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rrezartprebreza/spring-boot-skills34 installs

spring-data-redis

Use when implementing caching, session storage, rate limiting, or any Redis integration. Covers cache-aside pattern, key naming, TTL strategy, and serialization config.

How do I install this agent skill?

npx skills add https://github.com/rrezartprebreza/spring-boot-skills --skill spring-data-redis
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubwarn

    This skill provides templates and instructions for implementing Redis caching in Spring Boot applications. However, it contains a security vulnerability in its Java configuration template where insecure JSON deserialization is enabled. This configuration could allow an attacker to execute arbitrary code on the server if they are able to manipulate the data stored in the Redis cache.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

What does this agent skill do?

Spring Data Redis

Dependencies

<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>

Configuration

@Configuration
@EnableCaching
public class RedisConfig {

    @Bean
    public RedisTemplate<String, Object> redisTemplate(RedisConnectionFactory factory) {
        RedisTemplate<String, Object> template = new RedisTemplate<>();
        template.setConnectionFactory(factory);
        template.setKeySerializer(new StringRedisSerializer());
        template.setValueSerializer(new GenericJackson2JsonRedisSerializer()); // JSON, not Java serialize
        template.setHashKeySerializer(new StringRedisSerializer());
        template.setHashValueSerializer(new GenericJackson2JsonRedisSerializer());
        return template;
    }

    @Bean
    public RedisCacheManager cacheManager(RedisConnectionFactory factory) {
        RedisCacheConfiguration config = RedisCacheConfiguration.defaultCacheConfig()
            .entryTtl(Duration.ofMinutes(10))
            .serializeKeysWith(RedisSerializationContext.SerializationPair.fromSerializer(new StringRedisSerializer()))
            .serializeValuesWith(RedisSerializationContext.SerializationPair.fromSerializer(new GenericJackson2JsonRedisSerializer()))
            .disableCachingNullValues();

        return RedisCacheManager.builder(factory)
            .cacheDefaults(config)
            .withCacheConfiguration("orders", config.entryTtl(Duration.ofMinutes(5)))
            .withCacheConfiguration("products", config.entryTtl(Duration.ofHours(1)))
            .build();
    }
}

Key Naming Convention

{app}:{domain}:{id}          → orders:order:uuid-here
{app}:{domain}:list:{filter} → orders:order:list:status:PENDING
{app}:session:{userId}       → orders:session:uuid-here
{app}:ratelimit:{ip}         → orders:ratelimit:192.168.1.1

@Cacheable — Declarative Caching

@Service
@RequiredArgsConstructor
public class ProductService {

    @Cacheable(value = "products", key = "#id")
    public ProductResponse findById(UUID id) {
        return productRepository.findById(id)
            .map(ProductResponse::from)
            .orElseThrow(() -> new EntityNotFoundException("Product not found: " + id));
    }

    @CachePut(value = "products", key = "#result.id")  // update cache after write
    @Transactional
    public ProductResponse update(UUID id, UpdateProductRequest request) {
        Product product = productRepository.findById(id).orElseThrow();
        product.update(request);
        return ProductResponse.from(productRepository.save(product));
    }

    @CacheEvict(value = "products", key = "#id")  // invalidate on delete
    @Transactional
    public void delete(UUID id) {
        productRepository.deleteById(id);
    }

    @CacheEvict(value = "products", allEntries = true)  // clear all
    public void clearCache() {}
}

Manual Cache-Aside Pattern

@Service
@RequiredArgsConstructor
public class OrderCacheService {

    private final RedisTemplate<String, Object> redisTemplate;
    private final ObjectMapper objectMapper;
    private static final Duration TTL = Duration.ofMinutes(5);

    public Optional<OrderResponse> get(UUID orderId) {
        String key = "orders:order:" + orderId;
        Object cached = redisTemplate.opsForValue().get(key);
        if (cached == null) return Optional.empty();
        return Optional.of(objectMapper.convertValue(cached, OrderResponse.class));
    }

    public void put(OrderResponse order) {
        String key = "orders:order:" + order.id();
        redisTemplate.opsForValue().set(key, order, TTL);
    }

    public void evict(UUID orderId) {
        redisTemplate.delete("orders:order:" + orderId);
    }
}

Rate Limiting with Redis

@Component
@RequiredArgsConstructor
public class RateLimiter {

    private final RedisTemplate<String, String> redisTemplate;

    public boolean isAllowed(String identifier, int maxRequests, Duration window) {
        String key = "ratelimit:" + identifier;
        Long count = redisTemplate.opsForValue().increment(key);
        if (count == 1) {
            redisTemplate.expire(key, window);
        }
        return count <= maxRequests;
    }
}

application.yml

spring:
  data:
    redis:
      host: ${REDIS_HOST:localhost}
      port: ${REDIS_PORT:6379}
      password: ${REDIS_PASSWORD:}
      timeout: 2000ms
      lettuce:
        pool:
          max-active: 10
          max-idle: 5
          min-idle: 2
  cache:
    type: redis

Cache Stampede

When a hot key expires, every concurrent request misses at once and they all hammer the DB to recompute the same value (the "thundering herd"). For expensive, high-traffic loads, let one caller compute while the rest wait:

// sync = true — only one thread computes the value; others block on it
@Cacheable(value = "products", key = "#id", sync = true)
public ProductResponse findById(UUID id) { ... }

sync = true serializes recomputation per key within a single instance. For a fleet-wide guarantee, add a short Redis lock (SETNX with a TTL) around the recompute. Pair with jittered TTLs so a batch of keys written together doesn't all expire on the same second.

Gotchas

  • Agent uses Java serialization for values — always use JSON (GenericJackson2JsonRedisSerializer)
  • Agent caches entities with JPA lazy fields — cache DTOs/response objects, not entities
  • Agent uses no TTL — always set expiry, memory is not infinite
  • Agent forgets @EnableCaching@Cacheable silently does nothing without it
  • Agent caches null values — use .disableCachingNullValues() to avoid storing misses
  • Agent leaves hot keys unprotected — use @Cacheable(sync = true) to prevent stampede on expiry
  • Agent gives every entry the same TTL — add jitter so keys don't expire in a synchronized wave

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

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