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Backend Challenge - Distributed Caching System API

Introduction

The "Distributed Caching System API" challenge focuses on building a RESTful API that allows users to manage distributed caching for improved performance and scalability of applications.

Objectives

  • Set up a web server capable of handling API requests for caching operations.
  • Implement endpoints for storing, retrieving, updating, and deleting cached data.
  • Support distributed caching strategies for handling high-volume data access.
  • Understand HTTP methods (GET, POST, PUT, DELETE) and status codes.

Instructions

  1. Objective: Develop a Distributed Caching System API that provides functionality for managing cached data.

  2. Environment Setup: Choose your preferred programming language (e.g., JavaScript, Python, Java, Go) and set up the necessary environment.

  3. Implementation Details:

    • Define endpoints:
      • PUT /cache/{key}: Store data in the cache with a specified key.
      • GET /cache/{key}: Retrieve data from the cache based on the key.
      • POST /cache/{key}: Update existing data in the cache with a specified key.
      • DELETE /cache/{key}: Delete data from the cache based on the key.
    • Implement support for distributed caching strategies:
      • Choose a caching mechanism (e.g., Redis, Memcached) suitable for distributed environments.
      • Handle cache synchronization and consistency across multiple nodes or instances.
      • Implement caching policies such as expiration times and eviction strategies.
    • Ensure fault tolerance and error handling for cache operations and distributed environment failures.
    • Implement authentication and authorization mechanisms to restrict access to caching endpoints.

  4. Testing: Test your API using a HTTP client (e.g., Postman, curl).

    • Store, retrieve, update, and delete cached data through API endpoints.
    • Validate cache consistency and synchronization across distributed nodes.
    • Verify API responses include appropriate status codes and error handling for edge cases.

Possible Improvements

  • Cache Invalidation: Implement strategies for cache invalidation based on data updates or expiration policies.
  • Monitoring: Integrate with monitoring tools to track cache performance metrics (e.g., hit rate, miss rate).
  • Encryption: Implement data encryption for sensitive cached data.
  • Scaling: Optimize cache performance for scalability by adding or removing cache nodes dynamically.
  • Integration: Integrate with application frameworks (e.g., Spring Boot, Django) for seamless integration with existing systems.

Conclusion

By completing this challenge, you will gain practical experience in developing a Distributed Caching System API and learn essential practices for backend development related to caching and performance optimization. Explore additional improvements and challenges to further enhance your skills.

Happy coding!