t:.`9.csv
\t select sum v1 by id1 from t
\t select sum v1 by id1,id2 from t
\t select sum v1,avg v3 by id3 from t
\t select avg v1,avg v2,avg v3 by id1 from t
\t select sum v1,sum v2,sum v3 by id3 from t
\t select med v3,dev v3 by id1,id2 from t
\t select min v1,max v1 by id3 from t;
\t select 2 max v3 by id3 from t
\t select v1 dev v2 by id1,id2 from t
\\

https://h2oai.github.io/db-benchmark

data: 50GB csv: 1e9 rows[id1 id2 id3 id4 id5 id6 v1 v2 v3]
query: 9 multi-column aggregations[sum avg var dev correlation median 2max]
machine: amd epyc 9374f
code: k p/polars r/datatable [and much slower:clickhouse spark pandas arrow duckdb ..]

     query    csvload  (milliseconds)
k      950      1,600
?   97,000    606,000
p  258,000    265,000
r  257,000   1250,000

detail for the 9 queries
k      42   76   25  117    16   293    33    15   330
?     616 1509 6499  693  6260 20655  5817 51161  4231
p    1366 2401 42054 943 47177  5093 90847 29360 38500
r    3364 4494 7307 10008 7466 49770 63584 76673 31024

notes:
similar results for the 1e8 and 1e7
? is a 20th century version of k(32/64)
please contact fintan if you are a possible customer and would like to duplicate these timings