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debugger
answered Apr 24 '23 00:00
In Python, multithreading refers to the capability of executing multiple threads of execution concurrently within a single process. This allows a program to perform multiple tasks at the same time, improving its overall performance and efficiency.
Python's threading module provides built-in support for multithreading. You can use this module to create and manage threads within your Python program. Here's an example of how to create and run multiple threads in Python using the threading module:
import threading
def worker():
"""Function that represents a worker thread"""
print('Starting worker thread...')
# Do some work here
print('Worker thread finished')
# Create a list of threads
threads = []
# Create 5 worker threads and add them to the list
for i in range(5):
t = threading.Thread(target=worker)
threads.append(t)
# Start all threads in the list
for t in threads:
t.start()
# Wait for all threads to finish
for t in threads:
t.join()
print('All worker threads have finished')
In this example, we define a worker () function that represents the work that we want our threads to perform. We then create a list of Thread objects, and for each thread, we create a new Thread object using the target parameter to specify the function that the thread will execute.
We then start each thread in the list using the start () method, which causes the threads to begin executing concurrently. Finally, we use the join () method to wait for all the threads to finish before printing a message indicating that all threads have finished.
It's worth noting that Python's Global Interpreter Lock (GIL) limits the true concurrency of threads in Python, so multithreading may not always result in improved performance for CPU-bound tasks. However, it can still be useful for I/O-bound tasks or for tasks that involve waiting for external resources. In such cases, multithreading can help to improve the overall responsiveness and efficiency of a Python program.