Differences Between Microservices and Monolithic Architecture and Managing Data Sharing and Service Dependencies

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 has a specific responsibility; this reduces individual load and increases elasticity, but introduces communication overhead and data‑sharing challenges.

Problems of Data Sharing

Data‑sharing issues are similar to everyone speaking the same language but needing distinct specialists: front‑end issues go to designers, back‑end database problems go to DBAs. In microservices this translates to inter‑service data dependencies that must be carefully managed to achieve small, autonomous services.

In a lean team, I usually let each member own a single microservice module, enabling both team‑level and architectural autonomy, while code reviews help improve overall quality and foster learning.

How to Resolve Dependency Relationships

Service dependencies should generally be one‑way; for example, Service A may depend on data from Service B, but Service B should not depend on Service A, which helps avoid tangled relationships and simplifies troubleshooting and performance tuning.

Multiple Microservices Dependency

Given three services A, B, C where A depends on B and B depends on C, B should not expose an aggregated API of C directly to A. Instead, A should call B and C separately, allowing parallel calls and better performance.

Aggregation Service

When Service A frequently needs combined data from B and C, an aggregation service can act as a middle‑man, merging B and C’s data and presenting a single interface to A.

HTTP API Communication

Just as a browser communicates with a server, microservices communicate with each other in a client‑server relationship, and this pattern should be consistent across all service interactions.

Microservices provide APIs both to external clients and to other services. For client‑facing APIs, responses are usually wrapped with metadata (status, error messages, etc.). For inter‑service APIs, a simpler contract is preferred: return raw lists or objects and let the caller handle exceptions without excessive validation.

Between services, a trust relationship exists: the caller mainly worries about the callee’s stability, while the callee trusts the caller to be well‑behaved, allowing streamlined request/response structures. Between services and external clients, a distrust relationship requires strict parameter validation and clear error handling.

Redis Large Cache

In a microservice ecosystem, a base data system (e.g., user profile service) often manages a large cache that other services rely on for business aggregation. This cache is synchronized centrally and shared across services to improve performance.

Conclusion

Microservices, compared with monolithic applications, place data sharing and communication at the core of performance and reliability; therefore, service design should aim to simplify relationships, maximize abstraction, and keep the number of services as low as feasible.

The above content reflects personal understanding and summary of microservice development; any inaccuracies are welcome to be pointed out, and additional insights are encouraged in the comments.