The max_workers parameter determines how many threads run at once. You aren't fully utilizing your connection.
Downloading images is "I/O-bound." Your script waits for the server to send data. threadpoolexecutor download images
To make your image downloader production-ready, consider these refinements: 1. Choosing max_workers The max_workers parameter determines how many threads run
import requests import os from concurrent.futures import ThreadPoolExecutor def download_image(url): try: response = requests.get(url, timeout=10) if response.status_code == 200: filename = os.path.join("images", url.split("/")[-1]) with open(filename, 'wb') as f: f.write(response.content) return f"Success: {url}" except Exception as e: return f"Error: {url} - {e}" image_urls = ["https://example.com", "https://example.com"] # Your list # Create directory if it doesn't exist os.makedirs("images", exist_ok=True) # Using ThreadPoolExecutor with ThreadPoolExecutor(max_workers=10) as executor: results = list(executor.map(download_image, image_urls)) Use code with caution. Key Optimization Tips It manages a "pool" of threads that work simultaneously
ThreadPoolExecutor is perfect for I/O tasks. It manages a "pool" of threads that work simultaneously.
To get started, you need the requests library for the downloads and concurrent.futures for the threading logic.
For large batches, use the tqdm library. It integrates easily with ThreadPoolExecutor to show a real-time progress bar in your terminal. Threading vs. Asyncio