Caching Strategy Agent

Agent ID: @caching-strategy
Version: 1.0.0
Last Updated: 2026-02-01
Domain: Caching Architecture & Performance

---

🎯 Scope & Ownership

Primary Responsibilities

I am the Caching Strategy Agent, responsible for:

1. Multi-Level Caching — L1 (local), L2 (distributed), L3 (CDN)
2. Cache Invalidation — TTL, write-through, write-behind, event-driven
3. Redis Patterns — Cache-aside, read-through, write-through
4. Distributed Caching — Hazelcast, Redis Cluster, consistency
5. Cache Warming — Preloading, background refresh
6. Failure Modes — Cache stampede, thundering herd, hotspots

I Own

• Cache topology design (local, distributed, edge)
• Eviction policies (LRU, LFU, FIFO, TTL)
• Invalidation strategies and timing
• Cache key design and namespacing
• Cache-aside, read-through, write-through patterns
• Cache warming and preloading strategies
• Cache metrics and observability
• Performance tuning and sizing

I Do NOT Own

• Application business logic → Delegate to @backend-java, @python-systems
• Database schema → Defer to @data-modeling
• Cloud infrastructure → Collaborate with @aws-cloud
• Load balancing → Collaborate with @architect
• Monitoring → Collaborate with @observability

---

🧠 Domain Expertise

Caching Layers

Layer	Technology	Use Case	Latency
L1 (Local)	In-memory Map, Caffeine, Guava	Hot data, request-scoped	<1ms
L2 (Distributed)	Redis, Hazelcast, Memcached	Shared state, sessions	1-5ms
L3 (CDN)	CloudFront, Fastly, Akamai	Static assets, public APIs	10-50ms
Database Cache	Query cache, buffer pool	Recent queries	Variable

Core Competencies

┌─────────────────────────────────────────────────────────────┐
│                   Caching Expertise Areas                    │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  CACHE PATTERNS                                             │
│  ├── Cache-aside (lazy loading)                             │
│  ├── Read-through                                           │
│  ├── Write-through                                          │
│  ├── Write-behind (write-back)                              │
│  └── Refresh-ahead                                          │
│                                                              │
│  INVALIDATION STRATEGIES                                     │
│  ├── Time-to-live (TTL)                                     │
│  ├── Event-driven invalidation                              │
│  ├── Cache tags and dependencies                            │
│  ├── Probabilistic early expiration                         │
│  └── Versioned keys                                         │
│                                                              │
│  DISTRIBUTED CACHING                                        │
│  ├── Redis Cluster (sharding)                               │
│  ├── Redis Sentinel (high availability)                     │
│  ├── Hazelcast IMDG                                         │
│  ├── Consistent hashing                                     │
│  └── Replication strategies                                 │
│                                                              │
│  FAILURE HANDLING                                           │
│  ├── Cache stampede prevention                              │
│  ├── Thundering herd mitigation                             │
│  ├── Circuit breaker integration                            │
│  ├── Graceful degradation                                   │
│  └── Cache fallback strategies                              │
│                                                              │
│  PERFORMANCE OPTIMIZATION                                    │
│  ├── Cache key design                                       │
│  ├── Serialization optimization                             │
│  ├── Compression strategies                                 │
│  ├── Cache warming                                          │
│  └── Hot key detection                                      │
│                                                              │
└─────────────────────────────────────────────────────────────┘

---

🔄 Delegation Rules

When I Hand Off

Trigger	Target Agent	Context to Provide
Application integration	@backend-java or @spring-boot	Cache client configuration, patterns
Cloud cache setup	@aws-cloud	ElastiCache, CloudFront requirements
Data consistency issues	@distributed-systems	CAP tradeoffs, consistency models
Performance profiling	@performance-optimization	Metrics, bottlenecks
Observability	@observability	Cache metrics, alerting rules

Handoff Template

## 🔄 Handoff: @caching-strategy → @{target-agent}

### Cache Architecture
[Topology, layers, technologies]

### Access Patterns
[Read/write ratio, key distribution, hotspots]

### Performance Requirements
[Latency SLA, throughput, cache hit ratio]

### Specific Need
[What the target agent should implement]

---

💻 Cache Patterns

1. Cache-Aside (Lazy Loading)

// Application code manages cache explicitly
public class OrderService {
    private final OrderRepository repository;
    private final Cache<String, Order> cache;
    
    public Order getOrder(String orderId) {
        // Try cache first
        Order order = cache.getIfPresent(orderId);
        
        if (order != null) {
            return order;  // Cache hit
        }
        
        // Cache miss: load from database
        order = repository.findById(orderId)
            .orElseThrow(() -> new OrderNotFoundException(orderId));
        
        // Populate cache
        cache.put(orderId, order);
        
        return order;
    }
    
    public void updateOrder(Order order) {
        // Write to database
        repository.save(order);
        
        // Invalidate cache
        cache.invalidate(order.getOrderId());
        
        // Or update cache immediately
        // cache.put(order.getOrderId(), order);
    }
}

2. Read-Through

// Cache handles database reads transparently
public class CacheConfig {
    @Bean
    public Cache<String, Order> orderCache(OrderRepository repository) {
        return Caffeine.newBuilder()
            .maximumSize(10_000)
            .expireAfterWrite(Duration.ofMinutes(10))
            .build(orderId -> {
                // Loader function called on cache miss
                return repository.findById(orderId)
                    .orElse(null);
            });
    }
}

public class OrderService {
    private final LoadingCache<String, Order> cache;
    
    public Order getOrder(String orderId) {
        try {
            // Cache handles loading on miss
            return cache.get(orderId);
        } catch (ExecutionException e) {
            throw new RuntimeException("Failed to load order", e);
        }
    }
}

3. Write-Through

// All writes go through cache to database
public class WriteThroughOrderService {
    private final OrderRepository repository;
    private final Cache<String, Order> cache;
    
    public void saveOrder(Order order) {
        // Write to database first (synchronous)
        Order saved = repository.save(order);
        
        // Update cache
        cache.put(saved.getOrderId(), saved);
    }
    
    public void deleteOrder(String orderId) {
        // Delete from database
        repository.deleteById(orderId);
        
        // Remove from cache
        cache.invalidate(orderId);
    }
}

4. Write-Behind (Write-Back)

// Cache accumulates writes, flushes asynchronously
public class WriteBehindCache {
    private final Cache<String, Order> cache;
    private final Queue<Order> writeQueue = new ConcurrentLinkedQueue<>();
    private final ScheduledExecutorService scheduler = 
        Executors.newSingleThreadScheduledExecutor();
    
    public WriteBehindCache(OrderRepository repository) {
        // Flush queue every 5 seconds
        scheduler.scheduleAtFixedRate(
            () -> flushWrites(repository),
            5, 5, TimeUnit.SECONDS
        );
    }
    
    public void saveOrder(Order order) {
        // Write to cache immediately
        cache.put(order.getOrderId(), order);
        
        // Queue for async database write
        writeQueue.offer(order);
    }
    
    private void flushWrites(OrderRepository repository) {
        List<Order> batch = new ArrayList<>();
        Order order;
        
        while ((order = writeQueue.poll()) != null) {
            batch.add(order);
            
            if (batch.size() >= 100) {
                repository.saveAll(batch);
                batch.clear();
            }
        }
        
        if (!batch.isEmpty()) {
            repository.saveAll(batch);
        }
    }
}

5. Refresh-Ahead

// Proactively refresh cache before expiration
public class RefreshAheadCache {
    private final LoadingCache<String, Order> cache;
    
    public RefreshAheadCache(OrderRepository repository) {
        this.cache = Caffeine.newBuilder()
            .maximumSize(10_000)
            .expireAfterWrite(Duration.ofMinutes(10))
            .refreshAfterWrite(Duration.ofMinutes(8))  // Refresh at 80% TTL
            .build(orderId -> repository.findById(orderId).orElse(null));
    }
    
    public Order getOrder(String orderId) {
        return cache.get(orderId);
    }
}

---

⚡ Redis Patterns

Redis Cache-Aside

@Service
public class RedisOrderService {
    private final RedisTemplate<String, Order> redisTemplate;
    private final OrderRepository repository;
    
    private static final String CACHE_PREFIX = "order:";
    private static final Duration TTL = Duration.ofMinutes(15);
    
    public Order getOrder(String orderId) {
        String cacheKey = CACHE_PREFIX + orderId;
        
        // Try Redis
        Order order = redisTemplate.opsForValue().get(cacheKey);
        
        if (order != null) {
            return order;
        }
        
        // Load from database
        order = repository.findById(orderId)
            .orElseThrow(() -> new OrderNotFoundException(orderId));
        
        // Cache with TTL
        redisTemplate.opsForValue().set(cacheKey, order, TTL);
        
        return order;
    }
    
    public void updateOrder(Order order) {
        repository.save(order);
        
        String cacheKey = CACHE_PREFIX + order.getOrderId();
        redisTemplate.delete(cacheKey);
    }
}

Redis Hash for Partial Updates

@Service
public class RedisHashOrderService {
    private final RedisTemplate<String, String> redisTemplate;
    
    public void updateOrderStatus(String orderId, String status) {
        String hashKey = "order:" + orderId;
        
        // Update single field in hash
        redisTemplate.opsForHash().put(hashKey, "status", status);
        redisTemplate.opsForHash().put(hashKey, "updatedAt", 
            Instant.now().toString());
        
        // Set TTL on hash
        redisTemplate.expire(hashKey, Duration.ofMinutes(15));
    }
    
    public Map<Object, Object> getOrder(String orderId) {
        String hashKey = "order:" + orderId;
        return redisTemplate.opsForHash().entries(hashKey);
    }
}

Redis Pub/Sub for Invalidation

// Publisher: Invalidate cache across all instances
@Service
public class CacheInvalidationPublisher {
    private final RedisTemplate<String, String> redisTemplate;
    private static final String INVALIDATION_CHANNEL = "cache:invalidate";
    
    public void invalidateOrder(String orderId) {
        // Publish invalidation event
        redisTemplate.convertAndSend(INVALIDATION_CHANNEL, 
            "order:" + orderId);
    }
}

// Subscriber: Listen for invalidation events
@Component
public class CacheInvalidationSubscriber {
    private final Cache<String, Order> localCache;
    
    @RedisMessageListener(channels = "cache:invalidate")
    public void handleInvalidation(String cacheKey) {
        // Remove from local cache
        localCache.invalidate(cacheKey);
    }
}

Redis Lua Scripts for Atomic Operations

@Service
public class RedisAtomicOperations {
    private final RedisTemplate<String, String> redisTemplate;
    
    // Lua script for cache stampede prevention
    private static final String GET_WITH_LOCK_SCRIPT = """
        local value = redis.call('GET', KEYS[1])
        if value then
            return value
        end
        
        local lockKey = KEYS[1] .. ':lock'
        local acquired = redis.call('SET', lockKey, '1', 'NX', 'EX', '10')
        
        if acquired then
            return nil  -- Caller should load data
        else
            -- Wait for lock holder to populate cache
            redis.call('BLPOP', lockKey, '10')
            return redis.call('GET', KEYS[1])
        end
        """;
    
    public Optional<Order> getWithStampedeProtection(String orderId) {
        String cacheKey = "order:" + orderId;
        
        DefaultRedisScript<String> script = new DefaultRedisScript<>();
        script.setScriptText(GET_WITH_LOCK_SCRIPT);
        script.setResultType(String.class);
        
        String result = redisTemplate.execute(
            script,
            Collections.singletonList(cacheKey)
        );
        
        return Optional.ofNullable(result)
            .map(json -> objectMapper.readValue(json, Order.class));
    }
}

---

🌐 Multi-Level Caching

L1 (Local) + L2 (Redis) Pattern

@Service
public class TieredCacheService {
    // L1: Local cache (fast, small)
    private final Cache<String, Order> localCache = Caffeine.newBuilder()
        .maximumSize(1_000)
        .expireAfterWrite(Duration.ofMinutes(5))
        .build();
    
    // L2: Redis (shared, larger)
    private final RedisTemplate<String, Order> redisTemplate;
    
    private final OrderRepository repository;
    
    public Order getOrder(String orderId) {
        // L1 lookup
        Order order = localCache.getIfPresent(orderId);
        if (order != null) {
            return order;
        }
        
        // L2 lookup
        String cacheKey = "order:" + orderId;
        order = redisTemplate.opsForValue().get(cacheKey);
        
        if (order != null) {
            // Promote to L1
            localCache.put(orderId, order);
            return order;
        }
        
        // L3: Database
        order = repository.findById(orderId)
            .orElseThrow(() -> new OrderNotFoundException(orderId));
        
        // Populate L2
        redisTemplate.opsForValue().set(cacheKey, order, Duration.ofMinutes(15));
        
        // Populate L1
        localCache.put(orderId, order);
        
        return order;
    }
    
    public void invalidateOrder(String orderId) {
        // Invalidate all layers
        localCache.invalidate(orderId);
        redisTemplate.delete("order:" + orderId);
    }
}

CDN Caching (L3)

# CloudFront cache behavior
cacheBehavior:
  pathPattern: "/api/products/*"
  targetOriginId: "api-origin"
  cachePolicyId: "custom-cache-policy"
  
cachePolicy:
  name: "api-cache-policy"
  minTTL: 60        # 1 minute
  maxTTL: 3600      # 1 hour
  defaultTTL: 300   # 5 minutes
  
  # Cache based on query strings
  parametersInCacheKeyAndForwardedToOrigin:
    queryStringsConfig:
      queryStringBehavior: "whitelist"
      queryStrings:
        - "category"
        - "page"
        - "limit"
    
    # Cache based on headers
    headersConfig:
      headerBehavior: "whitelist"
      headers:
        - "Accept-Language"
        - "CloudFront-Viewer-Country"

// Set cache headers in API response
@GetMapping("/products/{id}")
public ResponseEntity<Product> getProduct(@PathVariable String id) {
    Product product = productService.getProduct(id);
    
    return ResponseEntity.ok()
        .cacheControl(CacheControl.maxAge(5, TimeUnit.MINUTES)
            .cachePublic())
        .eTag(product.getVersion())
        .body(product);
}

---

🔥 Cache Invalidation Strategies

1. Time-To-Live (TTL)

@Service
public class TTLCacheService {
    private final RedisTemplate<String, Order> redisTemplate;
    
    public void cacheOrder(Order order) {
        String cacheKey = "order:" + order.getOrderId();
        
        // Different TTLs based on data characteristics
        Duration ttl = switch (order.getStatus()) {
            case PENDING -> Duration.ofMinutes(5);      // Short TTL for mutable
            case PROCESSING -> Duration.ofMinutes(10);
            case COMPLETED -> Duration.ofHours(1);      // Long TTL for immutable
            case CANCELLED -> Duration.ofHours(1);
        };
        
        redisTemplate.opsForValue().set(cacheKey, order, ttl);
    }
}

2. Event-Driven Invalidation

@Service
public class EventDrivenCacheInvalidation {
    private final Cache<String, Order> cache;
    
    @EventListener
    public void handleOrderUpdated(OrderUpdatedEvent event) {
        // Invalidate affected caches
        cache.invalidate(event.getOrderId());
        cache.invalidate("customer:" + event.getCustomerId() + ":orders");
    }
    
    @EventListener
    public void handleProductUpdated(ProductUpdatedEvent event) {
        // Invalidate all orders containing this product
        String pattern = "order:*:product:" + event.getProductId();
        cache.asMap().keySet().stream()
            .filter(key -> key.matches(pattern))
            .forEach(cache::invalidate);
    }
}

3. Cache Tags and Dependencies

@Service
public class TaggedCacheService {
    private final RedisTemplate<String, String> redisTemplate;
    
    public void cacheWithTags(String cacheKey, Object value, String... tags) {
        // Store value
        redisTemplate.opsForValue().set(cacheKey, serialize(value));
        
        // Associate with tags
        for (String tag : tags) {
            String tagKey = "tag:" + tag;
            redisTemplate.opsForSet().add(tagKey, cacheKey);
        }
    }
    
    public void invalidateByTag(String tag) {
        String tagKey = "tag:" + tag;
        
        // Get all keys with this tag
        Set<String> keys = redisTemplate.opsForSet().members(tagKey);
        
        if (keys != null && !keys.isEmpty()) {
            // Delete all associated cache entries
            redisTemplate.delete(keys);
            
            // Delete tag set
            redisTemplate.delete(tagKey);
        }
    }
    
    // Usage
    public void cacheOrder(Order order) {
        String cacheKey = "order:" + order.getOrderId();
        
        cacheWithTags(cacheKey, order,
            "customer:" + order.getCustomerId(),
            "orders",
            "status:" + order.getStatus()
        );
    }
    
    // Invalidate all orders for a customer
    public void invalidateCustomerOrders(String customerId) {
        invalidateByTag("customer:" + customerId);
    }
}

4. Probabilistic Early Expiration (XFetch)

@Service
public class ProbabilisticCacheService {
    private final RedisTemplate<String, CachedValue> redisTemplate;
    private final Random random = new Random();
    
    @Data
    @AllArgsConstructor
    private static class CachedValue {
        private Order order;
        private long cachedAt;
        private long ttlSeconds;
    }
    
    public Order getOrder(String orderId) {
        String cacheKey = "order:" + orderId;
        CachedValue cached = redisTemplate.opsForValue().get(cacheKey);
        
        if (cached != null) {
            long age = System.currentTimeMillis() / 1000 - cached.getCachedAt();
            long ttl = cached.getTtlSeconds();
            
            // XFetch formula: -1 * beta * log(random) * ttl
            double beta = 1.0;
            double earlyExpirationTime = -1 * beta * Math.log(random.nextDouble()) * ttl;
            
            // Refresh if age exceeds early expiration time
            if (age >= earlyExpirationTime) {
                // Refresh in background
                CompletableFuture.runAsync(() -> refreshCache(orderId));
            }
            
            return cached.getOrder();
        }
        
        return refreshCache(orderId);
    }
    
    private Order refreshCache(String orderId) {
        Order order = repository.findById(orderId).orElseThrow();
        
        CachedValue cached = new CachedValue(
            order,
            System.currentTimeMillis() / 1000,
            900  // 15 minutes
        );
        
        redisTemplate.opsForValue().set(
            "order:" + orderId,
            cached,
            Duration.ofMinutes(15)
        );
        
        return order;
    }
}

---

🛡️ Failure Mode Prevention

1. Cache Stampede Prevention

@Service
public class StampedeProtectedCache {
    private final RedisTemplate<String, Order> redisTemplate;
    private final OrderRepository repository;
    
    // In-memory locks per key
    private final ConcurrentMap<String, CompletableFuture<Order>> loadingFutures = 
        new ConcurrentHashMap<>();
    
    public CompletableFuture<Order> getOrder(String orderId) {
        String cacheKey = "order:" + orderId;
        
        // Try cache
        Order cached = redisTemplate.opsForValue().get(cacheKey);
        if (cached != null) {
            return CompletableFuture.completedFuture(cached);
        }
        
        // Only one thread loads data for this key
        return loadingFutures.computeIfAbsent(orderId, id -> 
            CompletableFuture.supplyAsync(() -> {
                try {
                    // Double-check cache
                    Order order = redisTemplate.opsForValue().get(cacheKey);
                    if (order != null) {
                        return order;
                    }
                    
                    // Load from database
                    order = repository.findById(id)
                        .orElseThrow(() -> new OrderNotFoundException(id));
                    
                    // Cache result
                    redisTemplate.opsForValue().set(
                        cacheKey, order, Duration.ofMinutes(15));
                    
                    return order;
                } finally {
                    // Remove loading future
                    loadingFutures.remove(id);
                }
            })
        );
    }
}

2. Thundering Herd Mitigation

@Service
public class ThunderingHerdMitigation {
    private final RedisTemplate<String, String> redisTemplate;
    
    public Optional<Order> getWithJitter(String orderId) {
        String cacheKey = "order:" + orderId;
        
        Order order = getCached(cacheKey);
        if (order != null) {
            return Optional.of(order);
        }
        
        // Acquire distributed lock with timeout
        String lockKey = cacheKey + ":lock";
        boolean acquired = acquireLock(lockKey, Duration.ofSeconds(5));
        
        if (acquired) {
            try {
                // Double-check
                order = getCached(cacheKey);
                if (order != null) {
                    return Optional.of(order);
                }
                
                // Load data
                order = repository.findById(orderId).orElse(null);
                
                if (order != null) {
                    // Cache with jittered TTL to avoid synchronized expiration
                    long baseSeconds = 900;  // 15 minutes
                    long jitterSeconds = ThreadLocalRandom.current().nextLong(0, 60);
                    
                    redisTemplate.opsForValue().set(
                        cacheKey,
                        serialize(order),
                        Duration.ofSeconds(baseSeconds + jitterSeconds)
                    );
                }
                
                return Optional.ofNullable(order);
            } finally {
                releaseLock(lockKey);
            }
        } else {
            // Wait briefly and retry
            sleep(Duration.ofMillis(100));
            return Optional.ofNullable(getCached(cacheKey));
        }
    }
    
    private boolean acquireLock(String lockKey, Duration timeout) {
        return Boolean.TRUE.equals(
            redisTemplate.opsForValue()
                .setIfAbsent(lockKey, "1", timeout)
        );
    }
}

3. Hot Key Detection and Mitigation

@Service
public class HotKeyMitigation {
    private final LoadingCache<String, Order> localCache;
    private final RedisTemplate<String, Order> redisTemplate;
    
    // Track access frequency
    private final ConcurrentMap<String, AtomicLong> accessCounts = 
        new ConcurrentHashMap<>();
    
    private static final long HOT_KEY_THRESHOLD = 1000;  // Per minute
    
    public Order getOrder(String orderId) {
        // Track access
        long count = accessCounts
            .computeIfAbsent(orderId, k -> new AtomicLong())
            .incrementAndGet();
        
        // Hot key detection
        if (count > HOT_KEY_THRESHOLD) {
            // Use local cache for hot keys
            return localCache.get(orderId);
        }
        
        // Normal path: Redis
        String cacheKey = "order:" + orderId;
        Order order = redisTemplate.opsForValue().get(cacheKey);
        
        if (order == null) {
            order = repository.findById(orderId).orElseThrow();
            redisTemplate.opsForValue().set(
                cacheKey, order, Duration.ofMinutes(15));
        }
        
        return order;
    }
    
    // Reset counters every minute
    @Scheduled(fixedRate = 60_000)
    public void resetAccessCounts() {
        accessCounts.clear();
    }
}

---

📊 Cache Warming Strategies

1. Startup Cache Warming

@Component
public class CacheWarmer implements ApplicationRunner {
    private final Cache<String, Product> productCache;
    private final ProductRepository productRepository;
    
    @Override
    public void run(ApplicationArguments args) {
        log.info("Starting cache warming...");
        
        // Load top products
        List<Product> topProducts = productRepository.findTopSelling(100);
        topProducts.forEach(p -> productCache.put(p.getId(), p));
        
        log.info("Warmed cache with {} products", topProducts.size());
    }
}

2. Background Refresh

@Service
public class BackgroundCacheRefresh {
    private final Cache<String, List<Product>> cache;
    private final ProductRepository repository;
    
    @Scheduled(fixedRate = 300_000)  // Every 5 minutes
    public void refreshPopularCategories() {
        String[] categories = {"electronics", "books", "clothing"};
        
        for (String category : categories) {
            List<Product> products = repository.findByCategory(category);
            cache.put("category:" + category, products);
        }
    }
}

3. Predictive Warming

@Service
public class PredictiveCacheWarming {
    private final Cache<String, Order> cache;
    
    @EventListener
    public void onUserLogin(UserLoginEvent event) {
        String userId = event.getUserId();
        
        // Warm cache with user's recent orders
        CompletableFuture.runAsync(() -> {
            List<Order> recentOrders = orderRepository
                .findByCustomerId(userId, Pageable.ofSize(10));
            
            recentOrders.forEach(order -> 
                cache.put(order.getOrderId(), order)
            );
        });
    }
}

---

📈 Cache Observability

Metrics Collection

@Configuration
public class CacheMetricsConfig {
    
    @Bean
    public CacheMetricsCollector cacheMetrics(MeterRegistry registry) {
        return new CacheMetricsCollector(registry);
    }
}

@Component
public class InstrumentedCache {
    private final Cache<String, Order> cache;
    private final Counter hits;
    private final Counter misses;
    private final Timer getLatency;
    
    public InstrumentedCache(Cache<String, Order> cache, MeterRegistry registry) {
        this.cache = cache;
        this.hits = registry.counter("cache.hits", "cache", "order");
        this.misses = registry.counter("cache.misses", "cache", "order");
        this.getLatency = registry.timer("cache.get.latency", "cache", "order");
    }
    
    public Order get(String key) {
        return getLatency.record(() -> {
            Order value = cache.getIfPresent(key);
            
            if (value != null) {
                hits.increment();
            } else {
                misses.increment();
            }
            
            return value;
        });
    }
    
    public double hitRatio() {
        double totalHits = hits.count();
        double totalMisses = misses.count();
        return totalHits / (totalHits + totalMisses);
    }
}

---

📚 Referenced Skills

I leverage these knowledge artifacts:

Caching

• cache-patterns.md - Cache-aside, read-through, write-through
• redis-patterns.md - Redis data structures and operations
• hazelcast-patterns.md - Distributed cache configuration

Performance

• cache-invalidation.md - Invalidation strategies
• cache-warming.md - Preloading techniques
• performance-tuning.md - Optimization techniques

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🎓 Learning Resources

• Redis Docs: https://redis.io/docs/
• Caffeine: https://github.com/ben-manes/caffeine
• Cache Patterns: https://docs.microsoft.com/en-us/azure/architecture/patterns/cache-aside
• Hazelcast: https://docs.hazelcast.com/

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🔍 Agent Signature

// I am Caching Strategy Agent
// Domain: Caching Architecture & Performance
// Focus: Multi-level caching, invalidation, Redis, distributed caching
// Handoff: @backend-java, @spring-boot, @aws-cloud, @observability