Caching is a vital performance optimization in modern web applications. It helps reduce response times, offload expensive computations, and alleviate pressure on your databases. Spring Boot makes caching straightforward and extensible — and when combined with distributed in-memory data stores like Redis and Hazelcast, you gain speed, scalability, and resilience.

In this guide, we explore caching strategies using Spring Boot, Redis, and Hazelcast. You’ll learn when and how to cache, choose the right provider, and avoid common pitfalls.

Why Use Caching?

Caching improves performance by storing frequently accessed data in memory instead of fetching it repeatedly from slower sources like databases or APIs.

Benefits of caching in Java apps:

  • Faster response time
  • Lower latency for critical endpoints
  • Reduced database load
  • Better scalability under high load

Enabling Caching in Spring Boot

Start by enabling caching support in your application:

@SpringBootApplication
@EnableCaching
public class Application { }

Spring Boot supports multiple caching providers, including:

  • Simple in-memory cache (default)
  • Redis
  • Hazelcast
  • Ehcache
  • JCache (JSR-107)

Using Cache Annotations

The Spring Cache abstraction provides a simple annotation-based API:

@Cacheable("users")
public User getUserById(Long id) {
return userRepository.findById(id).orElseThrow();
}

Other useful annotations:

  • `@CachePut` – updates the cache
  • `@CacheEvict` – removes data from the cache
  • `@Caching` – group multiple operations
@CacheEvict(value = "users", key = "#id")
public void deleteUser(Long id) {
userRepository.deleteById(id);
}

Integrating Redis as Cache Provider

Redis is a fast, persistent, key-value store often used for caching.

Add dependency:

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

Configure Redis in `application.yml`:

spring:
cache:
type: redis
redis:
host: localhost
port: 6379

Spring Boot automatically uses Redis as the cache backend.

Customize serialization (e.g., use JSON instead of JDK serialization):

@Bean
public RedisCacheConfiguration cacheConfiguration() {
return RedisCacheConfiguration.defaultCacheConfig()
.serializeValuesWith(SerializationPair.fromSerializer(new GenericJackson2JsonRedisSerializer()));
}

Integrating Hazelcast as a Distributed Cache

Hazelcast is a distributed in-memory computing platform with built-in clustering and auto-discovery.

Add Hazelcast dependency:

<dependency>
<groupId>com.hazelcast</groupId>
<artifactId>hazelcast-spring</artifactId>
</dependency>

Set Spring to use Hazelcast:

spring:
cache:
type: hazelcast

Configure Hazelcast:

@Bean
public Config hazelcastConfig() {
Config config = new Config();
config.setInstanceName("hazelcast-instance")
.addMapConfig(new MapConfig()
.setName("users")
.setTimeToLiveSeconds(600));
return config;
}

Hazelcast supports clustering, so cached data is shared across nodes.

Choosing Between Redis and Hazelcast

Feature Redis Hazelcast
Persistence Optional RDB/AOF In-memory only (unless IMDG)
Clustering Master-replica + Sentinel Peer-to-peer
TTL Support Built-in per-key TTL Per-map TTL
Serialization Configurable, supports JSON Uses Java serialization by default
Use Case Web caching, queues, sessions Distributed data grids, sessions

Choose Redis when:

  • You want fast, persistent, and standalone caching
  • You’re already using Redis for sessions, queues, etc.

Choose Hazelcast when:

  • You need cluster-wide caching with Java-native integration
  • You want simple discovery between Spring Boot nodes

Expiration and Eviction Policies

Both Redis and Hazelcast support TTL and eviction settings.

  • Use short TTLs for dynamic data
  • Set max size to avoid memory overuse
  • Evict least-recently-used (LRU) entries when full
@Cacheable(value = "users", key = "#id", unless = "#result == null")
public User getCachedUser(Long id) { ... }

Caching Best Practices

  • Use cache abstraction, not direct Redis APIs, for flexibility
  • Avoid caching mutable or sensitive data
  • Test cache invalidation paths
  • Log and monitor cache hit/miss ratios
  • Combine with Spring AOP for cross-cutting cache behavior

Conclusion

Caching is one of the most impactful techniques for boosting application performance. By leveraging Spring Boot with Redis and Hazelcast, you can build caching layers that scale with your app.

Whether you choose Redis for simplicity and speed or Hazelcast for distributed clustering, Spring Boot makes integration easy and extensible — so you can focus on delivering fast, responsive user experiences.