Multithreading in Python

In Python programming, multithreading is a threading mechanism that enables many threads to run simultaneously by quickly switching between threads with the help of a CPU. It permits the primary threads inside a process to share its data space, facilitating communication and information transfer more readily than between separate processes. Multithreading aims to run multiple processes at once to improve the rendering and performance of the application. Let’s explore how to accomplish multithreading in Python. Discover why you should learn Python and enhance your skills for high-paying IT jobs.

What is a Process?

A process is a program scheduled for execution on the CPU after being dispatched from the ready state. It can be viewed as an independent entity with its own execution flow, memory, and resources. Because each process operates in its own address space, they are not dependent on one another for memory. Explore how method overloading in Python is possible. The Process Control Board is in charge of the procedure. A process can create smaller things. We refer to these as child processes. Due to the longer time it takes to finish, the process is slow.

The following states apply to the process:

New: The process is going to be developed at this stage.

Ready: After a process is built, it goes into the ready state, indicating that it is prepared to operate.

Run: In this state, the CPU has selected the process from the ready queue and it is currently operating.

Blocked or Wait: The process goes into this state when it asks for input from the user and demands access to I/O.

Terminated or Completed: In this situation, the PCB is erased and the operation is stopped.

Suspend Ready: This is the state that an initially ready process is in after moving out of main memory and into external storage.

Suspend Wait: They moved to secondary memory because the process of doing the I/O operation was out of the main memory. The status is ‘Suspend Wait’.

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What is a Thread?

The smallest component of an operating system-scheduled or independently executing program or process is called a thread. An operating system divides a process into threads to multitask a computer system. A thread is a quicker procedure that guarantees the system’s autonomous operation of every process.

A Thread Control Block (TCB) on a thread holds the following information:

Thread identifier: A distinct thread identification (TID) is assigned to each new thread.

Stack Pointer: Indicates the thread stack of the process. The stack holds the local variables for the thread.

Program Counter: This is a register that holds the address of the instruction that the thread is now executing.

Thread State: This can be completed, in progress, ready, waiting, or running.

Thread Register Set: The registers designated for the thread’s use in computations.

Parent Process Pointer: A pointer to the thread’s parent process’s Process Control Block (PCB).

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What is Multithreading in Python?

Multithreading is a threading technique that enables many threads to operate simultaneously. Furthermore, it permits the primary threads inside a process to share its data space, facilitating information transfer and communication more readily than single processes. Learn more about OOPs concepts in Python.

Within a single process, there can be several threads where:

• Every thread possesses an individual ‘Register Set’ and local variables, which are kept in a stack.
• Every thread shares ‘Program Code’ and ‘Global Variables’ within a process.

Context switching is the term for the frequent thread switching that occurs on a single-core CPU. When context switching happens, one thread’s state is stored and another thread’s state is loaded whenever an interrupt arises—whether from I/O or is expressly set.

• To boost processing power, multithreading divides a single process into numerous code segments.
• The goal of multithreading is to create several processing threads from a single process.
• Multithreading creates Threads that are executed concurrently
• Multiple threads can be executed at once with multithreading, which is quick to build and uses little resources.
• Multithreading uses a common address space for all of the threads.

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When do we use Multithreading in Python?

If you want to improve the performance of your application, multithreading can be a terrific option. Here are a few scenarios in which multithreading can be used: 

When Performing I/O Bound Tasks: When executing I/O-bound activities like reading from or writing to files, network sockets, or databases, multithreading can provide excellent performance. 

When Performing Parallel Processing: We can divide the work across several threads to expedite processing if your application involves a lot of calculations, such as processing a lot of photos or audio data. 

When Using Asynchronous Programming: Asynchronous programming methods like callbacks, promises, and coroutines are commonly combined with multithreading. Your program will perform better overall if you use many threads to carry out multiple asynchronous activities simultaneously.

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How do we accomplish Multithreading in Python?

In essence, a thread is a separate execution flow. There can be more than one thread in a single process. In Python, multithreading entails the creation and administration of several threads inside a program. Tasks can be completed in parallel thanks to the concurrent execution of threads. Particularly for I/O-bound jobs or processes requiring waiting, this can boost responsiveness, facilitate concurrent operations, and improve performance. Here is a list of the top 10 frameworks in Python.

Python multithreading can be accomplished with the help of the next two modules.

• The Thread module
• The Threading module

The Thread Module

Python’s thread module offers an interface for low-level thread creation and maintenance. It offers functions for creating threads and executing target functions, such as start_new_thread(). It is frequently used to accomplish Python multithreading. Nevertheless, several functionalities are absent, such as thread-safe data structures and synchronization primitives. The “thread” module in Python 3 is now called “_thread.”

Implementation of the Thread Module

The steps to use the Thread module for multithreading implementation are as follows:

Step 1: Import the thread module

import_thread

Step 2: Define a target method

Make a function that each thread will carry out. The precise operation or calculation you want the thread to carry out should be contained in this function.

def print_message(message):

for _ in range(5):

print(message)

time.sleep(1)

Step 3: Create a new thread

Using the target function as the first parameter and any necessary arguments as the following arguments, call the start_new_thread() method from the thread module. Explore the top reasons why you should choose Python for big data processing.

_thread.start_new_thread(print_message, (“Thread 1”,))

_thread.start_new_thread(print_message, (“Thread 2”,))

Step 4: Maintain the primary discussion

You can use a synchronization technique like ‘time.sleep()’ or ‘thread.join()’, or you can add a delay to prevent the main thread from terminating before the other threads finish their execution.

time.sleep(10)

The print_message() method was designated as the threads’ target function in the ensuing implementation. After a one-second pause, each thread will print a message five times. Using the start_new_thread() function, we create two threads by supplying the target function and the corresponding message as parameters. Lastly, we use time.sleep(10) to extend the life of the main thread by 10 seconds.

Output

Python’s threading module offers a more convenient and higher-level interface for working with threads than Python’s thread module, which is regarded as low-level.

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The Threading Module

An interface for developing and handling threads at a high level is provided by the Python `threading` module. It contains thread-safe data structures, synchronization primitives like locks and semaphores, and the `Thread` class for creating threads. Comparing the `threading` module to the lower-level `thread` module, the latter is less convenient and less capable of facilitating coordination across threads, while the former provides for concurrent task execution.

Implementation of the Threading Module

The steps to use the Threading module for multithreading implementation are as follows:

Step 1: Import the Threading module

import threading

Step 2: Define the target function

Make a function that each thread will carry out. The precise operation or calculation you want the thread to carry out should be contained in this function. The ‘print_message(message)’ function will serve as our model target function. Multithreading is one of the most frequently asked Python interview questions and answers in 2024.

def print_message(message):

for _ in range(5):

print(message)

time.sleep(1)

Step 3: Initiate the thread object

Create a pair of thread instances by providing the target function as a parameter.

thread1 = threading.Thread(target=print_message(“Thread 1”))

thread2 = threading.Thread(target=print_message(“Thread 2”))

Step 4: Start the thread

To begin the target function’s execution in a different thread, call the start() method on the Thread object. 

thread1.start()

thread2.start()

Step 5: Wait for thread completion

You can use the join() function on the thread object to pause the execution of the thread until it has finished. As a result, the program won’t run again until the thread is finished.

thread1.join()

thread2.join()

print(“All threads have completed.”)

Output

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Benefits of Multithreading in Python

The benefits of creating a multithreaded Python application are as follows:

• It ensures that computer system resources are used effectively.
• Apps with several threads are more responsive.
• Because it distributes resources and its state among child threads, it is more cost-effective.
• Because of this resemblance, it makes the multiprocessor design more efficient.
• It saves time by executing multiple threads at once.
• To store several threads, the system doesn’t require a lot of memory.

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Conclusion

We have covered multithreading in Python in this article, including definitions of threads and multithreading, as well as how to do multithreading in Python using code samples. Develop your Python skills with hands-on exposure through our Python training institute in Chennai at SLA Jobs.