ST_Intersects, ST_DWithin, and spatial join queries take minutes or hours to complete across large polygon or point tables.
PostGIS Consulting & Spatial Database Architecture
PostGIS Consulting, Spatial SQL Optimization & Enterprise Database Architecture
High-performance PostgreSQL/PostGIS database design, spatial indexing (GiST, SP-GiST, BRIN), spatial query optimization, and geodatabase scaling for enterprise applications.
// The buying trigger
You probably need this when…
Improper spatial indexing, missing GiST/BRIN indexes, and un-clustered geometry tables cause full sequential table scans and CPU saturation.
Desktop GIS analysts run heavy unconstrained queries that exhaust memory and lock operational transaction tables.
Teams lack automated pipelines to serve high-throughput Mapbox Vector Tiles (MVT) directly from PostgreSQL via ST_AsMVT.
// Concrete scope
What the engagement delivers
Every item is tied to an acceptance owner and evidence. Final scope is confirmed after data, security, and integration review.
Comprehensive PostGIS query plan diagnostic, execution cost analysis, and EXPLAIN ANALYZE profiling
Dynamic MVT vector tile generation pipeline (ST_AsMVT, ST_TileEnvelope) with edge caching
Custom spatial partitioning strategy (declarative table partitioning by geometry, tile grid, or administrative region)
GiST, SP-GiST, and BRIN spatial index optimization with geometry clustering (CLUSTER ON USING gist_idx)
Connection pooling and high-availability configuration using PgBouncer, Patroni, and read-replica routing
Spatial SQL query optimization runbook, automated regression benchmarks, and developer training
// Delivery process
From evidence to operational handover
- 01
Analyze Execution Plans
Profile slow queries using EXPLAIN (ANALYZE, BUFFERS), inspect disk I/O, cache hit ratios, and index usage.
- 02
Remediate Indexes & Storage
Rebuild spatial indexes, apply table clustering, partition large tables, and optimize postgresql.conf memory parameters.
- 03
Streamline Spatial SQL
Rewrite inefficient spatial joins, replace multi-step geometry buffers with bounding box pre-filters, and build ST_AsMVT tile endpoints.
- 04
Deploy HA & Guardrails
Implement PgBouncer connection pooling, read-replica query offloading, statement timeouts, and automated performance alerts.
// Proof before promises
How value is verified
No vanity accuracy number. Evidence is chosen around the operational decision and agreed before delivery starts.
- ✓Proven 10x to 100x query speedups on multi-million row spatial join operations
- ✓Sub-50ms dynamic vector tile generation supporting thousands of concurrent web GIS clients
- ✓Zero-downtime PostGIS version upgrades and cloud migrations (AWS RDS/Aurora, GCP Cloud SQL, Supabase, self-hosted)
// Buyer questions
Frequently asked questions
When should we choose BRIN indexes over GiST in PostGIS?+
BRIN (Block Range Index) is ideal for massive append-only or naturally clustered datasets (such as GPS trajectories or timestamped sensor points) where data is physically sorted on disk. It requires a tiny fraction of the disk space and RAM of a GiST index while providing rapid coarse filtering.
Can PostGIS generate vector tiles directly without middleware like GeoServer?+
Yes. Using ST_AsMVT, ST_AsMVTGeom, and ST_TileEnvelope, PostGIS can generate protocol buffer vector tiles directly inside the database in milliseconds, which can then be served via lightweight APIs (such as Martin, pg_tileserv, or FastAPI) and cached globally on a CDN.
How do you protect production transactional databases from heavy GIS analytical queries?+
We implement read-replica clusters dedicated to analytical read queries, configure PgBouncer pooling with separate transaction and session pools, and enforce statement_timeout and max_parallel_workers_per_gather guards.
Do you support PostGIS cloud migrations?+
Yes. We manage end-to-end migrations between on-premise PostgreSQL, AWS Aurora PostgreSQL, Google Cloud SQL, Azure Database for PostgreSQL, and Supabase with zero data loss and minimal cutover downtime.
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Bring us the problem, not a perfect brief.
Three fields start the conversation. An engineer will help determine fit, data readiness, and the smallest useful next step.
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