funcy VS pellet

On an unrelated note, Python folks should check out OP's library funcy [1]: "A collection of fancy functional tools focused on practicality. Inspired by clojure, underscore and my own abstractions."

Thanks for the library Suor!

[1] https://github.com/Suor/funcy

Functional semantics are available in Python, but IMO not that great. List, dict, and generator comprehensions allow you to perform most operations that you would use in a functional first programming language and there are third party libraries like toolz and funcy that implement some of the more advanced operations. The main issue I've found with using Python as a functional language is it doesn't support fluent syntax. With Scala you can do a relatively complex map/filter/reduce operation with syntactic ease list_of_ints.map(x => x*x).filter(x => x%2 ==0).reduce(x,y => x+y) With Python it's just clunky and less readable b/c of support of list comprehension syntax over fluent syntax. sum([x**2 for x in list_of_ints if x % 2 == 0]) A codebase with 5000 lines of the Scala style code will be much readable and maintainable than with the Python style code.

Also worse looking at: https://github.com/suor/funcy

Simple middleware can warn you about lazy loading/N+1 queries. Most of the time people just forget it happens.

Try using: https://github.com/har777/pellet

Self plug: Checkout https://github.com/har777/pellet to easily find and fix django N+1 issues.

I usually add it to existing integration tests so that they raise exceptions on N+1. If test coverage is low then I would suggest sending the N+1 metrics to something like datadog. That way your users using the product will reveal all the N+1 issues on your monitoring solution.