Using a context manager ( with statement) is highly recommended. It ensures that all threads are cleaned up and joined properly once the downloads are finished. 3. Execute Concurrently
By moving from sequential loops to ThreadPoolExecutor , you can often reduce the total time for a batch of downloads by , depending on your network conditions and the target server's speed. threadpoolexecutor download
from concurrent.futures import ThreadPoolExecutor urls = ["https://example.com", "https://example.com"] with ThreadPoolExecutor(max_workers=5) as executor: # Use .map to trigger the function for every URL in the list results = list(executor.map(download_file, urls)) print(results) Use code with caution. Best Practices and Considerations Using a context manager ( with statement) is
: Best for applying the same download function to a list of URLs and getting results in the same order. Execute Concurrently By moving from sequential loops to
: This defines the maximum number of threads. For downloads (I/O-bound), this number can often be much higher than your CPU core count (e.g., 10, 20, or even 50).
import requests def download_file(url): filename = url.split("/")[-1] response = requests.get(url) with open(filename, 'wb') as f: f.write(response.content) return f"Finished {filename}" Use code with caution. 2. Initialize the Pool
: If you need to process the data (like heavy image resizing) after downloading, consider using ProcessPoolExecutor instead to bypass the GIL.