Hacker News Reader

Comment by dbreunig | Hacker News Reader

Author here: what's special is that you can go from 0 to spatial data incredibly quickly, in the data generalist tool you're already using. It makes the audience of people working with geospatial data much bigger.

(Geopandas is great, too.)

View on HN

I’m very familiar with Postgres and spinning one with postgis seems easy enough. Do I get more with duckdb?

Most of the time I store locations and compute distance to them. Would that being faster to implement with duckdb

Probably 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'")

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.

I think the main point is not having to store a duplicate of the 600GB of input data.

> importing them into postgres shouldn't be that long and then you can do the same or more than with DuckDB.

Usually new data is generated regularly and would require creating a separate ETL process to ingest into Postgres. With DuckDB, no ETL is needed. New Parquet files are just read off the disk.

> Also as a side note, is everyone just using DuckDB in memory?

DuckDB is generally used as a single-user, and yes in-memory use case is most common. Not sure about use cases where a single user requires multiple sessions? But DuckDB does have read concurrency, session isolation etc. I believe write serialization is supported in multiple sessions.

With Parquet files, it's append-only so the "write" use-cases tend to be more limited. Generally another process generates those Parquet files. DuckDB just works with them.

> Usually new data is generated regularly

This part was not obvious. In a lot of cases geodata is mostly stable and reads/searches dominate over appends. And that’s why we keep this in DB (usually postgis, yes).

So DuckDB is optimised for very different use case and it is not always obvious when it’s mentioned

But DuckDB works just as well with static data.

This is the more trivial use case (static data, heavy reads) and DuckDB is absolutely optimized for this use case.

DuckDB also provides a vectorized, parallelized engine. When I run a query all of my 32 cores light up on htop.

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...

If the data is in Parquet they are already indexed in a sense. No further indexing necessary.

If they are stored in DuckDB’s native format (which I don’t use), it supports some state of the art indices.

https://duckdb.org/docs/stable/sql/indexes.html

I find Parquet plenty fast though.

Ah thanks, of course. I was thinking of dealing with millions of (Geo)JSON files adding up to terabytes, without copying/duplicating them though, mostly indexing. I used to do that with postgres foreign data wrappers and had hopes for duckdb :-). But that's a question for SO or other forum.

Reply on news.ycombinator.com