What if you could turn
thousands of lines of code into
simple queries?

Continuing our series of PostgreSQL Data Types today we’re going to introduce the PostgreSQL Point type.

In order to put the Point datatype in a context where it makes sense, we’re going to download a complete geolocation data set and normalize it, thus making good use of both the normalization good practice and those other PostgreSQL data types we’ve been learning about in the previous articles of this series.

Buckle-up, this is a long article with a lot of SQL inside.


I wrote a book!



Continuing our series of PostgreSQL Data Types today we’re going to introduce the PostgreSQL XML type.

The SQL standard includes a SQL/XML which introduces the predefined data type XML together with constructors, several routines, functions, and XML-to-SQL data type mappings to support manipulation and storage of XML in a SQL database, as per the Wikipedia page.


Continuing our series of PostgreSQL Data Types today we’re going to introduce the PostgreSQL array data types.

Arrays can be used to denormalize data and avoid lookup tables. A good rule of thumb for using them that way is that you mostly use the array as a whole, even if you might at times search for elements in the array. Heavier processing is going to be more complex than a lookup table.


Continuing our series of PostgreSQL Data Types today we’re going to introduce the PostgreSQL ranges data type.

Range types are a unique feature of PostgreSQL, managing two dimensions of data in a single column, and allowing advanced processing. The main example is the daterange data type, which stores as a single value a lower and an upper bound of the range as a single value. This allows PostgreSQL to implement a concurrent safe check against overlapping ranges, as we’re going to see in this article.


Dimitri Fontaine

PostgreSQL Major Contributor

Open Source Software Engineer

France