feat: enhance catalog and recommendation services with Redis caching

[TimilsinaBimal]

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Project	Deployment	Review	Updated (UTC)
watchly	Ready	Preview, Comment	Dec 30, 2025 10:03am

[gemini-code-assist]

Summary of Changes

Hello @TimilsinaBimal, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a comprehensive caching layer using Redis for the catalog and recommendation services. The primary goal is to enhance performance by storing frequently accessed and computationally expensive data, such as user library items, taste profiles, and watched content sets. By leveraging Redis, the system can retrieve this data much faster, leading to a more responsive user experience and reduced load on backend services.

Highlights

• Redis Caching for Library Items: User library items are now fetched once and cached in Redis, reducing redundant calls to external services and improving response times for catalog and recommendation services.
• Redis Caching for User Profiles and Watched Sets: Computationally intensive user taste profiles and watched content sets (TMDB and IMDb IDs) are now built and cached in Redis. This significantly speeds up the generation of personalized recommendations.
• Optimized Library Item Processing: The system now explicitly ignores 'removed' items when processing Stremio library updates, streamlining the data handling process.

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[gemini-code-assist]

Code Review

This pull request introduces Redis caching for catalog and recommendation data, which should significantly improve performance by reducing repeated computations and API calls. The implementation correctly separates the caching logic in the catalog_updater service (as a cache writer) and the recommendation service (as a cache reader with a fallback). My review includes a few suggestions to enhance the changes: one high-severity issue regarding redundant service initialization that impacts performance, and two medium-severity suggestions focused on improving maintainability by reducing code duplication and cleaning up now-unused code.

[TimilsinaBimal]

/gemini review

[gemini-code-assist]

Code Review

This pull request significantly improves the application by refactoring the manifest generation into a dedicated ManifestService and introducing Redis caching for catalog and recommendation data, which should greatly enhance performance. The pre-caching of user data upon token creation is a thoughtful touch for a better user experience.

My review focuses on a few key areas for improvement:

• Code Duplication: There's a fair amount of duplicated caching logic across ManifestService, CatalogUpdater, and CatalogService. Centralizing this logic would improve maintainability.
• Redis Usage: I've pointed out opportunities to use constants for Redis keys instead of magic strings and to batch Redis commands for better performance.
• Code Clarity: A minor suggestion to clean up some now-unused code in the Stremio library service.

Overall, this is a strong contribution that moves the application in the right direction. Addressing these points will make the new caching layer even more robust and easier to maintain.

[TimilsinaBimal]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces a significant and well-designed caching layer using Redis, which should greatly enhance the performance of catalog and recommendation services. The refactoring is excellent, with the introduction of ManifestService and UserCacheService leading to better separation of concerns and improved code organization. The pre-caching of user data upon token creation is a smart optimization. I've identified a few areas for improvement, including a security concern with logging unredacted tokens and a potential regression in cache TTL handling for catalogs.

[TimilsinaBimal]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces a significant and well-executed enhancement by adding a Redis caching layer to the catalog and recommendation services. The refactoring is excellent, particularly the introduction of ManifestService and UserCacheService, which greatly improves code organization, separation of concerns, and maintainability. The proactive caching strategy during token creation is a smart move to enhance user experience by speeding up initial manifest loads. The cache invalidation logic appears robust, ensuring data consistency. I've identified one critical bug that would prevent catalog caching from working and a medium-severity issue regarding logging consistency. Once these are addressed, this will be a solid improvement to the application's performance and architecture.

[TimilsinaBimal]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces a significant and well-executed refactoring to implement Redis caching for catalog and recommendation services. The logic is moved from API endpoints into dedicated services (ManifestService, UserCacheService), improving separation of concerns and maintainability. Caching is implemented at multiple levels (catalogs, user library items, user profiles) to optimize performance. The changes are solid, but I have a few suggestions to further improve code quality and performance by refining a caching utility function to avoid redundant operations and using more specific exception handling.

[TimilsinaBimal]

/gemini review

[gemini-code-assist]

Code Review

This is an excellent pull request that introduces a comprehensive and well-designed Redis caching layer for the catalog and recommendation services. The refactoring to create dedicated services like ManifestService and UserCacheService greatly improves the architecture, separation of concerns, and maintainability. The multi-level caching strategy is well-implemented and should provide significant performance benefits.

I have two main points of feedback:

• A critical issue in manifest.py where None user settings are not handled correctly, which could lead to runtime errors.
• A medium-severity suggestion to improve the performance of delete_by_pattern in redis_service.py by batching delete operations.

Overall, this is a very strong contribution. Addressing these points will make it even more robust.