Python is one of the most popular and versatile programming languages available today, deployed across many industries and used for web development, machine learning, and data science. Given its widespread use and high demand in interrelated fields such as machine learning and big data, it's not surprising that Python has surpassed Java as the top programming language.
In this article, you will learn about Python threading and how it works. We’ll cover:
- What is Python threading?
- What is the process?
- Advantages of threading
- Daemon thread
- Python threading functions
- threading.active_count()
- threading.main_thread()
- threading.timer()
- Synchronization using lock
What is Python Threading?
Threading is a sequence of instructions in a program that can be executed independently of the remaining process. You can see them as different units of your process that do jobs independently when scheduled. If they need to wait for a slow external operation to finish (such as a network request, or disk access), they sleep for a while and enable the scheduler to spend time executing another thread.
What is the Process?
A process is an executable instance of a computer program. Usually, a process is executed in a single sequence of control flow.
Advantages of Threading
- Threaded programs run faster on computer systems that have multiple CPUs.
- Threads of a process can share the memory of global variables. If the variable's value is changed in one thread, it is applicable for all threads.
Fig: Basic Python threading program
The above code executes as follows. We have imported the threading module and created a method thread labeled 't.' This thread 't' will run show() function because that is the target of a particular thread.
There are two types of thread:
- Daemon thread
- Non-daemon thread
Daemon Thread
A daemon thread runs without blocking the main program when it exits and when associated daemon threads are also killed.
Example:
Fig: Daemon thread
We create a daemon thread by adding extra argument daemon = true.
We added an extra argument—daemon = true—which created a daemon thread.
Python Threading Functions
1. threading.active_count()
This function tells us about the number of threads in execution.
Example:
Fig: threading.active_count()
2. threading.main_thread()
This function returns the main thread in the program.
Example:
Fig: threading.main_thread()
3. threading.Timer()
This function is used to create a new thread and specify the time after which it should start. Once it starts, it should call the specified function.
Example:
Fig: threading.Timer()
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Synchronization Using Lock
The lock helps us in the synchronization of two or more threads. It has two primary methods: acquire() and release().
acquire() method is used to acquire the lock.
release() is used to free an acquired lock.
Example:
Fig: lock() function in Python threading
Conclusion
In this article, we explored the basic concepts of Python threading. We learned about the different functions and objects associated with threading.
If you have any questions or concerns, please share them in the comments section, and we'll have our experts get back to you.
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