That said, as a predecessor to dbos in building durable workflows just using Postgres, I concur with the overall sentiment.
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
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.
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?
Typescript: https://www.pgflow.dev
Elixir: https://github.com/agoodway/pgflow/blob/main/docs/COMPARISON...
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.
@DBOS.workflow()
def checkout_workflow(items: Items):
order = create_order()
reserve_inventory(order, items)
payment_status = process_payment(order, items)
if payment_status == 'paid':
fulfill_order(order)
else:
undo_reserve_inventory(order, items)
cancel_order(order)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.
sgt•40m ago
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.