Hey guys, can someone help me with running daft the right way in kubernetes? For performing queries and operations that work on less than 5-10 GB of data, I believe running native daft runner is better. That being said from the daft documentation, what I see is that daft runs as k8s jobs. But in real scenarios, there will be AI agents or services that would want to run daft to read or write data to something like say - iceberg. I'm not sure but running a distributed compute engine within an agent or service can starve processes. What’s the pattern in which daft native runner is used?

We do not have the concept of running daft as a standalone service today! Ie Daft doesn't have the concept of a job or persistent deployment

I see. A ray cluster would be expensive for smaller scale. Which is why I was leaning more towards the native runner. Should I not be using daft as a query engine or for analytical query purposes?

It's incredible as a single node in-process query engine!

Yes I would love to see any reference of it if you have any to run it in an out of process single node cluster/deployment (which is not ray! ) Any code snippets or docs that you can share?

Daft doesn't have the concept of a deployment/cluster today

Oh got it

Not really at the moment, happy to take some proposals but we haven't been prioritizing this

Got it, thanks jay!

Np!