![]() ![]() Notice how all 3 strings are 1000 characters long, but they use different amounts of memory depending on which characters they contain. We can see how much memory an object needs using sys.getsizeof(): If the string uses more extended characters, it might end up using as many as 4 bytes per character. Then, if the string can be represented as ASCII, only one byte of memory is used per character. Python’s string representation is optimized to use less memory, depending on what the string contents are.įirst, every string has a fixed overhead. Why is that? A brief digression: Python’s string memory representation Once we load it into memory and decode it into a text (Unicode) Python string, it takes far more than 24MB. That’s why actual profiling is so helpful in reducing memory usage and speeding up your software: the real bottlenecks might not be obvious.Įven if loading the file is the bottleneck, that still raises some questions. However, in this case they don’t show up at all, probably because peak memory is dominated by loading the file and decoding it from bytes to Unicode. In addition, there should be some usage from creating the Python objects. So that’s one problem: just loading the file will take a lot of memory. Def load ( fp, *, cls = None, object_hook = None, parse_float = None, parse_int = None, parse_constant = None, object_pairs_hook = None, ** kw ): """Deserialize ``fp`` (a ``.read()``-supporting file-like object containing
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