System

Throughout the years, I’ve been part of a few medium- to large-scale system migrations. As in, rewriting old logic in a new language or stack to gain better scalability, resilience, and maintainability, or to respond more easily to changing business requirements. Whether rewriting your system is the right move is its own debate. A common question that shows up during a migration is, “How do we make sure the new system behaves exactly like the old one, minus the icky parts?” Another one is, “How do we build the new system while the old one keeps changing without disrupting the business?” ...

Python has a random.choice routine in the standard library that allows you to pick a random value from an iterable. It works like this: # src.py import random # The seed ensures that you'll get the same random choice # every time you run the script. random.seed(90) # This builds a list: ["choice_0", "choice_1", ..., "choice_9"] lst = [f"choice_{i}" for i in range(10)] print(random.choice(lst)) print(random.choice(lst)) This will print: choice_3 choice_1 I was looking for a way to quickly hydrate a table with random data in an SQLite database. To be able to do so, I needed to extract unpremeditated values from an array of predefined elements. The issue is, that SQLite doesn’t support array types or have a built-in function to pick random values from an array. However, recently I came across this trick from Ricardo Ander-Egg’s tweet, where he exploits SQLite’s JSON support to parse an array. This idea can be further extended to pluck random values from an array. ...