Implementing Bridging APIs

Bridging APIs act as intermediaries between Liferay and external systems, delivering customized, aggregated, and orchestrated API responses. They’re implemented as microservice client extensions and provide a flexible and extensible integration layer. While traditional Liferay headless endpoints primarily expose Liferay-native data, bridging APIs provide these capabilities:

• Aggregate data sources, including Liferay, external services, and proprietary systems.
• Decouple API architectures, mitigating the risk of upgrades disrupting third-party integrations.
• Contextually filter, transform, and enrich API responses.

Leveraging their microservice architecture, these APIs can also generate custom JSON or XML responses tailored to specific needs through selective field inclusion, facilitating data masking and field filtering. These capabilities make bridging APIs particularly beneficial for multi-system integrations, addressing data privacy concerns, and fulfilling unique business logic requirements.

This article explores the core concepts and advantages of producing bridging APIs. For detailed information on microservice client extensions, see Integrating Microservices or enroll in the Mastering Liferay Backend Client Extensions course.

Understanding Bridging

Leveraging Liferay's microservice client extensions, you can create bridging APIs with your preferred third-party tools and technologies. This approach exposes custom web services to extend Liferay's standard headless capabilities and provides a flexible method to create and manage custom APIs. These APIs provide significant utility across a variety of real-world scenarios. Consider these examples:

• Creating unified employee profiles by querying Liferay's user database, enriching the data with attributes from external HR systems, and delivering consolidated profiles.
• Creating unified custom catalog views aggregating product data from multiple e-commerce systems.
• Implementing atomic operations that dynamically update both Liferay and third-party systems, ensuring data consistency.

By leveraging Liferay's existing extensibility model, the bridging microservice can securely interact with Liferay's headless APIs to perform CRUD operations, invoke standalone object actions, utilize batch endpoints, and integrate seamlessly with Liferay’s security model through OAuth 2.0.

There are several strategies for implementing bridging APIs, such as invoking Liferay APIs through OAuth 2.0 access tokens, or integrating third-party APIs and proprietary data sources.

Invoking Liferay APIs via OAuth 2.0 Access Tokens

OAuth 2.0 authorization allows bridging APIs to securely interact with Liferay’s APIs. This enables

• Retrieving Liferay data through headless endpoints.
• Invoking standalone object actions to trigger automation workflows.
• Adding, updating, or deleting object entries and other entities.
• Leveraging Liferay batch APIs to make bulk changes to Liferay data.
• Executing search queries and aggregations using Liferay’s built-in capabilities.

OAuth 2.0 token-based API invocation transforms the bridging API into a powerful orchestration layer, enabling developers to coordinate multiple Liferay operations in a single request.

Integrating Third-Party APIs and Proprietary Data Sources

Bridging APIs extend beyond Liferay, facilitating interaction with diverse external API sources. This includes

• Third-party REST or GraphQL APIs, such as those provided by external CRMs, payment gateways, and analytics tools.
• Proprietary databases or file-based data sources.
• Other business applications requiring synchronization with Liferay.

Important Considerations for Bridging APIs

Since bridging APIs are implemented as microservice client extensions, they empower custom JSON or XML response generation. This level of control enables

• Masking sensitive data from Liferay responses.
• Selectively filtering fields before returning a response.
• Aggregating data from multiple sources into a single API response.

While this level of control provides high flexibility, the freedom of bridging APIs requires careful planning to mitigate potential challenges. Addressing the following considerations and implementing best practices is crucial for ensuring reliability, performance, and maintainability.

Caching and Stale Data

Since bridging APIs can aggregate data from multiple sources, response times can be impacted. Follow these recommendations to mitigate this:

• Implement in-memory caching mechanisms (e.g., Redis, EhCache).
• Define cache expiration policies to avoid serving stale data.
• Use conditional requests and ETags to validate data freshness when querying external sources.

Lazy Data Loading

Retrieving large datasets from multiple sources can degrade performance. To solve this challenge, consider these strategies:

• Implement lazy loading techniques to retrieve only essential data initially and only fetch additional details as needed.
• Enable pagination to manage large datasets effectively.
• Use GraphQL queries to request specific fields instead of full objects.

Clustered Microservice Client Extension Synchronization and Messaging

When deploying bridging APIs in clustered environments, ensure consistency across instances. To achieve this, consider these approaches:

• Use JGroups to facilitate inter-instance messaging and synchronization.
• Persist state changes in a Liferay object, using it as a source of truth for across microservice instances.
• Leverage event-driven architectures using message brokers like Kafka or RabbitMQ.

Non-Blocking Implementations

Microservice implementations should prevent API latency from affecting user experience. To achieve this, consider these strategies:

• Use asynchronous processing with queues or event-driven frameworks.
• Implement non-blocking I/O operations when handling large-scale data processing.
• Leverage reactive programming frameworks like Spring WebFlux to improve responsiveness.

Graceful Error Handling, Recovery, and Notifications

Robust error handling is crucial for API reliability. Ensure your strategy includes these elements:

• Implement retry mechanisms for transient failures (e.g., network timeouts).
• Use circuit breakers (e.g., Resilience4j) to prevent cascading failures.
• Log and monitor errors to ensure error details are available for analysis and resolution.
• Provide meaningful error messages in API responses and notify administrators when critical failures occur.

Conclusion

Bridging APIs offer a powerful method to extend Liferay's capabilities by seamlessly integrating with external systems and data sources. With their microservice architecture,they offer a decoupled approach to API production. This design facilitates data aggregation from diverse sources and the creation of flexible, custom response structures. With proper design and implementation, these APIs provide developers with a robust toolset to enhance Liferay’s headless ecosystem, ensuring secure, maintainable, and extensible solutions.