How to Monitor Progress During a cURL Download in Python

by

What will you learn?

In this tutorial, you will master the art of monitoring download progress using cURL in Python. You’ll delve into threading, subprocesses, and real-time output processing to keep users informed about the status of their downloads.

Introduction to Problem and Solution

Imagine downloading a large file using cURL in Python and wanting to display the progress every 10%. This guide tackles this challenge by implementing a solution that tracks and reports download progress at regular intervals. By incorporating threading and subprocesses, we ensure a seamless user experience while keeping them updated on the ongoing download process.

Diving Into the Problem and Solution

To address this challenge effectively, we utilize Python’s subprocess module to interact with cURL and monitor its output. Here’s how we approach the problem:

  1. Subprocess Module: We leverage subprocess to execute cURL commands within our Python script.
  2. Real-time Output Parsing: By parsing cURL’s stderr stream in real-time, we extract information about the download progress.
  3. Threading Implementation: Threading is used to run our monitoring logic concurrently without blocking other operations.

By combining these elements, we not only achieve progress tracking but also ensure that our application remains responsive throughout the download process.

Code

import subprocess
import threading

def monitor_progress(process):
    ten_percent = None
    while True:
        output = process.stderr.readline()
        if "Length" in output.decode('utf-8'):
            total_size = int(output.decode().split()[1])
            ten_percent = total_size / 10

        if "%" in output.decode('utf-8'):
            downloaded_bytes = int(output.decode().split()[0])
            percentage_completed = (downloaded_bytes / total_size) * 100

            if downloaded_bytes >= ten_percent:
                print(f"Downloaded {percentage_completed}%")
                ten_percent += total_size / 10

        if not output and process.poll() is not None:
            break

if __name__ == "__main__":
    cmd = "curl -# [YOUR_DOWNLOAD_URL_HERE]"
    process = subprocess.Popen(cmd, stderr=subprocess.PIPE, shell=True)

    thread = threading.Thread(target=monitor_progress, args=(process,))
    thread.start()

# Copyright PHD

Explanation

The code snippet above demonstrates how we monitor download progress by interacting with cURL through Python’s subprocess module. Here’s a breakdown of how it works:

  • The monitor_progress function reads cURL’s stderr stream to extract information about the download progress.
  • It calculates the percentage of completion at each 10% interval and prints updates accordingly.
  • Threading ensures that our monitoring logic runs concurrently without affecting the main program flow.

    1. How does threading improve performance? Threading enables parallel execution without blocking the main application flow, enhancing responsiveness during resource-intensive tasks like downloads.

    2. Can I use this method with other CLI tools? Yes! While tailored for cURL, similar approaches can be applied to any CLI tool that provides outputs for progress tracking.

    3. Is there a way to customize what gets printed at each milestone? Absolutely! You can modify the print statement inside monitor_progress based on your preferences for displaying download milestones.

    4. What happens if my internet connection drops during download? cURL handles network interruptions based on its settings; our script focuses on monitoring output rather than managing network issues.

    5. Will this work with both HTTP and FTP downloads? Yes! cURL supports various protocols including HTTP(S) and FTP(S), making this approach versatile for different types of downloads supported by cURL.

    6. What should I do if no progress updates appear? Ensure that your URL is correct and accessible; errors preventing downloads will result in no progress updates being reported.

    7. How do I add support for HTTPS URLs requiring authentication? Include -u user:password in your curl command within the script while securely handling credentials.

    8. Can I integrate this into GUI applications coded in Python? Certainly! Threading allows non-blocking operation suitable for GUIs; consider utilizing appropriate UI update mechanisms depending on your chosen GUI framework.

    9. Is there error handling included in case something goes wrong during execution? While basic error handling is absent here, integrating try-except blocks around critical sections enhances robustness when scaling up this code.

Conclusion

By combining subprocesses with threading, we’ve not only solved our initial challenge but also laid a foundation for more advanced interactions between Python scripts and external processes or tools. This skill set extends beyond individual tasks into broader applications development scenarios.

Leave a Comment