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Cache Me Outside: Speed Boosts in Python

rpalo profile image Ryan Palo Updated on ・3 min read

I just came across a little nugget of wisdom after reading this blog post about making words out of Periodic Table Elements. I highly recommend the read. However, that's not why we're here. We're here to cache in (#sorrynotsorry) on some of the benefits of Python's standard library.

For those that don't know, caching is really useful for when a function is getting called with the same arguments repeatedly and you can expect the same result each time. Instead of calculating the result each time, you save the result in your cache for later. This is especially useful for functions that intrinsically take a long time to run, like API calls, file I/O, or super-duper nested loops. Consider, for a moment, our humble friend the Fibonacci function. In Python, a common (but grossly inefficient) version goes something like this:

def fibonacci(n):
    """Finds the nth fibonacci number."""
    if n in [1, 2]:
        return 1
        return fibonacci(n - 2) + fibonacci(n - 1)

It looks really nice and is pretty understandable. But imagine it running at even n = 5. Here's a rough version of the call-stack:

        fibonacci(1) = 1
        fibonacci(2) = 1
        = 2
        fibonacci(2) = 1
            fibonacci(1) = 1
            fibonacci(2) = 1
            = 2
        = 3
2 + 3 = 5.

And that's only at # 5. You can see how fibonacci(2) gets called three times! Imagine what would happen if you tried to calculate n = 10 or n = 100! Don't worry. I did. Actually, for n = 100 I got too bored waiting for it to finish up. In fact, I couldn't even wait for n = 50 to finish! Even n = 35 took like 7 seconds or so. This is where the cache comes in. You could probably make your own without too much trouble. I tried a version way back in this blog post. It came out pretty good if I do say so myself. The only problem with adding a cache to a function is that it takes up a good number of lines of code and obscures what you actually want to show the function doing. There are ways around that too, but why struggle and fight with that problem when the standard library (trumpet fanfare) has got you covered?


# You should really look at the functools docs.
# I mean it, there's some awesome stuff in there!
# Also, FYI, LRU stands for Least Recently Used
# which is the item that gets booted from the cache when
# it hits its limit

from functools import lru_cache

@lru_cache(maxsize=128) # Works best with maxsize as a power of 2.
def fibonacci(n):
    """Finds the nth fibonacci number."""
    if n in [1, 2]:
        return 1
        return fibonacci(n - 2) + fibonacci(n - 1)

Notice how I didn't even change the main function? I just thought to myself, "Oh, caching might be nice to have here," and sprinkled a little decorator on top. I'll have to talk about decorators in another post, but just know that they're used to augment functions, usually to add side-effects like registering a function somewhere or, like you might expect, adding a cache. How much benefit did we gain?

Without the cache:

$ python3 -m cProfile fib.py -n 40
         204668315 function calls (7 primitive calls) in 89.028 seconds

Wowza. With the cache:

$ python3 -m cProfile fib.py -n 300
         323 function calls (24 primitive calls) in 0.001 seconds

How bow dah?

Originally posted on my blog :)

Posted on May 19 '17 by:

rpalo profile

Ryan Palo


Ryan is an engineer in the Sacramento Area with a focus in Python, Ruby, and Rust. Bash/Python Exercism mentor. Coding, physics, calculus, music, woodworking. Message me on DEV!


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This is neat, and love the Fibonacci example. Would it ever make sense to do something like "pre-emptive caching" whereby, in this case, you preload a few answers along the way so that even if something isn't cached there are bookmarks that will reduce the runtime? For example, would having the answers to powers of 2 baked in reduce the time required by half before the function was ever called?


Yeah, it seems like it. It doesn't look like there is an easy way to do that with the standard library @lru_cache, but you could definitely do it if you rolled your own. I guess the other option if you were going to have it run for a long time (like for a web-app or something) would be to provide a preload function that just exercises those inputs artificially:

def preload(func, inputs):
    """Runs the function with the provided list of inputs,
    filling in the cache before expected client use"""
    for input in inputs:

I'm working on another post about decorators, so I'll work a preloaded memoization example into it :)


Hello Ryan, how is lru_cache different from using a memoization decorator?
Thanks for the post!


It's pretty much the same. There are a couple benefits though.

  1. You don't have to write your own memoization function. You get to use one that has already been error checked, optimized for efficiency, and merged into the standard library. It's got Guido's stamp of approval 👍🏻
  2. LRU cache comes with an additional maxsize parameter which can help with tuning for memory management (i.e. not growing your cache to infinity). Setting maxsize to 0 effectively gives you a basic memoization decorator.
  3. You don't have to write it, you don't have to write tests for it, you don't have to maintain it, you don't have to write docs for it. The easiest code to debug is the code that never gets written. There are probably benefits to writing your own too though. Just depends on what you want.

So sad this is not available in Python 2.7 :(


All the more incentive to make the switch ;) but lookup repoze.lru. It's on github, and it does pretty much the same thing. There are actually a few backports if you google them.