Handling Concurrency in Python: Multithreading and Multiprocessing

Introduction
Multithreading
Multiprocessing
Conclusion

Introduction

Concurrency is a fundamental concept in computer science that refers to the ability of a program to execute multiple tasks seemingly at the same time. In Python, we can handle concurrency using two approaches: multithreading and multiprocessing.

In this tutorial, we will explore both multithreading and multiprocessing in Python. By the end of this tutorial, you will have a clear understanding of when and how to use multithreading and multiprocessing in your Python programs.

Before we begin, make sure you have a basic understanding of Python programming and are familiar with the concept of functions and modules.

Let’s get started!

Multithreading

What is Multithreading?

Multithreading is a technique in which multiple threads of execution coexist within a single process. A thread is a lightweight sub-process that can perform tasks concurrently, sharing the same memory space. This allows us to achieve a higher level of parallelism in our programs.

Why Use Multithreading?

Multithreading offers several benefits, such as:

Improved responsiveness: By executing tasks concurrently, we can keep the program responsive even when performing computationally intensive operations.
Better resource utilization: Multithreading allows us to take advantage of multicore processors, making our programs faster and more efficient.
Simplified program structure: By dividing a complex program into smaller threads, we can simplify the design and improve the overall readability.

Creating Threads in Python

To create and manage threads in Python, we can use the built-in threading module. Here’s a step-by-step example of creating and running threads:

Import the threading module:
```
 import threading
```

Define a function that will be executed by the thread:

 def print_numbers():
     for i in range(1, 11):
         print(i)

Create a Thread object and specify the target function:

 thread = threading.Thread(target=print_numbers)

Start the thread:
```
 thread.start()
```
Wait for the thread to finish (optional):
```
 thread.join()
```
In this example, the print_numbers function will be executed concurrently by a separate thread. The start method launches the thread, and the join method waits for the thread to complete before continuing with the main program.

Synchronizing Threads

When multiple threads access shared resources, synchronization becomes important to avoid data races and ensure the correctness of the program. Python provides several synchronization mechanisms, such as locks, semaphores, and condition variables.

Let’s take a look at an example of using a lock to synchronize threads: ```python import threading

counter = 0
lock = threading.Lock()

def increment():
    global counter
    with lock:
        counter += 1

def decrement():
    global counter
    with lock:
        counter -= 1
``` In this example, we have a shared variable `counter` that needs to be incremented and decremented by multiple threads. By using a lock, we ensure that only one thread can access the shared variable at a time, preventing race conditions.

Multiprocessing

What is Multiprocessing?

Unlike multithreading, which involves multiple threads within a single process, multiprocessing refers to the execution of multiple processes simultaneously. Each process has its memory space and runs independently of others.

Why Use Multiprocessing?

Multiprocessing offers several advantages over multithreading:

True parallelism: Since each process has its memory space, multiprocessing allows for true parallel execution, taking full advantage of multiple CPU cores.
Increased stability: If one process crashes, it doesn’t affect the other processes, making multiprocessing more robust than multithreading.
Isolation of resources: Processes have separate memory spaces, preventing unwanted interactions between different parts of the program.

Creating Processes in Python

To create and manage processes in Python, we can use the multiprocessing module. Here’s a step-by-step example:

Import the multiprocessing module:
```
 import multiprocessing
```

Define a function to be run by the process:

 def print_numbers():
     for i in range(1, 11):
         print(i)

Create a Process object and specify the target function:

 process = multiprocessing.Process(target=print_numbers)

Start the process:
```
 process.start()
```
Wait for the process to finish (optional):
```
 process.join()
```
In this example, the print_numbers function will be executed concurrently by a separate process. The start method launches the process, and the join method waits for the process to complete before continuing with the main program.

Managing Processes

The multiprocessing module provides various features for managing processes, such as process pools, inter-process communication, and shared memory.

For example, let’s see how to use a process pool to execute multiple processes in parallel: ```python import multiprocessing

def square(x):
    return x * x

if __name__ == '__main__':
    numbers = [1, 2, 3, 4, 5]
    
    with multiprocessing.Pool() as pool:
        result = pool.map(square, numbers)
    
    print(result)
``` In this example, the `square` function calculates the square of a number. We use the `Pool` class from the `multiprocessing` module to create a pool of worker processes. The `map` method then distributes the tasks across the available processes, executing them in parallel. The result is a list of squared numbers.

Conclusion

In this tutorial, we explored two powerful techniques for handling concurrency in Python: multithreading and multiprocessing. We learned about their benefits, saw how to create and manage threads and processes, and discussed synchronization and process management.

By leveraging the power of concurrency, you can write more efficient and responsive programs, taking advantage of the full potential of modern hardware. With this knowledge, you can now start incorporating multithreading and multiprocessing into your Python projects and take your coding skills to the next level!

Remember, practice is the key to mastering concurrency in Python. Keep exploring and experimenting with different scenarios to gain a deeper understanding of how to handle concurrency effectively.

Happy coding!

Published: 8 November 2020