On 5/19/26 7:27 AM, Martin Mueller wrote:
> I use Postgres with a GUI frontend (Aquafold) as a very large
> spreadsheet on steroids that analyzes rare or defective spellings in a
> corpus of 65,000 texts and1.5 billion words. I typically extract data
> from the corpus with python scripts, turn them into tables and load them
> into the database.
>
>
> On my Mac with 32 GB of memory performance is OK with queries that
> typically within seconds extract data rows from tables with up to ten
> million rows. If the result set is large, I suspect that most of time
> machine's time is spent displaying result sets. I have used indexing
> sparingly. While it helps, the time savings often don't matter much.
This is going to need more information:
1) Postgres version.
2) The table schema including indexes.
3) An example of the query.
4) Where you are measuring the time.
5) The client you are displaying the results in.
>
>
> I am thinking about scaling up to table with about 60 million rows. Are
> there things to do or watch out for? Or should I proceed on the
> assumption that that 60 million records are within scope and that the
> added timecost is roughly linear?
>
> Martin Mueller
>
> Professor emeritus of English and Classics
>
> Northwestern University
>
--
Adrian Klaver
adrian.klaver@aklaver.com
