H3 is a geospatial indexing system using a hexagonal grid that can be (approximately) subdivided into finer and finer hexagonal grids. It includes functions for converting from latitude and longitude coordinates to the containing H3 cell, finding the center of H3 cells, finding the boundary geometry of H3 cells, finding neighbors of H3 cells, and more.
H3-PG is a PostgreSQL extension that adds bindings for H3 functions and compatibility with PostGIS defined types.
For information about H3, see https://h3geo.org/
The Greenplum H3-PG extension package is installed with Greenplum Database.
Greenplum Database supports the H3 extension with these component versions:
For information about the supported Greenplum extension packages and software versions, see Extensions.
For information about Greenplum PostGIS feature support, see Greenplum PostGIS.
To enable H3 support first enable plpython3u support with the CREATE LANGUAGE plpython3u
command on your target database. If your database already has this language enabled you can skip this step. Next, use the CREATE EXTENSION h3_postgis CASCADE
command on your target database. This command will install the following extensions: postgis
, postgis_raster
, h3
, and h3_postgis
.
You use the DROP EXTENSION
command to remove support for the H3 extension and the extensions that are used with H3.
CautionRemoving H3 support from a database drops H3 database objects from the database without warning. Users accessing H3 objects might interfere with the dropping of H3 objects. Please refer to the following note from PostGIS documentation for a detailed explanation: Notes.
Depending on the extensions you enabled for PostGIS, drop support for the extensions in the database.
Use the following commands to drop the H3-PG SQL bindings and H3 library.
DROP EXTENSION IF EXISTS h3_postgis;
DROP EXTENSION IF EXISTS h3;
If you want to drop support for PostGIS and PostGIS raster as well, use the following commands.
DROP EXTENSION IF EXISTS postgis_raster;
DROP EXTENSION IF EXISTS postgis;
Create a table with a geometry column. The INSERT
statements add 10 geopoints to the table.
DROP TABLE IF EXISTS test_buildings;
CREATE TABLE test_buildings (gid INTEGER, type VARCHAR(32), center GEOGRAPHY) DISTRIBUTED BY (gid);
INSERT INTO test_buildings (gid, type, center)
VALUES (1, '', 'POINT(-78.88054167312363 43.03597938296738)');
INSERT INTO test_buildings (gid, type, center)
VALUES (2, '', 'POINT(-78.25103469999999 42.987796800000005)');
INSERT INTO test_buildings (gid, type, center)
VALUES (3, '', 'POINT(-77.278319 43.056357)');
INSERT INTO test_buildings (gid, type, center)
VALUES (4, '', 'POINT(-76.53755992503561 42.486039090444734)');
INSERT INTO test_buildings (gid, type, center)
VALUES (5, '', 'POINT(-77.35210400000001 42.745183)');
INSERT INTO test_buildings (gid, type, center)
VALUES (6, '', 'POINT(-76.4586205 42.20662450000001)');
INSERT INTO test_buildings (gid, type, center)
VALUES (7, '', 'POINT(-73.99734793983018 41.99385572155095)');
INSERT INTO test_buildings (gid, type, center)
VALUES (8, 'house', 'POINT(-72.97949770000001 40.83532615000001)');
INSERT INTO test_buildings (gid, type, center)
VALUES (9, 'house', 'POINT(-72.95980417527308 40.8958175447567)');
INSERT INTO test_buildings (gid, type, center)
VALUES (10, 'house', 'POINT(-72.46734516736213 41.046857297778395)');
Find H3 indexes at resolution 7 and redistribute the data.
SELECT h3_redistribute_table('test_buildings', 'h3_test_buildings', 'gid', 'center', 7, 'type');
Create a different table with existing H3 indexes.
DROP TABLE IF EXISTS h3_test_incidents;
CREATE TABLE h3_test_incidents (gid INTEGER, h3_res7 h3index) DISTRIBUTED BY (gid);
INSERT INTO h3_test_incidents (gid, h3_res7)
VALUES (1,'872a1070bffffff');
INSERT INTO h3_test_incidents (gid, h3_res7)
VALUES (2,'872a10764ffffff');
INSERT INTO h3_test_incidents (gid, h3_res7)
VALUES (3,'872a1072dffffff');
INSERT INTO h3_test_incidents (gid, h3_res7)
VALUES (4,'872a10743ffffff');
INSERT INTO h3_test_incidents (gid, h3_res7)
VALUES (5,'872a100d9ffffff');
INSERT INTO h3_test_incidents (gid, h3_res7)
VALUES (6,'872a1070bffffff');
INSERT INTO h3_test_incidents (gid, h3_res7)
VALUES (7,'872a10764ffffff');
INSERT INTO h3_test_incidents (gid, h3_res7)
VALUES (8,'872a10740ffffff');
INSERT INTO h3_test_incidents (gid, h3_res7)
VALUES (9,'872a10741ffffff');
INSERT INTO h3_test_incidents (gid, h3_res7)
VALUES (10,'872a10763ffffff');
Find every incident that happened within 50 hexes for each building.
DROP TABLE IF EXISTS h3_out_table;
SELECT h3_dwithin_batch('h3_test_buildings', 'h3_test_incidents', 'h3_out_table', 'gid', 'gid', '__h3_res7', 'h3_res7', 50);
SELECT * FROM h3_out_table ORDER BY 1,2;
Find the hex distance and approximate real world distance between every building of type 'house' and every incident.
DROP TABLE IF EXISTS h3_out_table;
CREATE VIEW h3_test_buildings_view AS SELECT * FROM h3_test_buildings WHERE type = 'house';
SELECT h3_distance_batch('h3_test_buildings_view', 'h3_test_incidents', 'h3_out_table', 'gid', 'gid', '__h3_res7', 'h3_res7');
SELECT * FROM h3_out_table ORDER BY 1,2;