How Can Python's Requests Library Be Optimized for Streaming Large File Downloads?

Stream-Optimized File Downloading in Python with Requests

Requests, a renowned HTTP library, faces a challenge in handling large file downloads that exceed available memory. To overcome this limitation, it's crucial to implement a streaming approach that reads and writes file chunks as they are received.

The conventional approach, as seen in the provided code snippet, falls short in this regard. Despite utilizing r.iter_content(), which iterates over the response content in chunks, the response is still cached in memory.

To address this issue, consider introducing streaming capabilities into the code. The key modification lies in the implementation of requests.get() with the stream parameter set to True. This allows the library to retrieve the response content without storing it in memory:

def download_file(url):
    local_filename = url.split('/')[-1]
    with requests.get(url, stream=True) as r:
        r.raise_for_status()
        with open(local_filename, 'wb') as f:
            for chunk in r.iter_content(chunk_size=8192): 
                f.write(chunk)
    return local_filename

Through this optimization, Python's memory consumption remains bounded regardless of the file size being downloaded. The use of iter_content with a specified chunk size ensures that data is written to the file in manageable portions, avoiding memory exhaustion.

Note that the number of bytes returned in each chunk might not align precisely with the specified chunk size. It's common for the retrieved chunk size to vary and be significantly larger than the designated size. For details on this behavior, refer to the official documentation for iter_content and body content workflow.

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