Threading vs. Multiprocessing in Python: When to Choose Which?

Understanding the Difference Between Threading and Multiprocessing

Background:
In Python, concurrency and parallelism can be achieved through the threading and multiprocessing modules. However, understanding the subtle differences and appropriate usage of each module can be challenging.

Threading and Multiprocessing Essentials:

• Threading: Involves creating multiple threads within a single process. These threads share memory and other resources, enabling fast communication but potentially causing race conditions with shared data.
• Multiprocessing: Creates multiple processes, each with its own memory and resources. Processes do not share memory, leading to slower communication but eliminating race conditions and offering better utilization of multiple cores.

When to Use Threading and Multiprocessing:

• Use threading if:

  • Code is CPU-bound and doesn't require extensive shared data (e.g., network server, GUI).

• Use multiprocessing if:

  • Tasks are CPU-intensive and benefit from core-level parallelism.
  • Heavy computations are performed in custom libraries with proper GIL handling.

Python's GIL and Threading:
Python's Global Interpreter Lock (GIL) restricts threads in the same process from executing Python code simultaneously. This limits the performance gains when using multiple threads for CPU-bound operations.

Resource Management:

• Threads: Easier and cheaper to create and destroy than processes.
• Processes: More expensive but allow for independent resource management and memory isolation.

Sharing Data:

• Threads: Share memory by default, leading to potential race conditions.
• Processes: Do not share memory. Data transfer requires serialization and deserialization (pickling).

Additional Features:

• Multiprocessing offers features not available in threading, such as process pools, shared memory objects, and queues.

Best Practices:

• Design code with self-contained jobs that avoid shared data to maximize performance.
• Use concurrent.futures for seamless switching between threads and processes.
• Employ locking and synchronization mechanisms (locks, semaphores) for manual data sharing in complex scenarios.

Further Reading:

• [Concurrency and Multiprocessing in Python](https://realpython.com/python-concurrency/)
• [Python Multithreading vs. Multiprocessing](https://www.thepythoncorner.com/2018/06/python-multithreading-vs-multiprocessing-indepth-tutorial/)
• [GIL and Multithreading in Python](https://www.oreilly.com/library/view/python-in-a/0596001886/re278.html)

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