many processes performing work in parallel, with a central database as a coordination point, used as little as possible. A mutex wouldn't get quite the same performance :)
Can you use postgres as a state store for a distributed application?
It seems this article is trending toward that view: If you can maintain transactional consistency along with application workflow state, then would this generalize to maintaining distributed application state in general?
The follow-up would be: Would this be preferable to Valkey/Redis?
Yes you can - usually I think it's advisable to wrap postgres in a shim application to provide a consistently defined surface you can control but postgres can absolutely serve as the authority node on data correctness.
As to which technical solution would be optimal there are a bunch of factors to consider and I think preferences around features could lead you to a variety of options. Postgres is excellent as long as you're minimizing the amount of data piping directly through it or operating at a reasonable scale.
i don't understand the last point of UDF. Either you need the state to be updated atomically across different systems or you don't. But writing a row in a system in order to update the second one at any random time in the future isn't really much different from enqueuing a job in queue.
The key is that the UDF's enqueue is transactional with the database update. Let's say the database update is inserting a new order. This provides the guarantee that if a new order is inserted, a job to process the order is also enqueued. It's impossible for a new order to be inserted without its processing job also being enqueued. Then the durable workflow/queue system is responsible for making sure the processing job, once enqueued, actually executes.