Python Program on Lock in Multithreading without using Thread Class

Exploring the practical side of Python, let’s look into using locks in multithreading without the Thread class. In the world of doing things simultaneously, locks are essential for handling shared stuff and making sure threads run smoothly. As we dig into this exploration, our goal is to make the use of locks clearer, showing how they keep things in order and avoid clashes between threads.

This practical journey promises insights into effective multithreading practices, showcasing how locks contribute to the harmonious orchestration of parallel processes.

Topic Explanation:

Multithreading involves the simultaneous execution of threads, which may lead to conflicts when multiple threads access shared data. To tackle this, locks act as safeguards, allowing only one thread at a time to access the shared resource. In this practical journey, we’ll unravel the intricacies of implementing locks without relying on the Thread class.

Purpose of Using Locks:

Locks serve as traffic controllers in the multithreading world, preventing collisions when threads attempt to access shared resources concurrently. This ensures data integrity and prevents potential issues arising from simultaneous interactions.

Implementing Locks without Thread Class:

Rather than using the conventional Thread class, we’ll explore alternative methods to implement locks. This approach provides flexibility and sheds light on different techniques to synchronize threads without relying on built-in classes.

Prerequisite:

• Basic understanding of Python programming.
• Familiarity with multithreading concepts in Python.
• Knowledge of shared resources and potential issues in concurrent programming.
• Understanding of the role of locks in managing concurrent access to shared data.
• Awareness of the challenges associated with simultaneous thread execution.

Code With Comments:

# Importing necessary libraries
from threading import *
import time

# Creating a lock instance
l = Lock()

# Function to print the multiplication table
def printtable(n):
    # Acquiring the lock to ensure exclusive access to shared resources
    l.acquire()
    for i in range(1, 11):
        # Printing the product of the number and the iteration
        print(n * i)
        # Introducing a delay to simulate a real-world scenario
        time.sleep(1)
    # Releasing the lock after completing the task
    l.release()

# Creating thread instances with the printtable function and arguments
T1 = Thread(target=printtable, args=(5,), name="First: ")
T2 = Thread(target=printtable, args=(7,), name="Second: ")
T3 = Thread(target=printtable, args=(9,), name="Third: ")

# Starting the threads
T1.start()
T2.start()
T3.start()

Output
5 10 15 20 25 30 35 40 45 50 7 14 21 28 35 42 49 56 63 70 9 18 27 36 45 54 63 72 81 90

Code Explanation:

Importing Libraries:

• from threading import *: Importing the Thread class and other threading-related functions from the threading module.

Creating Lock:

• l = Lock(): Creating a lock instance using the Lock class.

Print Table Function:

• def printtable(n): …: Function to print the multiplication table of a given number, with a lock to ensure thread safety.

Acquiring Lock:

• l.acquire(): Acquiring the lock before entering the critical section to prevent concurrent access to shared resources.

Printing Table:

• print(n * i): Printing the product of the number and the iteration.

Introducing Delay:

• time.sleep(1): Introducing a 1-second delay to simulate a scenario where threads perform tasks with varying durations.

Releasing Lock:

• l.release(): Releasing the lock after completing the critical section.

Creating Threads:

• T1 = Thread(target=printtable, args=(5,), name=”First: “): Creating three threads (T1, T2, T3) with the printtable function and different arguments.

Starting Threads:

• T1.start(): Starting the threads to execute the specified functions concurrently.

Conclusion:

In summary, our journey through multithreading with locks in Python, without using the Thread class, highlights the crucial role of locks in coordinating access to shared resources. As threads run simultaneously, the use of locks maintains a regulated and organized flow, preventing conflicts and upholding the integrity of the data.

This practical journey unveils the significance of locks in managing the complexities of parallel execution, paving the way for robust and harmonious multithreading in Python.