Spatial Functions in MySQL 8: A Comprehensive Guide

Updated: January 26, 2024 By: Guest Contributor Post a comment

Introduction

With the launch of MySQL 8, spatial data processing has become more robust and simpler. Spatial data, which relates to the positions, shapes, and orientation of objects in space, has applications ranging from GIS systems to property management. MySQL 8 introduced various improvements and features for spatial data manipulation. This guide aims to delve into these spatial functions, from the basic to more advanced use cases, each accompanied by clear code examples and expected outputs.

Getting Started with Spatial Data Types

Before delving into spatial functions, it is important to understand the spatial data types MySQL supports:

  • POINT – Represents a single location in coordinate space.
  • LINESTRING – A sequence of points that form a straight line.
  • POLYGON – A shape with a boundary defined by line strings.
  • MULTIPOINT, MULTILINESTRING, MULTIPOLYGON, GEOMETRYCOLLECTION – Collections of the basic types.

Creating a spatial column requires using the spatial data type.

CREATE TABLE geo_points (id INT PRIMARY KEY, location POINT NOT NULL, SPATIAL INDEX(location));

Inserting Spatial Data

Inserting spatial data must be done using the ST_GeomFromText() function:

SET @g = 'POINT(0 0)';
INSERT INTO geo_points (id, location) VALUES (1, ST_GeomFromText(@g));

Finding Distance Between Points

To find the distance between two points, use the ST_Distance() function:

SET @pt1 = ST_GeomFromText('POINT(0 0)');
SET @pt2 = ST_GeomFromText('POINT(1 1)');
SELECT ST_Distance(@pt1, @pt2);

The output of this will show the distance which is calculated using the spatial reference system of the points.

Basic Spatial Functions

There are various spatial functions to perform operations like checking geometry types or relationships between geometries. Let’s look at some basic ones:

Checking Geometry Type

SELECT ST_GeometryType(geom) FROM geo_points;

This will return the type, such as 'POINT', of each geometry in the geo_points table.

Boundary of Geometries

SELECT ST_AsText(ST_Boundary(geom)) FROM geo_shapes;

The function ST_Boundary() will return the boundary of a geometry.

Intermediate Spatial Functions

Once you’re comfortable with basic functions, explore some intermediate spatial functions.

Relationship Between Geometries

You can check spatial relationships between geometries using functions such as ST_Contains() and ST_Within():

SELECT ST_Contains(g1, g2) FROM geometries;

Converting Between Geometries and Text

MySQL allows you to convert geometries to Well-Known Text (WKT) using ST_AsText() and vice versa using ST_GeomFromText() to work with human-readable string formats:

-- Geometry to text
SELECT ST_AsText(geom) FROM geo_points;

-- Text to geometry
SELECT ST_GeomFromText('POINT(1 1)');

Advanced Spatial Functions

Advanced functions allow for more complex geometry manipulation and spatial relationship determinations.

Buffer Zones Around Points

SELECT ST_AsText(ST_Buffer(geom, 10)) FROM geo_shapes;

This function creates a 10 unit buffer zone around the geometry.

Aggregating Spatial Data

To combine spatial objects into complex structures, use functions such as ST_Collect() and ST_Union():

SELECT ST_AsText(ST_Collect(geom)) FROM geo_shapes;

Putting It Together: A Real-World Example

Let’s consider a real-world scenario. Imagine you’re tasked with finding properties within a given radius of a central point. You would need to build a query incorporating several spatial functions.

-- Set central point
SET @center = ST_GeomFromText('POINT(10.77472 106.69639)');
SET @radius = 1000; -- in meters

-- Find properties within radius
SELECT id, ST_Distance_Sphere(location, @center) AS distance
FROM properties
WHERE ST_Distance_Sphere(location, @center) <= @radius;

In this example, you use ST_Distance_Sphere() to find the spherical distance between the properties’ positions and the central point, constrained by the radius.

Conclusion

In conclusion, MySQL 8 provides powerful spatial functions that cater to a wide array of applications, from querying simple distances to performing complex spatial analyses. The clear organizing principle from basic to advanced functions should aid any user in finding the right tools for their spatial data needs.