vyuh_cache 1.4.5

A flexible caching solution for Vyuh Framework Apps, offering time-based caching with pluggable storage backends to reduce API calls and boost performance.

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Vyuh Cache 💾

A lightweight, flexible caching solution for Vyuh-framework applications. Provides time-based caching with pluggable storage backends. Perfect for reducing API calls, improving app performance, and managing temporary data.

Features ✨

• Time-based Caching: Automatic expiration with TTL (Time To Live) ⏱️
• Type Safety: Generic implementation for type-safe caching 🛡️
• Pluggable Storage: Built-in memory storage with extensible storage interface 💾
• Async API: Full async support for all operations ⚡
• Value Generation: Built-in support for generating missing cache values 🔄
• Expiration Handling: Automatic cleanup of expired entries 🧹

Installation 📦

Add this to your package's pubspec.yaml:

dependencies:
  vyuh_cache: any

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Usage 💡

Basic Usage

// Create a cache instance with memory storage
final cache = Cache(
  CacheConfig(
    storage: MemoryCacheStorage<String>(),
    ttl: Duration(minutes: 5),
  ),
);

// Store a value
await cache.set('greeting', 'Hello, World!');

// Check if key exists
if (await cache.has('greeting')) {
  // Retrieve the value
  final value = await cache.get('greeting');
  print(value); // Hello, World!
}

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Value Generation

// Get value with automatic generation if missing
final value = await cache.build(
  'user-123',
  generateValue: () async {
    // Expensive operation (e.g., API call)
    return await fetchUserFromApi('123');
  },
);

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Cache Management

// Remove a specific entry
await cache.remove('old-key');

// Clear all entries
await cache.clear();

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Custom Storage

Implement CacheStorage interface for custom storage backends:

class RedisStorage<T> implements CacheStorage<T> {
  @override
  Future<CacheEntry<T>?> get(String key) async {
    // Implement Redis get
  }

  @override
  Future<void> set(String key, CacheEntry<T> value) async {
    // Implement Redis set
  }

  // ... implement other methods
}

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Best Practices 🎯

1. Choose Appropriate TTL: Set TTL based on data volatility
2. Handle Errors: Always handle potential cache misses
3. Type Safety: Use specific types instead of dynamic
4. Memory Management: Clear unused cache entries
5. Storage Selection: Use appropriate storage for your use case

Learn More 📚

• Visit docs.vyuh.tech for detailed documentation
• Check out the GitHub repository
• Report issues on the issue tracker

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