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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Latitude and longitude are angles, not interchangeable linear measurements. Subtracting two coordinate pairs can help identify nearby candidates, but it does not give a general-purpose distance in meters. A fast GIS query therefore needs two distinct steps: use a spatial index to find plausible candidates, then apply a coordinate-aware exact predicate. “Sub-millisecond” is a performance target—not a result established for the design described here.
Why degree differences do not give a general distance
Latitude and longitude describe positions on Earth in angular coordinates. A difference of 0.01 degrees in latitude does not represent the same ground distance as a difference of 0.01 degrees in longitude: the east–west distance represented by a longitude change varies with latitude. The coordinate reference system (CRS) and the operation applied to its coordinates determine what values mean and what units a result uses.
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This is why a calculation such as sqrt((lat1 - lat2)^2 + (lon1 - lon2)^2) is not a general metric distance formula. It combines angular differences as though they were equivalent planar lengths. It may be useful as a rough heuristic in a tightly bounded context, but it is not a sound substitute for a distance operation with explicit coordinate semantics.
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Choose a coordinate model before choosing a query
In PostGIS, geometry and geography encode different assumptions. geometry represents features in a planar coordinate system, and its operations use the units of the geometry’s spatial reference system. geography represents geodetic features and accounts for an ellipsoidal Earth model in its operations. These are not interchangeable labels for the same calculations. See the PostGIS manual on data management.
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- Use projected geometry when appropriate: a suitable local projected CRS can make planar operations meaningful in its linear units for the area and accuracy your application requires.
- Use geography when geodetic behavior is needed: it is designed for Earth-based features and ellipsoidal calculations, with different operation semantics and costs.
- Make metadata explicit: establish coordinate order, SRID, coordinate type, and intended distance units at ingestion and in every query interface.
PostGIS documents WGS84 longitude/latitude with SRID 4326 as a geography example. In such a representation, coordinate values are angular degrees; a radius supplied to a geography distance operation is not thereby a degree value. Confirm the relevant function’s units and the column’s type and SRID rather than inferring them from the numeric coordinates.
Separate candidate lookup from exact spatial evaluation
A spatial index makes a query faster by narrowing the search, not by proving that every returned candidate satisfies the requested spatial relationship. PostGIS explains that spatial indexes store geometry bounding boxes as a primary filter; a secondary spatial predicate checks the more specific relationship. As its manual puts it, “Spatial indexes store only the bounding box of geometries.”
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- Primary filter: the index identifies features whose bounding boxes could overlap the query region.
- Exact check: a spatial predicate or distance operation evaluates those candidates against the actual geometry and requested semantics.
Candidate selectivity matters: if many bounding boxes intersect the search region, many rows still require exact evaluation. Index hit counts are therefore not the same as counts of true geometric matches.
Choose an index for the data and workload
PostGIS supports several spatial index approaches; none is best for every dataset. Consider data ordering, update frequency, index size, and the query mix together.
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| Index family | How it is characterized in the PostGIS manual | When to evaluate it |
|---|---|---|
| GiST | The common spatial index; PostGIS implements an R-Tree over GiST. | A general-purpose starting point for spatial query workloads. |
| BRIN | Summarizes extents for ranges of table records; smaller and quicker to build, but generally slower to query than GiST. | Spatially ordered data that is infrequently updated, where a smaller index is useful. |
| SP-GiST | Supports partitioned search structures such as quad-trees and k-d trees. | Workloads and data structures that fit a partitioned search approach. |
These descriptions are tradeoffs, not performance guarantees. Compare the candidate count, exact-predicate cost, storage footprint, write behavior, and representative query latency on your own data. Index options and operational details are documented in the PostGIS data-management manual.
Write radius searches so the database can use the index
For a PostGIS radius query, ST_DWithin is index-aware: it uses an expanded bounding box to reduce the rows considered when a suitable spatial index is available, then checks distance to confirm matches. A bare ST_Distance(...) < radius threshold does not itself use an index to optimize the scan. The PostGIS spatial-query documentation explains this distinction.
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SELECT geom
FROM geom_table
WHERE ST_DWithin(geom, :query_point, :radius);This is a PostGIS-specific pattern, not a universal spatial API prescription. Before using it, verify that geom has the intended spatial type and SRID, that the radius is expressed in the intended units for that type, and that the query plan uses an appropriate index.
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For a geometry column, create a GiST index with the documented form:
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CREATE INDEX geom_table_geom_idx
ON geom_table
USING GIST (geom);Then inspect the execution plan for the actual query and data. PostGIS also notes that VACUUM ANALYZE can refresh table statistics used by the planner; it does not guarantee that a particular plan or latency will result. Consult the manual for the deployed PostGIS version, especially where documentation is marked development.
How to test a sub-millisecond target honestly
The documentation establishes query behavior, not the latency of a proposed GIS engine. No benchmark here demonstrates sub-millisecond performance. Treat that figure as a target until a reproducible benchmark shows it under stated conditions.
A useful benchmark report should specify:
- Dataset size, geometry types, spatial distribution, CRS, and coordinate model.
- Query mix, search-radius distribution, data-update rate, and expected candidate selectivity.
- Hardware, database and PostGIS versions, configuration, and concurrency.
- Cache conditions and whether measurements include network, parsing, and result serialization.
- The latency statistic reported, such as median or a stated percentile, along with sample size and measurement method.
- Correctness checks against an exact reference implementation, including edge cases near the radius boundary.
Measure both the indexed candidate path and end-to-end behavior. A low average alone can conceal slow-tail requests, and a fast result is not useful if coordinate assumptions produce incorrect matches. Report observed latency only alongside the conditions that produced it.
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