Multithreading in Python
What is a Thread?
A thread is a small unit of a program or process executed independently or scheduled by the Operating System. In the computer system, an Operating System achieves multitasking by dividing the process into threads.
A thread is a lightweight process that ensures the execution of the process separately on the system. In Python 3, when multiple processors are running on a program, each processor runs simultaneously to execute its tasks separately.
Multithreading is a threading technique in Python programming to run multiple threads concurrently by rapidly switching between threads with a CPU help. Besides, it allows sharing of its data space with the main threads inside a process that share information and communication with other threads easier than individual processes. Multithreading aims to perform multiple tasks simultaneously, which increases performance, speed and improves the rendering of the application.
Benefits of Multithreading
Multithreaded applications are more responsive.
It shares resources and its state with sub-threads (child) which makes it more economical.
It makes the multiprocessor architecture more effective due to similarity.
It saves time by executing multiple threads at the same time.
The system does not require too much memory to store multiple threads.
It ensures effective utilization of computer system resources.
When Multithreading is required?
It is a very useful technique for time-saving and improving the performance of an application. Multithreading allows the programmer to divide application tasks into sub-tasks and simultaneously run them in a program. It allows threads to communicate and share resources such as files, data, and memory to the same processor. Furthermore, it increases the user’s responsiveness to continue running a program even if a part of the application is the length or blocked.
Modules used to execute Multithreading in python
The thread module
The threading module
To implement the thread module in Python, we need to import a thread module and then define a function that performs some action by setting the target with a variable.
A thread has a beginning, an execution sequence, and a conclusion. It has an instruction pointer that keeps track of where within its context it is currently running.
- It can be pre-empted (interrupted)
- It can temporarily be put on hold (also known as sleeping) while other threads are running — this is called yielding.
Syntax: thread.start_new_thread ( function, args[, kwargs] )
This method call enables a fast and efficient way to create new threads in both Linux and Windows. The method call returns immediately and the child thread starts and calls function with the passed list of args. When function returns, the thread terminates.
The threading module is a high-level implementation of multithreading used to deploy an application in Python. To use multithreading, we need to import the threading module in Python Program.
- threading.activeCount() − Returns the number of thread objects that are active.
- threading.currentThread() − Returns the number of thread objects in the caller’s thread control.
- threading.enumerate() − Returns a list of all thread objects that are currently active.
In addition to the methods, the threading module has the Thread class that implements threading. The methods provided by the Thread class are as follows −
- run() − The run() method is the entry point for a thread.
- start() − The start() method starts a thread by calling the run method.
- join([time]) − The join() waits for threads to terminate.
- isAlive() − The isAlive() method checks whether a thread is still executing.
- getName() − The getName() method returns the name of a thread.
- setName() − The setName() method sets the name of a thread.
To implement a new thread using the threading module, you have to do the following −
- Define a new subclass of the Thread class.
- Override the __init__(self [,args]) method to add additional arguments.
- Then, override the run(self [,args]) method to implement what the thread should do when started.
Once you have created the new Thread subclass, you can create an instance of it and then start a new thread by invoking the start(), which in turn calls run() method.
Synchronizing Threads in Python
It is a thread synchronization mechanism that ensures no two threads can simultaneously execute a particular segment inside the program to access the shared resources. The situation may be termed as critical sections. We use a race condition to avoid the critical section condition, in which two threads do not access resources at the same time.
The 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 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.