Best practices for optimizing spatial queries in PostGIS 3.5

General
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:rocket: Indexing Strategies

  • Use GiST or SP-GiST indexes on geometry/geography columns to accelerate spatial filtering.
  • Leverage BRIN indexes for large datasets with spatial clustering—especially useful for time-series geospatial data.
  • Maintain index health with REINDEX and VACUUM ANALYZE to prevent bloat and ensure planner accuracy.

:brain: Query Design Tips

  • Use bounding box filters (&&) before expensive spatial functions like STIntersects or STDWithin.
  • Avoid unnecessary transformations—keep geometries in the same SRID to skip ST_Transform overhead.
  • Simplify geometries with ST_SimplifyPreserveTopology when precision isn’t critical, especially for visualizations or exploratory analysis.

:bar_chart: Performance Enhancers

  • Partition large spatial tables using PostgreSQL native partitioning—ideal for hazard layers by region or time.
  • Use parallel queries where possible—PostGIS 3.x supports parallelism for certain spatial operations.
  • Cache derived geometries in materialized views or temporary tables to avoid recomputation.

:mag: Diagnostic Tools

  • Enable auto_explain to log slow spatial queries and identify bottlenecks.
  • Use EXPLAIN (ANALYZE, BUFFERS) to inspect query plans and index usage.
  • Monitor pgstatstatements to track frequently executed spatial queries.

:test_tube: Advanced Techniques

  • Apply STClusterKMeans or STClusterDBSCAN for spatial clustering in risk synthesis models.
  • Use ST_Subdivide to break complex geometries into manageable chunks for faster processing.
  • Integrate with FDWs (Foreign Data Wrappers) to query external spatial sources without duplication.
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Could you elaborate on that point? I do a lot of the expensive spatial functions, but am less familiar with bounding box filters.