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Quack-Cluster: A Serverless Distributed SQL Query Engine with DuckDB and Ray
What is the lifetime of the Ray workers, or, in other words, what is the scalability / scale-to-zero story that makes this serverless?
So DuckDB was developed to allow queries for bigish data finally without the need for a cluster to simplify data analysis... and we now put it to a cluster?

I think there are solutions for that scale of data already, and simplicity is the best feature of DuckDB (at lest for me).

  • AnEro
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  • 11 minutes ago
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Big fan of this push back, because there are alot of projects that have that smell over engineering with the wrong base. (especially with vibecoding now) Thought there are use cases where some have lots of medium-sized data divided up. For compliance, I have a lot of reporting data split such that duckdb instances running in separate processes work amazing for us especially with lower complexity to other compute engines in that environment. If I wanted to move everything into somewhere a clickhouse/trino/databrick/etc would work well the compliance complexity skyrockets and makes it so we have to have perfect configs and tons of extra time invested to get the same devex
feels like a missed opportunity to call it cluster-quack xD
Surely “clusterduck” would be better…
In my experience ray clusters don't scale well and end up costing you more money. You need to run permanent per-user instances etc.

What you need is a multi-tenancy shared infrastructure that is elastic.

neat. i'm pretty novice in the guts of this kind of stuff, but how does this work under the hood for blocking operators where they "cannot output a single row until the last row of their input has been seen"?

i think this is where spark shuffling comes in? but how does it work here.

https://duckdb.org/docs/stable/guides/performance/how_to_tun...