I’ve been working on a high-traffic e-commerce application recently, and the database was struggling under load during peak sales. Queries that usually took milliseconds were stretching into seconds, frustrating users and risking lost revenue. That’s when I turned to distributed caching with Redis and Spring Boot to solve these performance bottlenecks. Let me walk you through how I implemented it, step by step.

Setting up the project is straightforward. I use Maven for dependency management. Here’s the core setup I started with in my pom.xml file. This includes Spring Boot starters for web, data JPA, Redis, and cache support. I also add Jedis for Redis connectivity and H2 for in-memory database testing.

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-data-redis</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-cache</artifactId>
</dependency>

For local development, I run Redis in Docker. A simple docker-compose.yml file sets up Redis with persistence and password protection. This keeps everything isolated and easy to manage. Have you considered how containerization can simplify your cache setup?

Configuring Redis in Spring Boot involves creating connection factories and templates. I define a JedisConnectionFactory with settings for host, port, and password. Then, I set up a RedisTemplate to handle serialization. Using Jackson for JSON makes object storage clean and readable.

@Bean
public RedisTemplate<String, Object> redisTemplate() {
    RedisTemplate<String, Object> template = new RedisTemplate<>();
    template.setConnectionFactory(jedisConnectionFactory());
    Jackson2JsonRedisSerializer<Object> serializer = new Jackson2JsonRedisSerializer<>(Object.class);
    template.setDefaultSerializer(serializer);
    return template;
}

Implementing basic caching starts with the @Cacheable annotation. I apply this to methods that fetch data, like product details. When a method is called, Spring checks the cache first. If the data isn’t there, it executes the method and stores the result. This simple pattern cut my database calls by over 80% in some cases.

What happens when data changes, though? That’s where @CacheEvict comes in. I use it on update or delete methods to remove stale entries. For example, when a product price changes, I evict the old cache entry to ensure fresh data on the next request.

Advanced strategies involve handling cache misses and concurrency. I implement a cache-aside pattern where the application code manages cache reads and writes. This gives fine-grained control but requires careful invalidation. In one project, I added a TTL (time-to-live) to entries to auto-expire old data, preventing memory bloat.

Monitoring is crucial. I integrate Spring Boot Actuator to track cache metrics like hit rates and miss counts. This helps me spot issues early, like when a cache isn’t being used effectively. Did you know that poor cache configuration can sometimes make performance worse?

Testing cached applications requires mocking Redis. I use Testcontainers to spin up a real Redis instance in tests. This ensures my cache logic works as expected in a production-like environment. Here’s a snippet from a test method:

@Test
void testProductCaching() {
    Product product = productService.getProductById(1L);
    // First call hits DB, second should hit cache
    Product cachedProduct = productService.getProductById(1L);
    assertThat(cachedProduct).isEqualTo(product);
}

Common pitfalls include cache penetration, where missing keys cause repeated database hits. I solve this by caching null values or using bloom filters. Another issue is cache stampede—multiple requests updating the cache simultaneously. I use locking or atomic operations to handle this.

As applications scale, consider Redis clustering for high availability. I set up sentinel nodes for failover, ensuring the cache remains available even if a node goes down. This added resilience has saved me during unexpected outages.

In my experience, starting simple and iterating works best. Begin with basic caching on read-heavy endpoints, then expand as needed. Always measure performance before and after to validate improvements.

I hope this guide helps you speed up your applications. If you’ve faced similar challenges or have tips to share, I’d love to hear from you in the comments. Don’t forget to like and share this article if it added value to your development journey!