Posted on Jul 1

How coupled are your microservices?

#microservices #softwareengineering #architecture #api

How Coupled Are Your Microservices?

In this article, we address a crucial topic in software architecture: coupling and its main types in the context of microservices architecture.

The motivation for this writing comes from direct experience with the negative impact of coupling on system design. In one project, delivered by a software house, we had to fully refactor it before going live due to excessive domain, pass-through, shared data, and content coupling. These interdependencies made the services fragile and undermined their autonomy, scalability, and maintainability.

Understanding the different forms of coupling and their implications is essential for designing robust and scalable microservice systems.

Before we explore the types of coupling, let’s understand how components communicate in different architectural styles.

Component Communication: In-Process vs. Inter-Process

In monolithic systems, modules interact within the same process, sharing memory, objects, and direct calls. This in-process communication is fast, reliable, and flexible. Although coupling exists between modules, developers can manage them more easily since everything resides in the same space and they can coordinate changes directly.

In contrast, microservices-based architectures run each service in separate processes, often in distributed environments. Inter-process communication occurs over the network using REST APIs, asynchronous events, and gRPC, among others. This introduces challenges such as latency, network failures, versioning, and observability, requiring well-defined contracts and service decoupling.

Thus, while coupling in monolithic systems is more tolerable, it can directly compromise system autonomy, scalability, and resilience in microservices.

How Communication Affects Coupling in Microservices

The way services or modules communicate determines the type and severity of coupling. In monoliths, content coupling or shared data coupling is common (we’ll discuss these later), as modules can access each other’s internal structures directly.

In microservices, the ideal is to maintain loose domain coupling, where services depend only on public, stable contracts, such as APIs or events, and never on internal logic or data structures. This separation preserves service autonomy and prevents cascading side effects.

Now that we understand the different communication contexts and their impact on coupling, let’s examine the main types of coupling.

Types of Coupling

Coupling refers to the degree of dependency between two modules or services and how much one must know or rely on the other to function properly. The lower the coupling, the greater the service autonomy and flexibility.

Not all coupling is inherently bad; eliminating it completely is unfeasible in real-world systems. The goal is to reduce unnecessary or harmful forms of coupling as much as possible.

Figure 1 presents the four main types of coupling that can occur between microservices, ordered from the most desirable (low coupling) to the least desirable (high coupling).

Figure 1: Types of coupling in order of increasing dependency

The most common types of coupling in microservices are domain coupling, pass-through coupling, shared data coupling, and content coupling, which are ordered in increasing dependency. They are organized from the most desirable (low coupling) to the least desirable (high coupling).

Designing effective microservices requires careful attention to these forms of dependency and applying best practices to mitigate them, such as using asynchronous events, well-defined APIs, and clear separation of responsibilities. Next, we analyze each of these types, starting with domain coupling.

Domain Coupling

Domain coupling occurs when one microservice directly depends on another to perform a business function. It is a common and acceptable type of dependency in microservices since each service handles a specific business domain. Communication between services is natural to fulfill broader system requirements. For example, in a digital library system, a Loan service might need to query a User service to obtain user details such as name, email, address, and the Catalog service to get book details. Figure 2 illustrates domain coupling between the loans, users, and catalog services.

Figure 2: Domain coupling

The Loan service is domain-coupled with both the User and Catalog services. The Catalog service is also domain-coupled with the Loan service, while the User service remains independent and has no domain coupling to other services.

It is worth noting that domain coupling becomes particularly problematic when the boundaries of responsibility between services are not clearly defined. In many projects, the lack of explicit domain modeling leads to situations where one service depends on rules or decisions belonging to another or, worse, replicates them in parallel. This results in data inconsistencies, duplicated logic, semantic coupling, and difficulty evolving the system.

Without clear boundaries, what would be a natural coupling, such as querying data from a neighboring domain, becomes a fragile and dysfunctional link that requires coordinated deployments, increases the risk of regressions, and undermines service autonomy. In the long run, the system loses the benefits of modularity and tends to become a distributed monolith, where everything depends on everything else.

Therefore, defining business domains, as Domain-Driven Design (DDD) proposed, is fundamental to ensure that domain coupling remains healthy and controlled.

Pass-through Coupling

Pass-through Coupling occurs when a service passes data to another, not because it needs the data itself, but because a third service will use it. Figure 3 illustrates a pass-through coupling scenario where the delivery of a book needs to notify other users about its availability via email.

Figure 3: Pass-through coupling

The loan service queries the User service to retrieve user emails and passes them to the Notification service. The Loan service merely forwards the user data to the notification service without using it directly, creating a pass-through coupling.

Drawbacks of Pass-Through coupling

Pass-through coupling may initially seem harmless, but it introduces several architectural issues that undermine distributed systems’ cohesion, maintainability, and evolution. The harms of pass-through coupling are:

Increase in unnecessary coupling: The intermediary service, in this case, the Loan service, does not need the data for itself, yet it becomes dependent on the format, semantics, and contract of data that belongs to the Notification service. As a result, changes in the source (User service) or the destination (Notification service) can break the intermediary service (Loan service).
Violation of the single responsibility principle: The intermediary service starts handling responsibilities outside its domain, merely to satisfy the needs of another service. This leads to a loss of cohesion, as the service becomes too aware of foreign domains.
Unnecessary exposure of internal details: Internal data from one service, such as email structure or addresses, is leaked to another service, even if that service doesn’t use it directly. This increases the risk of abstraction leakage and breaks encapsulation.
Maintenance fragility: Changes to how data is represented or used now affect three services instead of two: the original producer (User service), the final consumer (Notification service), and the intermediary (loan service). This increases maintenance costs and the risk of regressions.
Unnecessary complexity in the data flow: The data path becomes longer and harder to trace. Understanding where the data came from and why it is there becomes more challenging, complicating debugging, observability, and auditing.
Testing and isolation become harder: Since the intermediary service now depends on the data and contracts of two other services, its tests must mock or simulate more external behaviors, making them more fragile and time-consuming.

Pass-through coupling violates the principles of modularity and service autonomy. It causes a service to become responsible for data and contracts that do not belong to its domain, making the system more rigid, fragile, and difficult to evolve.

How can we avoid pass-through coupling?

A viable alternative would be for the Loan service to forward only the IDs of the users waiting for the book delivery to the Notification service. The Notification service would then be responsible for querying the User service to retrieve the corresponding email addresses. Figure 4 illustrates this alternative.

Figure 4: Alternative to avoid pass-through coupling

To avoid temporal coupling, a topic that will be addressed later, we can adopt the same logic of interaction between services, but use asynchronous communication via a message broker instead of direct calls. Figure 5 illustrates this approach.

Figure 5: Alternative to avoid pass-through coupling using message brokers

As we have seen, pass-through coupling introduces unnecessary dependencies by making a service intermediate data that does not fall under its responsibility. Now, let’s examine shared data coupling.

Shared Data Coupling

At an even more concerning level, shared data coupling occurs when two or more services directly share the same data source, compromising the independence and isolation of functionalities. Figure 6 illustrates a shared data coupling.

Figure 6: Shared data coupling

Using the previous scenario where users on the waiting list must be notified of a book delivery, shared data coupling would arise if the Notification service accessed the User service’s database directly to retrieve email addresses.

Drawbacks of shared data coupling

The main problems associated with shared data coupling are:

Loss of service autonomy: When two or more services access the same data source, be it a database, memory, or shared file system, they are no longer truly autonomous. As a result, any change to the data schema can impact multiple services simultaneously.
Fragility in structural changes: Since services share the physical data structure, changes such as renaming columns, adding indexes, or modifying constraints require team coordination. This makes independent and safe service evolution more difficult.
Versioning difficulties: It becomes nearly impossible to version data independently per service, as all services are tied to the same model and schema. This limits practices such as controlled migrations or safe rollbacks.
Concurrency and integrity conflicts: Different services may attempt to update the same data simultaneously without orchestration or control logic. This can result in inconsistencies, data loss, or transactional corruption.
Increased testing and deployment complexity: Integration testing and deployments become more difficult, as a change in one service may require regression testing in all other services that share the data. This leads to tighter organizational coupling, delays, and increased risk in production environments.
Unintentional exposure of internal structures: Sharing a data source often reveals implementation details such as relationships and normalization. This can result in misuse, duplicated logic, and inconsistent interpretations of the data across services.

When shared data coupling may be accepted

Shared data coupling is one of the most problematic forms of coupling, as it violates the principle of service autonomy. Despite the risks, shared data coupling can be tolerated in specific scenarios, such as reading static data, such as lists of usernames, countries, or even cities, provided this information is relatively stable and rarely changes. However, services should never directly access another service’s database, as when the Notification service accesses the User service’s database.

In situations like the country list example, a more appropriate approach would be to expose this data through a shared cache, such as Redis, thus preserving the independence between services.

Next, we will examine content coupling, which is considered the most critical among all coupling types.

Content Coupling

This is the most undesirable type of coupling. It occurs when an external service directly accesses the internal data of another service, modifying its internal state while completely bypassing the API or business logic responsible for protecting that access. Figure 7 illustrates this scenario.

Figure 7: Content coupling

Using our digital library case study, this would be equivalent to the Payment service directly accessing the loans table to update the status field to “Regularized” after a fine is paid without calling the Loan service’s API, which should be responsible for validating and applying this change.

Drawbacks of content coupling

The main consequences of content coupling are:

Violation of encapsulation: The consuming service bypasses the business logic of the service that owns the data, breaking domain boundaries and allowing changes without validation or control.
High risk of regressions: Any change in the internal structure, such as field names, data types, or business rules, can silently break the consuming service. Local tests may fail to catch these issues.
Inability to evolve safely: The service that owns the data becomes constrained, as it can no longer refactor its internal structure without directly affecting other services. This creates hidden bidirectional coupling.
Difficulty in tracing and auditing: Since changes occur outside the official API, logs, events, and traces become inconsistent. Another service may modify the data without the service owner even realizing it.

Even in emergencies we must avoid the content coupling. Communication between services should always occur through well-defined public interfaces that encapsulate business rules and ensure data consistency. Otherwise, the system becomes fragile, difficult to maintain, and prone to errors that are hard to diagnose.

Temporal Coupling

Temporal coupling occurs when a service depends on another being available at the exact moment of interaction for a functionality to complete successfully. Unlike logical coupling types such as domain, pass-through, content, or shared data coupling, temporal coupling is related to runtime availability.

An example would be the Loan service making a synchronous HTTP call to the User service to verify if the user is active before completing a book checkout. The loan process is blocked or fails if the User service is unavailable or experiencing high latency. Figure 8 illustrates a synchronous call to the User service from the Loan service.

Temporal coupling is not always harmful, but it is essential to recognize its presence. In scenarios where a sequence of microservices communicates synchronously, the challenges of this type of coupling tend to intensify, potentially impacting the system’s scalability and resilience. Figure 8 presents a blocking, synchronized network call.

Figure 8: Loan service makes a synchronous call to the user service

One way to reduce temporal coupling is to adopt asynchronous, event-based communication using a broker such as Kafka or RabbitMQ. In addition, it is essential to apply fault-tolerance best practices to mitigate the effects of this type of coupling, such as circuit breakers, retries, timeouts, and degraded responses. The use of local or distributed caching, as well as the separation of critical and non-critical flows, also helps prevent non-essential functionalities from blocking the main operation.

Conclusion

Designing microservices requires awareness of the different types of coupling, how they arise, and how to mitigate them. Understanding coupling types such as domain, pass-through, shared data, content, and temporal is essential to ensure autonomy, scalability, and maintainability in distributed architectures.

While certain coupling levels, such as domain coupling, are inevitable, the goal should always be to minimize unnecessary coupling by favoring well-defined contracts, asynchronous event-driven communication, fault tolerance, and proper encapsulation of each service’s responsibilities.

By correctly identifying the type of coupling involved in an interaction and applying the appropriate strategies to reduce it, we can build more resilient, evolvable systems aligned with the principles of service-oriented architecture. Ultimately, good microservice design depends less on the complete absence of coupling and more on its correct identification, management, and isolation.