The Many Spatial Indexes of PostGIS

I. Exploring Spatial Indexes in PostGIS

1. What are Spatial Indexes?

Spatial indexes in PostGIS are specialized data structures used to efficiently retrieve spatial objects from a database. They optimize the performance of spatial queries, allowing fast lookups for objects that:

• Reside within a specific area (e.g., “Find all points within a given polygon”).
• Are close to other objects (e.g., “Find all roads near a building”).

Spatial data like coastlines or polygons can be complex, involving thousands of points, making traditional indexing inefficient. To solve this, PostGIS uses bounding boxes (small, fixed-size rectangular envelopes) as spatial search keys.

• Bounding Box Characteristics:
  • A bounding box for a 2D object consists of 4 floating-point numbers (representing its minimum and maximum X, Y coordinates).
  • Bounding boxes are computationally cheap to compare, making them ideal for checking spatial relationships such as containment or intersection.

2. How Spatial Indexes work?

A spatial index speeds up searches by reducing the number of comparisons. For instance, instead of checking every vertex in a polygon to see if a point is inside it, the system first checks the bounding box, which is much faster.

II. Creating and managing Spatial Indexes in PostGIS

1. Creating a Spatial Index

Creating a spatial index in PostGIS is straightforward. To enable efficient querying, you must create an index on the geometry column of your table:

CREATE INDEX mytable_geom_x ON mytable USING GIST (geom);

• Explanation:
  • This SQL command creates a GIST index on the geom column of mytable.
  • GIST (Generalized Search Tree) is the default index type for spatial queries in PostGIS, which is suitable for most use cases.

Creating this index significantly improves the speed of spatial queries such as ST_Contains, ST_Within, or ST_Intersects, which check the spatial relationships between geometries.

2. Understanding different index types

PostGIS provides several types of indexes that can be used for geometry columns. You can view the available operator classes and their corresponding access methods (which define how the database performs lookups):

SELECT opcname, amname
FROM pg_opclass oc
JOIN pg_am am ON (am.oid = oc.opcmethod)
JOIN pg_type typ ON (oc.opcintype = typ.oid)
WHERE typ.typname = 'geometry';

The query above shows the different operator classes available for the geometry type in PostGIS:

Using the appropriate operator class for your spatial data type is crucial for optimal performance. For example, to create a GIST index on a 2D geometry column:

CREATE INDEX mytable_geom_gist ON mytable USING GIST (geom gist_geometry_ops_2d);

3. Choosing the Right Index

• B-tree and hash indexes are not typically used for spatial queries. They are more relevant for generic database functions.
• GIST, SPGIST, and BRIN indexes are designed for spatial queries and vary by their strengths in handling different types of spatial data.

III. GIST, SPGIST, and BRIN: Differences and use Cases

1. GIST (Generalized Search Tree)

• R-Tree Structure: GIST implements an R-tree structure, which is highly flexible and handles overlapping spatial data well.
• Use Case: Best for general spatial queries where geometries may overlap, and data points vary in size and location.

2. SPGIST (Space-Partitioned GIST)

• Quad-Tree Structure: SPGIST uses a quad-tree, splitting data into non-overlapping partitions, making it more efficient for certain types of data.
• Use Case: Optimal for uniformly distributed data with minimal overlap (e.g., city grid layouts, uniformly spaced points).

3. BRIN (Block Range INdex)

• Compact and Efficient: BRIN stores ranges of values in blocks, leading to extremely small indexes.
• Use Case: Suitable for very large datasets that are pre-sorted by geometry (e.g., geographic data arranged by latitude and longitude).

4. Comparison of Index Builds

To see how these index types perform, let’s compare the time taken to build each index type on a dataset of 1 million random points:

• Analysis: BRIN is significantly faster to build and takes up less space, but it sacrifices query performance, especially for complex spatial queries.

5. Query Performance

Using a query that joins 1000 random bounding boxes to the data table, let’s compare the query times:

• Analysis:
  • GIST is a solid performer across most use cases, especially for queries involving overlapping or large geometries.
  • SPGIST is faster in scenarios with less overlap, such as uniformly distributed points.
  • BRIN, while quick to build, struggles with complex queries, making it more suitable for simpler, large-scale datasets.

6. Real-World application: SPGIST vs GIST

In a real-world scenario with 9349 non-overlapping polygons, the performance of GIST and SPGIST may vary:

• GIST tends to perform better when there is a lot of overlap between geometries.
• SPGIST outperforms GIST when the data is more uniform and less overlap occurs.

IV. Conclusion

1. Summary of key insights

• Spatial Indexes Are Essential: Always use spatial indexes for spatial data in PostGIS to improve query performance.
• GIST: The go-to index for general spatial queries involving complex, overlapping geometries.
• SPGIST: A specialized index for uniformly distributed data with little overlap.
• BRIN: Use BRIN for very large, sorted datasets where space efficiency is critical, but expect slower performance on complex queries.

2. Best practices

• Use GIST as the default index for most spatial datasets unless specific conditions favor SPGIST or BRIN.
• For datasets with regular, non-overlapping geometries, SPGIST can provide a performance boost.
• Consider BRIN when working with very large datasets that are pre-sorted or when building speed and storage space are critical concerns.

References:

• PostGIS Documentation
• (The Many) Spatial Indexes of PostGIS