Why is the MainThread still running after closing threads and processes in Python?

Discover why your Python multithreading and multiprocessing code continues to run even after terminating threads and processes, and learn how to fix it effectively.
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Understanding Python Thread and Process Management

When working with Python's multithreading and multiprocessing libraries, it's common for beginners to encounter unexpected behavior, such as the MainThread continuing to run after closing other threads and processes. This can be frustrating, especially when you're trying to manage resources or understand your program's flow. In this post, we'll explore the underlying reasons for this behavior and provide a structured solution to help you effectively manage your threads and processes.

The Problem

A user encountered a scenario where they were unsure why their code continued to execute even after calling the join() method on their threads and processes. They found that their main thread reported active threads still running, leading to confusion about how to properly terminate all active threads and processes.

Here's the setup they were working with:

Multiple threads for video processing (read_video and display_video)

A multiprocessing task for model predictions (Model)

All threads and processes were set as daemon threads.

Despite these measures, the MainThread remained active after the expected termination.

Analyzing the Code

Let's dive into the issues arising from the user's implementation:

Daemon Threads and join():

The user made all threads and the process daemon but still used join() to wait for their completion in the main process. This combination doesn't provide the intended benefits, as daemon threads and processes will terminate when the main program exits, rather than completing their tasks.

Calling terminate():

After invoking join() on their process, they subsequently called terminate(). However, since the process was already joined, this call was redundant and ineffective.

Queue Management:

They also called close() on the multiprocessing.Queue objects. This wasn't necessary because these queues are managed as background threads and do not require explicit closure by the main process.

Potential Hang Due to Queues:

If threads or processes have items in a queue that haven't been read, they will not be able to terminate fully. Therefore, if any writing threads (like read_video) were still producing items for queues that had not been completely read by a reader thread (like display_video), this could cause hangs.

Solution Approach

To address these issues, here are some recommended solutions:

1. Avoid Redundant Daemon Threads

Keep Them Daemon: Continue to keep them as daemon threads if you want them to terminate when the main program exits.

Avoid join(): Do not use join() for these threads if you intend them to run as daemon threads. Instead, let the main process run independently. Here is how you can modify the main thread:

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

2. Use Sentinel Values

Utilize sentinel values to indicate when a thread or process has finished sending data. For example, if None is used as a sentinel, your reading threads can listen for this special value to know when to stop processing:

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

Make sure your reader checks for this sentinel before terminating.

3. Ensure Complete Processing

Allow your readers to completely empty their queues before finishing. For example:

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

Conclusion

Understanding the lifecycle of threads and processes in Python is key to effective parallel programming. By avoiding conflicts between daemon behaviors and join(), utilizing sentinel values, and ensuring all items are processed, you can effectively manage your parallel tasks without leaving threads or processes unexpectedly hanging.

With these strategies, you'll be able to close y