On Quadtree and Geohash algorithm

How do you partition a quadtree?
- All the places, i.e. points of interest such as restaurants, can be partitioned the usual way: think of them as IDs and use key-range or hash-based partitioning.
- What about the tree itself?
  - That’s not entirely clear but I think this can be replicated in full on all the nodes since the data should be pretty small.
How do you decide hash length for geohashing?
- Longer = more precision.
  - Can lead to more empty hashes.
  - If search radius is high, this will require searches for more number of hashes.
    - These can run in parallel though. Results can be aggregated and ranked once at the end.
- Smaller length:
  - Some hashes may have huge number of points.
    - If we partition the hashes randomly across multiple machines (for example, by hashing the geohashes), the distribution might turn out to be uniform on average.