Unraveling Threading: A Beginner’s Gateway to Enhanced Responsiveness with Python

Introduction

Threading in Python offers a gateway to optimizing responsiveness in applications, making it crucial for developers, especially beginners. This guide explores threading fundamentals in Python, highlighting its significance in improving responsiveness, while also comparing its ease of learning with other languages like Java.

We also have a YouTube video of creating and using Python threads for absolute beginners, do check it out.

• Original article on using threading python for absolute beginner
• Source codes for Python threading

Why Threading Matters for Improved Responsiveness

In modern software development, responsiveness is key to providing a smooth user experience. Threading allows programs to perform multiple tasks concurrently, preventing blocking operations that can lead to sluggishness. By leveraging threading, developers can keep applications responsive even when executing demanding tasks.

Getting Started with Threading in Python

Here,we’ll explore the fundamental concepts of threading, providing you with a solid foundation to start incorporating concurrency into your Python projects. Throughout this tutorial, we’ll cover essential steps such as creating threads, synchronizing thread execution, passing arguments to threads, and more. By mastering these basics, you’ll be well-equipped to leverage threading to enhance the performance and responsiveness of your Python applications. Let’s begin our exploration of threading in Python

Do check out our original article on creating and using threads in Python for absolute beginners.

1. Thread Creation
2. Thread Synchronization passing data to threads
3. Exchanging data between running threads

Creating a Thread in Python

First thing to do is to import the threading module
The threading module in Python provides support for working with threads.

import threading

Define a function representing the task you want to execute concurrently:
Before creating a thread, define a function that encapsulates the task you intend to perform concurrently. This function will serve as the target for the thread.

def do_something():
 # Your concurrent task code goes here
 pass

This function can contain any code you want to run concurrently, such as data processing, I/O operations, or network communication.

Create a Thread object, specifying the target function.Once you have defined the target function, create a Thread object and specify the target function using the target parameter.

my_thread = threading.Thread(target=do_something)
my_thread.start()

This step initializes a thread object named my_thread, which will execute the specified target function when started.

After creating the thread object, start the thread by invoking the start() method.

Program flow for threading function

This initiates the execution of the target function in a separate thread of control, allowing it to run concurrently with other parts of your program.

Why use .join() in Python threading

the join() method is used to ensure that the main program waits for all threads to complete their execution before proceeding further. This is important for coordinating the execution of multiple threads and handling dependencies between them.

Here are some key reasons why join() is commonly used in Python threading:

1. Synchronization: join() allows the main thread to synchronize with other threads, ensuring that critical tasks are completed before proceeding. Without join(), the main thread may continue execution, leading to unpredictable behavior or incorrect results.
2. Managing Dependencies: When multiple threads are performing related tasks or sharing resources, join() helps manage dependencies between them. By waiting for threads to finish their work, you can ensure that subsequent operations are executed in the correct sequence or that shared resources are released safely.
3. Waiting for Results: If threads are performing computations or tasks that produce results needed by the main program, join() allows you to wait for these results to become available before continuing execution. This is particularly useful when aggregating results from multiple threads or when the main program depends on the output of worker threads.
4. Clean Shutdown: join() ensures a clean shutdown of the program by waiting for all threads to complete before exiting. This helps avoid terminating threads prematurely, which could lead to resource leaks or other undesirable consequences.

An example of using join

# _1_create_python_threads_no_join.py
import time
import threading   #required for threading
def do_something():
   print(f'\nEntered do_something() ')
   time.sleep(2)
   print('\nDone Sleeping in thread\n')
   
print(f'\nStart of Main Thread ')
t1 = threading.Thread(target = do_something)   #create the thread t1
t1.start()                                     #start the threads
print('\nEnd of Main Thread\n+---------------------------+')

Source code for the Python threading can be downloaded from here

Explanation of using join() in Python code above

1. The main thread starts and prints a message indicating its start.
2. A thread named t1 is created and started with the target function do_something().
3. The join() method is called on the thread t1 immediately after starting it. This instructs the main thread to wait for the completion of thread t1 before proceeding further
4. As a result, the main thread waits until thread t1 completes its task (which includes a 2-second sleep), and only then prints the “End of Main Thread” message.

In summary, the join() method ensures that the main thread waits for the completion of a specified thread before continuing its execution. This helps in synchronizing the execution of multiple threads and handling dependencies between them.

Passing arguments to Python threads using args keyword

Passing arguments to Python threads allows you to customize the behavior of each thread and enable them to work with different data or parameters. Let’s explore how to pass arguments to threads:

Define a Function with Parameters: Start by defining a function that represents the task you want each thread to perform. This function should accept parameters that will be passed to it when the thread is created.

def my_function(arg1, arg2):
    # Your thread task code goes here
    print(f"Thread with arguments {arg1} and {arg2} is executing")

Create a Thread Object with Arguments: When creating a thread, specify the target function and provide the arguments to be passed to it using the args parameter.and start the thread.

import threading

# Arguments to be passed to the thread function
arg1 = "Hello"
arg2 = "World"

# Create a thread with arguments
my_thread = threading.Thread(target=my_function, args=(arg1, arg2))
my_thread.start()

When the thread executes, it will run the target function with the provided arguments.

Thread with arguments Hello and World is executing

By following these steps, you can pass arguments to Python threads and customize their behavior based on your requirements. This flexibility allows you to create versatile and dynamic multi-threaded applications that efficiently process data or perform tasks concurrently

Exchanging data between running threads in Python

Exchanging data between running threads in Python involves using shared data structures or synchronization mechanisms to ensure safe and efficient communication. Let’s explore some techniques for exchanging data between threads:

Shared Data Structures: Shared data structures such as lists, dictionaries, queues, or other mutable objects can be accessed and modified by multiple threads. When using shared data structures, it’s essential to ensure thread safety to prevent race conditions and data corruption.

Sharing data between python threads using a Queue:

import queue
import threading

# Create a shared queue
shared_queue = queue.Queue()

# Thread function to add data to the queue
def producer():
    data = "Hello from producer"
    shared_queue.put(data)

# Thread function to retrieve data from the queue
def consumer():
    data = shared_queue.get()
    print("Consumer received:", data)

# Create and start threads
producer_thread = threading.Thread(target=producer)
consumer_thread = threading.Thread(target=consumer)
producer_thread.start()
consumer_thread.start()

Synchronization Mechanisms
Synchronization mechanisms such as locks, semaphores, or condition variables can be used to coordinate access to shared resources and prevent data races. These mechanisms ensure that only one thread accesses the shared resource at a time, maintaining data integrity.

Example using a Lock:

import threading

# Create a shared variable
shared_variable = 0

# Create a lock
lock = threading.Lock()

# Thread function to increment the shared variable
def increment():
    global shared_variable
    with lock:
        shared_variable += 1
        print("Incremented value:", shared_variable)

# Create and start threads
threads = []
for _ in range(5):
    thread = threading.Thread(target=increment)
    threads.append(thread)
    thread.start()

# Wait for all threads to complete
for thread in threads:
    thread.join()

Threads can communicate with each other using event objects, condition variables, or other synchronization primitives. These mechanisms allow threads to signal each other, coordinate their execution, and exchange information as needed.

Example using Condition Variables to communicate between running python threads:

import threading

# Create a condition variable
condition = threading.Condition()

# Shared data
shared_data = []

# Thread function to append data to the shared list
def producer():
    with condition:
        shared_data.append("Data produced")
        condition.notify()

# Thread function to consume data from the shared list
def consumer():
    with condition:
        while not shared_data:
            condition.wait()
        data = shared_data.pop()
        print("Data consumed:", data)

# Create and start threads
producer_thread = threading.Thread(target=producer)
consumer_thread = threading.Thread(target=consumer)
producer_thread.start()
consumer_thread.start()

By using shared data structures and synchronization mechanisms, you can effectively exchange data between running threads in Python, enabling efficient communication and coordination in multi-threaded applications. Choose the appropriate technique based on your specific requirements and concurrency needs.