Caching Problems

Caching is a technique used to store copies of data in a high-speed data storage layer to improve retrieval times. While caching can significantly improve application performance, improper implementation can lead to various issues. This document outlines common anti-patterns related to caching and provides optimization strategies.

Cache Invalidation Failures

Anti-Pattern

Java Example:

public class ProductService {
    private final Map<Long, Product> productCache = new HashMap<>();
    private final ProductRepository repository;
    
    public ProductService(ProductRepository repository) {
        this.repository = repository;
    }
    
    public Product getProduct(Long id) {
        // Check if product exists in cache
        if (productCache.containsKey(id)) {
            return productCache.get(id);
        }
        
        // If not in cache, get from database and cache it
        Product product = repository.findById(id);
        if (product != null) {
            productCache.put(id, product);
        }
        return product;
    }
    
    public void updateProduct(Product product) {
        // Update product in database
        repository.save(product);
        
        // Cache invalidation is missing here!
        // The cache still contains the old version of the product
    }
}

JavaScript Example:

class UserService {
  constructor() {
    this.userCache = new Map();
    this.api = new ApiClient();
  }
  
  async getUser(userId) {
    // Check if user exists in cache
    if (this.userCache.has(userId)) {
      return this.userCache.get(userId);
    }
    
    // If not in cache, fetch from API and cache it
    const user = await this.api.fetchUser(userId);
    this.userCache.set(userId, user);
    return user;
  }
  
  async updateUser(userId, userData) {
    // Update user via API
    await this.api.updateUser(userId, userData);
    
    // Cache invalidation is missing here!
    // The cache still contains the old user data
  }
}

// Usage
const userService = new UserService();

// First call fetches and caches
const user = await userService.getUser('user123');
console.log(user); // {id: 'user123', name: 'John', email: 'john@example.com'}

// Update user
await userService.updateUser('user123', {name: 'John Updated'});

// Second call returns stale data from cache
const updatedUser = await userService.getUser('user123');
console.log(updatedUser); // Still {id: 'user123', name: 'John', email: 'john@example.com'}

Description

This anti-pattern occurs when an application fails to properly invalidate or update cached data after the underlying data has changed. This leads to stale data being served to users, potentially causing inconsistencies, incorrect business decisions, or confusing user experiences.

Optimization

Java Example:

public class ProductService {
    private final Map<Long, Product> productCache = new HashMap<>();
    private final ProductRepository repository;
    
    public ProductService(ProductRepository repository) {
        this.repository = repository;
    }
    
    public Product getProduct(Long id) {
        // Check if product exists in cache
        if (productCache.containsKey(id)) {
            return productCache.get(id);
        }
        
        // If not in cache, get from database and cache it
        Product product = repository.findById(id);
        if (product != null) {
            productCache.put(id, product);
        }
        return product;
    }
    
    public void updateProduct(Product product) {
        // Update product in database
        repository.save(product);
        
        // Properly invalidate cache
        productCache.remove(product.getId());
        // Or update the cache with the new version
        // productCache.put(product.getId(), product);
    }
    
    // Additional method to handle cache invalidation for bulk updates
    public void invalidateCache() {
        productCache.clear();
    }
}

JavaScript Example:

class UserService {
  constructor() {
    this.userCache = new Map();
    this.api = new ApiClient();
  }
  
  async getUser(userId) {
    // Check if user exists in cache
    if (this.userCache.has(userId)) {
      return this.userCache.get(userId);
    }
    
    // If not in cache, fetch from API and cache it
    const user = await this.api.fetchUser(userId);
    this.userCache.set(userId, user);
    return user;
  }
  
  async updateUser(userId, userData) {
    // Update user via API
    const updatedUser = await this.api.updateUser(userId, userData);
    
    // Properly update cache with new data
    this.userCache.set(userId, updatedUser);
    return updatedUser;
  }
  
  // Method to invalidate a specific user in cache
  invalidateUser(userId) {
    this.userCache.delete(userId);
  }
  
  // Method to invalidate entire cache
  invalidateCache() {
    this.userCache.clear();
  }
}

// Usage
const userService = new UserService();

// First call fetches and caches
const user = await userService.getUser('user123');
console.log(user); // {id: 'user123', name: 'John', email: 'john@example.com'}

// Update user (cache is updated)
await userService.updateUser('user123', {name: 'John Updated'});

// Second call returns updated data from cache
const updatedUser = await userService.getUser('user123');
console.log(updatedUser); // {id: 'user123', name: 'John Updated', email: 'john@example.com'}

Implement proper cache invalidation strategies to ensure data consistency. When data is updated, either remove the corresponding cache entry or update it with the new value. For distributed systems, consider using cache invalidation events or time-based expiration policies. Always ensure that all code paths that modify data also handle cache invalidation appropriately.

Cache Stampede

Anti-Pattern

Java Example:

public class ExpensiveDataService {
    private final Map<String, CachedData> cache = new ConcurrentHashMap<>();
    private final ExpensiveDataRepository repository;
    
    public ExpensiveDataService(ExpensiveDataRepository repository) {
        this.repository = repository;
    }
    
    public Data getData(String key) {
        CachedData cachedData = cache.get(key);
        
        // Check if data is in cache and not expired
        if (cachedData != null && !cachedData.isExpired()) {
            return cachedData.getData();
        }
        
        // If not in cache or expired, fetch from database
        Data freshData = repository.fetchExpensiveData(key);
        cache.put(key, new CachedData(freshData, System.currentTimeMillis() + 60000)); // 1 minute TTL
        return freshData;
    }
    
    private static class CachedData {
        private final Data data;
        private final long expiryTime;
        
        public CachedData(Data data, long expiryTime) {
            this.data = data;
            this.expiryTime = expiryTime;
        }
        
        public Data getData() {
            return data;
        }
        
        public boolean isExpired() {
            return System.currentTimeMillis() > expiryTime;
        }
    }
}

JavaScript Example:

class DatabaseCache {
  constructor(dbClient) {
    this.cache = new Map();
    this.dbClient = dbClient;
    this.cacheTTL = 60000; // 1 minute in milliseconds
  }
  
  async getRecord(recordId) {
    const cachedRecord = this.cache.get(recordId);
    
    // Check if record is in cache and not expired
    if (cachedRecord && Date.now() < cachedRecord.expiresAt) {
      return cachedRecord.data;
    }
    
    // If not in cache or expired, fetch from database
    const data = await this.dbClient.fetchRecord(recordId);
    
    // Store in cache with expiration time
    this.cache.set(recordId, {
      data,
      expiresAt: Date.now() + this.cacheTTL
    });
    
    return data;
  }
}

// Usage in a high-traffic web server
app.get('/api/products/:id', async (req, res) => {
  const productId = req.params.id;
  const dbCache = new DatabaseCache(dbClient);
  
  try {
    // If many requests come in at once for the same expired product,
    // all will miss the cache and hit the database simultaneously
    const product = await dbCache.getRecord(productId);
    res.json(product);
  } catch (error) {
    res.status(500).send('Error fetching product');
  }
});

Description

Cache stampede (also known as thundering herd or cache avalanche) occurs when many concurrent requests attempt to access a cached item that is either expired or missing, causing all of them to simultaneously try to fetch the data from the underlying data source. This can overwhelm the database or service, leading to increased latency, timeouts, or even system failures.

Optimization

Java Example:

public class ExpensiveDataService {
    private final Map<String, CachedData> cache = new ConcurrentHashMap<>();
    private final Map<String, CompletableFuture<Data>> ongoingFetches = new ConcurrentHashMap<>();
    private final ExpensiveDataRepository repository;
    
    public ExpensiveDataService(ExpensiveDataRepository repository) {
        this.repository = repository;
    }
    
    public CompletableFuture<Data> getData(String key) {
        CachedData cachedData = cache.get(key);
        
        // Check if data is in cache and not expired
        if (cachedData != null && !cachedData.isExpired()) {
            return CompletableFuture.completedFuture(cachedData.getData());
        }
        
        // Check if there's already an ongoing fetch for this key
        CompletableFuture<Data> ongoingFetch = ongoingFetches.get(key);
        if (ongoingFetch != null) {
            // Return the existing future to avoid duplicate fetches
            return ongoingFetch;
        }
        
        // Create a new fetch operation
        CompletableFuture<Data> newFetch = CompletableFuture.supplyAsync(() -> {
            try {
                // Fetch from database
                Data freshData = repository.fetchExpensiveData(key);
                
                // Update cache
                cache.put(key, new CachedData(freshData, System.currentTimeMillis() + 60000)); // 1 minute TTL
                
                return freshData;
            } finally {
                // Remove from ongoing fetches when complete
                ongoingFetches.remove(key);
            }
        });
        
        // Register the ongoing fetch
        ongoingFetches.put(key, newFetch);
        
        return newFetch;
    }
    
    // Proactive cache refresh before expiration
    public void startBackgroundRefresh() {
        ScheduledExecutorService executor = Executors.newScheduledThreadPool(1);
        executor.scheduleAtFixedRate(() -> {
            for (Map.Entry<String, CachedData> entry : cache.entrySet()) {
                String key = entry.getKey();
                CachedData cachedData = entry.getValue();
                
                // If approaching expiration (e.g., 75% of TTL elapsed) and no ongoing fetch
                long ttl = 60000; // 1 minute in milliseconds
                long refreshThreshold = System.currentTimeMillis() + (ttl / 4);
                
                if (cachedData.getExpiryTime() < refreshThreshold && !ongoingFetches.containsKey(key)) {
                    // Refresh in background
                    getData(key);
                }
            }
        }, 15, 15, TimeUnit.SECONDS);
    }
    
    private static class CachedData {
        private final Data data;
        private final long expiryTime;
        
        public CachedData(Data data, long expiryTime) {
            this.data = data;
            this.expiryTime = expiryTime;
        }
        
        public Data getData() {
            return data;
        }
        
        public long getExpiryTime() {
            return expiryTime;
        }
        
        public boolean isExpired() {
            return System.currentTimeMillis() > expiryTime;
        }
    }
}

JavaScript Example:

class DatabaseCache {
  constructor(dbClient) {
    this.cache = new Map();
    this.fetchPromises = new Map();
    this.dbClient = dbClient;
    this.cacheTTL = 60000; // 1 minute in milliseconds
    
    // Start background refresh
    this.startBackgroundRefresh();
  }
  
  async getRecord(recordId) {
    const cachedRecord = this.cache.get(recordId);
    
    // Check if record is in cache and not expired
    if (cachedRecord && Date.now() < cachedRecord.expiresAt) {
      return cachedRecord.data;
    }
    
    // Check if there's already an ongoing fetch for this record
    if (this.fetchPromises.has(recordId)) {
      // Return the existing promise to avoid duplicate fetches
      return this.fetchPromises.get(recordId);
    }
    
    // Create a new fetch promise
    const fetchPromise = this.fetchAndCacheRecord(recordId);
    
    // Register the ongoing fetch
    this.fetchPromises.set(recordId, fetchPromise);
    
    try {
      return await fetchPromise;
    } finally {
      // Remove from ongoing fetches when complete
      this.fetchPromises.delete(recordId);
    }
  }
  
  async fetchAndCacheRecord(recordId) {
    try {
      // Add small jitter to prevent synchronized expiration
      const jitter = Math.random() * 5000; // Random jitter up to 5 seconds
      
      const data = await this.dbClient.fetchRecord(recordId);
      
      // Store in cache with expiration time
      this.cache.set(recordId, {
        data,
        expiresAt: Date.now() + this.cacheTTL + jitter
      });
      
      return data;
    } catch (error) {
      // If fetch fails, remove from ongoing fetches
      this.fetchPromises.delete(recordId);
      throw error;
    }
  }
  
  // Proactively refresh cache before items expire
  startBackgroundRefresh() {
    setInterval(() => {
      const now = Date.now();
      
      this.cache.forEach((record, recordId) => {
        // If approaching expiration (e.g., 75% of TTL elapsed) and no ongoing fetch
        const refreshThreshold = record.expiresAt - (this.cacheTTL * 0.25);
        
        if (now > refreshThreshold && !this.fetchPromises.has(recordId)) {
          // Refresh in background without blocking
          this.getRecord(recordId).catch(error => {
            console.error(`Background refresh failed for ${recordId}:`, error);
          });
        }
      });
    }, 15000); // Check every 15 seconds
  }
}

// Usage in a high-traffic web server
const dbCache = new DatabaseCache(dbClient); // Single instance for the server

app.get('/api/products/:id', async (req, res) => {
  const productId = req.params.id;
  
  try {
    // All concurrent requests for the same product will share the same fetch operation
    const product = await dbCache.getRecord(productId);
    res.json(product);
  } catch (error) {
    res.status(500).send('Error fetching product');
  }
});

Optimization Strategies

Request Coalescing: When multiple requests for the same resource arrive simultaneously, only one request should fetch from the underlying data source while others wait for that result.
Staggered Expiration Times: Add random jitter to cache expiration times to prevent many items from expiring simultaneously.
Background Refresh: Proactively refresh cache items before they expire, ideally during low-traffic periods.
Soft Expiration: Continue serving stale data while asynchronously refreshing the cache.
Cache Lock: Use a distributed lock to ensure only one process can refresh a particular cache entry at a time.
Fallback to Stale Data: If fetching fresh data fails, temporarily continue serving stale data rather than failing completely.
Circuit Breaker Pattern: Implement circuit breakers to prevent overwhelming the backend system during outages.
Tiered Caching: Implement multiple layers of caching with different expiration policies.

By implementing these strategies, you can prevent cache stampedes and ensure your application remains responsive even under high load or when cached data expires.

Inefficient Cache Eviction Policies

Anti-Pattern

Java Example:

public class SimpleCache<K, V> {
    private final Map<K, V> cache = new HashMap<>();
    private final int maxSize;
    
    public SimpleCache(int maxSize) {
        this.maxSize = maxSize;
    }
    
    public V get(K key) {
        return cache.get(key);
    }
    
    public void put(K key, V value) {
        // Check if cache is full
        if (cache.size() >= maxSize) {
            // Inefficient: randomly remove an entry when cache is full
            K randomKey = cache.keySet().iterator().next();
            cache.remove(randomKey);
        }
        
        cache.put(key, value);
    }
}

JavaScript Example:

class SimpleCache {
  constructor(maxSize) {
    this.cache = new Map();
    this.maxSize = maxSize;
  }
  
  get(key) {
    return this.cache.get(key);
  }
  
  set(key, value) {
    // Check if cache is full
    if (this.cache.size >= this.maxSize) {
      // Inefficient: clear the entire cache when it's full
      this.cache.clear();
    }
    
    this.cache.set(key, value);
  }
}

// Usage
const cache = new SimpleCache(100);
cache.set('user:1', { name: 'John', age: 30 });
// ... more items added
// When cache reaches 100 items, the next item will cause the entire cache to be cleared

Description

This anti-pattern occurs when an application uses inefficient or inappropriate cache eviction policies. Common issues include clearing the entire cache when it’s full, randomly removing entries without considering their usage patterns, or using a one-size-fits-all approach for all types of data. This leads to poor cache hit ratios, unnecessary recomputation of values, and degraded application performance.

Optimization

Java Example:

public class LRUCache<K, V> {
    private final LinkedHashMap<K, V> cache;
    private final int maxSize;
    
    public LRUCache(int maxSize) {
        this.maxSize = maxSize;
        // Use LinkedHashMap with access order to implement LRU
        this.cache = new LinkedHashMap<K, V>(maxSize, 0.75f, true) {
            @Override
            protected boolean removeEldestEntry(Map.Entry<K, V> eldest) {
                return size() > maxSize;
            }
        };
    }
    
    public V get(K key) {
        return cache.get(key);
    }
    
    public void put(K key, V value) {
        cache.put(key, value);
    }
}

// Alternative implementation with more control and metrics
public class AdvancedCache<K, V> {
    private final Map<K, CacheEntry<V>> cache = new HashMap<>();
    private final int maxSize;
    private final EvictionPolicy evictionPolicy;
    
    // Cache statistics
    private long hits = 0;
    private long misses = 0;
    
    public AdvancedCache(int maxSize, EvictionPolicy evictionPolicy) {
        this.maxSize = maxSize;
        this.evictionPolicy = evictionPolicy;
    }
    
    public V get(K key) {
        CacheEntry<V> entry = cache.get(key);
        if (entry == null) {
            misses++;
            return null;
        }
        
        // Update access statistics
        entry.incrementAccessCount();
        entry.setLastAccessTime(System.currentTimeMillis());
        hits++;
        
        return entry.getValue();
    }
    
    public void put(K key, V value) {
        // Check if cache is full
        if (cache.size() >= maxSize && !cache.containsKey(key)) {
            // Apply the selected eviction policy
            evict();
        }
        
        // Add or update the entry
        CacheEntry<V> entry = cache.get(key);
        if (entry == null) {
            entry = new CacheEntry<>(value);
            cache.put(key, entry);
        } else {
            entry.setValue(value);
            entry.setLastUpdateTime(System.currentTimeMillis());
        }
    }
    
    private void evict() {
        switch (evictionPolicy) {
            case LRU:
                evictLRU();
                break;
            case LFU:
                evictLFU();
                break;
            case FIFO:
                evictFIFO();
                break;
        }
    }
    
    private void evictLRU() {
        K oldestKey = null;
        long oldestAccessTime = Long.MAX_VALUE;
        
        for (Map.Entry<K, CacheEntry<V>> entry : cache.entrySet()) {
            if (entry.getValue().getLastAccessTime() < oldestAccessTime) {
                oldestAccessTime = entry.getValue().getLastAccessTime();
                oldestKey = entry.getKey();
            }
        }
        
        if (oldestKey != null) {
            cache.remove(oldestKey);
        }
    }
    
    private void evictLFU() {
        K leastFrequentKey = null;
        long leastFrequentCount = Long.MAX_VALUE;
        
        for (Map.Entry<K, CacheEntry<V>> entry : cache.entrySet()) {
            if (entry.getValue().getAccessCount() < leastFrequentCount) {
                leastFrequentCount = entry.getValue().getAccessCount();
                leastFrequentKey = entry.getKey();
            }
        }
        
        if (leastFrequentKey != null) {
            cache.remove(leastFrequentKey);
        }
    }
    
    private void evictFIFO() {
        K oldestKey = null;
        long oldestCreationTime = Long.MAX_VALUE;
        
        for (Map.Entry<K, CacheEntry<V>> entry : cache.entrySet()) {
            if (entry.getValue().getCreationTime() < oldestCreationTime) {
                oldestCreationTime = entry.getValue().getCreationTime();
                oldestKey = entry.getKey();
            }
        }
        
        if (oldestKey != null) {
            cache.remove(oldestKey);
        }
    }
    
    public double getHitRatio() {
        long total = hits + misses;
        return total == 0 ? 0 : (double) hits / total;
    }
    
    public enum EvictionPolicy {
        LRU, // Least Recently Used
        LFU, // Least Frequently Used
        FIFO // First In First Out
    }
    
    private static class CacheEntry<V> {
        private V value;
        private final long creationTime;
        private long lastAccessTime;
        private long lastUpdateTime;
        private long accessCount;
        
        public CacheEntry(V value) {
            this.value = value;
            this.creationTime = System.currentTimeMillis();
            this.lastAccessTime = this.creationTime;
            this.lastUpdateTime = this.creationTime;
            this.accessCount = 0;
        }
        
        public V getValue() {
            return value;
        }
        
        public void setValue(V value) {
            this.value = value;
        }
        
        public long getCreationTime() {
            return creationTime;
        }
        
        public long getLastAccessTime() {
            return lastAccessTime;
        }
        
        public void setLastAccessTime(long lastAccessTime) {
            this.lastAccessTime = lastAccessTime;
        }
        
        public long getLastUpdateTime() {
            return lastUpdateTime;
        }
        
        public void setLastUpdateTime(long lastUpdateTime) {
            this.lastUpdateTime = lastUpdateTime;
        }
        
        public long getAccessCount() {
            return accessCount;
        }
        
        public void incrementAccessCount() {
            this.accessCount++;
        }
    }
}

JavaScript Example:

class LRUCache {
  constructor(maxSize) {
    this.cache = new Map();
    this.maxSize = maxSize;
    this.hits = 0;
    this.misses = 0;
  }
  
  get(key) {
    if (!this.cache.has(key)) {
      this.misses++;
      return undefined;
    }
    
    // Get the entry
    const entry = this.cache.get(key);
    
    // Update access statistics
    entry.lastAccessed = Date.now();
    entry.accessCount++;
    this.hits++;
    
    // LRU implementation: remove and re-add to put at the end of the insertion order
    this.cache.delete(key);
    this.cache.set(key, entry);
    
    return entry.value;
  }
  
  set(key, value) {
    // If key already exists, update it
    if (this.cache.has(key)) {
      const entry = this.cache.get(key);
      entry.value = value;
      entry.lastUpdated = Date.now();
      
      // Move to the end of insertion order
      this.cache.delete(key);
      this.cache.set(key, entry);
      return;
    }
    
    // Check if cache is full
    if (this.cache.size >= this.maxSize) {
      // LRU: Remove the first item (oldest by insertion order in Map)
      const firstKey = this.cache.keys().next().value;
      this.cache.delete(firstKey);
    }
    
    // Add new entry
    this.cache.set(key, {
      value,
      created: Date.now(),
      lastAccessed: Date.now(),
      lastUpdated: Date.now(),
      accessCount: 0
    });
  }
  
  getHitRatio() {
    const total = this.hits + this.misses;
    return total === 0 ? 0 : this.hits / total;
  }
  
  clear() {
    this.cache.clear();
    this.hits = 0;
    this.misses = 0;
  }
}

// More sophisticated cache with configurable eviction policies
class AdvancedCache {
  constructor(maxSize, evictionPolicy = 'LRU') {
    this.cache = new Map();
    this.maxSize = maxSize;
    this.evictionPolicy = evictionPolicy; // 'LRU', 'LFU', or 'FIFO'
    this.hits = 0;
    this.misses = 0;
  }
  
  get(key) {
    if (!this.cache.has(key)) {
      this.misses++;
      return undefined;
    }
    
    const entry = this.cache.get(key);
    entry.lastAccessed = Date.now();
    entry.accessCount++;
    this.hits++;
    
    return entry.value;
  }
  
  set(key, value) {
    // If key exists, update it
    if (this.cache.has(key)) {
      const entry = this.cache.get(key);
      entry.value = value;
      entry.lastUpdated = Date.now();
      return;
    }
    
    // Check if cache is full
    if (this.cache.size >= this.maxSize) {
      this.evict();
    }
    
    // Add new entry
    this.cache.set(key, {
      value,
      created: Date.now(),
      lastAccessed: Date.now(),
      lastUpdated: Date.now(),
      accessCount: 0
    });
  }
  
  evict() {
    switch (this.evictionPolicy) {
      case 'LRU':
        this.evictLRU();
        break;
      case 'LFU':
        this.evictLFU();
        break;
      case 'FIFO':
        this.evictFIFO();
        break;
      default:
        this.evictLRU();
    }
  }
  
  evictLRU() {
    let oldestKey = null;
    let oldestAccessTime = Infinity;
    
    for (const [key, entry] of this.cache.entries()) {
      if (entry.lastAccessed < oldestAccessTime) {
        oldestAccessTime = entry.lastAccessed;
        oldestKey = key;
      }
    }
    
    if (oldestKey) {
      this.cache.delete(oldestKey);
    }
  }
  
  evictLFU() {
    let leastFrequentKey = null;
    let leastFrequentCount = Infinity;
    
    for (const [key, entry] of this.cache.entries()) {
      if (entry.accessCount < leastFrequentCount) {
        leastFrequentCount = entry.accessCount;
        leastFrequentKey = key;
      }
    }
    
    if (leastFrequentKey) {
      this.cache.delete(leastFrequentKey);
    }
  }
  
  evictFIFO() {
    let oldestKey = null;
    let oldestCreationTime = Infinity;
    
    for (const [key, entry] of this.cache.entries()) {
      if (entry.created < oldestCreationTime) {
        oldestCreationTime = entry.created;
        oldestKey = key;
      }
    }
    
    if (oldestKey) {
      this.cache.delete(oldestKey);
    }
  }
  
  getHitRatio() {
    const total = this.hits + this.misses;
    return total === 0 ? 0 : this.hits / total;
  }
}

// Usage
const lruCache = new LRUCache(1000);
const advancedCache = new AdvancedCache(1000, 'LFU'); // Use LFU for frequently accessed items

Optimization Strategies

Choose Appropriate Eviction Policies:
- LRU (Least Recently Used): Best for data with temporal locality.
- LFU (Least Frequently Used): Best for data with frequency-based access patterns.
- FIFO (First In First Out): Simple but effective for data with uniform access patterns.
- Time-Based Expiration: Good for data that becomes stale after a certain period.
Use Different Policies for Different Data Types:
- Configuration data might benefit from time-based expiration.
- User session data might benefit from LRU.
- Popular content might benefit from LFU.
Implement Size-Aware Caching:
- Consider both the number of items and their size.
- Evict larger items first when memory pressure is high.
Monitor Cache Effectiveness:
- Track hit/miss ratios to evaluate policy effectiveness.
- Adjust cache sizes and policies based on metrics.
Consider Hybrid Approaches:
- Combine multiple policies (e.g., TinyLFU with window TinyLFU).
- Use adaptive policies that change based on workload.
Implement Segmented Caching:
- Divide the cache into segments with different policies.
- Allocate more space to segments with higher hit rates.
Pre-emptive Eviction:
- Proactively evict items that are likely to become stale.
- Use predictive models to anticipate which items will be needed.

By implementing appropriate cache eviction policies, you can significantly improve cache hit ratios, reduce resource consumption, and enhance application performance.

Introduction

Getting Started

Cortex Features

Patterns

Integrations

Enterprise

Caching Problems

Caching Problems

Anti-Pattern

Description

Optimization

Anti-Pattern

Description

Optimization

Optimization Strategies

Anti-Pattern

Description

Optimization

Optimization Strategies

Introduction

Getting Started

Cortex Features

Patterns

Integrations

Enterprise

​Caching Problems

Caching Problems