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Latitude and longitude are angles, not interchangeable linear measurements: subtracting two coordinate pairs does not give a general-purpose distance. A reliable fast GIS query separates the work into two stages: use a spatial index to find plausible candidates, then apply a spatial predicate or distance calculation with the intended coordinate model and units. “Sub-millisecond” is a performance target, not a result established for this design; it must be demonstrated with a reproducible benchmark.
Why degree differences do not give a reliable distance
Latitude and longitude describe angular position. A change in longitude does not represent the same ground distance everywhere: its east-west span varies with latitude. Consequently, raw subtraction such as lon1 - lon2 and lat1 - lat2 cannot be treated as a general metric distance. Even when a small-area approximation is acceptable, its assumptions and error bounds must be explicit.
Before choosing a distance operation, establish what coordinate reference system (CRS) the coordinates use, which spatial reference identifier (SRID) identifies that system, and what units the operation returns. In PostGIS, geometry operations are planar and use the units of the geometry’s spatial reference system. geography represents geodetic features, models Earth as an ellipsoid, and accounts for that model in its operations. These are different semantics, not interchangeable labels. PostGIS documents the distinction between geometry, geography, and spatial reference systems.
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Make coordinate order, SRID, and units explicit
PostGIS documentation uses WGS84 longitude/latitude with SRID 4326 as a geography example. Do not assume that every API or data source uses the same coordinate order; define it at your system boundary and verify it when building points. Also confirm whether a distance argument is expected in projected-coordinate units or, for geography operations, the units specified by that operation. A query can be fast and still be wrong if its coordinates or radius are interpreted in the wrong system.
Why a spatial index needs an exact check
A spatial index narrows the search; it does not necessarily decide whether a feature truly satisfies the requested spatial relationship. PostGIS explains that spatial indexes store geometry bounding boxes. A bounding box is a convenient approximation used as a primary filter, so a query commonly needs a secondary filter that checks the actual geometry or distance. The PostGIS manual describes this primary-filter and secondary-filter model.
- Primary filter: Use the index to find records whose bounding boxes could intersect the search region.
- Exact or more specific filter: Apply the appropriate spatial predicate or distance operation to those candidates to confirm the requested relationship.
The number of candidates surviving the first stage matters: a loose bounding-box filter can leave many records for the more expensive check. Measure candidate selectivity alongside final result counts when tuning a workload.
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Choose an index for the data and workload
PostGIS offers several index approaches; no one type is best for every dataset. Selection depends on how data is organized, how often it changes, index footprint, and the queries the service actually runs. The PostGIS manual covers GiST, BRIN, and SP-GiST spatial indexing.
| Index family | What it offers | Workload considerations |
|---|---|---|
| GiST | PostGIS’s common spatial index, implemented as an R-Tree over GiST. | A general-purpose starting point for spatial searches; evaluate its candidate counts and performance on the real query mix. |
| BRIN | Summarizes extents for ranges of table records; the index is smaller and quicker to build than GiST. | Can suit spatially ordered, infrequently updated data. It is generally slower to query than GiST, so test it against the intended workload. |
| SP-GiST | Supports partitioned search structures, including quad-trees and k-d trees. | Consider it when the data and search pattern fit a partitioned structure; benchmark rather than assuming it will outperform alternatives. |
Write radius searches so the planner can use an index
For PostGIS radius filtering, ST_DWithin is index-aware. It uses an expanded bounding box internally, allowing an available spatial index to reduce the rows considered before distance is checked to confirm matches. By contrast, a threshold filter written only as ST_Distance(...) < radius does not itself use an index to optimize the scan. PostGIS documents index-aware predicates and radius-query behavior.
SELECT geom
FROM geom_table
WHERE ST_DWithin(geom, :query_point, :radius);This is a PostGIS-specific pattern, not a universal spatial API. It assumes the column type, SRID, point construction, and radius units all match the intended calculation. Check the actual execution plan to verify that an appropriate index is being used and that the query is not scanning the full table.
Index setup and planner statistics
PostGIS documents GiST index creation with CREATE INDEX ... USING GIST (...). Its manual also notes that VACUUM ANALYZE can refresh statistics used by the query planner. These steps do not guarantee a particular plan or latency: inspect the plan for the deployed PostGIS version and representative parameters. See the PostGIS data-management manual for index creation and related guidance.
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What a credible sub-millisecond benchmark must show
The PostGIS documentation explains spatial semantics and query techniques; it does not establish that a proposed GIS engine achieves sub-millisecond latency. Treat that figure as an ambition until measured. A useful benchmark must report enough detail for another engineer to understand both the workload and the result.
- Data: Record dataset size, geographic distribution, geometry complexity, and whether the data resembles production.
- Queries: Describe the query mix, search radii or regions, result sizes, and the proportion of requests that return no matches.
- Environment: Identify hardware, software versions, concurrency, and cache conditions.
- Metric: Name the latency statistic reported, such as a percentile, and state how many requests were measured and over what period.
- Correctness: Compare results against an exact reference implementation using the same coordinate model and query semantics; check for false positives and missed matches.
Keep the performance comparison fair: an index-backed candidate search followed by an exact predicate should be compared with an equivalent query and correctness standard. Report how many candidates reach the exact stage, because that helps explain whether latency comes from indexing, candidate volume, or geometry evaluation.
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