Building durable workflows on Postgres

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.

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

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.

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.

This feels like the sort of architecture that starts clean and then gradually grows most of the things a workflow-native system already has. I've seen systems like this, seen companies that are built out of this idea, and built small systems like this over time.

Once you need retries, backoff, timeouts, cancellation, versioning, visibility, task routing, rate limits, leases, heartbeats, stuck-worker detection, replay/debugging semantics, workflow migration, fanout/fanin, long timers, audit trails, and operator tooling, the “just use a database” story becomes “build a poor copy of a workflow engine plus a bunch of workers.” pretty quick.

That may still be a good tradeoff for many applications, especially if Postgres is already the core operational dependency. But the comparison shouldn’t be “database vs overcomplicated orchestrator.” It’s more like “what complexity do you want to own, and what do you want to buy / offload to a professional system?”