Redis Caching

Introduction

Redis is an in-memory data store used for caching, session storage, rate limiting and pub/sub. In Spring Boot, spring-boot-starter-data-redis + @Cacheable makes caching a one-annotation affair.

Informative example

Cache-aside with Spring Cache + Redis:

@Configuration
@EnableCaching
public class CacheConfig {
    @Bean RedisCacheManager cacheManager(RedisConnectionFactory f) {
        return RedisCacheManager.builder(f)
            .cacheDefaults(defaultConfig().entryTtl(Duration.ofMinutes(10)))
            .build();
    }
}

@Service
public class ProductService {
    @Cacheable(value = "products", key = "#id")
    public Product findById(Long id) {
        return repo.findById(id).orElseThrow();  // hits DB only on miss
    }

    @CacheEvict(value = "products", key = "#product.id")
    public Product update(Product product) { return repo.save(product); }
}

Best practices

• Set TTL on every cache — stale data is worse than a cache miss.
• Cache the result of expensive reads, not writes.
• Use Redis Cluster for production HA.

Purpose of this lesson

Master Redis Caching so you can apply it confidently in production Java code, technical interviews, and code reviews.

Step-by-step explanation

1. Understand the core idea behind Redis Caching.
2. Walk through the runnable example and tweak it in the playground.
3. Apply the pattern in a small Spring Boot or CLI exercise of your own.
4. Re-read the common mistakes and interview Q&A to lock the concept in.

Interactive workflow diagram

Debugging tips

• Read the full stack trace — Java's exception messages name the offending class and line.
• Reproduce in the smallest possible main() method before fixing in the real app.
• Use IntelliJ's debugger breakpoints and 'Evaluate Expression' rather than scattering System.out.

Optimization strategies

• Avoid caching large objects — Redis memory is expensive. Compress or store IDs and lazy-load.

Enterprise example

GitHub uses Redis for rate limiting and caching hot repository metadata — sub-ms reads at billion-request scale.

Interview questions & answers

Summary

In this lesson you learned Redis Caching — the concept, syntax, a runnable example, and the production pitfalls to avoid. Apply it in the playground before moving on.