In late 2020 when osm2pgsql released the flex output I eagerly jumped on that bandwagon. The osm2pgsql flex output enabled the type of data structure and cleanup abilities I had always wanted from osm2pgsql. By January 2021 the PgOSM Flex project was up and running and I was phasing out my legacy OpenStreetMap processes. Since then, I have written more than a dozen posts exploring different improvements and use cases for the OpenStreetMap data loaded via PgOSM Flex. This post looks at a few notable improvements to version 0.6.0 over prior versions. The two areas of focus are:

• Data quality
• Usability

Data quality improvements

The set of improvements that gave me the idea for this post were made in PgOSM Flex versions 0.5.1 and 0.6.0. Version 0.5.1 took advantage of the long awaited addition of multilinestring support to osm2pgsql. Adding that feature in osm2pgsql allowed relations of lines to be added in the same manner that relations of polygons had used. Without the multilinestring support, relations such as 13642053, shown in the following screenshot, were being skipped by the PgOSM Flex import. This improvement targeted roads, waterways, and public transport layers.

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I'm excited to announce my book, Mastering PostGIS and OpenStreetMap, is available to purchase as of October 1, 2022! This book provides a practical guide to introduce readers to PostGIS, OpenStreetMap data, and spatial querying. Queries used for examples are written against real OpenStreetMap data (included) to help you learn how to navigate and explore complex spatial data. The examples start simple and quickly progress through a variety of clever spatial queries and powerful techniques.

Section 12.3, Create Denver specific tables, is available as a free preview section. The full Table of Contents is available from the free preview page.

Who is this book for?

Mastering PostGIS and OpenStreetMap is for anyone that wants to learn more about PostGIS and/or OpenStreetMap data. The hefty Appendix helps keep new users on track without distracting users with more experience. The following table gives an idea of the topics covered.

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Postgres 15 beta 2 was released recently! I enjoy Beta season... reviewing and testing new features is a fun diversion from daily tasks. This post takes a look at an improvement to UNIQUE constraints on columns with NULL values. While the nuances of unique constraints are not as flashy as making sorts faster (that's exciting!), improving the database developer's control over data quality is always a good benefit.

This email chain has the history behind this change. The Postgres 15 release notes summarize this improvement:

"Allow unique constraints and indexes to treat NULL values as not distinct (Peter Eisentraut)

Previously NULL values were always indexed as distinct values, but this can now be changed by creating constraints and indexes using UNIQUE NULLS NOT DISTINCT."

Two styles of UNIQUE

To take a look at what this change does, we create two tables. The null_old_style table has a 2-column UNIQUE constraint on (val1, val2). The val2 allows NULL values.

CREATE TABLE null_old_style
(
    id BIGINT GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,
    val1 TEXT NOT NULL,
    val2 TEXT NULL,
    CONSTRAINT uq_val1_val2
        UNIQUE (val1, val2)
);

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I recently started using the H3 hex grid extension in Postgres with the goal of making some not-so-fast queries faster. My previous post, Using Uber's H3 hex grid in PostGIS, has an introduction to the H3 extension. The focus in that post, admittedly, is a PostGIS focused view instead of an H3 focused view. This post takes a closer look at using the H3 extension to enhance performance of spatial searches.

The two common spatial query patterns considered in this post are:

• Nearest neighbor style searches
• Regional analysis

Setup and Point of Focus

This post uses two tables to examine performance. The following queries add an h3_ix column to the osm.natural_point and osm.building_polygon tables. This approach uses GENERATED columns and adds an index to the column. Going through these steps allow us to remove the need for PostGIS joins at query time for rough distance searches. See my previous post for details about installing the H3 extension and the basics of how it works.

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This post explores using the H3 hex grid system within PostGIS. H3 was developed by Uber and has some cool benefits over the PostGIS native ST_HexagonGrid() function used in my post Find missing crossings in OpenStreetMap with PostGIS. The hex grid built-in to PostGIS is great for one-off projects covering a specific region, though it has shortcomings for larger scale consistency. On the other hand, the H3 grid is a globally defined grid that scales up and down through resolutions neatly. For more details, read Uber's description.

This post works through a few of the functions available in the H3 extension and how they can be used for spatial aggregation in an analysis. One additional focus is how to generate a table of H3 hexagons for a given resolution.

Note: This post does not focus on using H3 for the best performance. See my post H3 indexes for performance with PostGIS data for a look into high performance spatial searches with H3.

Install H3 in Postgres

The H3 library is available to PostGIS as a Postgres extension. I am using the bytesandbrains h3-pg project available on GitHub. The extension can be installed using pgxn install h3. Once installed, create the H3 extension in the database.

CREATE EXTENSION h3;

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