Question

I’m using a Python object with a couple attributes to organize a data model, but I’m wondering if this is any less efficient than using a key based dictionary. My model stores MP3 tag data and looks as such:

class Mp3Model:
    def __init__(self, path, filename):
        self.path = path
        self.filename = filename
        self.artist = ''
        self.title = ''
        self.album = ''
        self.tracknumber = ''
        self.genre = ''
        self.date = ''

The model is used as such:

mp3s = []
for file in files:
    if os.path.splitext(file)[1] == '.mp3':
        # Append a new Mp3Model to the mp3s list for each file found
        mp3s.append(Mp3Model(os.path.join(self.dir, file), file))

Would using a key based dictionary, or even a simple list provide much performance enhancement? The length of the mps[] object list is highly variable depending on how many files are found in a given directory, and the program can slow to a crawl (I haven’t implemented any threading yet) when I scan directories with tons of files.

Answer 1

Unless you declare __slots__ for your object, object attributes are stored in an underlying dict anyway so using a dict would be marginally faster than an object. However the difference would be negligible compare to the rest of your code.

The selection of a data structure should depend on various other factors:

• do you need to store the results?
• how are you accessing the data? Serial or random access?
• which keys would you be searching your data on?
• if you’re planning to parallelise your task, can it handle concurrent writes? what would the locking overheads be?
• …

Optimising for your use case would lead to much higher returns.