From Junior to Senior Developer: Mastering Parallelism for Faster Code and Better Performance

Introduction

As developers, we constantly look for ways to improve performance, optimize code, and build scalable applications. One of the key differences between a junior and a senior developer is the understanding of parallelism — executing independent operations concurrently instead of sequentially.

A common mistake junior developers make is writing mutually exclusive operations in a synchronous manner, which leads to unnecessary performance bottlenecks. This article will guide you through understanding parallel execution, when to use it, and how it can boost your application’s performance.

Understanding Parallelism vs. Concurrency

Before we dive into the code, let’s clarify some basic concepts:

• Concurrency: The ability of a system to handle multiple tasks by switching between them. This doesn’t necessarily mean tasks are executing at the same time.
• Parallelism: The ability to execute multiple tasks simultaneously, utilizing multiple cores or threads.

For example, imagine you are making breakfast:

Synchronous execution (single-threaded):

• Make coffee
• Toast bread
• Fry eggs

Parallel execution (multi-threaded):

• Make coffee (Thread 1)
• Toast bread (Thread 2)
• Fry eggs (Thread 3)

By running these tasks in parallel, you reduce the total execution time.

Why Should You Use Parallelism?

If tasks are independent (i.e., one doesn’t depend on the result of another), executing them in parallel can:

• Reduce execution time
• Optimize CPU utilization
• Enhance user experience (faster responses in web applications)

Let’s look at a bad approach (synchronous execution) vs. a better approach (parallel execution).

Example 1: Fetching Data from Multiple APIs

Let’s assume you need to fetch data from three different APIs and process them.

Bad Approach: Sequential Execution

import time
import requests

def fetch_data(url):
    response = requests.get(url)
    return response.json()

start_time = time.time()
data1 = fetch_data("https://api.example.com/data1")
data2 = fetch_data("https://api.example.com/data2")
data3 = fetch_data("https://api.example.com/data3")
end_time = time.time()
print(f"Total time taken: {end_time - start_time:.2f} seconds")

Here, each API call waits for the previous one to complete. If each call takes 2 seconds, the total time is around 6 seconds.

Better Approach: Using Parallel Execution with ThreadPoolExecutor

import time
import requests
from concurrent.futures import ThreadPoolExecutor

def fetch_data(url):
    response = requests.get(url)
    return response.json()

urls = [
    "https://api.example.com/data1",
    "https://api.example.com/data2",
    "https://api.example.com/data3"
]

start_time = time.time()
with ThreadPoolExecutor(max_workers=3) as executor:
    results = list(executor.map(fetch_data, urls))

end_time = time.time()
print(f"Total time taken: {end_time - start_time:.2f} seconds")

Result

Instead of executing sequentially, this approach allows all three API calls to execute simultaneously, reducing total execution time to around 2 seconds instead of 6.

Visualizing Parallel Execution

To better understand parallel execution, let’s consider the following clock diagram.

Sequential Execution (Bad Approach)

Time -->
API Call 1 |--------2s--------|
API Call 2           |--------2s--------|
API Call 3                     |--------2s--------|
Total time taken: 6s

Parallel Execution (Good Approach)

Time -->
API Call 1 |--------2s--------|
API Call 2 |--------2s--------|
API Call 3 |--------2s--------|
Total time taken: 2s

In the parallel approach, all API calls execute simultaneously, leading to a massive performance gain.

Example 2: Processing Large Files in Parallel

Another common mistake is processing large files sequentially instead of leveraging multiple cores.

Bad Approach: Synchronous File Processing

import time

def process_file(filename):
    with open(filename, "r") as file:
        data = file.read()
        return len(data)

start_time = time.time()
file_sizes = []
for file in ["file1.txt", "file2.txt", "file3.txt"]:
    file_sizes.append(process_file(file))
end_time = time.time()
print(f"Total time taken: {end_time - start_time:.2f} seconds")

Better Approach: Using Multiprocessing for True Parallelism

import time
from multiprocessing import Pool

def process_file(filename):
    with open(filename, "r") as file:
        data = file.read()
        return len(data)

start_time = time.time()
with Pool(processes=3) as pool:
    file_sizes = pool.map(process_file, ["file1.txt", "file2.txt", "file3.txt"])

end_time = time.time()
print(f"Total time taken: {end_time - start_time:.2f} seconds")

Using multiprocessing ensures that each file is processed on a separate CPU core, significantly reducing execution time.

Key Takeaways

1. Never write mutually exclusive operations synchronously — if operations don’t depend on each other, parallelize them.
2. Use multithreading (ThreadPoolExecutor) for I/O-bound tasks (e.g., API calls, database queries).
3. Use multiprocessing (Pool) for CPU-bound tasks (e.g., file processing, heavy computations).
4. Visualize execution flow with clock diagrams to understand performance improvements.
5. Senior developers optimize for performance, juniors execute linearly — start thinking in parallel!

Conclusion

Growing from a junior to a senior developer isn’t just about learning syntax — it’s about thinking efficiently. Understanding when to use parallelism can make your applications significantly faster and more scalable.

Start identifying synchronous bottlenecks in your code and rewrite them using parallel execution. Not only will you boost performance, but you’ll also develop a senior mindset that prioritizes efficiency and scalability.

What are some areas in your code that could benefit from parallelism? Let’s discuss this in the comments!