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Case Study

Integration Challenges Solved: Contract Driven Development and API Specifications to Fulfill Executable Contracts

Overview

There are several challenges to integration testing that can be solved using Contract-Driven Development and API Testing. Using this methodology, our experts ensure testing of integration points within each application are performed in isolation. We check if all messages sent or received through these integration points are in conformance of the documentation or contract.

A contract is a mutually agreed API specification that brings consumers and providers on the same page. What however makes contract-driven API development complex is the way data is often interpreted by both the provider and consumer.

Let’s consider an example where two microservices, Order Service and Payment Service, need to exchange data about an order. The Order Service provides the order details, including the total amount and customer information, while the Payment Service processes payments.

Typical Scenario: When the Order Service sends the order amount as a floating-point number (e.g., 99.99), but the Payment Service expects the amount as an integer representing cents (e.g., 9999).

Expertise Required:

API Contract: Define the API contract specifying that the order amount is sent as a string representing the amount in cents (e.g., “9999”).

Data Transformation: Implement a data transformation layer that converts the floating-point number to the expected integer format before sending the data to the Payment Service.

Validation: Add validation checks to ensure that the order amount is in the correct format before processing payments.

Our Solution: Enabling API Specifications as Executable Contracts

  • Enabled adherence of API specification as an executable contract
  • Defined API specifications at a component level for consumer and provider applications
  • Deployed API specifications as contract test cases
  • Leveraged Automation Testing Tools to check backward compatibility with existing API Consumers/Clients
  • Automated creation of new connections and test cases on introduction of new environment
  • Built API Specifications that are machine learning parsable codes stored in a central version control system

Challenge

Semantic Differences:

  • Microservices may have different interpretations of the same data types, leading to semantic mismatches.
  • For example, one service may interpret a “date” as a Unix timestamp, while another may expect a date in a specific format.

Data Serialization:

  • When services communicate over the network, data must be serialized and deserialized.
  • Different serialization frameworks or libraries may handle data types differently, causing mismatches.

Language-Specific Data Types:

  • Microservices may be implemented in different programming languages with their own data type systems.
  • For example, a string in one language may not map directly to the string type in another language.
  • Versioning and Evolution:
  • Changes to data types over time can lead to compatibility issues between different versions of microservices
  • Adding new fields or changing existing data types can break backward compatibility

Null Handling:

  • Null values may be handled differently across services, leading to unexpected behavior
  • Some services may expect null values, while others may not handle them gracefully

How We Helped

API Contract and Documentation:

  • Clearly defined and document API contracts with agreed-upon data types
  • Specify data formats, units, and constraints in API documentation to ensure consistency

Use Standardized Data Formats:

  • Adopt standardized data formats like JSON Schema or OpenAPI to describe API payloads.
  • Standard formats help ensure that all services understand and interpret data consistently.

Data Transformation Layers:

  • Implement data transformation layers or microservices responsible for converting data between different formats
  • Use tools like Apache Avro or Protocol Buffers for efficient data serialization and deserialization

Shared Libraries or SDKs:

  • Develop and share libraries or SDKs across microservices to ensure consistent handling of data types
  • Centralized libraries can provide functions for serialization, validation, and conversion

Schema Registry:

  • Use a schema registry to centrally manage and evolve data schemas
  • Services can fetch the latest schema from the registry, ensuring compatibility and consistency

Schema Evolution Strategies:

  • Implement schema evolution strategies such as backward compatibility
  • When introducing changes, ensure that older versions of services can still understand and process data from newer versions

Validation and Error Handling:

  • Implement robust validation mechanisms to catch data type mismatches early
  • Provide clear error messages and status codes when data types do not match expected formats

Testing:

  • Conduct thorough testing, including unit tests, integration tests, and contract tests
  • Test scenarios should include data type edge cases to uncover potential mismatches

Versioning and Compatibility:

  • Use versioning strategies such as URL versioning or header versioning to manage changes
  • Maintain backward compatibility when making changes to data types

Code Reviews and Collaboration:

  • Encourage collaboration between teams to review API contracts and data models
  • Conduct regular code reviews to identify and address potential data type mismatches

Runtime Type Checking:

  • Some programming languages offer runtime type checking or reflection mechanisms
  • Use these features to validate data types at runtime, especially when integrating with external services

The Impact

Improved Interoperability: Ensures seamless communication between microservices regardless of the languages or frameworks used.

Reduced Errors: Minimizes the chances of runtime errors and unexpected behavior due to data type inconsistencies.

Faster Integration: Developers spend less time resolving data type issues and can focus on building features.

Easier Maintenance: Centralized data transformation layers and standardized contracts simplify maintenance and updates.

Contract Driven Development

 

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