Python Multi-Threading

If you are an expert in python then you are looking to improve your editing skills and this article is useful for you. The ability of the system is enhanced by using a thread in the python. Python threading allows us to run a different part of the program simultaneously and simplify design skills.

What a thread

Thread is a very small unit of process or program that can operate independently of an application. Most functionality in a computer system is achieved by splitting the process into strands by an application. A thread can be defined as a command line within a code that operates independently of another code. The cable contains the following information in the thread control block

Thread Identifier: Every new thread is provided with a Unique id (TID).

Stack Indicator: Stack process and contains local variables below the width of the cables.

Counter Calculation system: a register that keeps the address of the current command line.

Thread Status: can work, ready, wait, start or done.

Thread Register set: registers assigned to a computer network.

Parent process interface: Indicator in the control block block (PCB) of the system in which the cable resides.

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Multi- Threading  

Multiple threading is a way to use no of threads simultaneously by quickly switching between threads with the help of a CPU. Multiple threading also allows for the sharing of data space and large strands within the process of sharing information and communicating with other threads more easily than single processes. Multithreading aims to accomplish multiple tasks simultaneously, by making this functionality, speed and improve app delivery.

Benefits of a multi-threading in Python

If we use multi-threading in python the following are the benefits

1. Ensures the effective use of computer software resources.
2. Multithreaded programs are very responsive.
3. Share resources with its state through lower (child) thread which makes it more economical.
4. It makes multiprocessor construction more efficient due to the similarity.
5. Save time by doing multiple strings at once.
6. The system does not require a lot of memory to store multiple threads.
7. Multi-threading are faster than the sequential threading. 

 

Using Multi-threading in Python?

The multi-learning process greatly helps save time and improve app performance. This allows the programmer to classify tasks by the number of sub-tasks and at the same time perform them in the program. Resources data, file, and memory are transferred and shared with the same processor. In addition, it increases the user’s response to continue running the program even if part of the system is long or blocked.

How to read more access to python

Two multi-threading application modules are used to manage threads in python.

1.Thread module

2. Threading module

Thread Module

This started with the Python 3 version, which is designed to be outdated and can be accessed with threads that support backward interaction. In order to use this module in python we first need to import the cable module and define the function that performs certain actions with a flexible target setting.

Syntax:

thread.start_new_thread (function_name, args [, kwargs])

Threading Modules

The cable module is an advanced read-through module used to install the application in Python. In order to use multi-threading, we need to import the series module into the Python program.

Threading Class Methods

start () method: 

start () is used to promise a string function. And it only costs once per thread to make the thread start.

run () method:

run () is used to describe the function of a string and can be overridden by a section that extends the string section.

join () join method:

 join() is used to block the processing of another code until the end of the thread.

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Follow the steps below to use the threading module in Python Multi-threading:

1. Enter the series module

Create a new thread by importing a series module, as shown. Syntax:

1.Import thread: 

The thread module is built into the Thread section, designed to create a Python thread.

2. Declaration of Thread parameter:

Contains target function, argument and query as parameters in the Thread () section.

• Target: Specifies the name of the function performed by the string.
• Args: Defines disputes referred on behalf of a targeted employee.

For example:

1. import thread
2. def print_hello (n):
3. Print (“Hello, how old are you”, n)

Thread (target = target_hello, args = (18,))

In the code above, we have requested the print_hello () function as the specified parameter. Print_hello () contains one parameter n, passed to the args parameter.

3. Start a new thread:

To start the thread in Python multi-threading, call the thread section item. The start method () can be called once for each string item; otherwise, it throws a different error.

Syntax:

• T1 start ()
• T2 start ()

4. Join method:

It is the join method () used in the string section to stop the production of the main thread and wait until the wire object is completely removed. When the rope object is finished, it begins to make a large thread in Python.

5. Synchronizing Threads in Python

It is a thread synchronization method that ensures that no two strands can simultaneously parse within a system to access shared resources. The condition can be termed as critical stages. We use race mode to avoid a critical stage situation, where two strands do not reach resources at the same time. 

Priority Queue in Multi-threading

If we want to start new queue object that can hold certain number of items then queue module is used. The queue module can be control by following way

• Get(): if we want to remove and return the object from queue
• Put(): to add the object in queue
• Qsize(): to identify the number of objects in queue currently. 
• Empty(): to check that whether the queue is empty or not if empty then return true otherwise false. 
• Full(): to check that whether the queue is full or having some space. If it is full then return true otherwise false 

Limitations of Multi-threading

1. The code writing in multi-threading is very difficult only highly experienced programmer can write the code for multi-threading. 
2. It is much difficult to identify the error in multi-threading than others. It very hard to find the root causes of errors. 
3. Existing code often requires a great deal of redesign to take advantage of more multi-threading. The programmers need to eliminate static variable, if any variable or code is not safe for thread then need to replace.
4. The task of concurrency management is difficult because it has potential to bring new problems in application.

Author:

 Madhuri Diwan

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