Fun with PostgreSQL! Using the French Wordle list, this post shows you some sql to find out the most often used letters in a table, how to reduce possibilities once you’ve found some letters, and how to solve wordle puzzles using the back end.
Regular expressions? Exceptional expressions according to Paul! Some nice examples and tips for using regrex inside Postgres. Topics include true/false regex, text extraction, text substitutions, and using regex flags.
Some helpful queries and examples for revenue accruing per day over the course of a month.
Data Definition Language (DDL) in SQL itself is notoriously non-dynamic but this next post on Devious SQL provides some examples of Dynamic DDL Postgres syntax for utility queries where you need to review the current state of the database.
An interesting question came up on the postgresql IRC channel about how to use native PostgreSQL features to handle some sort of queuing behavior. While the specific usage was related to handling serialization of specific events to some external broker, the specifics are less important than the overall structure.
PostgreSQL can provide high performance summaries over multi-million record tables, and supports some great SQL sugar to make it concise and readable, in particular aggregate filtering, a feature unique to PostgreSQL and SQLite.
PostgreSQL has built-in JSON generators that can be used to create structured JSON output right in the database, upping performance and radically simplifying web tiers.
Data checksums are a great feature in PostgreSQL. They are used to detect any corruption of the data that Postgres stores on disk. Every system we develop at Crunchy Data has this feature enabled by default. It's not only Postgres itself that can make use of these checksums.
We spend time day in, day out, answering the questions that matter and coming up with solutions that make the most sense. However, sometimes a question comes up that is just so darn … interesting that even if there are sensible solutions or workarounds, it still seems like a challenge just to take the request literally. Thus was born this blog series, Devious SQL. (Devious - longer and less direct than the most straightforward way.)
There have certainly been times when using PostgreSQL, that I’ve yearned for an UPDATE or DELETE statement with a LIMIT feature. While the SQL standard itself has no say in the matter as of SQL:2016, there are definite cases of existing SQL database dialects that support this.
This is the first in a series of blogs on the topic of using PostgreSQL for "data science". I put that in quotes because I would not consider myself to be a practicing "data scientist", per se. Of course I'm not sure there is a universally accepted definition of "data scientist". This article provides a nice illustration of my point.
The page "Falsehoods Programmers Believe About Names" covers some of the ways names are hard to deal with in programming. This post will ignore most of those complexities, and deal with the problem of matching up loose user input to a database of names.
Today we are going to walk through some of the preliminary data shaping steps in data science using SQL in Postgres.
Today we are going to finish up by showing how to use that stored model to make predictions on new data. By the way, I did all of the Postgres work for the entire blog series in Crunchy Bridge. I wanted to focus on the data and code and not on how to run PostgreSQL.
Greetings friends! We have finally come to the point in the Postgres for Data Science series where we are not doing data preparation. Today we are going to do modeling and prediction of fire occurrence given weather parameters… IN OUR DATABASE!
In our last blog post on using Postgres for statistics, I covered some of the decisions on how to handle calculated columns in PostgreSQL. I chose to go with adding extra columns to the same table and inserting the calculated values into these new columns. Today’s post is going to cover how to implement this solution using Pl/pgSQL.
For those of you who have a bad taste in your mouth from earlier run-ins with regexs, this will be more use case focused and I will do my best to explain the search patterns I used.