Understanding Why Threading Pool is Faster Than Starting Threads Individually in Python 2.7

Discover the advantages of using a `threading pool` for multithreading in Python 2.7, compared to starting threads one by one. Learn how to optimize your code effectively!
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Understanding Why Threading Pool is Faster Than Starting Threads Individually in Python 2.7

Multithreading in Python can be an effective way to speed up your applications, especially when dealing with tasks that can run concurrently. However, many programmers have noticed notable differences in execution time when comparing a threading pool approach to starting individual threads one by one. Today, we’re going to dive deep into this issue, understand it, and see how you can optimize your threading code for better performance.

The Problem: Threading One by One

Imagine you want to execute several tasks simultaneously - perhaps you want to process multiple strings or perform calculations. Initially, many might think the best approach is to create and start a new thread for each individual task:

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

Execution Time Observation

When testing the above approach, one might observe that it takes a notably longer execution time, up to 45 seconds for 9 threads. This raises the question: why is this method significantly slower when using individual threads?

The Solution: Using a Threading Pool

To enhance performance, Python offers a ThreadPool that manages a pool of threads and can efficiently handle multiple tasks. Here’s a comparison of how to implement this using a ThreadPool:

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

When running this code, you’ll notice a dramatic decrease in execution time, down to approximately 5.393 seconds for the same number of tasks.

Understanding the Performance Boost

Function Call Issue: In the first approach, you are inadvertently calling stringFunction instead of passing the function as a target to threading.Thread. This looks like:

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

This means you are waiting for stringFunction to complete before creating the next thread, which severely hampers concurrency.

Proper Thread Initiation: By modifying it to:

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

You ensure that each thread is initiated without waiting for the previous task to finish, allowing tasks to be processed more independently.

Concurrency Managed: The ThreadPool automatically manages a number of threads, freeing you from manually tracking the threads and their states. It reuses threads from the pool and can handle many tasks in a much quicker fashion.

Conclusion

In summary, utilizing a threading pool dramatically improves the efficiency of concurrent execution in Python 2.7. Always ensure you are passing your functions correctly to Thread or ThreadPool constructors, and take advantage of the built-in functionalities that help in managing threads effectively.

By adopting these optimization practices, not only can you reduce execution time significantly, but also streamline your code for better readability and maintenance.

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