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Concurrency And Multithreading

πŸ™‡β€β™€οΈ Β Students & Apprentices

Hello there, fellow tech enthusiasts! Aren't you excited to learn about concurrency and multithreading? I am absolutely thrilled to share this amazing journey with you.

Imagine having a superpower that allows you to be at several places at once, doing multiple things simultaneously. That's exactly what concurrency and multithreading offer in the world of computer programming!

In this colorful odyssey, we will explore the wondrous world of parallel code execution, delve into the idiosyncrasies of concurrent programming, and have a taste of just how fantastic multithreading can be. So buckle up, because this is going to be one electrifying ride!

A Tale of Two Threads: What are Concurrency and Multithreading? 🧡

Before we dive into the nitty-gritty, let's discuss the basics. Concurrency and multithreading are intricately related concepts, but they're not quite the same thing. Allow me to explain:

  • Concurrency refers to the ability of a program to manage multiple tasks seemingly at the same time.
  • Multithreading is a specific method of achieving concurrency by utilizing multiple threads of execution within a single process.

Think of concurrency as the big picture and multithreading as one of its masterpieces. They work hand in hand to unlock the potential of modern hardware , particularly in multi-core processors where each core can execute a separate thread.

Enough talking though; let's dive straight into code!

import threading
import time

def print_numbers():
    for i in range(10):

def print_letters():
    for letter in 'abcdefghij':

t1 = threading.Thread(target=print_numbers)
t2 = threading.Thread(target=print_letters)



print("All threads are done!")

In this Python example, we have two functions: print_numbers and print_letters. They both print their respective characters with a slight delay. If we were to call these functions sequentially, it would take around 22 seconds to complete (10 seconds for numbers and 12 seconds for letters).

However, with multithreading (using the threading.Thread class), we can run both functions concurrently, completing the entire execution in just 12 seconds!

A Symphony of Synchronization 🎼

When working with concurrent programming, it's imperative to consider potential hazards like race conditions and deadlocks. These might occur due to incorrect synchronization between threads when they access shared resources.

For instance, let's say we have two threads trying to withdraw money from a shared bank account. If they both read the balance, subtract the withdrawal amount, and write the new balance at the same time, one of them might end up overwriting the other's changes. This is a classic example of a race condition!

To prevent such catastrophes, we need to enforce proper synchronization using tools like locks, semaphores, or barriers. Here's an example in Java that demonstrates synchronization:

class BankAccount {
    private int balance;

    public BankAccount(int initialBalance) {
        balance = initialBalance;

    public synchronized void deposit(int amount) {
        balance += amount;

    public synchronized void withdraw(int amount) {
        balance -= amount;

    public synchronized int getBalance() {
        return balance;

In this Java code snippet, we define a BankAccount class that encapsulates account balance management. By using the synchronized keyword, we ensure that only one thread can access the deposit, withdraw, and getBalance methods at a time, preventing race conditions from ever occurring.

The Art of Asynchronous Programming 🎨

Promises, callbacks, and async/await are asynchronous programming techniques commonly found in languages like JavaScript. They allow us to write code that doesn't block other tasks from running while waiting for an operation to complete.

For example, suppose we're fetching data from an API. Instead of waiting and blocking other tasks, we can use async/await to run the data-fetching code asynchronously:

async function fetchData() {
    const response = await fetch('');
    const data = await response.json();

console.log('This will run before data is fetched.');

Notice the usage of the async and await keywords in the fetchData function. This ensures that the function runs asynchronously, allowing other tasks (like the second console.log) to execute while waiting for the data to be fetched.

Concurrency: The Road Less Traveled 🏞️

Concurrency is an essential concept in modern programming, and while it comes with its own set of challenges, mastering it can be incredibly rewarding. From distributed systems to game engines, concurrency and multithreading are used in countless applications that shape our lives.

As Alan Kay, a pioneering computer scientist, once said:

β€œThe best way to predict the future is to invent it.”

Embrace concurrency, harness its power, and join the ranks of those who strive to create a better, faster, and more efficient future with code! is a collection of articles on a variety of technology and programming articles assembled by James Padolsey. Enjoy! And please share! And if you feel like you can donate here so I can create more free content for you.