What we learned when a user tried to load a 1 GB GML file in a browser
5 hours ago
- #vector tiles
- #data scalability
- #geospatial web performance
- Traditional GIS formats (GML, Shapefile, GeoJSON) fail at scale in browsers due to memory constraints, parsing bottlenecks, and rendering inefficiencies.
- Vector tiles solve these issues by pre-tiling data into small chunks (e.g., Mapbox Vector Tiles), loading only visible portions on demand, enabling smooth 60 FPS rendering with minimal memory use.
- Client-side tiling solutions like geojson-vt are insufficient for large datasets (>100 MB) as they still require full data loading into memory.
- A vector tile pipeline involves converting raw data (e.g., GML) to optimized formats (e.g., PMTiles) via tools like Tippecanoe or Planetiler, with pre-clipping, simplification, and zoom-level filtering.
- The OSM ecosystem demonstrates vector tiles' scalability, handling planet-scale data (~92 GB) efficiently through precomputed tiling rather than loading entire datasets.
- Adoption gaps persist as data publishers often distribute non-web-optimized formats; bridging this requires offering pre-tiled downloads (e.g., PMTiles) and simpler tooling.
- Formats like GeoParquet or FlatGeoBuf optimize storage but not rendering, reinforcing that vector tiles uniquely address web-based geospatial visualization challenges.