Speeding up function calls with lru_cache in Python

This technique is called Memoization [0]<p>Here is an implementation of a memoize decorator in Python that will support all inputs [1]. You&#x27;d have to modify to not use all the Pylons framework stuff.<p>[0] <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Memoization" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Memoization</a><p>[1] <a href="https:&#x2F;&#x2F;github.com&#x2F;reddit-archive&#x2F;reddit&#x2F;blob&#x2F;master&#x2F;r2&#x2F;r2&#x2F;lib&#x2F;memoize.py" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;reddit-archive&#x2F;reddit&#x2F;blob&#x2F;master&#x2F;r2&#x2F;r2&#x2F;l...</a>

Caching the result is not speeding up function calls.

I had worked on a open source project called &#x27;safecache&#x27; on the similar note. As others has already commented, @lru_cache does not play well with mutable data structures. So my implementation handles for both immutable and mutable data structures as well as multi-threaded operations.<p><a href="https:&#x2F;&#x2F;github.com&#x2F;Verizon&#x2F;safecache" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Verizon&#x2F;safecache</a>

(Fibonacci numbers have a closed form analytic solution that can be computed in constant time: <a href="https:&#x2F;&#x2F;www.evanmiller.org&#x2F;mathematical-hacker.html" rel="nofollow">https:&#x2F;&#x2F;www.evanmiller.org&#x2F;mathematical-hacker.html</a>)

I recently discovered that joblib can do something similar, both on disk and in memory: <a href="https:&#x2F;&#x2F;joblib.readthedocs.io&#x2F;en&#x2F;latest&#x2F;memory.html" rel="nofollow">https:&#x2F;&#x2F;joblib.readthedocs.io&#x2F;en&#x2F;latest&#x2F;memory.html</a>

&gt; As, we can see the optimal cache size of fib function is 5. Increasing cache size will not result in much gain in terms of speedup.<p>Try it with fib(35), curious what you find.

Maybe I&#x27;m abusing lru_cache but another use for it is debouncing.<p>We had a chatbot that polls a server and sends notifications, but due to clock skew it would sometimes send two notifications. So I just added the lru_cache decorator to the send(username, message) function to prevent that.

Functools’ lru_cache also has good methods for getting more info about the cache’s utilisation (.cache_info() I think), which is quite helpful when found in logs.

One line summary: Use lru_cache decorator<p>@functools.lru_cache(maxsize=31)<p>def fib(n):

lru_cache doesn&#x27;t work with lists or dicts, or indeed any non-hashable data, so it&#x27;s not quite a transparent change. About half the time I use a cache I end up implementing my own.

This is neat and I learned something new. TLDR: use the functools.lru_cache decorator to add caching to slow functions.<p>I must admit I was hoping for a general approach to speeding up all function calls in python. Functions are the primary mechanism for abstraction and yet calls are relatively heavy in their own right. It would be neat if python had a way to do automatic inlining or some such optimization so that I could have my abstractions but avoid the performance hit of a function call (even at the expense of more byte code).

What&#x27;s the ruby equivalent of `functools` and specifically `functools.lru_cache`?<p>Note that `lru_cache` doesn&#x27;t just do caching, it also provides convenient methods on the original function to get cache stats etc in a pythonic way.

The last bit of deterministic functions is a main selling point of functional programming (and get help from compilers) or any design that promotes creating pure functions.

&gt; One line summary: Use lru_cache decorator<p>Okay, at least got the decency of providing a TL;DR. But if your summary is literally three words, why not put it in the title: “speed up Python function calls with functools.lru_cache”?<p>God I hate clickbait.

Huh? He sped it up much more by rewriting as a loop . . .

dvic CV function on problems

Cool

tl;dr – memoization.

Instead of the last quote in the article, I prefer this one (got it from [0])<p>&gt;&quot;There are two hard things in computer science: cache invalidation, naming things, and off-by-one errors.&quot; – Martin Fowler<p>And there&#x27;s plenty of similar articles, for example [1] [2]<p>[0] <a href="https:&#x2F;&#x2F;www.mediawiki.org&#x2F;wiki&#x2F;Naming_things" rel="nofollow">https:&#x2F;&#x2F;www.mediawiki.org&#x2F;wiki&#x2F;Naming_things</a><p>[1] <a href="https:&#x2F;&#x2F;dbader.org&#x2F;blog&#x2F;python-memoization" rel="nofollow">https:&#x2F;&#x2F;dbader.org&#x2F;blog&#x2F;python-memoization</a><p>[2] <a href="https:&#x2F;&#x2F;mike.place&#x2F;2016&#x2F;memoization&#x2F;" rel="nofollow">https:&#x2F;&#x2F;mike.place&#x2F;2016&#x2F;memoization&#x2F;</a>

&gt; @functools.lru_cache<p>Avoid these tricks if you care about thread safety.