This workload is overwhelmingly read-heavy and repeatedly queries the same relatively static reference data (postal codes mapped to coordinates). The best way to reduce latency and offload Amazon RDS is to introduce an application-side cache using a lazy loading (cache-aside) pattern and choose a TTL that reflects how often the underlying data changes.
With lazy loading, the application first checks the cache for the postal code. If the value is present (a cache hit), the application returns the coordinates immediately with low latency and without querying the database. If the value is absent (a cache miss), the application reads from RDS, returns the result, and then populates the cache for subsequent requests. This pattern is operationally simple and widely used because the application controls what is cached and when.
A large TTL is appropriate here because postal-code-to-coordinate mappings typically do not change frequently. A longer TTL maximizes cache hit ratio, which is critical because each postal code is requested thousands of times per day. High cache hit rates reduce database read load, decrease query contention, and substantially improve response times. In contrast, a small TTL would cause frequent expirations and repeated database lookups, reducing the performance benefit and increasing database load.
Write-through and write-behind caching (options C and B) are designed to coordinate caching with writes. They are useful when write consistency and write performance are central concerns. Here, writes are rare; the performance issue is repeated reads. Write-behind can also introduce consistency delays, which is unnecessary for this scenario.
Therefore, the best change is D: implement a lazy loading (cache-aside) caching strategy with a large TTL to keep hot, rarely changing postal code lookups in cache and dramatically reduce read latency and database load.
