Think you'd want to compare with a columnar database since this is an analytic workload, not a transactional one.

And a 4 node Spark cluster with Parquet or Arrow files and a Scala job will compare favorably to this because it's also a lazy evaluator and the benchmark problems here are embarrassingly parallel.

PS this is a very cool project!

I've never benchmarked against postgres, but would be interested about the results. I once tried monetdb, and it was orders of magnitude slower for simple calculations, so I stopped looking at RDMS'es after that.

I think they solve a different problem, smaller data, data/relational integrity, data mutations. Dataframes can take shortcuts here.

If you want to do a serious benchmark, feel free to contact us. Github: https://github.com/vaexio/vaex/issues Email: (I'm easy to google).

I am currently doing an internship as part of my masters degree where I am analyzing ~30 GB of data. I'm using Postgres + Python and it is working quite well, even on my 2014 MacBook Air.

It would indeed be interesting to see how this approach with Vaex compares to Postgres. Though, I would be quite sad giving up SQL in favour of Pandas DataFrame indexing and Python looping :)

No Python looping happening in Vaex :), otherwise, we wouldn't get this performance.

We are also working on GraphQL support, with a Hasura-like API: https://docs.vaex.io/en/latest/example_graphql.html

I think GraphQL is easier in combinations with front end development, and you can tab-complete your way out. Early days for this sub-project, but I think very promising.

Ah, of cause. It makes sense you don't loop in Python.

This all seems pretty interesting. I will give it a go.

Ive been switching an reporting system that did analysis in postgres, to analysis in pandas (mostly business-stats type summaries).

It feels like growing wings and a jetpack. Almost everthing is waaay easier and faster.