A monolith isn’t just a single, giant codebase; it’s an architectural choice that often outmaneuvers distributed systems for certain problems.
Let’s see it in action with a simple web application. Imagine a blog:
# blog_app.py
from flask import Flask, render_template, request
app = Flask(__name__)
# In-memory "database" for simplicity
posts = [
{"id": 1, "title": "My First Post", "content": "This is the content of my first post."},
{"id": 2, "title": "Another Thought", "content": "Exploring monoliths today."},
]
@app.route('/')
def index():
return render_template('index.html', posts=posts)
@app.route('/post/<int:post_id>')
def show_post(post_id):
post = next((p for p in posts if p['id'] == post_id), None)
return render_template('post.html', post=post)
if __name__ == '__main__':
app.run(debug=True)
And the templates:
<!-- templates/index.html -->
<!DOCTYPE html>
<html>
<head>
<title>My Blog</title>
</head>
<body>
<h1>Blog Posts</h1>
<ul>
{% for post in posts %}
<li><a href="{{ url_for('show_post', post_id=post.id) }}">{{ post.title }}</a></li>
{% endfor %}
</ul>
</body>
</html>
<!-- templates/post.html -->
<!DOCTYPE html>
<html>
<head>
<title>{{ post.title }}</title>
</head>
<body>
<h1>{{ post.title }}</h1>
<p>{{ post.content }}</p>
<a href="{{ url_for('index') }}">Back to list</a>
</body>
</html>
Running this with python blog_app.py and visiting http://127.0.0.1:5000/ shows a functional blog. All the logic for displaying posts, fetching them, and rendering them resides within this single Python process. There are no network calls between services, no serialization/deserialization overhead, no distributed transaction headaches.
The core problem a monolith solves efficiently is coupling. In a well-designed monolith, components are tightly coupled internally. This means function calls are direct, data sharing is via memory, and state management is straightforward. The system’s complexity is managed through code organization, not distributed coordination. This makes development faster initially because developers don’t need to worry about network latency, partial failures, or consistency across services. Refactoring is also significantly easier: changing a function signature in one part of the codebase immediately flags all its callers.
Consider debugging. If you encounter an issue, you can attach a single debugger to the running process and trace the execution flow seamlessly across different logical modules. There’s no need to correlate logs from multiple services, understand distributed tracing, or guess which service might be the culprit. The entire application’s state is accessible and observable within one context.
The biggest misconception about monoliths is that they are inherently difficult to scale. While scaling a monolith horizontally (running multiple instances behind a load balancer) can be less granular than scaling individual microservices, it’s often simpler to manage. If your monolith has a bottleneck in its database layer, you scale the database. If it’s CPU-bound, you run more instances of the entire monolith. For many applications, this is perfectly adequate and much easier to set up and maintain than orchestrating the scaling of dozens of independent services.
The reason monoliths often win for internal tools, simple CRUD applications, or new projects where the domain is not yet fully understood is that they minimize operational overhead and development friction. The cost of building, deploying, and operating a single application is substantially lower than managing a distributed system. The cognitive load on developers is also reduced, allowing them to focus on feature development rather than infrastructure.
When you’re dealing with a system where different parts of the application need to communicate very frequently and with extremely low latency, the overhead of inter-process or network communication in a distributed system can become a significant performance bottleneck. In a monolith, these communications are often just function calls, which are orders of magnitude faster.
The next challenge you’ll inevitably face with a growing monolith is managing its internal complexity and ensuring maintainability as the codebase expands.