alt Hacker NewsProbably no difference for your use-case (ST_Distance). If you already have data in Postgres, you should continue using Postgis.
In my use case, I use DuckDB because of speed at scale. I have 600GBs of lat-longs in Parquet files on disk.
If I wanted to use Postgis, I would have to ingest all this data into Postgres first.
With DuckDB, I can literally drop into a Jupyter notebook, and do this in under 10 seconds, and the results come back in a flash: (no need to ingest any data ahead of time)
import duckdb
duckdb.query("INSTALL spatial; LOAD spatial;")
duckdb.query("select ST_DISTANCE(ST_POINT(lng1, lat1), ST_POINT(lng2, lat2)) dist from '/mydir/*.parquet'")Replies
touisteur • 05/04/2025
And now I'm curious whether there's a way to actually index external files (make these queries over 600GB faster) and have this index (or many indices) be persistent. I might have missed that when I looked at the docs...
➕ show 1 reply
I haven't yet understood this pattern (and I tried using duckdb). Unless you're only ever going to query those files once or twice in your life, importing them into postgres shouldn't be that long and then you can do the same or more than with DuckDB.
Also as a side note, is everyone just using DuckDB in memory? Because as soon as you want some multiple session stuff I'd assume you'd use DuckDB on top of a local database, so again I don't see the point but I'm sure I'm missing something.