Python threading get all threads

Python Multithread

It is not necessary to retain an explicit handle to all of the daemon threads in order to ensure they have completed before exiting the main process.

threading.enumerate() returns a list of all Thread objects currently alive. The list includes daemonic threads, dummy thread objects created by current_thread(), and the main thread. It excludes terminated threads and threads that have not yet been started.

import threading import time import logging import random logging.basicConfig(level=logging.DEBUG, format='(%(threadName)-9s) %(message)s',) def f(): t = threading.currentThread() r = random.randint(1,10) logging.debug('sleeping %s', r) time.sleep(r) logging.debug('ending') return if __name__ == '__main__': for i in range(3): t = threading.Thread(target=f) t.setDaemon(True) t.start() main_thread = threading.current_thread() for t in threading.enumerate(): if t is main_thread: continue logging.debug('joining %s', t.getName()) t.join()

After threading.enumerate() gathers all active threads including the calling thread (main_thread), the code calls join() methods of those threads except the main_thread.

(daemon ) Starting (non-daemon) Starting (non-daemon) Exiting

(Thread-1 ) sleeping 6 (Thread-2 ) sleeping 10 (MainThread) joining Thread-1 (Thread-3 ) sleeping 5 (Thread-3 ) ending (Thread-1 ) ending (MainThread) joining Thread-2 (Thread-2 ) ending (MainThread) joining Thread-3

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Get All Running Threads in Python: A Comprehensive Guide to Threading

Learn how to improve program performance by managing concurrent threads in Python. Use threading.enumerate() to get a list of all running threads. Discover best practices for managing threads today!

• Introduction to Threading in Python
• Access All Current Thread Objects
• How to List all the Threads of a Python Process
• Wait Until All Threads Finish Their Work
• Managing Threads in Python
• Best Practices for Managing Threads
• Other simple code examples for getting all running threads in Python
• Conclusion
• How do I list all threads in Python?
• How do I see active threads in Python?
• How do you retrieve data from a thread in Python?
• How do I find the number of active threads?

Python is a widely used programming language that provides built-in support for multithreading. managing concurrent threads in python is essential for improving program performance. In this article, we will discuss how to get a list of all running threads in Python.

Introduction to Threading in Python

Threading in Python allows you to manage concurrent threads doing work at the same time. The threading module in Python allows access to all current thread objects using the threading.enumerate() method. Both processes and threads are created and managed by the threading module.

Access All Current Thread Objects

To get a list of all running threads, use the threading.enumerate() method. This method returns a list of all Thread objects currently alive. The program keeps a list of Thread objects. Here’s an example:

import threadingdef worker(): print('Worker')threads = []for i in range(5): t = threading.Thread(target=worker) threads.append(t) t.start()for thread in threads: thread.join()print(threading.enumerate()) 

This code will create 5 threads and add each thread to a list. The join() method is called on each thread to wait for it to finish its work. Finally, threading.enumerate() is called to print out the list of threads.

How to List all the Running Threads of a Python ProcessMultithreading$ps -T -p process-id Duration: 3:32

To wait until all threads finish their work, put the threads in a list and then use the join() method. The main thread is a non-daemon thread. Python program will only exit when all non-daemon threads have finished exiting. Here’s an example:

import threading import timedef worker(): print('Worker') time.sleep(1)threads = []for i in range(5): t = threading.Thread(target=worker) threads.append(t) t.start()for thread in threads: thread.join()print('All threads have finished') 

This code will create 5 threads and add each thread to a list. The join() method is called on each thread to wait for it to finish its work. Finally, a message is printed to indicate that all threads have finished.

To manage threads in Python, use the threading module which makes working with threads even easier. The activeCount() method returns the number of active threads in the current thread’s thread group. The list includes daemonic threads and dummy thread objects created by current_thread() . Here’s an example:

import threadingdef worker(): print('Worker')print(threading.activeCount()) t = threading.Thread(target=worker) t.start() print(threading.activeCount()) 

This code will print out the number of active threads before and after a new thread is created and started.

Excessive use of threads can lead to issues such as race conditions and deadlocks. To avoid such issues, it’s important to use synchronization mechanisms such as locks and semaphores. Best practices for managing threads include using thread pools, avoiding global variables , and using exception handling.

Using Thread Pools

A thread pool is a group of threads that are created in advance and are available for use as needed. This can be more efficient than creating and destroying threads on demand. The concurrent.futures module in Python provides a ThreadPoolExecutor class for managing thread pools. Here’s an example:

from concurrent.futures import ThreadPoolExecutordef worker(): print('Worker')with ThreadPoolExecutor(max_workers=5) as executor: for i in range(10): executor.submit(worker) 

This code will create a thread pool with a maximum of 5 workers. The submit() method is called on the executor to add tasks to the pool.

Avoiding Global Variables

Global variables can cause issues when multiple threads access them simultaneously. To avoid this, use thread-local variables or pass variables as arguments to functions.

Using Exception Handling

When working with threads, it’s important to use exception handling to catch and handle errors. This can prevent the entire program from crashing if one thread encounters an error. Here’s an example:

import threadingdef worker(): print(1/0)try: t = threading.Thread(target=worker) t.start() t.join() except Exception as e: print('Caught an exception:', e) 

This code will catch the exception raised by the worker thread and print an error message.

In Python , for example, python get threads

import threading for thread in threading.enumerate(): print(thread.name)

Conclusion

Threading in Python is useful for managing concurrent tasks and improving program performance. To get a list of all running threads, use the threading.enumerate() method. Best practices for managing threads include using synchronization mechanisms, Avoiding Global Variables , and using exception handling. With these techniques, you can create efficient and reliable Python programs that take advantage of the power of multithreading.

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How to Use Thread Utility Functions in Python

You can access the threads in a Python program using threading module functions.

In this tutorial you will discover how to access and query threads in Python using thread utility functions.

Need Thread Utility Functions

A thread is a thread of execution in a computer program.

Every Python program has at least one thread of execution called the main thread. Both processes and threads are created and managed by the underlying operating system.

Sometimes we may need to create additional threads in our program in order to execute code concurrently.

Python provides the ability to create and manage new threads via the threading module and the threading.Thread class.

In some applications we may have many threads and may need to access details about the number of threads that are running or access properties of threading.Thread instances for specific threads.

How can we get access to the threads in a Python program?

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How to Use Thread Utility Functions

There are a number of utilities we can use when working with threads within a Python process.

These utilities are provided as “threading” module functions.

Examples of popular threading module utility functions are as follows:

• active_count(): Returns an integer count of the number of running threads.
• current_thread(): Returns a threading.Thread instance for the current thread.
• main_thread(): Returns a threading.Thread instance for the main thread.
• get_ident(): Returns the thread identifier integer assigned by the Python interpreter.
• get_native_id(): Returns the native thread identifier integer assigned by the operating system.
• enumerate(): Returns a list of threading.Thread instances that are currently running.

Some additional threading module functions that are less commonly used include the following:

• excepthook(): Set the exception handling function for all threads.
• settrace() and gettrace(): Set and get the trace function for all threads.
• setprofile() and getprofile(): Set and get the profile function for all threads.
• stack_size(): Set and get the default stack size used for new threads.

Now that we are familiar with the threading module functions, let’s look at some worked examples.

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How to Get The Number of Active Threads

We can discover the number of active threads in a Python process.

This can be achieved via the threading.active_count() function that returns an integer that indicates the number threads that are “alive“.

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Programming for beginners

import threading import time class MyThread(threading.Thread): def run(self): print(threading.current_thread().getName()," with id ",threading.get_ident()," Started") time.sleep(2) print(threading.current_thread().getName()," with id ",threading.get_ident()," Finished") thread1 = MyThread(name="Thread_1") thread2 = MyThread(name="Thread_2") thread3 = MyThread(name="Thread_3") thread1.start() thread2.start() thread3.start() active_threads = threading.enumerate(); for thread in active_threads: print("Active Thread : ", thread.getName()) time.sleep(3) active_threads = threading.enumerate(); for thread in active_threads: print("Active Thread : ", thread.getName())

$ python3 MyThread.py Thread_1 with id 4318040064 Started Thread_2 with id 4333768704 Started Thread_3 with id 4339023872 Started Active Thread : MainThread Active Thread : Thread_1 Active Thread : Thread_2 Active Thread : Thread_3 Thread_1 with id 4318040064 Finished Thread_2 with id 4333768704 Finished Thread_3 with id 4339023872 Finished Active Thread : MainThread 

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