PyCanopy: A spatial query layer for Polars, competitive with DuckDB, SedonaDB
PyCanopy is a spatial query layer built on top of Polars, offering geospatial capabilities that aim to compete with DuckDB and SedonaDB. It brings spatial join, spatial indexing, and geometric operations into the Polars ecosystem.
Background
- **Polars** is a high-performance DataFrame library for Python, similar to Pandas but designed for speed and memory efficiency on large datasets. It lacks built-in spatial (geographic) query capabilities.
- **DuckDB** is an in-process SQL OLAP database that recently added native spatial functions (e.g., distance, intersection), making it a popular lightweight alternative for geospatial analysis.
- **SedonaDB** (formerly GeoSpark) is a distributed geospatial query system built on Apache Spark, used for processing massive-scale spatial data across clusters.
- **PyCanopy** is a new open-source Python library that adds spatial query support directly to Polars, enabling operations like point-in-polygon, distance joins, and bounding-box filtering without switching to a different engine. It aims to offer performance competitive with DuckDB's spatial extension and SedonaDB, but within the Polars ecosystem.