Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Speeding up function calls with lru_cache in Python

hackeregg.github.io

Post reply on HN
Print view View original

81–85 of 85 posts

Re: Speeding up function calls with lru_cache in Python

#81

Post by mac-chaffee »

zo1 wrote: post #75
mac-chaffee wrote: post #18

Maybe I'm abusing lru_cache but another use for it is debouncing. 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.

That comment entry that you get back from JIRA should have a unique "ID" attached to it that you can use to do a duplicate check on. According to you, your function signature is send(username, message), in which case this solution will fail if the same user makes the same comment on two different issues . Have a look at their REST API docs for retrieving comments on an Issue: https://docs.atlassian.com/software/jira/…

I checked the code (probably should have done that in the first place) and it turns out we include a Jira link in the message, which contains the comment ID.

Re: Speeding up function calls with lru_cache in Python

#82

Post by asicsp »

fanf2 wrote: post #64
asicsp wrote: post #5

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

That attribution is wrong - the mediawiki page links to Martin Fowler’s collection of quotes, where he says the off-by-one version came from Leon Bambrick https://twitter.com/secretgeek/status/7269997868?s=21

thanks for the correction, I cannot edit the post though :(

Re: Speeding up function calls with lru_cache in Python

#83

Post by asicsp »

Immortal333 wrote: post #24
asicsp wrote: post #5

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

Thanks for similar articles. I am aware of similar articles and that's why I have kept it short and simple. I will add these as references, later. I have enjoyed reading Python Tricks[0] book by Dan bader (author of https://dbader.org/blog/python-memoization ). It's awesome. Might be outdated but I still recommend it to any body who has just started learning python. [0] https://realpython.com/products/python-tricks-b…

Hey, sorry if my post came off wrongly. In hindsight, I should've worded better, something like "similar articles for further reading".

Re: Speeding up function calls with lru_cache in Python

#84

Post by akdas »

eindiran wrote: post #78
akdas wrote: post #72

Earlier quoted context omitted.

Plugging my own writing, but I did a deep dive on top-down vs. bottom-up dynamic programming on my blog: https://avikdas.com/2019/04/15/a-graphical-introduction-to-d... The follow-up posts go into even more detail on individual problems that can be solved using either form of dynamic programming, including some real-world problems like content-aware image resizing.

Thank you for the link, I enjoyed the post a lot. The drawings you've done add a lot to the explanation. Do you have any other deep dives you've done in the same style that you'd recommend?

Thanks! I'm glad the drawings were helpful.

Most of my writing can be found at my blog (https://avikdas.com/), but here are some from the same dynamic programming series:

- Deep-dive into the Chain Matrix Multiplication Problem - https://avikdas.com/2019/04/25/dynamic-programming-deep-dive... - Real-world applications of DP: https://avikdas.com/2019/05/14/real-world-dynamic-programmin... and https://avikdas.com/2019/07/29/improved-seam-carving-with-fo... - Another real-world application, this time in machine learning - https://avikdas.com/2019/06/24/dynamic-programming-for-machi...

If you look on my blog, you'll also see my recent series is on scalability, things like read-after-write consistency and queues for reliability.

Re: Speeding up function calls with lru_cache in Python

#85

Post by nimithryn »

WJW wrote: post #56
nimithryn wrote: post #45

Earlier quoted context omitted.

Does this work in practice for large values of n? You will be limited by numerical error in phi.

The Fibonacci series grows so fast that for "large" values of N you'll need a arbitrary size integer implementation anyway, so at that point you might as well go for a (non-IEEE) arbitrary size float type and get all the significant digits you need.

You’ll still need to compute the digits of Phi though, and then exponentiate that. My intuition is that using ints is still faster.
Post reply on HN