The modern calendar is a trap for the young engineer’s mind. We deal with the calendar on a daily basis and until exposed to its insanity it’s rather common to think that calendar based computations are easy. That’s until you’ve tried to do it once. A very good read about how the current calendar came to be the way it is now is Erik’s Naggum The Long, Painful History of Time.
Category “Postgresql” — 86 articles
Business logic is supposed to be the part of the application where you deal with customer or user facing decisions and computations. It is often argued that this part should be well separated from the rest of the technical infrastructure of your code. Of course, SQL and relational database design is meant to support your business cases (or user stories), so then we can ask ourselves if SQL should be part of your business logic implementation. Or actually, how much of your business logic should be SQL?
Sometimes you need to dive in an existing data set that you know very little about. Let’s say we’ve been lucky to have had a high level description of the business case covered by a database, and then access to it. Our next step is figuring out data organisation, content and quality. Our tool box: the world’s most advanced open source database, PostgreSQL, and its Structured Query Language, SQL.
Kris Jenkins cooked up a very nice way
to embed SQL in your
code: YeSQL for Clojure. The main
idea is that you should be writing your SQL queries in
.sql files in your
code repository and maintain them there.
The idea is very good and it is now possible to find alternative
implementations of the Clojure yesql library in
other languages. Today, we are going to have a look at one of them for
the python programming
A recent interview question that I had to review was spelled like this:
Find missing int element into array 1..100
Of course at first read I got it wrong, you have only one integer to look
for into the array. So while the obvious idea was to apply classic sorting
techniques and minimize array traversal to handle complexity (time and
space), it turns out there’s a much simpler way to do it if you remember
your math lessons from younger. But is it that much simpler?
In our previous article
Aggregating NBA data, PostgreSQL vs MongoDB we spent
time comparing the pretty new
MongoDB Aggregation Framework with the decades
old SQL aggregates. Today, let’s showcase more of those SQL aggregates,
producing a nice
histogram right from our SQL console.
When reading the article Crunching 30 Years of NBA Data with MongoDB Aggregation I coulnd’t help but think that we’ve been enjoying aggregates in SQL for 3 or 4 decades already. When using PostgreSQL it’s even easy to actually add your own aggregates given the SQL command create aggregate.
In our Tour of Extensions today’s article is about advanced tag indexing. We have a great data collection to play with and our goal today is to be able to quickly find data matching a complex set of tags. So, let’s find out those lastfm tracks that are tagged as blues and rhythm and blues, for instance.
We’re going to use the Last.fm dataset from the Million Song Dataset project here.
At the Open World Forum two weeks ago I had the pleasure to meet with Colin Charles. We had a nice talk about the current state of both MariaDB and PostgreSQL, and even were both interviewed by the Open World Forum Team. The interview is now available online. Dear French readers, it’s in English.
Here’s the video:
Executive Summary: MariaDB is a drop-in fully Open Source replacement for MySQL and sees quite some progress and innovation being made, and PostgreSQL is YeSQL!