It's neat seeing postgres gearing up for async support. There's also folks like OrioleDB doing massive revamps of postgres & doing disaggregated storage in public. https://github.com/orioledb/orioledb https://hn.algolia.com/?query=orioledb&sort=byDate
Oh they were bought by Suprabase two months ago... Fingers crossed! The Suprabase CEO commented at the time, with a nice basic overview, https://news.ycombinator.com/item?id=40039138
that link doesn't do any performance comparison. They claim that CedarDB executes less "code branches" than duckdb, which may or may not translate to faster performance.
> less "code branches" than duckdb, which may or may not translate to faster performance.
In that case it was about 2.5x faster than DuckDB end to end, so a bit less than the difference in branches.
If you want to see some independent benchmarks on Umbra, our underlying technology, its currently first place on Clickbench [1]. You can compare against duckdb there as well.
Benchmarking full tcp-h (not just one query like in your post) on sizable dataset (few TBs) would be very good close to real world scenario, but vendors usually avoid this.
What counts as large or small definitely varies a lot depending on the context of the conversation/analysis.
MotherDuck's "Big Data is Dead" post [0] sticks in mind:
> The general feedback we got talking to folks in the industry was that 100 GB was the right order of magnitude for a data warehouse. This is where we focused a lot of our efforts in benchmarking.
Another point of reference is [1]
> [...] Umbra achieves unprecedentedly low query latencies. On small data sets, it is even faster than interpreter engines like DuckDB
> TPC-H Small Dataset = 866k tuples, sf 0.1
[0] https://motherduck.com/blog/big-data-is-dead/
[1] https://db.in.tum.de/~kersten/Tidy%20Tuples%20and%20Flying%2...
and then user discovers that DuckDB is plagued with OOMs and dramatic performance degradations when his data is slightly larger than memory.