{"id":7213,"date":"2020-07-21T15:21:26","date_gmt":"2020-07-21T15:21:26","guid":{"rendered":"https:\/\/www.askpython.com\/?p=7213"},"modified":"2022-08-06T13:14:59","modified_gmt":"2022-08-06T13:14:59","slug":"daemon-threads-in-python","status":"publish","type":"post","link":"https:\/\/www.askpython.com\/python-modules\/daemon-threads-in-python","title":{"rendered":"Daemon Threads in Python – What Are They and How to Create Them?"},"content":{"rendered":"\n

Hello everyone! In today’s post, we’ll be looking at using Daemon Threads in Python. Before we start off with the main topic, let us look at what a Daemon Thread is first!<\/p>\n\n\n\n

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Daemon Threads<\/h2>\n\n\n\n

A Daemon Thread<\/em> is a type of thread that can run independently in the background. These kinds of threads execute independently of the main thread. So these are called non-blocking threads.<\/p>\n\n\n\n

When might you need Daemon threads in Python?<\/strong><\/p>\n\n\n\n

Suppose you need to have a long running task which tries to read log files. This task must alert the user when it detects an error message in the logs.<\/p>\n\n\n\n

We can assign a daemon thread for this task, which can keep monitoring our log files, while our main program does it’s usual work!<\/p>\n\n\n\n

The best part about daemon threads is that they will automatically stop the execution once the main program finishes!<\/p>\n\n\n\n

In case you need a short task, a daemon thread will stop execution after it returns. However, due to this nature, daemon threads are extensively used for long-running background tasks.<\/p>\n\n\n\n

Now, let’s look at an example which shows how we can use them in Python!<\/p>\n\n\n\n

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Using Daemon Threads in Python – A Hands-On Implementation<\/h2>\n\n\n\n

These examples in Python will use the threading module in Python<\/a>, which is part of the standard library.<\/p>\n\n\n

\nimport threading\n<\/pre><\/div>\n\n\n
To illustrate the power of daemon threads, let’s first create two threads, A and B.<\/p>\n\n\n\n
We’ll make thread A<\/strong> perform a short computation, while thread B<\/strong> tries to monitor a shared resource.<\/p>\n\n\n\n
If this resource is set to True<\/code>, we’ll make thread B alert the user about the status.<\/p>\n\n\n
\nimport threading\nimport time\n\n# Set the resource to False initially\nshared_resource = False \n # A lock for the shared resource\nlock = threading.Lock()\n\ndef perform_computation():\n    \n    # Thread A will call this function and manipulate the resource\n    print(f'Thread {threading.currentThread().name} - performing some computation....')\n    shared_resource = True\n    print(f'Thread {threading.currentThread().name} - set shared_resource to True!')\n    print(f'Thread {threading.currentThread().name} - Finished!')\n    time.sleep(1)\n\ndef monitor_resource():\n    # Thread B will monitor the shared resource\n    while shared_resource == False:\n        time.sleep(1)\n    print(f'Thread {threading.currentThread().name} - Detected shared_resource = False')\n    time.sleep(1)\n    print(f'Thread {threading.currentThread().name} - Finished!')\n\n\nif __name__ == '__main__':\n    a = threading.Thread(target=perform_computation, name='A')\n    b = threading.Thread(target=monitor_resource, name='B')\n\n    # Now start both threads\n    a.start()\n    b.start()\n<\/pre><\/div>\n\n\n
Here, Thread A<\/strong> will set shared_resource<\/code> to True<\/code>, and thread B<\/strong> will wait for this resource to be True.<\/p>\n\n\n\n
Output<\/strong><\/p>\n\n\n
\nThread A - performing some computation....\nThread A - set shared_resource to True!\nThread A - Finished!\nThread B - Detected shared_resource = False\nThread B - Finished!\n<\/pre><\/div>\n\n\n
Notice that both threads are normal threads. Now let’s make thread B to be a daemon thread. Let’s see what happens now.<\/p>\n\n\n\n
To do this, we can set it as a parameter in the threading.Thread(daemon=True)<\/code> method.<\/p>\n\n\n
\nimport threading\nimport time\n\nshared_resource = False # Set the resource to False initially\nlock = threading.Lock() # A lock for the shared resource\n\ndef perform_computation():\n    # Thread A will call this function and manipulate the resource\n    print(f'Thread {threading.currentThread().name} - performing some computation....')\n    shared_resource = True\n    print(f'Thread {threading.currentThread().name} - set shared_resource to True!')\n    print(f'Thread {threading.currentThread().name} - Finished!')\n    time.sleep(1)\n\ndef monitor_resource():\n    # Thread B will monitor the shared resource\n    while shared_resource == False:\n        time.sleep(1)\n    print(f'Daemon Thread {threading.currentThread().name} - Detected shared_resource = False')\n    time.sleep(1)\n    print(f'Daemon Thread {threading.currentThread().name} - Finished!')\n\n\nif __name__ == '__main__':\n    a = threading.Thread(target=perform_computation, name='A')\n    b = threading.Thread(target=monitor_resource, name='B', daemon=True) # Make thread B as a daemon thread\n\n    # Now start both threads\n    a.start()\n    b.start()\n<\/pre><\/div>\n\n\n
Output<\/strong><\/p>\n\n\n
\nThread A - performing some computation....\nThread A - set shared_resource to True!\nThread A - Finished!\nDaemon Thread B - Detected shared_resource = False\n<\/pre><\/div>\n\n\n
Here, notice that the daemon thread does not finish. This is because it will automatically get killed by the main thread.<\/p>\n\n\n\n
The non-blocking nature of daemon threads make it very useful for a lot of Python applications.<\/p>\n\n\n\n
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Conclusion<\/h2>\n\n\n\n
In this article, we learned about how we can use Daemon Threads in our Python application<\/p>\n\n\n\n
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References<\/h2>\n\n\n\n