Python

A Python dictionary lookup takes about 100 nanoseconds whether the dict holds 1,000 keys or 1,000,000. A nested loop that checks a list for duplicates takes half a millisecond on 200 items and 136 milliseconds on 3,200. Sixteen times the input, nearly 280 times the wait. Same machine, same Python. Big O notation is the name for that difference. What Big O notation measures Big O describes how the running time (or memory use) of a piece of code grows as the input gets larger. It is not a stopwatch reading, and it does not care whether your laptop is fast or slow. It cares about the shape of the growth curve: when you double the input, does the time double, quadruple, or barely move? ...

Install two Python projects on the same machine and the second one can break the first. One needs requests==2.28, the other needs requests==2.32, and pip has exactly one system-wide site-packages directory to write into. The last install wins, silently. You find out when the other project starts throwing errors that have nothing to do with the code you just changed. A virtual environment gives each project its own site-packages. Nothing global gets touched, and two projects can depend on incompatible versions of the same library without either one noticing. ...

A function can have 100% unit test coverage and the page that calls it can still return a 500 in production. That is not a contradiction. Unit tests, integration tests, and end-to-end tests check different things, and a suite that’s heavy on one and light on another has a specific, predictable hole in it. What unit, integration, and end-to-end tests each check Take a sign-up feature: a function validates the input, writes a row to a database, and sends a welcome email. Three tests can cover it, and each one answers a different question. ...