Is python good for multi threading?
No, it is not a good idea. Multithreading is not possible in Python due to something called the Global Interpreter Lock. A multi-threaded program contains two or more parts that can run concurrently and each part can handle a different task at the same time making optimal use of the available resources specially when your computer has multiple CPUs. Multi-threading enables you to write in a way where multiple activities can proceed concurrently in the same program. Python doesn't allow multi-threading, but if you want to run your program at a speed that needs to wait for something like IO, then it's used a lot. whereas the threading package couldn't let you use extra CPU cores. Python doesn't support multi-threading because Python on the Cpython interpreter does not support true multi-core execution via multithreading. However, Python does have a threading library. The GIL does not prevent threading. All the GIL does is make sure only one thread is executing Python code at a time; control still switches between threads. However, if you mix in C extensions and I/O (such as PIL or numpy operations), any C code can run in parallel with one active Python thread. The threading ModuleWe can still perform multi-threading using the threading module. Multiple threads within a process share the same data space with the main thread and can therefore share information or communicate with each other more easily than if they were separate processes. The newer threading module included with Python 2.4 provides much more powerful, high-level support for threads than the thread module discussed in the previous section. The threading module exposes all the methods of the thread module and provides some additional methods −
The threading module has the Thread class that implements threading. The methods provided by the Thread class are as follows −
ExampleThe threading module provided with Python includes a simple-to-implement locking mechanism that allows you to synchronize threads. A new lock is created by calling the Lock() method, which returns the new lock. The acquire(blocking) method of the new lock object is used to force the threads to run synchronously. The optional blocking parameter enables you to control whether the thread waits to acquire the lock. If blocking is set to 0, the thread returns immediately with a 0 value if the lock cannot be acquired and with a 1 if the lock was acquired. If blocking is set to 1, the thread blocks and wait for the lock to be released. The release() method of the new lock object is used to release the lock when it is no longer required.
OutputStarting Thread-1 Starting Thread-2 Thread-1: Fri Aug 12 05:49:52 2022 Thread-1: Fri Aug 12 05:49:53 2022 Thread-1: Fri Aug 12 05:49:54 2022 Thread-2: Fri Aug 12 05:49:56 2022 Thread-2: Fri Aug 12 05:49:58 2022 Thread-2: Fri Aug 12 05:50:00 2022 Exiting Main Thread
Updated on 12-Aug-2022 12:33:31
Is multithreading faster in Python?Both multithreading and multiprocessing allow Python code to run concurrently. Only multiprocessing will allow your code to be truly parallel. However, if your code is IO-heavy (like HTTP requests), then multithreading will still probably speed up your code.
Does Python allow threading?Python threading allows you to have different parts of your program run concurrently and can simplify your design. If you've got some experience in Python and want to speed up your program using threads, then this tutorial is for you!
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