Boost Your File Copying Speed with Multithreading in Python

---

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to perform file copying in multiple threads or processor in python?

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Boost Your File Copying Speed with Multithreading in Python

When working with large sets of files, the speed of copying can be a significant bottleneck, especially when using a sequential approach. If you're facing long wait times due to file transfer operations, then you're not alone. In this post, we'll explore how to enhance your file copying process using multithreading in Python, which can dramatically cut down the time needed to complete these tasks.

The Challenge: Slow Sequential File Copying

Many developers first attempt file copying with a straightforward loop, as shown below:

[[See Video to Reveal this Text or Code Snippet]]

While it's simple, this approach is often slow, taking about 1.2 seconds to complete for a number of files. Trying to optimize the process led to experimentation with multiprocessing, but the results were disappointing. A rewritten version utilizing processes took an unacceptably long 168.8 seconds to finish.

Understanding the Misstep

The crux of the issue is that spawning new processes is inherently slow, especially on Windows systems, where the overhead associated with starting and managing new processes can outweigh the benefits for I/O-bound tasks like file copying.

Instead of using multiprocessing, we can leverage multithreading, which is well-suited for tasks that are I/O-bound. Python's Global Interpreter Lock (GIL) restricts CPU-bound tasks, but for I/O operations such as file reading and writing, multithreading can significantly improve performance.

The Solution: Using ThreadPoolExecutor

Step 1: Import Necessary Libraries

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Define the Copy Function

Define a simple function that takes in the source and destination paths, allowing it to encapsulate the logic for copying files.

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Execute with ThreadPoolExecutor

Now, set up the ThreadPoolExecutor to parallelize the copying of files.

[[See Video to Reveal this Text or Code Snippet]]

Important Notes

Performance May Vary: Depending on the speed of the underlying storage, you might not see a dramatic difference. While multithreading can help, external factors like disk read/write speeds can still impact overall performance.

Testing and Tuning: As with any optimization, it's wise to test your implementation and adjust settings as needed.

Conclusion

By switching from multiprocessing to multithreading, we can significantly reduce the time taken to copy files in Python. Utilizing ThreadPoolExecutor provides an efficient way to manage thread creation and allows your application to take full advantage of available I/O bandwidth.

If you are looking for a faster file copying method, implementing these changes will optimize your operations and free up valuable time for tasks that matter!

Happy coding!