In Python, multithreading is a way to achieve concurrency. Multithreading allows a program to perform multiple tasks at the same time, thereby improving the efficiency and execution speed of the program.
This article will introduce all the ways of multithreading in Python, including using the threading module, using the concurrent.futures module, using the multiprocessing module, and using the asyncio module.
1. Use threading module
The threading module in Python provides basic support for multi-threaded programming. Use this module to create and manage threads to achieve concurrent execution. The following is a sample code that uses the threading module to implement multi-threading:
import threading
def worker():
print('Worker thread started')
# do some work here
print('Worker thread finished')
if __name__ == '__main__':
print('Main thread started')
# create a new thread
t = threading.Thread(target=worker)
# start the new thread
t.start()
print('Main thread finished')
In the above code, we first define a worker function, which will be executed in a new thread.
Then, a new thread t is created in the main thread and the worker function is targeted by this thread.
Finally, start the new thread by calling the start method. Running the above code, the output results are as follows:
Main thread started Worker thread started Main thread finished Worker thread finished
As can be seen from the above output, the program first executes the code in the main thread, then creates a new thread and executes the worker function in the new thread.
The main thread and the new thread are executed in parallel, so the execution speed of the program is improved.
2. Use concurrent.futures module
The concurrent.futures module is a new module in Python 3, which provides implementations of thread pools and process pools. Parallel execution can be more easily achieved using this module.
The following is sample code for implementing multi-threading using the concurrent.futures module:
import concurrent.futures
def worker():
print('Worker thread started')
# do some work here
print('Worker thread finished')
if __name__ == '__main__':
print('Main thread started')
# create a thread pool
with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
#submit worker function to the pool
future = executor.submit(worker)
print('Main thread finished')
In the above code, we first define a worker function, which will be executed in a new thread.
Then, a thread pool executor is created in the main thread and the maximum number of threads is set to 2. Next, submit the worker function to the thread pool by calling the submit method.
Finally, we output a message indicating that the main thread has completed execution. Running the above code, the output results are as follows:
Main thread started Main thread finished Worker thread started Worker thread finished
As can be seen from the above output, the program first executes the code in the main thread, and then executes the worker function through the thread pool. The thread pool automatically manages the creation and destruction of threads, making the program more efficient.
3. Use multiprocessing module
The multiprocessing module in Python provides support for multi-process programming. Use this module to execute tasks in different processes to achieve concurrent execution.
The following is sample code for implementing multi-threading using the multiprocessing module:
import multiprocessing
def worker():
print('Worker process started')
# do some work here
print('Worker process finished')
if __name__ == '__main__':
print('Main process started')
# create a new process
p = multiprocessing.Process(target=worker)
# start the new process
p.start()
print('Main process finished')
In the above code, we first define a worker function, which will be executed in a new process. Then, a new process p is created in the main process and the worker function is targeted by the process.
Finally, start the new process by calling the start method. Running the above code, the output results are as follows:
Main process started Main process finished Worker process started Worker process finished
As can be seen from the above output, the program first executes the code in the main process, then creates a new process, and executes the worker function in the new process.
The main process and the new process are executed in parallel, so the execution speed of the program is improved.
4. Using asyncio module
The asyncio module in Python provides support for asynchronous programming. Use this module to implement coroutines to achieve concurrent execution in a single thread.
The following is sample code for implementing multithreading using the asyncio module:
import asyncio
async def worker():
print('Worker task started')
# do some work here
print('Worker task finished')
if __name__ == '__main__':
print('Main task started')
# create a new event loop
loop = asyncio.get_event_loop()
# run the worker coroutine
loop.run_until_complete(worker())
# close the event loop
loop.close()
print('Main task finished')
In the above code, we first define an asynchronous function worker, which will be executed in a coroutine.
Then, a new event loop loop is created in the main task and the worker coroutine is run by calling the run_until_complete method.
Finally, we close the event loop. Running the above code, the output is as follows:
Main task started Worker task started Worker task finished Main task finished
As can be seen from the above output, the program first executes the code in the main task, and then executes the worker coroutine through the event loop.
Coroutines are executed in a single thread, so the execution speed of the program is improved.
5. Summary
This article introduces all the ways to multithread in Python, including using the threading module, using the concurrent.futures module, using the multiprocessing module, and using the asyncio module.
Different methods are suitable for different scenarios, and you can choose the most appropriate method according to your needs.
Multi-threaded programming can improve the efficiency and execution speed of the program, but you need to pay attention to thread safety and the use of locks.
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