Outlined below are common real-world anti-patterns seen in systems built with microservices where development was done as an outsourced project — and how to remedy them.

1. The Distributed Monolith

The Problem:

You could claim that this is the most common anti-pattern. A team breaks up a monolithic application into microservices, but each individual member of the team fails to decouple the application correctly both logically or technically. 

Root Causes:

• No domain-driven design
• Shared databases
• No boundaries or API versioning
  
  

Fix:

• Usage of Domain-driven Design, to define bounded contexts clearly.
• Each microservice should own its data store, and communicate via defined APIs or events.
• Utilize asynchronous communication  for inter-service communication. 
  
  

2. Over-Engineering the Microservices

The Problem: 

Some outsourcing teams overly fragment services — producing dozens of tiny services that create more complexity than value.

Root Cause: 

• A misunderstanding of “micro” in microservices.
• A need to demonstrate architectural sophistication to management.
• Inconsistency with business domains.
  
  

Fix:

• Size services relative to business capabilities, not from a technical framework.
• Combine microservices that have high inter-dependencies.
• Assess the ROI of each service. If it doesn’t provide autonomy or scale, simplify it.
  
  

Finding that right balance is important. We have seen that working with a good Software development company in Rajkot or an experienced custom software development team can help you appropriately size your architecture from day one.

3. Shared Database Anti-Pattern

The Problem: 

Multiple microservices are wired to one shared database. This creates no data autonomy, lack of independent data scaling, and excessive dependency between services.

Root Cause: 

• A legacy monolithic mentality.
• The database schema and accesses are shared across teams.
• A misunderstanding of data ownership.
  
  

Fix:

• Utilize the Database per Service pattern.
• Share data using APIs or event-driven methods.
• If necessary, implement a data synchronization layer for read-only aggregation.