naver @RequestCache: Developing a Custom (opens in new tab)
The development of `@RequestCache` addresses the performance degradation and network overhead caused by redundant external API calls or repetitive computations within a single HTTP request. By implementing a custom Spring-based annotation, developers can ensure that specific data is fetched only once per request and shared across different service layers. This approach provides a more elegant and maintainable solution than manual parameter passing or struggling with the limitations of global caching strategies. ### Addressing Redundant Operations in Web Services * Modern web architectures often involve multiple internal services (e.g., Order, Payment, and Notification) that independently request the same data, such as a user profile. * These redundant calls increase response times, put unnecessary load on external servers, and waste system resources. * `@RequestCache` provides a declarative way to cache method results within the scope of a single HTTP request, ensuring the actual logic or API call is executed only once. ### Limitations of Manual Data Passing * The common alternative of passing response objects as method parameters leads to "parameter drilling," where intermediate service layers must accept data they do not use just to pass it to a deeper layer. * In the "Strategy Pattern," adding a new data dependency to an interface forces every implementation to change, even those that have no use for the new parameter, which violates clean architecture principles. * Manual passing makes method signatures brittle and increases the complexity of refactoring as the call stack grows. ### The TTL Dilemma in Traditional Caching * Using Redis or a local cache with Time-To-Live (TTL) settings is often insufficient for request-level isolation. * If the TTL is set too short, the cache might expire before a long-running request finishes, leading to the very redundant calls the system was trying to avoid. * If the TTL is too long, the cache persists across different HTTP requests, which is logically incorrect for data that should be fresh for every new user interaction. ### Leveraging Spring’s Request Scope and Proxy Mechanism * The implementation utilizes Spring’s `@RequestScope` to manage the cache lifecycle, ensuring that data is automatically cleared when the request ends. * Under the hood, `@RequestScope` uses a Singleton Proxy that delegates calls to a specific instance stored in the `RequestContextHolder` for the current thread. * The cache relies on `RequestAttribute`, which uses `ThreadLocal` storage to guarantee isolation between different concurrent requests. * Lifecycle management is handled by Spring’s `FrameworkServlet`, which prevents memory leaks by automatically cleaning up request attributes after the response is sent. For applications dealing with deep call stacks or complex service interactions, a request-scoped caching annotation provides a robust way to optimize performance without sacrificing code readability. This mechanism is particularly recommended when the same data is needed across unrelated service boundaries within a single transaction, ensuring consistency and efficiency throughout the request lifecycle.
