Hi Alessandro, The performance of different index and partitioning methods is studied in our paper: "Spatial Data Management in Apache Spark: The GeoSpark Perspective and Beyond" <https://link.springer.com/epdf/10.1007/s10707-018-0330-9?author_access_token=uN-xyHoyjIdl5eEkjN1PGPe4RwlQNchNByi7wbcMAY5FIy3715vvIQJ9siUDSsvKSCPSmrCsBtxOtdEB_GyuKrtBlViQhVx6JCZOXsifPdBZY7jWZcukeQQzhfGNyMG_XMyrXooXxi9zorwme2tlMw%3D%3D> (research paper). Jia Yu, Zongsi Zhang, Mohamed Sarwat. Geoinformatica Journal 2018. https://link.springer.com/epdf/10.1007/s10707-018-0330-9?author_access_token=uN-xyHoyjIdl5eEkjN1PGPe4RwlQNchNByi7wbcMAY5FIy3715vvIQJ9siUDSsvKSCPSmrCsBtxOtdEB_GyuKrtBlViQhVx6JCZOXsifPdBZY7jWZcukeQQzhfGNyMG_XMyrXooXxi9zorwme2tlMw%3D%3D
Most likely, you won't need to change other parameters. To change the number of partitions, you can do df.repartition, similar to this: https://sedona.apache.org/tutorial/Advanced-Tutorial-Tune-your-Application/#be-aware-of-spatial-rdd-partitions You can also try broadcast join if one side of your join query is very small: https://sedona.apache.org/api/sql/Optimizer/#broadcast-join Thanks, Jia On Sun, Oct 3, 2021 at 12:23 AM Alessandro Saccoia < [email protected]> wrote: > Hi list, > > In view of using framework up to its full potential, I wonder if you can > point to any resource, or anyone is even willing to give a short tutorial > about the differences and the use cases of the different types of spatial > partitioning listed here: > > https://sedona.apache.org/api/sql/Parameter/ > > Thank you, > Best > Alessandro >
