Also about the kafka API, I tried to implement that earlier, I had a sort of `translation` layer for that earlier, but it gets pretty complicated to maintain that because kafka is offset based, while walrus is message based.
tl;dr they write to s3 once every 250ms to save costs. IIRC, they contend that when you keep things organized by writing to different files for each topic, it's the Linux disk cache being clever that turns the tangle of disk block arrangement into a clean view per file. They wrote their own version of that, so they can cheaply checkpoint heavily interleaved chunks of data while their in-memory cache provides a clean per-topic view. I think maybe they clean up later async, but my memory fails me.
I don't know how BufStream works.
The thing that really stuck with me from that interview is the 10x cost reduction you can get if you're willing and able to tolerate higher latency and increased complexity and use S3. Apparently they implemented that inside Datadog ("Labrador" I think?), and then did it again with WarpStream.
I highly recommend the whole episode (and the whole podcast, really).