API Caching Strategies [Redis]

API caching is the practice of storing the result of expensive or frequently repeated operations, then serving that stored result for later requests until it expires or is invalidated. In a NestJS application, that can mean caching full HTTP responses, specific service-layer queries, or derived dashboard-style aggregates.
For this project, caching matters because event listing and event detail endpoints often have read-heavy traffic patterns. Without caching, the application repeats the same database queries, serialization work, and network round-trips even when the underlying data has not changed.
The main benefits are:

• Lower latency: cached responses return much faster than recomputing the same result on every request.
• Reduced database load: fewer duplicate reads reach PostgreSQL, which helps preserve capacity for writes and complex queries.
• Better scalability: the API can handle more concurrent traffic before CPU, I/O, or database bottlenecks appear.
• More predictable performance: hot endpoints stay responsive during traffic spikes because repeated requests reuse previously computed results.
• Improved cost efficiency: less repeated work means better use of container, database, and infrastructure resources.