Have always wanted to dip my toe in EventStoreDB/Kurrent but this looks super intuitive and nice to use. Especially like the js projections, can imagine it being really handy in prototyping or ad-hoc reporting.
Interesting point about SlateDB - I've been thinking about how different architectures handle event sourcing and stream processing. SierraDB's append-only model with fixed partitions is really compelling for event sourcing, but I'm curious how it compares to something like SlateDB when you need more general-purpose streaming capabilities. Do you think the trade-offs between these approaches are starting to converge, or are they solving fundamentally different problems? Also, SierraDB's use of RESP3 is smart - anything that reduces client complexity is a win in my book.
This looks really cool!
Have always wanted to dip my toe in EventStoreDB/Kurrent but this looks super intuitive and nice to use. Especially like the js projections, can imagine it being really handy in prototyping or ad-hoc reporting.
In memory partitions, yeah?
It's persisted to S3 storage, but SlateDB feels like it might sort of have some fit, maybe, as a scale-out distributed LSM-tree. https://slatedb.io https://news.ycombinator.com/item?id=41714858
There's an old 404 post too that looks like a reasonably on target introduction: Why SlateDB is the right choice for Stream Processing. https://web.archive.org/web/20241102212325/https://www.respo...
Interesting point about SlateDB - I've been thinking about how different architectures handle event sourcing and stream processing. SierraDB's append-only model with fixed partitions is really compelling for event sourcing, but I'm curious how it compares to something like SlateDB when you need more general-purpose streaming capabilities. Do you think the trade-offs between these approaches are starting to converge, or are they solving fundamentally different problems? Also, SierraDB's use of RESP3 is smart - anything that reduces client complexity is a win in my book.