paleolimbot commented on issue #686: URL: https://github.com/apache/sedona-db/issues/686#issuecomment-4000908277
This one is from the release post: deduplicating buildings from a large ish Parquet file. I think DuckDB is faster at this one because of its hash join but I'm not sure. ```python # pip install "apache-sedona[db]" --force-reinstall --pre --extra-index-url=https://pypi.fury.io/sedona-nightlies/ # curl -L https://github.com/geoarrow/geoarrow-data/releases/download/v0.2.0/microsoft-buildings_point.parquet -o buildings.parquet import sedona.db sd = sedona.db.connect() sd.options.memory_limit = 'unlimited' url = "buildings.parquet" sd.read_parquet(url).to_view("buildings") sd.sql( """ SELECT ROW_NUMBER() OVER () AS building_id, geometry FROM buildings """ ).to_parquet("buildings_idx.parquet") sd.sql( """ SELECT l.building_id, r.building_id AS nearest_building_id, l.geometry AS geometry, ST_Distance(l.geometry, r.geometry) AS dist FROM "buildings_idx.parquet" AS l JOIN "buildings_idx.parquet" AS r ON l.building_id <> r.building_id AND ST_DWithin(l.geometry, r.geometry, 1e-10) """ ).to_memtable().to_view("duplicates", overwrite=True) # 30.8s sd.view("duplicates").count() #> 1017476 # pip install --force-reinstall --pre duckdb import duckdb duckdb.load_extension("spatial") # 19s df = duckdb.sql(""" SELECT l.building_id, r.building_id AS nearest_building_id, l.geometry AS geometry, ST_Distance(l.geometry, r.geometry) AS dist FROM "buildings_idx.parquet" AS l JOIN "buildings_idx.parquet" AS r ON l.building_id <> r.building_id AND ST_DWithin(l.geometry, r.geometry, 1e-10) """) len(df) #> 1017476 ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
