redis-problem.md

Redis Cache Integration in Microservices Architecture: Documentation for Architectural Review

Objective:

This document outlines the strategy for integrating Redis as a shared cache in the process layer of our microservices architecture to support efficient data sharing and pagination.

Problem Statement:

In the process layer, managing and orchestrating multiple API calls efficiently is crucial. This orchestration requires temporary storage of data to support functionalities like pagination effectively.

Proposed Solution:

• Redis as a Shared Cache in the Process Layer:
  • Data Storage for Orchestration: During the orchestration of API calls, intermediate data is temporarily stored. Redis is utilized to cache these responses, which facilitates quick data retrieval and minimizes the need for repeated API calls.
  • Support for Pagination: To effectively support pagination, it is essential to temporarily store information about each page’s data. Redis caches this information, allowing for quick access and manipulation of page data without the need to re-fetch or re-calculate data from the source systems.
  • Exposing Redis Hash Keys: To optimize data transfer and reduce overhead, Redis hash keys (keyed by requestId or correlationId) are exposed. This method allows direct access to cached data, enabling the experience layer to retrieve necessary data without additional processing or data transfer.
  • Automatic Data Cleanup: Each Redis hash created for storing orchestration and pagination data includes an expiry timestamp. This feature ensures that data is automatically cleaned up after the expiration period, which is typically set between 3 to 5 seconds, depending on the context. This automated cleanup reduces maintenance overhead and ensures the system is not overloaded with stale data.

Benefits:

1. Improved Response Times: Caching responses in Redis reduces the time taken to fetch data on subsequent requests, thereby improving overall response times.
2. Reduced Data Transfer: By exposing Redis hash keys, the system minimizes data transfer between the process and experience layers, leading to increased efficiency and reduced bandwidth consumption.
3. Enhanced Scalability and Performance: Redis’s ability to handle high concurrency and significant workloads makes it ideal for environments that require rapid scaling and high performance.
4. Efficient Resource Management: The automated expiry of data helps in managing the resources efficiently, ensuring that the cache does not retain obsolete data, which could otherwise impact performance.

Conclusion:

The integration of Redis within the process layer serves as a strategic enhancement to our microservices architecture, addressing specific challenges associated with data orchestration and pagination. By leveraging Redis for caching and exposing hash keys for direct access, along with automated data cleanup, we enhance system performance and efficiency, aligning with our architectural goals and business needs.