In Part 1 of this series, we plunged into the core principles that set successful microservice architectures apart: Encapsulation, Module Connections, Cohesion, and Coupling. We covered the theoretical underpinnings, best practices, and organizational dynamics that influence whether you end up with a clean, maintainable system—or a “distributed monolith” in disguise.

Now it’s time to roll up our sleeves and see these principles in action. In this post, we’ll:

1. Analyze real-world microservice scenarios that highlight healthy vs. unhealthy cohesion and coupling.
2. Show you how to recognize common architectural smells that signal trouble on the horizon.
3. Walk you through refactoring steps to turn a tangled mess into a system that’s easy to maintain and evolve over time.

By the end of this deep dive, you’ll have a roadmap for diagnosing issues in your own services, plus concrete strategies for nudging them back to a healthy state.

1. When Cohesion Goes Awry: A Real-World Glimpse

Let’s imagine you have a “Utility Service” that sprang up as a way to handle routine tasks—things like user verification, time-zone conversions, and analytics data transformations. Over time, that service became a dumping ground for any code snippet that didn’t seem to fit neatly anywhere else.

Symptoms of Low Cohesion

• Overstuffed Endpoints: Endpoints start looking more like Swiss Army knives: /utility/doEverything.
• Unclear Ownership: Multiple teams commit to the same repository, each with drastically different use cases and domains in mind.
• Ongoing Rework: Every small change or fix for one responsibility inadvertently breaks another.

Why this is a problem: Low cohesion means higher cognitive load—engineers have to know about user verification, data transformations, and random analytics math just to change a single line of code. Over time, this friction slows down delivery and escalates the risk of accidental breakage.

Refactoring into Cohesive Domains

1. Identify Bounded Contexts:

   Use techniques from Domain-Driven Design to spot which functionalities naturally group together—e.g., user verification might pair well with authentication and identity services, while analytics transformations may belong in a dedicated “Reporting” or “Analytics” service.

2. Carve Out the Modules:

   Extract code into services that match these bounded contexts. For instance, the “Utility” service might split into:

   • Verification Service (handling identity checks, user KYC, email confirmations)
   • Analytics Transformer Service (handling data aggregation, transformations, and summarization)

3. Establish Clear Contracts:

   Each new service exposes lean, purposeful endpoints. For example:

   POST /verification/users/{userId}/check
   POST /analytics/transform
   
   

   This reduces “surface area” and paves the way for simpler versioning and documentation.

2. Recognizing Dangerous Coupling