Microsoft has their own Durable Task framewor[1] for that kind of stuff, and it supports both running as a self-hosted standalone service like temporal, and running serverless on Azure Functions. It actually predated airflow, temporal, etc., IIRC.

This one seems to be more database-specific use case. The advantage is probably that you can track the exact state of the job in the database itself, rather than having to cross-reference the workflow log with the codebase and trace through it line by line to figure out what the state is. Plus I assume it's less overhead and latency, and operationally one less thing to spin up.

[1] https://learn.microsoft.com/en-us/azure/durable-task/common/...

(Author of both durable task framework and pg_durable/duroxide here)

Indeed Durable tasks is an exceptional project and was a unique innovation at the time.

pg_durable brings the same reliability and durablity semantics to long running operations within the database.

We have tons of interesting scenarios on the roadmap. Stay tuned! :)

Does ai.backfill() fill incomplete/dirty rows or does pg_durable have some notion of partial completion?

For one, Airflow (or anything external, for that matter) has no insight into DB load, so when devs slam 200 concurrent workers at the DB, other workloads may be impacted. In contrast, this could (I don’t think it does at this time) get near realtime feedback on performance without the RTT cost, and adjust itself accordingly.

it also feels strange to query for DB load before starting a job.. i'm not even sure how you would do it, how you would adjust a job given a load value, and what would you do if there's too much load.