How to effectively implement a scalable caching strategy in a Spring Boot application with Redis?
Quick question that's been bugging me - I'm running a Spring Boot application (version 2.5.4) that serves a REST API for a high-traffic e-commerce site... We're experiencing latency issues during peak hours, especially when fetching product data from our PostgreSQL database. To alleviate this, I decided to implement caching using Redis (version 6.2.5). I added the following dependency to my `pom.xml`: ```xml <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-data-redis</artifactId> </dependency> ``` Next, I configured Redis in my `application.properties`: ```properties spring.redis.host=localhost spring.redis.port=6379 ``` In my service class, I annotated the method that fetches product data with `@Cacheable`: ```java @Cacheable(value = "products", key = "#productId") public Product getProductById(Long productId) { return productRepository.findById(productId).orElse(null); } ``` However, I'm noticing that not all requests are hitting the cache as expected. Sometimes, I see repetitive database queries in my logs even though I believe products should be cached. For example, when fetching the product with ID 1, I expect subsequent calls to hit Redis, but I see this log: ``` 2023-09-15 12:00:01 INFO com.example.service.ProductService - Fetching product from database for ID 1 ``` I also tried setting the cache expiration time in my configuration: ```properties spring.cache.redis.time-to-live=60000 ``` But it doesn't seem to resolve the issue, as I still see multiple database hits for the same product ID within short intervals. I verified that Redis is running and can be accessed through a Redis client, and I confirmed that the cache entries are being created. I also checked the cache key generation, and everything seems to align with what I expect. Could there be an issue with how I'm using `@Cacheable`? Are there specific configurations or best practices I should follow to ensure efficient caching? Any insights or suggestions would be greatly appreciated! This is part of a larger service I'm building. Is there a better approach?