[_kdhr]

> but waiting 5 minutes for a vlookup against a few hundred thousand records to complete because I need one single column in another sheet is maddening when it would take nearly no time at all if I loaded the data into Python -> Pandas.

I replied elsewhere in the thread about me working on a new spreadsheet alternative that’s like a reactive Haskell. (Attempting to preserve community norms by not self-promoting more than once per submission.)

One thing that I’m planning is that for any table/array/tree etc. that exceeds a certain size, the data itself and any outputs of functions that work on that data will be offloaded into a real database as rows which can be indexed. Either via SQLite, using PostgreSQL, or even BigQuery (but I don’t trust Google much), this would let users transparently grow their data sets from 1,000 rows to 100,000 to 1,000,000 rows without having to suddenly switch languages or representations. (As an online product this is easier to do, but I think a desktop app equivalent could do it too.) Array functions would in the end be streaming functions rather than literally loading all data in memory.

In Inflex tables are literally arrays of records, but as the language is purely functional and statically typed, the compiler has a lot of freedom to rewrite code into more efficient representations (e.g. streaming), as done in Haskell or SQL.