AWS Aurora, ElastiCache Patterns & DynamoDB — The Complete Data Layer

The article discusses the AWS data layer, focusing on Aurora, ElastiCache, and DynamoDB. It covers two key concepts of Aurora: writer vs reader endpoints and the failover process. Writer Endpoint always points to the current primary/writer instance, handling all write operations. If a failover occurs, Aurora automatically promotes a replica to become the new writer, redirecting the reader endpoint without requiring application configuration changes.

The reader endpoint distributes read-only queries across Aurora Replicas, reducing the load on the writer instance and allowing horizontal scaling of read capacity. In a typical application, reads far outnumber writes; thus, Aurora's reader endpoint improves read performance and scalability.

The article then delves into ElastiCache caching strategies, outlining three primary patterns: Lazy Loading, Write Through, and Session Store. Lazy Loading caches data only on the first read, introducing a potential stale data window. Write Through updates both the cache and database together, ensuring consistency but adding latency and cache memory usage.

Session Store temporarily stores session data like login tokens and user preferences with a Time To Live (TTL), automatically deleting entries when the TTL expires. The choice between these patterns depends on factors such as consistency requirements, read/write ratios, and whether temporary state should be stored in the database.