"Get or create" is a very common operation for syncing data in the database, but implementing it correctly may be trickier than you may expect. If you ever had to implement it in a real system with real-life load, you may have overlooked potential race conditions, concurrency issues and even bloat!
I'm fairly sure that Excel is the most common way to store data, manipulate data, and yes(!), even pass data around. This is why it's not uncommon to find yourself reading Excel in Python. In this article I compare several ways to read Excel from Python.
Choosing to use a block range index (BRIN) to query a field with high correlation is a no-brainer for the optimizer. However, under some easily reproducible circumstances, a BRIN index can result in significantly slower execution even when the indexed field has very high correlation. In this article I describe how using a BRIN index in presumably "ideal circumstances" can result in degraded performance, and suggest a recent new feature of PostgreSQL as a remedy.
There are many best practices for maintaining backward and forward compatibility in application code, but it's not very commonly mentioned in relation to SQL. SQL is used to produce critical business information for applications and decision-making, so there's no reason it shouldn't benefit from similar practices. In this article, I present a simple way to future-proof SQL.
Concurrency is not very intuitive - you need to train your brain to consider what happens when multiple processes execute a certain code block at the same time. In this article I present common concurrency challenges and how to overcome them with minimal locking.
What I've been up to in 2021...
A list of useful features you already have, but may not know about! In this article I share lesser known features of PostgreSQL.
A story about how I ended up sending hundreds of users messages saying they got paid when they didn't! In the process we've learned a valuable lesson about nested transactions and Django signals.
Pandas is by far the most popular tool for data analysis. It's packed with useful features, it's battle tested and widely accepted. However, pandas comes at a cost which is often overlooked. SQL databases has been around since the 1970s. They contain many features that most developers never heard of, and I want to bring some of them to light.
Django 3.2 is just around the corner and it's packed with new features. Django versions are usually not that exciting (it's a good thing!), but this time many features were added to the ORM, so I find it especially interesting!
