Differences Between Microservices and Monolithic Architecture

Differences Between Microservices and Monolithic Architecture

If a monolithic application is like a single person doing everything, a microservice system resembles a team where each member focuses on a specific responsibility, offering greater flexibility but also increasing communication overhead and data‑sharing complexity.

Where Does Data Sharing Become a Problem?

Data sharing issues arise when services depend on each other's data, similar to how everyone speaking Mandarin still needs clear channels for specific concerns; each service should manage its own data and expose APIs, forming a collaborative yet loosely coupled system.

In streamlined teams, assigning each member a dedicated microservice promotes autonomy at both the team and architectural levels, and peer code reviews improve overall code quality while fostering mutual learning.

How to Resolve Dependency Relationships

Service dependencies should generally be unidirectional—e.g., Service A depends on Service B's data, but Service B should not depend on Service A—to avoid tangled relationships, simplify troubleshooting, and enhance performance.

How Multiple Microservices Depend on Each Other

Given three services A, B, and C where A depends on B and B depends on C, B should not aggregate C's data and expose it directly to A. Instead, A should call B and C separately, allowing parallel calls and better performance.

Aggregation Service

When A frequently needs combined data from B and C, an aggregation service can act as a middleman, merging the data before delivering it to A.

HTTP API Communication

Just as browsers communicate with servers, microservices communicate with each other and with clients. API design must differentiate between client‑facing (2C) APIs, which require comprehensive wrappers, status codes, and error handling, and service‑to‑service APIs, which can be simpler and trust‑based.

For service‑to‑service calls, the relationship is based on trust: the caller assumes the callee is reliable, so parameters and return structures are kept minimal. For client‑to‑service calls, stricter validation and error reporting are necessary due to potential malicious usage.

Redis Large Cache

In a microservice ecosystem, a foundational system (e.g., base‑data) often manages core data such as user profiles. This system maintains a large Redis cache that other services synchronize with and use for business aggregation.

Conclusion

Microservices, compared to monolithic applications, place data sharing and communication at the core of performance and reliability; during design, relationships should be simplified, highly abstracted, and services kept as few as necessary.