All you need is Postgres until you scale into TBs of data. We use Postgresql as a durable workflow engine, vector search, time-series data, BM25 search, OLTP/OLAP engine, and a queue. It's basically the only dependency we have for https://lobu.ai
The main benefit is centralizing all the data in one place so we don't need to worry about copying data in between multiple systems. Once something becomes the bottleneck, you can eventually migrate to a purpose specific tool to scale out.To be honest, LISTEN/NOTIFY in my opinion is the most fragile part of PG but it's fine as start until you scale out.
I'm in the same camp. Do you use any specific extensions? Especially for OLAP and time series (partitioned tables + related extensions work fine, but curious if you use anything else)
The native extensions are fine but I don't have good experience with any third party extensions, so far tried Timescale, pg_lake, citus, and pgvectorscale. They look very appealing but it's usually a trap as you can't get the value without using the vendor's cloud offerings.
I think if you grow enough to look for these extensions, it's usually better to bet on purpose-specific tooling. For example, I use DuckDB/Iceberg combination extensively for columnar data and connect DuckDB to PG when I need it.
I don't see logs mentioned. I agree with most those applications but would keep my OLAP stuff (metrics, logs, traces) in a separate store like VictoriaMetrics, both for capacity and read activity.
My dream is, instead of separating data storage, state machines, valid state constraints, and the logic that transitions between valid states, we can actually unify these into some kernel of app state. Honestly, Postgres already has a lot of these capabilities, but I don’t see an obvious story on the app or product level, providing provably correct sets of states that apps can transition between, and which they can automatically expose to clients in informative ways (this user can like this post, but not edit). It looks colored Petri net shaped to me, but I don’t yet see a simple app state paradigm in the same way that the database has obvious successful boundaries.
https://github.com/agentspan-ai/agentspan which is essentially an agentic SDK layer for Conductor can convert any of your langgraph, openAI, vercel, or ADK agent and makes it durable and adds orchestration with no code changes.
Curious to know experience of people using DBOS and Temporal.
I have used Temporal in the past, works really good, my only problem with it was some limits on request payload or event sizes, created some inconveniences to us when building solutions. It also enforces good engineering practices, but sometimes you don't want to write special logic if your CSV file is larger than 2Mb, upload it to S3, pass link, then download it in the workflow.
What is your experience with DBOS? How does it compare to Temporal in terms of operational complexity, feature parity and anything else
I thought Temporal was overly complex, but as you said the best part is it does enforce good engineering practices.
Then I tried their Cloud offering and was appalled at their pricing. I burned through the $1,000 free credits before I even got something to production. Didn't want to bother with running a local Temporal, either.
Best solution is to just take inspiration from their architecture and then do it yourself in Postgres, IMO.
They've just released an external storage approach to solve the large payload issue. I don't 100% love it (it's bolted on, not an intrinsic part), and it's an early release right now - but you can consider this effectively solved for now.
That's good because back in the day if you were putting entire documents in a message queue I would laugh people out the door, putting something in object storage + linking is much more useful (though the distributed system part/backup current state part can be annoying!)
we're using dbos for ai gen workflows and processing video files. understanding how to migrate from celery took time, but for our case it was worth it.
I run a large on-prem temporal setup - throwaway acct as they will likely out me.
Temporal is, in my opinion having run it in prod for over a year - poorly designed, slow and ridicliously heavy infra wise.
If you're doing anything non-trivial (say, 200+ events/workflow) and you need to run only a couple hundred of them concurrently all day, you're going to spend millions on infra, and it's still going to absolutely suck.
Try running their own benchmarks, the numbers are pathetic.
Their sales team is also absolutely appalling and desperate.
From a Developer standpoint, the SDK is quite nice though.
Don't get trapped into nexus, and if the sales team call you make sure legal is in the room.
Agree. Have worked in a codebase using Temporal, and is pretty much a nightmare. I don't know about the infra side, but from the developer side, all the abstractions they bring to the table are poorly designed. Wouldn't recommend
Since DBOS doesn't support Rust, we implemented a very minimal Rust version of this at https://github.com/tensorzero/durable. It has been quite stable and extensible but of course you need to be very careful with the SQL implementations. Hope this is interesting to readers here.
Continuously amazed by what you can do with few tools, as long as Postgres is a part of your toolkit.
I recently developed a distributed queue and it works really great - benchmarks great too, with no race conditions or conflicts. I used SKIP LOCKED so that workers can compete safely.
You can also have multiple workers across nodes avoid conflict by using session wide mutexes i.e. pg advisory lock.
We have a durable queue built into postgres to handle some complex notification-ish logic. It's worked excellently and while there are services various cloud providers would love to sell us to do that it's extremely cheap to run.
For that particular usage, the volume we process and business criticality make it a good choice for inventing here - but for other durable processes we just use off the shelf tools since the cost of maintenance would quickly outstrip the value.
Postgres is a great tool to use and far more powerful than most people give it credit for - but there's always the balance of in-house maintenance vs. paying rent for someone else's solution.
You pay for everything you build - the more complexity you put into it the more that costs over time. Dependencies need to be updated, language/framework upgrades usually break something, new features/requirements introduce additional complexity and code to manage. Software just costs money every day - not a lot, our industry is much lower margin than, say, stamping sheets of metal into tools - but it still has operational costs beyond just the money to operate the hardware we run our products on.
I know that. This looks like some lib you update once a year/every new CVE, and it is compared to a lib from cloud vendor and also update once a year/every new CVE, which is why I asked what it costed YOU in this particular case.
I feel it's way too hand wavy on consistency and correctness. My opinion as someone who've implemented marketing workflows that breaks all the time (and tons of painful lessons).
Strong correctness guarantee is something that should not be undermine. Even more important than availability.
The examples on the website is simple but heavily undermines the importance of correctness. Anyone who implement similar pseudo-code directly will eventually suffer from data correctness issue in crashes.
As you said, the example is simple and it might not be obvious to people without prod experience what the problems can be. Postgres can give you all the primitives you need to solve this at the application layer. Durable workflows on Postgres is an effective way to access these primitives.
Having inherited a few of these - you tend to home-grow an ad-hoc version of many of the existing OSS tools, but with less of the patterns baked in.
Not sure where the NIH ends and where you're actually better off with a supported orchestration approach. I suppose if you expect your program to be around a while (or need advanced features), maybe think about using something a bit more battle tested?
Convex has a workpool component that gives the ability to compose big complicated flows in an understandable way, and give you realtime updates on status of various pieces: https://www.convex.dev/components/workflow
The efforts we've undergone to make Oban (and Pro) work with CRDB have been ridiculous. Feature detection all over because of a lack of common operators and functions that can't be used in indexes. The worst is the rampant "serialization_failure" errors that force continual transaction retries. Not how I'd suggest scaling Postgres.
That said, as a predecessor to dbos in building durable workflows just using Postgres, I concur with the overall sentiment.
Can you expand on why you chose to use CRDB with Oban? I have no opinion here, I’m genuinely curious as someone using Oban myself (with Postgres). I haven’t hit the point of really needing to scale it out yet and I’d rather avoid the traps others have figured out.
I am not convinced that using a special software for "durable workflows" is necessary. If one has a stateful message queue or job task queue, e.g. RabbitMQ or Celery, one can use it. Irrespective, many jobs can be made idempotent. The most that you ought to residually need is a column in an existing table of your own database which keeps track of what remains to be done.
Given the above, it would seem that durable workflow software is pushed forward by those who have a surplus of VC money to spend. As for the vendors, there is no shortage of people trying to sell you things that you don't need.
I find it strange that some think in terms of AWS architecture as the default. You could replace nearly the entire AWS stack with an Elixir (Erlang) monolith + Postgres.
All you need is Postgres until you scale into TBs of data. We use Postgresql as a durable workflow engine, vector search, time-series data, BM25 search, OLTP/OLAP engine, and a queue. It's basically the only dependency we have for https://lobu.ai
The main benefit is centralizing all the data in one place so we don't need to worry about copying data in between multiple systems. Once something becomes the bottleneck, you can eventually migrate to a purpose specific tool to scale out.To be honest, LISTEN/NOTIFY in my opinion is the most fragile part of PG but it's fine as start until you scale out.
I'm in the same camp. Do you use any specific extensions? Especially for OLAP and time series (partitioned tables + related extensions work fine, but curious if you use anything else)
The native extensions are fine but I don't have good experience with any third party extensions, so far tried Timescale, pg_lake, citus, and pgvectorscale. They look very appealing but it's usually a trap as you can't get the value without using the vendor's cloud offerings.
I think if you grow enough to look for these extensions, it's usually better to bet on purpose-specific tooling. For example, I use DuckDB/Iceberg combination extensively for columnar data and connect DuckDB to PG when I need it.
Listen/notify is poised to become much better in PG 18 and 19
Why’s that?
I don't see logs mentioned. I agree with most those applications but would keep my OLAP stuff (metrics, logs, traces) in a separate store like VictoriaMetrics, both for capacity and read activity.
Yeah I have logs in Sentry, which also uses Postgresql.
Armin Ronacher's `absurd` is an implementation of durable workflows for postgres:
https://lucumr.pocoo.org/2025/11/3/absurd-workflows/
https://github.com/earendil-works/absurd
https://earendil-works.github.io/absurd/
I've not used it, but it's worth comparing to other options
My dream is, instead of separating data storage, state machines, valid state constraints, and the logic that transitions between valid states, we can actually unify these into some kernel of app state. Honestly, Postgres already has a lot of these capabilities, but I don’t see an obvious story on the app or product level, providing provably correct sets of states that apps can transition between, and which they can automatically expose to clients in informative ways (this user can like this post, but not edit). It looks colored Petri net shaped to me, but I don’t yet see a simple app state paradigm in the same way that the database has obvious successful boundaries.
Conductor OSS does this quite well https://docs.conductor-oss.org/devguide/ai/index.html
https://github.com/agentspan-ai/agentspan which is essentially an agentic SDK layer for Conductor can convert any of your langgraph, openAI, vercel, or ADK agent and makes it durable and adds orchestration with no code changes.
Curious to know experience of people using DBOS and Temporal.
I have used Temporal in the past, works really good, my only problem with it was some limits on request payload or event sizes, created some inconveniences to us when building solutions. It also enforces good engineering practices, but sometimes you don't want to write special logic if your CSV file is larger than 2Mb, upload it to S3, pass link, then download it in the workflow.
What is your experience with DBOS? How does it compare to Temporal in terms of operational complexity, feature parity and anything else
I thought Temporal was overly complex, but as you said the best part is it does enforce good engineering practices.
Then I tried their Cloud offering and was appalled at their pricing. I burned through the $1,000 free credits before I even got something to production. Didn't want to bother with running a local Temporal, either.
Best solution is to just take inspiration from their architecture and then do it yourself in Postgres, IMO.
They've just released an external storage approach to solve the large payload issue. I don't 100% love it (it's bolted on, not an intrinsic part), and it's an early release right now - but you can consider this effectively solved for now.
That's good because back in the day if you were putting entire documents in a message queue I would laugh people out the door, putting something in object storage + linking is much more useful (though the distributed system part/backup current state part can be annoying!)
we're using dbos for ai gen workflows and processing video files. understanding how to migrate from celery took time, but for our case it was worth it.
I run a large on-prem temporal setup - throwaway acct as they will likely out me.
Temporal is, in my opinion having run it in prod for over a year - poorly designed, slow and ridicliously heavy infra wise.
If you're doing anything non-trivial (say, 200+ events/workflow) and you need to run only a couple hundred of them concurrently all day, you're going to spend millions on infra, and it's still going to absolutely suck.
Try running their own benchmarks, the numbers are pathetic.
Their sales team is also absolutely appalling and desperate.
From a Developer standpoint, the SDK is quite nice though.
Don't get trapped into nexus, and if the sales team call you make sure legal is in the room.
Since I'm in a ranting mode -- here's a good example: you're limited to _ONE_ IO per shard in the history service:
https://github.com/temporalio/temporal/blob/e22e6304b3c4a409...
https://github.com/temporalio/temporal/blob/e22e6304b3c4a409...
Temporal does a crazy amount of database operations and all of these are behind that mutex.
Oh, and you can't change the shard count on existing clusters.
Great stuff.
Agree. Have worked in a codebase using Temporal, and is pretty much a nightmare. I don't know about the infra side, but from the developer side, all the abstractions they bring to the table are poorly designed. Wouldn't recommend
Since DBOS doesn't support Rust, we implemented a very minimal Rust version of this at https://github.com/tensorzero/durable. It has been quite stable and extensible but of course you need to be very careful with the SQL implementations. Hope this is interesting to readers here.
Continuously amazed by what you can do with few tools, as long as Postgres is a part of your toolkit.
I recently developed a distributed queue and it works really great - benchmarks great too, with no race conditions or conflicts. I used SKIP LOCKED so that workers can compete safely.
You can also have multiple workers across nodes avoid conflict by using session wide mutexes i.e. pg advisory lock.
Advisory locks are preferred for this anyways because holding a lot of SELECT FOR UPDATE doesn’t scale too well.
We have a durable queue built into postgres to handle some complex notification-ish logic. It's worked excellently and while there are services various cloud providers would love to sell us to do that it's extremely cheap to run.
For that particular usage, the volume we process and business criticality make it a good choice for inventing here - but for other durable processes we just use off the shelf tools since the cost of maintenance would quickly outstrip the value.
Postgres is a great tool to use and far more powerful than most people give it credit for - but there's always the balance of in-house maintenance vs. paying rent for someone else's solution.
what's "maintenance" here ? If app is also using PostgreSQL it should be just initial effort of writing/importing code to run it, no ?
You pay for everything you build - the more complexity you put into it the more that costs over time. Dependencies need to be updated, language/framework upgrades usually break something, new features/requirements introduce additional complexity and code to manage. Software just costs money every day - not a lot, our industry is much lower margin than, say, stamping sheets of metal into tools - but it still has operational costs beyond just the money to operate the hardware we run our products on.
I know that. This looks like some lib you update once a year/every new CVE, and it is compared to a lib from cloud vendor and also update once a year/every new CVE, which is why I asked what it costed YOU in this particular case.
I feel it's way too hand wavy on consistency and correctness. My opinion as someone who've implemented marketing workflows that breaks all the time (and tons of painful lessons).
Strong correctness guarantee is something that should not be undermine. Even more important than availability.
The examples on the website is simple but heavily undermines the importance of correctness. Anyone who implement similar pseudo-code directly will eventually suffer from data correctness issue in crashes.
As you said, the example is simple and it might not be obvious to people without prod experience what the problems can be. Postgres can give you all the primitives you need to solve this at the application layer. Durable workflows on Postgres is an effective way to access these primitives.
Having inherited a few of these - you tend to home-grow an ad-hoc version of many of the existing OSS tools, but with less of the patterns baked in.
Not sure where the NIH ends and where you're actually better off with a supported orchestration approach. I suppose if you expect your program to be around a while (or need advanced features), maybe think about using something a bit more battle tested?
Convex has a workpool component that gives the ability to compose big complicated flows in an understandable way, and give you realtime updates on status of various pieces: https://www.convex.dev/components/workflow
Isn't this Just Oban from elixir? :)
Citing CockroachDB as an example of scaling Postgres made me spit out coffee. Was this LLM-written?
Yeah that seems off to me too. But I guess they meant that since CockroachDB is compatible with Pg, it would also serve the same prupose?
The efforts we've undergone to make Oban (and Pro) work with CRDB have been ridiculous. Feature detection all over because of a lack of common operators and functions that can't be used in indexes. The worst is the rampant "serialization_failure" errors that force continual transaction retries. Not how I'd suggest scaling Postgres.
That said, as a predecessor to dbos in building durable workflows just using Postgres, I concur with the overall sentiment.
Can you expand on why you chose to use CRDB with Oban? I have no opinion here, I’m genuinely curious as someone using Oban myself (with Postgres). I haven’t hit the point of really needing to scale it out yet and I’d rather avoid the traps others have figured out.
How do you incorporate secrets in this kind of implementation? Stored in db?
Secrets are orthogonal to durable execution--what are your concerns about using them together?
how is this compared to hatchet?
I am not convinced that using a special software for "durable workflows" is necessary. If one has a stateful message queue or job task queue, e.g. RabbitMQ or Celery, one can use it. Irrespective, many jobs can be made idempotent. The most that you ought to residually need is a column in an existing table of your own database which keeps track of what remains to be done.
Given the above, it would seem that durable workflow software is pushed forward by those who have a surplus of VC money to spend. As for the vendors, there is no shortage of people trying to sell you things that you don't need.
PgFlow is pretty awesome for DAG workflows - it's built on pgmq (which does the heavy lifting, making it backend agnostic).
Typescript: https://www.pgflow.dev
Elixir: https://github.com/agoodway/pgflow/blob/main/docs/COMPARISON...
Temporal is an insane piece of software, always surprised people dont know about it. You could replace almost youre whole AWS stack with temporal
Sure, if you wanna run a 48 node cassandra cluster...
I find it strange that some think in terms of AWS architecture as the default. You could replace nearly the entire AWS stack with an Elixir (Erlang) monolith + Postgres.