Optimizing Geospatial SQL Queries with H3 Indexes for 400× Performance Gains
By
matheusalmeida
Pulled from the oven just right. Trustworthy, fact-dense, deeply satisfying.
Summary
The article explains how to dramatically speed up geospatial SQL queries (geo joins) using H3 indexes. It describes the performance problems with traditional geo joins that use spatial functions like ST_Intersects, which can become extremely slow at scale due to quadratic complexity. The solution involves automatically rewriting queries to leverage H3's hierarchical hexagonal grid system, which enables efficient spatial indexing and filtering, resulting in up to 400× performance improvements for geospatial operations in databases.
Key quotes
· 4 pulledGeo joins look innocent: SELECT * FROM A JOIN B ON ST_Intersects(A.geo, B.geo); ...but at scale they can become the query that ruins your day.
Geospatial functions are expensive, and they often force a loop join that starts to feel quadratic as your tables grow.
The core idea in this post is simple: we'll see how Floe automatically rewrites this kind of query and takes advantage of H3 indexes for dramatic speedup.
Orders of magnitude improvements can be gained with the right data structures
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