efficiency of long (str) keys in python dictionary

Question

I\'m parsing some xml (with some python 3.4 code) and want to retrieve both the text from a node and its id attribute. Example:

Some text here

Answer 1

For the most parts, @Martijn Pieters 's answer is correct, that is, theoretically. However, practically, you want to consider a lot of things when it comes to performance.

I recently ran into this problem of hashing long strings as keys, and I got a time out error in the practice I am doing, just because of python's dictionary key hashing. I knew this because I solved the question using JavaScript object as a "dictionary" and it worked just fine, which means no time out error.

Then since my keys are in fact long string of lists of numbers, I made them tuples of numbers instead (immutable object can be a key). That works perfectly as well.

That being said, I tested the timing with hashing function @Martijn Pieters wrote in the example with a long string of list of numbers as keys against a tuples version. The tuples version takes way longer on repl.it, their python compiler. I am not talking about 0.1 difference. It's a difference between 0.02 and 12.02.

Odd isn't it ?! :>

Now the point is, every environment varies. The volume of your operations accumulates. Thus you CANNOT simply say if certain operation is gonna take longer or shorter. Even it's a 0.01 sec operation, doing it only 1000 times, makes the user waits 10 secs.

For any production environment, you really want to try to optimize your algorithm, if needed, and use better design, always. For normal software, it saves your users' valuable time. For cloud services, it will be dollar bills we are talking about.

At last, I definitely DO NOT recommend use long strings as keys just because the inconsistent results I got in different environments. You definitely want to use the ids as keys and iterate through string values to find the ids if you need. But if you have to use the long string as keys, consider limit the number of operations on the dictionary. Keeping two versions is definitely a waste of space/RAM. The topic of performance and memory is another lesson.