How do you choose between REST API caching and database caching?

For example, when a client (such as a browser) submits a REST API call, it has the option of caching the API answer. The server doesn't have to process that request twice when the client makes it again, so the response time is quicker. Coming to benefits: Few of them are as follows; -Your level of service increases. -There is reduced bandwidth use on the website. -The latency of the webpage decreases. -Also, the server load is reduced.

REST API caching involves storing the responses from a RESTful API request and reusing them for subsequent requests instead of fetching the data again from the server. ? How it Works: The server includes caching information (such as expiration time) in the HTTP headers of the API response. The client (or intermediary caching server) can store a copy of the response and reuse it until it expires or is invalidated. ? Key Points: Caching occurs at the API level, reducing the need for redundant requests to the server. Improves performance by minimizing data transfer and server load.

REST API caching is a technique used to store responses from API requests, aiming to boost performance and lighten the load on servers. This can occur either on the client-side or server-side, leveraging various methods such as browser caching, edge caching, reverse proxy caching, and application-level caching. Cache-Control headers are utilized to define caching behavior, while mechanisms for cache invalidation ensure that cached responses stay accurate. Overall, caching helps by serving cached responses instead of processing redundant requests, which ultimately improves performance and scalability.

REST API caching which is every incoming request through network, is already captured with parameters and unified resource identity in cache server, and how to manage those cache or what is invalid cache depend on situation.

Caches are high-speed data storage layers that keep a subset of the data that is frequently requested for reading. Future queries for this data are fulfilled more quickly to the transitory storage layer than by directly querying the main database.

Database caching involves storing frequently accessed database query results or objects in a cache to reduce the need for repeated database queries. ? How it Works: Query results or objects are stored in a cache, and subsequent requests can be served directly from the cache rather than querying the database. ? Key Points: Caching occurs at the database level, optimizing the retrieval of data. Effective for read-heavy applications where the same data is queried frequently.

? Improved Performance: Reduces latency by serving cached responses, minimizing the time needed to retrieve data from the server. ? Reduced Server Load: Fewer requests reaching the server means less load on the server, leading to improved scalability. ? Bandwidth Savings: Minimizes data transfer between client and server as cached responses can be reused. ? Enhanced User Experience: Faster response times contribute to a better user experience, especially for applications with real-time requirements.

A real-world system would likely use a mix of both techniques, tailored to specific APIs and their usage patterns. Some examples: High-frequency trading: Every millisecond counts. API-level caching of market data minimizes roundtrips to the database, giving traders that edge. Stale data is unacceptable. Reconciliation system: Overnight batch processing? Database caching likely offers great efficiency gains. It reduces load during peak hours and allows reads from a replicated source.