Multithreading in Python

November 16, 2022 | Comments(0) |

TABLE OF CONTENT

1. Introduction
2. Multithreading Concepts
3. Demo on Multithreading
4. Advantages of Multithreading
5. Disadvantages of Multithreading
6. Conclusion
7. About CloudThat
8. FAQs

 

Introduction

Threading is used in scenarios where two tasks are performed concurrently i.e., Tasks that spend much of their time waiting for external events to occur. It utilizes the multiprocessor architectures for greater efficiency.

Let us try to understand what a thread is:

Imagine an application like MS Word, it has sub functionalities like the user can write content, change the fonts, insert attachments, and save those files in various formats. Breaking down these tasks into smaller tasks, each task will be classified as a thread

Thread is a single sequential flow of execution of tasks of a process or lightweight process where every process can have one or more threads. Thread can read/write and modify data into another thread. It minimizes the context-switching time.

Python provides numerous functions that can be utilized while implementing multithreading. Let us explore each one of these functions.

Multithreading Concepts

Python has two built-in modules for implementing multithreading concepts, i.e., thread and threading

  1. active_count()

This work returns the number of currently active Thread objects. This value is broken even with the length of the list that the function enumerate() returns.

2. TIMEOUT_MAX

This is often a consistent value in this module that holds the maximum allowed value for the timeout parameter for blocking capacities like Lock.acquire() and RLock.acquire().

  1. get ident()

This function returns the identifier of the current thread. We will use this identifier as an index of a dictionary to get thread-specific information.

  1. current_thread()

This function returns the current Thread object. The thread returned will depend on the thread of control at the caller’s conclusion. In case this thread is not through threading,

The function returns a dummy thread object that has constrained functionality.

Demo on Multithreading

Let us try understanding multithreading with a simple python program.

This program includes two separate functions as follows:

  1. Calculating the square number
  2. Calculating the cube number

calc_square() function calculates the square of the respective number in the list.

calc_cube() function calculates the cube of the respective number in the list.

The time.sleep() is used to add delay in the execution of a program.

The thread method takes two inputs, the function name target and its arg(arguments), as a tuple. This function is what will be executed when a thread begins execution. When we instantiate the Thread class, the constructive method will be invoked automatically, and it will create a new thread.

To begin the execution, call the start method of the Thread class.

Calling the join method of the Thread class to hold up for the thread to complete in the main thread.

thread1

While the time.sleep method suspends the execution of the calc_square() function for 0.05 secs, another function is executed and prints out the cube value in the list, then it goes into sleep, and the calc_square() function will be executed. The operating system is concurrently executing both threads running each one a little bit at a time.

Advantages of Multithreading

The advantage of using a thread is that if a thread gets an exception or an error at the time of its execution, it does not affect the execution of the other threads. Each thread has its stack, program counter, and local variables, threads share a common memory

Using multiple threading we can reduce the execution time of lambda which will directly impact the cost and memory consumption.

Disadvantages of Multithreading

  1. A multithreading system cannot be hindered.
  2. The overhead related to managing distinctive threads may be too expensive for basic tasks.
  3. Multithreaded and multi-contexed applications are not easy to develop
  4. The task of managing concurrency among threads is troublesome and has the potential to introduce new issues in an application
  5. Existing code frequently requires critical re-architecting to take advantage of multithreading and multicontexting

Conclusion

Threads have a negligible impact on the system’s resources. The overhead of creating, keeping up, and managing threads is lower than a general process. Multithreading in an interactive application empowers a program/process to continue running even in case a section is blocked or executes a long process, expanding client responsiveness. 

About CloudThat

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Drop a query if you have any questions regarding Multithreading and I will get back to you quickly.

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FAQs

  1. What are the major contrasts between Thread and Process?

A. Thread is a single sequential flow of execution of tasks of a process or lightweight process where the process can have one or more threads. Proper synchronization between processes isn’t required. Whereas threads must be synchronized to avoid unforeseen scenarios.

2. What are the key differences between Multithreading and Multiprocessing?

A. Multiprocessing executes numerous tasks/programs at the same time, while multithreading executes numerous threads at the same time.


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