I am creating an appEngine application in python that will need to perform efficient geospatial queries on datastore data. An example use case would be, I need to find the first 20 posts within a 10 mile radius of the current user. Having done some research into my options, I have found that currently what seems like the 2 best approaches for achieving this type of functionality would be:
- Indexing geoHashed geopoint data using Python's GeoModel library
- Creating/deleting documents of structured data using Google's newer SearchAPI
It seems from a high level perspective that indexing geohashes and performing queries on them directly would be less costly and much faster than having to create and delete a document for every geospatial query, however i've also read that geohashing can be very inaccurate along the equator or along 'faultlines' created by the hashing algorithm. I've seen very few posts contrasting the best methods in detail, and I think stack is a good place to have this conversation, so my questions are as follows:
- Has anyone implemented similar features and had positive experiences with either methods?
- Which method would be the cheaper alternative?
- Which would be the faster alternative?
- Is there another important method I'm leaving out?
Thanks in advance.