In this article we’ll play with the Magic: the Gathering card data in JSON format data set, provided with a CC0 licence, and process the information provided. We also see how to normalize a JSON document into a proper database model that benefits from some PostgreSQL advanced features, and how to then inject the JSON documents into the normalized database schema. Finally, we compare some non-trivial processing done against both versions of the database schema.
57 Articles tagged “Yesql”
There’s a very rich set of PostgreSQL functions to process text, you can find them all at the String Functions and Operators documentation chapter, with functions such as overlay, substring, position or trim. Or aggregates such as string_agg. And then regular expression functions, including the very powerful regexp_split_to_table.
In a previous article here we saw How to Write SQL in your application code. The main idea in that article is to maintain your queries in separate SQL files, where it’s easier to maintain them. In particular if you want to be able to test them again in production, and when you have to work and rewrite queries.
The reason why I like Unicode a lot is because it allows me to code in text based environments and still have nice output. Today, we’re going to play with Regional Indicator Symbol, which is implemented as a Unicode combinaison of letters from 🇦 to 🇿. For instance, if you display 🇫 then 🇷 concatenated together, you get 🇫🇷. Let’s try that from our PostgreSQL prompt!
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.
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 language: anosql.
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?