The Single Most Useful Decorator in Python

hello and welcome today i wanted to share  with you what in my opinion is the single   most useful decorator in python taking a look at  the code that's already written here first we have   a function which computes the nth fibonacci  number and it does so in a recursive fashion   so it's extremely inefficient next in  our main function we have just a loop   going through 400 numbers just printing  out the number and the ith fibonacci number   and then at the end we print done now if i  were to actually run this code you would see   that while the first few go very quickly it starts  grinding to a halt around 31 32 33 and essentially   each number after that takes twice as long as  the number before it and this is because of the   you know bad implementation of the  fibonacci function that we've given here   the main problem with this implementation  is that when i compute fib of n minus 1 well   part of that computation is to compute  fib of n minus 2. so i'm doing a lot of   repeated work here every level down i'm going  to be repeating work that has already been done   and then just was thrown away in a different  function call an easy way to fix this is just by   remembering values that have already been computed  and that's what the decorator does so my favorite   decorator in python is the cache decorator from  the functools library so functools is a built-in so you don't have to install anything and  then you just hit your fibonacci function   with cache and now let's go  ahead and try running it again   you see it goes through all 400 of them  immediately and the reason for that was   that even though we have this very inefficient  definition what cache does is it just remembers   all of the values of the function that it's  computed before so if you compute fib of 398   then when i compute fib of 399 it looks up fib of  398 and 397 and it says oh i've already computed   both of those things instead of recomputing  them using this recursive definition it will   just use the value that it's remembered from  the previous time that you've run the function   so i find this to be extremely useful because  it allows you to avoid repeated computations   there's also a variant of cache called lru  cache lru stands for least recently used   and it does essentially the same thing but  instead of storing all previous values of   the function that you've called it with it only  stores a certain number of them so let's just   replace this with an lru cache and give it a  max size of say five this means that it's just   going to store the five most recent values of  the function that i've called now if i'm just   running up values of the function in a for loop  like this then that's plenty just to do this   so still if i run the function it completes just  as fast as before and this way i'm only using a   little bit more memory but it's a huge speed  up compared to when there was no cache at all   so i found myself using cache or lru cache  in a lot of different projects just anywhere   where i can see myself accidentally repeating  computations that might take a long time that's   a great place to just throw it in a cache or an  lru cache you don't have to do any other work   and it can provide a lot of speed up  if you know the right place to use it

Channel: mCoding

Views: 210,261

Rating: 4.9707298 out of 5

Keywords: python, programming, cached, decorator, functools

Id: DnKxKFXB4NQ

Length: 3min 50sec (230 seconds)

Published: Sat Dec 12 2020

Reddit Comments

[u/delta_tee]

Interesting video username 😁

[u/DorchioDiNerdi]

True dat. Just remember that internally, both cache and lru_cache use a dictionary in which the arguments of the function are the key, and the result is the value. So be careful not to use mutable data structures as the arguments.

[u/JimOBeano]

This is great 👍

[u/whateverathrowaway00]

Lol. I facepalmed hard when I first learned of this decorator as I spent a ton of time making a custom cache decorator project.

I don’t regret it as I learned a ton, but yes this is a fantastic decorator and everyone should be aware of it and how it works ( really, just know not to use mutable keys).

[u/spicypixel]

The cachetools package offers other cache strategies, it's been great. Having a TTLcache is very handy with some applications.