Geohashing and spatial indexing
Learn how a coordinate can be encoded into progressively smaller cells for indexing and search.
Why this matters
A database with millions of records needs a fast way to narrow down which records might be nearby before doing more exact geometry calculations.
Learn it
A geohash turns longitude and latitude into a text code representing a rectangular cell. Adding characters refines the cell into a smaller area. Shared prefixes mean positions share a containing cell at that precision. Nearby positions can still have different prefixes when they fall on opposite sides of a cell boundary, so geohash prefix similarity is not a universal distance measure.
Work through an example
Two points a few meters apart can fall in neighboring cells and have different-looking hashes. Two points sharing a long prefix are in the same small cell, but exact distance still requires coordinates or geometry.
coarse cell → shorter geohash
finer cell → longer geohash
exact relationship → calculate with coordinates/geometryPractice
- 1Move the point in the geohash explorer slowly across a cell boundary.
- 2Watch which part of the code changes.
- 3Explain why a geohash is useful for candidate search but not a replacement for exact distance.
Try it
Geohash as a spatial index
A geohash converts a coordinate into a hierarchical text code identifying a bounding cell. Longer hashes represent smaller cells. Nearby points often share prefixes, but cell boundaries mean closeness cannot be inferred from prefix length alone.
What to watch for
Geohash precision means cell size. It does not mean measurement accuracy, and it does not guarantee that a longer shared prefix always ranks every nearby point correctly.
Where this takes you
Geohashes are useful for spatial bucketing, partitioning, caching, approximate filtering, and fast candidate lookup.