Key Challenges in Microservices Testing

When you make the switch from a monolithic design to a microservices-based design, you are setting up multiple points of failure. Those failure points become difficult to identify and fix in such an intricately dependent infrastructure.

As an application grows in size, the dependency, communication, and coordination between different individual services also increase, adding to the overall complexity of the design. The greater the number of such connections, the more difficult it becomes to prevent failure.

According to a DevOps survey, testing microservices is a challenge for 72% of engineering teams.

Inter-service Dependency

Each individual service is dependent on another for its proper functioning. The more services there are, the higher the number of inter-service communications that might fail. In this complex web of inter-service communications, a breakdown in any of the services has a cascading effect on all others dependent on it.

Calls between services can go through many layers, making it hard to understand how they depend on each other. If the nth dependency has a latency spike, it can cause a chain of problems further upstream.

Consider a retail e-commerce application composed of microservices like user authentication, product catalog, shopping cart, and payment processing. If the product catalog service is updated or fails, it can affect the shopping cart and payment services, leading to a cascading failure. Testing must account for these dependencies and the ripple effect of changes.

Data Management

Managing data in a microservices architecture can be a complex task. With services operating independently, data may be stored in various databases, data lakes, or data warehouses. Managing data consistency across services can be challenging, and errors can occur, which can cause significant problems.

Customer data may be stored in several databases, and ensuring data consistency can be challenging. For example, if a customer updates their details, the change must reflect in all databases.

Ensuring data consistency across different microservices, which might use different databases, is challenging. Testing must cover scenarios where data needs to be synchronized or rolled back across services.

An e-commerce application uses separate microservices for order processing and inventory management. Tests must ensure that when an order is placed, the inventory is updated consistently, even if one of the services temporarily fails.

class OrderService:
    def process_order(order_id, product_id, quantity):
        # Process the order
        try:
            InventoryService.update_inventory(product_id, -quantity)
            Database.commit()  # Commit both order processing and inventory update
        except InventoryUpdateFailure:
            Database.rollback()  # Rollback the transaction in case of failure
            raise OrderProcessingFailure("Failed to process order due to inventory issue.")

class InventoryService:
    def update_inventory(product_id, quantity_change):
        # Update the inventory
        if not InventoryDatabase.has_enough_stock(product_id, quantity_change):
            raise InventoryUpdateFailure("Not enough stock.")
        InventoryDatabase.update_stock(product_id, quantity_change)

class Database:
    @staticmethod
    def commit():
        # Commit the transaction
        pass

    @staticmethod
    def rollback():
        # Rollback the transaction
        pass

# Exception classes for clarity
class InventoryUpdateFailure(Exception):
    pass

class OrderProcessingFailure(Exception):
    pass

# Example usage
order_service = OrderService()
try:
    order_service.process_order(order_id="1234", product_id="5678", quantity=1)
    print("Order processed successfully.")
except OrderProcessingFailure as e:
    print(f"Error: {e}")

Communication and Coordination between services

The microservices architecture approach involves many services communicating with each other to provide the desired functionality. Services communicate with each other through APIs. Service coordination is essential to ensuring that the system works correctly. Testing communication and coordination between services can be challenging, especially when the number of services increases.

Diverse Technology Stacks

The challenge of a diverse technology stack in microservices testing stems from the inherent nature of microservices architecture, where each service is developed, deployed, and operated independently. This autonomy often leads to the selection of different technologies best suited for each service's specific functionality. While this flexibility is a strength of microservices, it also introduces several complexities in testing.

👉Expertise in Multiple Technologies

👉Environment Configuration

👉Integration and Interface Testing

👉Automated Testing Complexity

👉Error Diagnosis and Troubleshooting

👉Consistent Quality Assurance

A financial services company uses different technologies for its microservices; some are written in Java, others in Python, and some use different databases. This diversity requires testers to be proficient in multiple technologies and complicates the setup of testing environments.

Finding the root cause of failure

When multiple services talk to each other, a failure can show up in any service, but the cause of that problem can originate from a different service deep down. Doing RCA for the failure becomes extremely tedious, time-consuming and high effort for teams of these distributed systems.

Uber has over 2200 microservices in its web of interconnected services; if one service fails, all upstream services suffer the consequences. The more services there are, the more difficult it is to find the one that originated the problem.

Unexpected Functional changes

Uber decided to move to a distributed code base to break down application logic into several small repositories that can be built and deployed with speed.

Though this gave teams the flexibility to make frequent changes, it also increased the speed at which new failures were introduced.

A study by Dimensional Research found that the average cost of an hour of downtime for an enterprise is $300,000, highlighting the importance of minimizing unexpected functionality changes in microservices.

So these rapid and continuous code changes, makes multi-repo systems more vulnerable to unintended breaking failures like latency, data manipulation etc.

Difficulty in localizing the issue

Each service is autonomous, but when it breaks, the failure it triggers can propagate far and wide, with damaging effects.

This means the failure can show up elsewhere, but the trigger could be several services upstream. Hence, identifying and localizing the issue is very tedious, sometimes impossible without the right tools.

How to overcome such challenges?

Challenges like complexity and inter-service dependency are inherent to microservices. To tackle such intricacies, the conventional testing approach won’t work for testing these multi-repo systems.

Since microservices themselves offer smarter architecture, testing them also needs a tailored approach. The usual method that follows unit testing, integration testing, and end-to-end testing won’t be the right one. The unit tests depend largely on mocks, making them less reliable, whereas E2E testing unnecessarily requires the whole system up and running as they test the complete user flow, leaving them tedious and expensive.

You can find here how a tailored approach to test these independent services will help you take all these challenges away. A slight deviation from the traditional testing pyramid to a more suitable test pyramid for microservices is needed.

The Solution Approach

Microservices have a consumer-provider relationship between them. In a consumer-provider model, one microservice (the consumer) relies on another microservice (the provider) to perform a specific task or provide a specific piece of data.

The consumer and provider communicate with each other over a network, typically using a well-defined API to exchange information. This means the consumer service could break irreversibly if the downstream service (provider) changes its response that the consumer is dependent on.

So an approach that focuses on testing these contract schema between APIs to ensure the smooth functioning of services is needed. The easiest way to achieve this is to test every service independently for contracts [+data], by checking the API response of the service.

In recent years, the trend of adopting microservices architecture has been increasing among organizations. This approach allows developers to build and deploy applications more quickly, enhance scalability, and promote flexibility.

The HyperTest Way to Approach Microservices Testing

HyperTest is a unique solution to run these contract[+data] tests or integration tests that can test end-to-end scenarios. It works on Real-time traffic replication (RTR), which monitors real user activity from production using a SDK set-up in your repo and automatically converts real-world scenarios into testable cases. These can be run locally or via CI to catch first-cut regressions and errors before a merge request moves to production.

It implements these modes to test services:

👉Record Mode

👉Replay/ Test Mode

Learn more about this approach here.

• HyperTest is an API test automation platform that helps teams generate and run integration tests for their microservices without ever writing a single line of code.

• It can use your application traffic to build integration tests in hours or days that can take teams months, if not years, to build.

• Not just that this builds very high coverage without effort, it by design makes it impossible for teams to introduce a breaking change or failure in your apps that is not first reported by HyperTest.

HyperTest localizes the root cause of the breaking change to the right service very quickly, saving debugging time.

5 Best Practices For Microservices Testing

Microservices testing is a critical aspect of ensuring the reliability and performance of applications built using this architectural style. Here are five best practices for microservices testing, each accompanied by an example for clarity:

1. Implement Contract Testing

Contract testing ensures that microservices maintain consistent communication. It involves validating the interactions between different services against a contract, which defines how these services should communicate.

Imagine a shipping service and an order service in an e-commerce platform. The order service expects shipping details in a specific format from the shipping service. Contract testing can be used to ensure that any changes in the shipping service do not break this expected format.

2. Utilize Service Virtualization

Service virtualization involves creating lightweight, simulated versions of external services. This approach is useful for testing the interactions with external dependencies without the overhead of integrating with the actual services.

In a banking application, virtualized services can simulate external credit score checking services. This allows testing the loan approval microservice without the need for the actual credit score service to be available.

3. Adopt Consumer-Driven Contract (CDC) Testing

CDC testing is a pattern where the consumers (clients) of a microservice specify the expectations they have from the service. This helps in understanding and testing how consumers interact with the service.

A mobile app (consumer) that displays user profiles from a user management microservice can specify its expected data format. The user management service tests against these expectations, ensuring compatibility with the mobile app.

4. Implement End-to-End Scenario Testing

End-to-end scenario testing involves testing the entire application. It's crucial for ensuring that the entire system functions correctly as a whole.

A tool like HyperTest works perfect for implementing this approach where all the scenarios will be covered without the need to keep the db, other services up and running.

5. Continuous Integration and Testing

Continuously integrating and testing microservices as they are developed helps catch issues early. This involves automating tests and running them as part of the continuous integration pipeline whenever changes are made.

A content management system with multiple microservices for article creation, editing, and publishing could use a CI/CD pipeline. Automated tests run each time a change is committed, ensuring that the changes don't break existing functionality.

By following these best practices, teams can significantly enhance the quality and reliability of microservices-based applications. Each practice focuses on a different aspect of testing and collectively they provide a comprehensive approach to effectively handle the complexities of microservices testing.

Conclusion

Contract [+data] tests are-the optimal solution to test distributed systems. These service level contract tests are simple to build and easy to maintain, keeping the microservices in a 'releasable' state.

As software systems become more complex and distributed, testing each component individually and as part of a larger system can be a daunting task. We hope this piece has helped you with your search of finding the optimal solution to test your microservices. Download the ultimate testing guide for your microservices.

Schedule a demo here to see how HyperTest fits in your software and never allows bugs to slip away.