Itay azolay <itayazo...@gmail.com> added the comment:
Yes, you're right. It's a bad example, I tried to simplify it, and I ended up oversimplifying it. Real-life cases are of course more complicated. What I wanted to accomplish, is very similar to the `key` argument in sorted/min/max/etc.. let my try and give you an example. assume we have a complex data type, with a timestamp-signed attribute. our single item will look as follows: SingleItem = (unique_timestamp, <mutable_data_structure> ) now assume we have a function "extensive_computation" def extensive_computation(items: List[SingleItem]): # very hard work sleep(60) As developer, I know that the every item has unique timestamp. So for a list of N timestamp, when they are the same, the result of the computation will be the same. def item_cache_key(items: List[SingleItem]): return (timestamp for timestamp, data in items) I would like to then create: @lru_cache(128, key=item_cache_key): def cache_extensive_computation(items): extensive_computation(items) Does that makes more sense? ---------- _______________________________________ Python tracker <rep...@bugs.python.org> <https://bugs.python.org/issue41220> _______________________________________ _______________________________________________ Python-bugs-list mailing list Unsubscribe: https://mail.python.org/mailman/options/python-bugs-list/archive%40mail-archive.com
