Over the past year, I have participated in several migration projects involving traditional monolithic applications. These endeavors were quite insightful, motivating me to share my experience through this new article series.
Before we begin, I'd like to point out that the series is an exercise in planning and continuously publishing long-form content â thus, the contents of this series will revolve around a fictitious application.
Defining the Entry Point
Let's consider a monolithic application employing a popular framework like Symfony, Rails or Django. This framework handles the majority of our application business logic in the backend and is the backbone of our fictitious product.
Our frontend utilizes the framework's provided templating system and relies on a bridge for interactions between client and server. We might also expose an external API for mobile applications or external tenants to utilize.
These considerations condense into the traditional definition of a monolithic stack â one codebase to rule them all. This architecture works well for its intended use case most of the time, as proven by time itself.
Although this structure is effective initially, it has certain architectural limitations that become apparent as complexity increases. These limitations lead to challenges such as API misalignment, difficulty with product expansion, and reduced cohesion.
The first Problems appear
This series requires introducing the necessary crux and foundational understanding to motivate migration to a different architectural approach. To that end, we will present and analyze concrete implementations and design patterns that directly conflict with the traditional monolithic approach.
Problem 1: APIs
External applications, such as mobile apps and third-party vendors, rely on an API to interact with the core application. In a monolithic architecture, the API layer often becomes tightly coupled with internal business logic, exposing implementation details rather than business capabilities.
This coupling means internal refactoring can break external integrations, and API versioning becomes complex as changes ripple through the codebase. Different external consumers requiring different data representations lead to API bloat where endpoints inefficiently serve multiple use cases.
Problem 2: Products
As the application grows, distinct products within the monolith begin to exhibit different scaling requirements, release cycles, and team ownership patterns. Each product may need specialized data models, unique business rules, and different performance characteristics.
However, in a monolithic architecture, all products must share the same technology stack. This creates bottlenecks where teams cannot iterate independently, leading to coordination overhead and slower feature delivery.
Problem 3: Cohesion
As the application expands to serve multiple products, the codebase develops inappropriate coupling between unrelated business domains. Teams inadvertently create shared utilities and services that become tightly coupled across product boundaries, making changes risky and testing complex.
The shared database becomes a bottleneck where schema changes require coordination across all teams. Additionally, UI components become overly generic to accommodate different product requirements, resulting in suboptimal user experiences and harder-to-maintain frontend code.
This creates a situation where business logic that should be cohesive within product boundaries becomes scattered across shared components, while unrelated products become coupled through these shared dependencies.