In-Memory Caching
In-Memory Caching
Our application is growing rapidly. Thousands of users are logging in every minute. Many of them are asking exactly the same questions. "What are today's best-selling products? " "What is my profile? " "What are the latest offers? " Each time, the application sends the request to the database. The database looks up the answer... ... again. ... and again. ... and again. The engineering problem became obvious. Do we really need to query the database every single time for frequently requested data? The engineering concept that solved this problem is In-Memory Caching. Instead of repeatedly asking the database, frequently accessed data is temporarily stored in memory, where it can be retrieved much faster. The database is consulted only when necessary or when the data changes. This reduces database load and significantly improves application performance. Amazon Web Services provides this through ElastiCache, supporting Redis and Memcached. Microsoft Azure provides Azure Cache for Redis. Google Cloud provides Memorystore. Different names. One engineering concept. The database now focuses on storing and updating data, while the cache serves repeated requests at high speed. But another challenge soon emerged. Modern applications no longer consisted of one program. They were made up of many independent services. How do these services communicate without depending on each other?