I walked away from a job interview a few years ago on this point.
One of the technical questions was "if you have a db and a message queue, how do you get your update to alter both or neither (i.e. transactionally)"?
I thought about it for a couple of minutes, then came back with something like "I can't, and you can't either." Then I proposed the usual spiel about using a replicated-state-machine/write-ahead-log/event-sourcing (whatever it might be called at the time) and leaning into eventual consistency as the only practical solution.
He asked if I'd heard about the outbox pattern, so I let him describe it. Sure enough it sounded like this article. The secret to transacting across the database D and the message queue Q:
(D,Q)
is to split D into two parts (the State and the Outbox), transact across those instead
(S,O) Q
and then just pretend that you have a transaction across D and Q.
FWIW The article literally talks about the challenges with getting this to actually work and recommends removing it and just using the DB for everything.
Step 1: identify that you and at least one other node are separated by distance, and some lossy communication channel, and therefore form a distributed system.
Step 2: propose a source of truth that everyone can listen to. Hearing the same facts in the same order should put everyone in the same state (eventual consistency)
Step 3 (you are here): try to do better than EC, by merging the external queue into one of the nodes, making it the master.
Step 4: Now there's no distance between the nodes, so no need to solve the distributed systems problem and you can retire the queue.
That's what I generally choose. You don't need to worry about distributed system semantics, if you choose to not make the system distributed.
However the way Postgres keeps around obsolete rows (deleted or modified) until they're vacuumed can cause problems for high throughput queues. So for those systems the complexity might be worth it. But I bet 90% of the time the choice to use a separate queue is premature optimization. And hopefully OrioleDB (undo based storage engine for postgres) will avoid most of these pitfalls reducing the need for separate queues even further.
Every item will be written to the queue exactly once (as the update is transactional). Queue processing may need at-least-once semantics, yes, depending on what exactly you're doing.
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.
I have built and maintain a system that uses a very similar system - we register artifacts with UUIDs into S3 in a specifically write-once, never edit, never remove approach and then store those UUIDs in a postgres system. We simply juggle around the connection of other model objects to UUIDs as needed allowing us to achieve safe guarantees without burdening the centralized system with the massive volume (these artifacts are often 50MB+ PDFs). I will mention that I am quite fond of this approach but it's good to be aware that introducing levels of abstraction like this do necessarily widen some fail points on the storage side - if your service uses multiple persistence stores each additional store exposes yet another point where inconsistency could be introduced and/or a message could be lost. Still, fragmenting your data over multiple stores that are particularly well suited for their specialized usages can be huge for performance and cost.
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 :)
A more modern term is your system is a single architectural quantum’
Neal Ford calls this a distributed monolith because a change to a database schema can break every single service at once, but there are very valid uses of this method.
There are decades of books on the foot guns as we used this even back in the client-server days.
One suggestion I have is to research where the first version of SoA failed, especially as these systems tend to erode into Enterprise Service Busses.
Products like Apache airflow tend to have value not because of the persistence layer, but because they force workflows into DAGs, which is an enforceable structural constraint, while SQL, being declarative, can sometimes force you into trying to enforce governance through observing behavior.
The former is not subject to Rice’s theorem, while the latter is.
If you actively control for these it will greatly increase the lifetime of this system before (or if) you reach the point you have to replace the system.
We've leveraged the atomicity of transactions with a fail-safe approach for external service interactions for client email sending. This could certainly be done with a formal queue though it'd operate very similarly and achieve the same guarantees as we have today (and was built when we were too small to justify such an infra spend). Internally we have jobs that execute complex logic to transform data from a pending state to a computed state which lean on the DB's atomicity to guarantee that data is successfully transitions and those tasks are all incredibly resilient - but when a secondary persistence store is involved transactional guarantees need to be compromised in some manner. In our email sending example we have the opinion that it is more important to guarantee a client receives all notifications compared to a notification being guaranteed to be sent precisely once so our mechanism in sending is to confirm email sending was successful and then close a transaction that removes that message from the pending list.
There will always be a window for potential loss due to solar flares/whatever but the key in designing a system like this is to make sure you're aware of how the system can fail, accept that outcome and then work to, as much as possible, shrink the distance in cycles/logic between each persistence committal. Logic should be front-loaded to do as much prep work as possible before any irreversible actions happen and then those irreversible actions should be ordered to your preference and dispatched as quickly and cheaply as possible in a safe manner.
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.
OK. I've read it a few times and still don't understand. Where is the distributed part? You store data in a single transaction into postgres. What/who is notifying the message queue?
You build a distributed system on top of this! For example, you may have many distributed workers durably executing workflows from the Postgres-backed task queue. The Postgres transactions allow you to atomically perform operations spanning both your task queue and your business data.
We’ve got an in-house pubsub solution that lives in the main applications database, so pretty much exactly as described in the article. And the atomicity it allows is indeed really nice!
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.
This sounds a lot like reinventing a message queue. Someone trying this in the future might learn painful lessons about ordering, commits, partitioning, dead-letter-queues, replayability, don't-call-me-I'll-call-you, and anything else a Kafka-like comes with out of the box.
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.
One of the technical questions was "if you have a db and a message queue, how do you get your update to alter both or neither (i.e. transactionally)"?
I thought about it for a couple of minutes, then came back with something like "I can't, and you can't either." Then I proposed the usual spiel about using a replicated-state-machine/write-ahead-log/event-sourcing (whatever it might be called at the time) and leaning into eventual consistency as the only practical solution.
He asked if I'd heard about the outbox pattern, so I let him describe it. Sure enough it sounded like this article. The secret to transacting across the database D and the message queue Q:
is to split D into two parts (the State and the Outbox), transact across those instead and then just pretend that you have a transaction across D and Q.FWIW The article literally talks about the challenges with getting this to actually work and recommends removing it and just using the DB for everything.
Step 2: propose a source of truth that everyone can listen to. Hearing the same facts in the same order should put everyone in the same state (eventual consistency)
Step 3 (you are here): try to do better than EC, by merging the external queue into one of the nodes, making it the master.
Step 4: Now there's no distance between the nodes, so no need to solve the distributed systems problem and you can retire the queue.
However the way Postgres keeps around obsolete rows (deleted or modified) until they're vacuumed can cause problems for high throughput queues. So for those systems the complexity might be worth it. But I bet 90% of the time the choice to use a separate queue is premature optimization. And hopefully OrioleDB (undo based storage engine for postgres) will avoid most of these pitfalls reducing the need for separate queues even further.
Is it really a distributed system or just a bunch of services with a central database?
*: edit, maybe a better example here is a rail system with a single central dispatcher is centralized but may still be distributed
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.
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/
Neal Ford calls this a distributed monolith because a change to a database schema can break every single service at once, but there are very valid uses of this method.
There are decades of books on the foot guns as we used this even back in the client-server days.
One suggestion I have is to research where the first version of SoA failed, especially as these systems tend to erode into Enterprise Service Busses.
Products like Apache airflow tend to have value not because of the persistence layer, but because they force workflows into DAGs, which is an enforceable structural constraint, while SQL, being declarative, can sometimes force you into trying to enforce governance through observing behavior.
The former is not subject to Rice’s theorem, while the latter is.
If you actively control for these it will greatly increase the lifetime of this system before (or if) you reach the point you have to replace the system.
There will always be a window for potential loss due to solar flares/whatever but the key in designing a system like this is to make sure you're aware of how the system can fail, accept that outcome and then work to, as much as possible, shrink the distance in cycles/logic between each persistence committal. Logic should be front-loaded to do as much prep work as possible before any irreversible actions happen and then those irreversible actions should be ordered to your preference and dispatched as quickly and cheaply as possible in a safe manner.
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.
Here's another blog post about how a Postgres-backed task queue can run at scale: https://www.dbos.dev/blog/making-postgres-queues-scale
When workers query the db for jobs the rows get locked by the select and there are no race conditions or duplicate assigned jobs
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?
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.
This sounds a lot like reinventing a message queue. Someone trying this in the future might learn painful lessons about ordering, commits, partitioning, dead-letter-queues, replayability, don't-call-me-I'll-call-you, and anything else a Kafka-like comes with out of the box.