> I took a brief look at your docs. What would you say is the main difference of yours vs some of the other options? Just the simplicity of it being a single sql file and a sdk wrapper? Sorry if the docs answer this already - trying to take a quick look between work.

It's really just trying to be as simple as possible. I was motivated by trying to just do the most simple thing I could come up with after I did not really find the other solutions to be something I wanted to build on.

I'm sure they are great, but I want to leave the window open to having people self host what we are building / enable us to deploy a cellular architecture later and thus I want to stick to a manageable number of services until until I can no longer. Postgres is a known quantity in my stack and the only postgres only solution was DBOS which unfortunately did not look ready for prime time yet when I tried it. That said, I noticed that DBOS is making quite some progress so I'm somewhat confident that it will eventually get there.

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jedberg 8 hours ago | parent | next [-]

Could you provide some more specifics as to why DBOS isn’t “ready for prime time”? Would love to know what you think is missing!

FWIW DBOS is already in production at multiple Fortune 500 companies.

	▲	the_mitsuhiko an hour ago \| parent \| next [-]
		> Could you provide some more specifics as to why DBOS isn’t “ready for prime time”? Would love to know what you think is missing! Some time in September I was on a call with Qian Li and Peter Kraft and gave them direct feedback. The initial reason this call happened was related to a complaint of mine [1] about excessive dependencies on the Python client which was partially remedied [2] but I felt it should be possible to offload complexity away from the clients even further. My main experiments were pretty frustrating when I tried it originally because everything in the client is global state (you attach to the DBOS object) which did not work for how I was setting up my app. I also ran into challenges with passing through async and I found the step based retry not to work for me. (There are also some other oddities in the Python client in particular: why does the client need to know about Flask?) In the end, I just felt it was a bit too early and I did not want to fight that part of the infrastructure too much. I'm sure it will get there, and I'm sure it has happy users. I was just not one of them. [1]: https://x.com/mitsuhiko/status/1958504241032511786 [2]: https://x.com/qianl_cs/status/1971242911888281608
	▲	biasafe_belm 7 hours ago \| parent \| prev [-]
		I'd love to hear both of your thoughts! I'm considering durable execution and DBOS in particular and was pretty happy to see Armin's shot at this. I'm building/architecting a system which will have to manage many business-critical operations on various schedules. Some will be daily, some bi-weekly, some quarterly, etc. Mostly batch operations and ETL, but they can't fail. I have already designed a semblance of persistent workflow in that any data ingestion and transformation is split into atomic operations whose results are persisted to blob storage and indexed in a database for cataloguing. This means that, for example, network requests can be "replayed", and data transformation can be resumed at any intermediate step. But this is enforced at the design stage, not runtime like other solutions. My system also needs to be easily auditable and written in Python. There are many, many ways to build this (especially if you include cloud offerings) but, like Armin, I'm trying to find the simplest architecture possible so our very small team can focus on building and not maintaining.

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8 hours ago | parent | prev [-]

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