Why microservices may end when monoliths come back with a vengeance

The shift toward microservices began to achieve momentum within the early 2010s as technology corporations recognized the constraints of monolithic architectures. However, many corporations, e.g Amazon (Prime Video), Invision, Istio and Segment they go back to monolithic architecture. In this text, we’ll discuss why many organizations fail when transitioning to a microservices architecture.

What is a monolith?

Monolithic architecture is straightforward: the user requests data, and all business logic and data resides in a single service. However, monolithic systems face challenges resembling limited scalability, difficulty deploying updates, and vulnerability to single points of failure.

To solve this problem, many organizations have tried to maneuver to a microservices-based architecture to benefit from benefits resembling abstraction and encapsulation, faster deployment, easier maintenance, and tighter alignment of every service with team ownership.

Why microservices?

In a great microservices architecture, each business domain runs as a separate, independent service with its own database. This configuration provides advantages resembling improved scalability, flexibility, and resiliency. Consider the diagram below.

Reality

However, recent trends show that many corporations are moving away from this and sticking to monolithic architecture. This is since it is difficult to attain this level of harmony in the true world. The reality often looks just like the diagram below.

Migrating to a microservices architecture is thought to cause complex interactions between services, cyclic calls, data integrity issues, and admittedly, it is nearly not possible to completely eliminate the monolith. Let’s discuss why a few of these issues occur when you migrate to a microservices architecture.

Invalid domain boundaries

In a great scenario, a single service should contain a number of complete business domains, in order that each domain is a stand-alone service. A site should never be split into multiple services as this will result in interdependencies between services. The diagram below shows how a single service can contain a number of entire domains to keep up clear boundaries.

In complex real-world systems, defining domain boundaries will be difficult, especially when data has traditionally been conceptualized in specific ways. The diagram below shows what real-world systems often appear like in a microservices architecture when boundaries aren’t predefined or engineers add latest services without considering domain boundaries.

If domains aren’t well defined, dependency on other services increases, resulting in many problems:

• Cyclic dependencies or excessive invocations: When services are interdependent, they require frequent data exchange.
• Data integrity issues: Splitting a single domain into services splits deeply related data into multiple services.
• Unclear team ownership: Multiple teams may must collaborate in overlapping domains, resulting in inefficiency and confusion.

Deeply connected data and functionality

In a monolithic architecture, clients often bypass designated interfaces and access the database directly since it is difficult to implement encapsulation in a single codebase. This can tempt developers to chop corners, especially if the interfaces are unclear or seem complicated. Over time, this creates a network of clients which are tightly coupled to specific database tables and business logic.

When moving to a microservices architecture, each client have to be updated to work with the brand new service APIs. However, because customers are so tied to the monolith’s business logic, this requires refactoring their logic during migration.

It takes time to untangle these dependencies without breaking existing functionality. Some client updates are sometimes delayed as a result of the complexity of the work, leading to some clients continuing to make use of the Monolith database after migration. To avoid this, engineers can create latest data models in the brand new service but keep existing models within the monolith. When models are deeply coupled, it results in data and functions being partitioned between services, causing multiple inter-service calls and data integrity issues.

Data migration

Data migration is one of the crucial complex and dangerous elements of the transition to microservices. It is very important to accurately and fully transfer all relevant data to the brand new microservices. Many migrations end at this stage as a result of complexity, but successful data migration is essential to realizing the advantages of microservices. Common challenges include:

• Data integrity and consistency: Errors during migration can result in data loss or inconsistencies.
• Data volume: Transferring large amounts of information will be resource-intensive and time-consuming.
• Downtime and business continuity: Data migration may require downtime, potentially disrupting business operations. A smooth transition with minimal user input is crucial.
• Testing and Validation: Rigorous testing is required to be certain that migrated data is accurate, complete, and performs well in the brand new service.

Application

A microservices architecture may look attractive, but moving from a monolith is difficult. Many corporations are in an in-between state, which increases system complexity, causing data integrity issues, circular dependencies, and unclear team ownership. The inability to totally leverage the advantages of microservices in the true world is causing many corporations to revert to a monolithic approach.