Today, we’re going to begin a dive into the PostgreSQL Data Types. As my colleague Will Leinweber said recently in his talk Constraints: a Developer’s Secret Weapon that he gave at pgDay Paris: database constraints in Postgres are the last line of defense.

The most important of those constraints is the data type, or the attribute domain in normalization slang. By declaring an attribute to be of a certain data type, then PostgreSQL ensures that this property is always true, and then implements advanced processing features for each data type, so that you may push the computation to the data, when needed.

This article is the first of a series that will go through many of the PostgreSQL data types, and we open the journey with boolean.

In our previous article we saw three classic Database Modelization Anti-Patterns. The article also contains a reference to a Primary Key section of my book The Art of PostgresQL, so it’s only fair that I would now publish said Primary Key section!

So in this article, we dive into Primary Keys as being a cornerstone of database normalization. It’s so important to get Primary Keys right that you would think everybody knows how to do it, and yet, most of the primary key constraints I’ve seen used in database design are actually not primary keys at all.

Next week we see two awesome PostgreSQL conferences in Europe, back to back, with a day in between just so that people may attend both! In chronological order we have first Nordic pgDay in Oslo where I will have the pleasure to talk about Data Modeling, Normalization and Denormalization. Then we have pgday.paris with an awesome schedule and a strong focus on the needs of application developers!

Current trend in software deployments is to rely on open source software for entire production stacks. You can find open source software in the core technical stacks of every startup out there, I’m told. If you’re using Cloud based offerings, most of Cloud providers are running Free/Libre Open Source Software as their foundation.

This article is a deep dive into the economic models behind successful open source projects and communities, and how as a professional, enterprise grade user, you depend on the long-term sustainability of all the open source projects you’re using. And because you depend on the projects you’re using to be successful, how to contribute and guarantee their success.

Today I want to react to an article that claims that Relational Algebra Is the Root of SQL Problems in which the author hand-waves the following position:

SQL becomes more a hindrance to data manipulation than an efficient tool. SQL’s greatest problem isn’t in the implementation level, but at its theory foundation. The problem can’t be solved by application optimization. Relational algebra isn’t sophisticated enough for handling the complicated data manipulation scenarios.

Then they go on to several arguments from authority to “prove” their point. My reading of the article is that SQL is very hard when you didn’t care to learn it, as most technologies are.

In this article, we’re going to look at the simple examples provided where apparently SQL makes it so much harder to find a solution compared to writing some Java or C++ code. Contrary to the original article, we go as far as to actually writing both the SQL solution and a complete Python solution, so that we can compare.

In another article here, entitled on JSON and SQL, we saw in great details how to import a data set only available as a giant JSON file. Then we normalized the data set, so as to be able to write SQL and process our data. This approach is sometimes very useful and was a good way to learn some of the JSON functions provided by PostgreSQL.

In this article, we’re going to use SQL to export the data from our relational model into a JSON document. The trick that makes it complex in this example is that we have a recursive data model, with a notion of a parent row that exists in the same table as the current one. That’s a nice excuse to learn more about the SQL construct WITH RECURSIVE.