DuckDB is probably the most important geospatial software of the last decade

Original Article ↗ Hacker News Discussion ↗

Debate over the headline claim

  • Many see DuckDB (with spatial) as a big quality-of-life upgrade, but argue calling it “most important geospatial software of the decade” is marketing hyperbole.
  • Others nominate QGIS, PostGIS, H3, new formats (GeoParquet, COG), or web platforms as more impactful, noting most core GIS innovation dates back to early 2000s and progress since has been incremental.

Installation, accessibility, and packaging

  • Supporters emphasize INSTALL SPATIAL as a one-line way to get a full FOSS GIS stack (GEOS, GDAL, PROJ DB) with no transitive deps, no server, and cross‑platform/WASM builds.
  • Critics counter that pip install geopandas, apt install postgis, or Docker/Postgres.app are already one‑line or near‑zero friction, so “night and day” is overstated.

Workflow and performance

  • Big praised feature: work directly on Parquet/GeoParquet and other file formats (local or S3) without ETL into a database, using vectorized, parallel SQL.
  • Users report strong performance on hundreds of GBs of Parquet; CSV at that scale often “chokes” any engine and needs conversion and partitioning.
  • Columnar OLAP design shines for analytical reads but can be slower than Spatialite for heavy spatial-index queries and is not suited for high‑concurrency OLTP.
  • Some concrete pain points: globbing limitations with GDAL-backed readers and difficulty with many small files (e.g., millions of GeoJSONs).

Comparisons to existing tools

  • Functionally, spatial SQL looks very similar to PostGIS; what’s “new” is local, serverless analytics on data-lake formats rather than new geospatial algorithms.
  • Several note DuckDB is best viewed as an analytics engine that complements, not replaces, PostGIS/QGIS/GRASS/ESRI.
  • Others are pursuing similar directions with Polars + geospatial extensions, Trino/Athena, or ClickHouse.

Who uses it: SQL, generalists, and risk

  • Some claim embedding spatial in a familiar data tool greatly broadens participation by “data generalists.”
  • Others argue many developers are weak at SQL and will lean on LLMs, raising concerns about correctness and performance of generated queries.
  • Several geospatial practitioners worry that lowering barriers without exposing CRS/projection issues will encourage subtle, high‑impact mistakes.

Projections, geometry models, and stagnation

  • Long discussion on projections vs spheroidal geometry: projections are entrenched, fast, and tooling-centric but unintuitive and error‑prone; spheroidal models are more “correct” but slower and poorly optimized in most OSS.
  • Some see overall geospatial software as stagnant and too map‑ and cartography‑centric, arguing many important spatial analyses don’t need maps at all.

Licensing and ecosystem

  • Concerns raised about DuckDB spatial’s dependency on LGPL‑licensed GEOS and implications of static linking in closed‑source builds.
  • There is also mild worry DuckDB might eventually follow other databases into more restrictive licensing.