# Caching Strategies

Caching is a critical performance optimization technique that can significantly reduce database load and improve application response times. This document explores various caching strategies available in rhosocial ActiveRecord and provides guidance on implementing them effectively.

## Introduction to Caching

Database operations, especially complex queries, can be resource-intensive. Caching stores the results of expensive operations so they can be reused without repeating the operation. rhosocial ActiveRecord provides several caching mechanisms at different levels of the application.

## Types of Caching in ActiveRecord

rhosocial ActiveRecord supports several types of caching:

1. **Model-level Caching**: Caching entire model instances
2. **Query Result Caching**: Caching the results of database queries
3. **Relationship Caching**: Caching related records loaded through relationships

Each type of caching is suitable for different scenarios and comes with its own considerations.

## Model-level Caching

Model-level caching stores entire model instances in the cache, allowing them to be retrieved without hitting the database.

### Basic Model Caching

```python
from rhosocial.activerecord.models import User
from rhosocial.activerecord.cache import ModelCache

# Fetch a user from the database and cache it
user = User.objects.get(id=1)
ModelCache.set(User, 1, user, ttl=300)  # Cache for 5 minutes

# Later, retrieve the user from cache
cached_user = ModelCache.get(User, 1)
if cached_user is None:
    # Cache miss, fetch from database
    cached_user = User.objects.get(id=1)
    ModelCache.set(User, 1, cached_user, ttl=300)
```

### Automatic Model Caching

rhosocial ActiveRecord can be configured to automatically cache model instances:

```python
from rhosocial.activerecord.models import User
from rhosocial.activerecord.cache import enable_model_cache

# Enable automatic caching for the User model
enable_model_cache(User, ttl=300)

# Now fetches will automatically use the cache
user = User.objects.get(id=1)  # Checks cache first, then database if needed

# Updates will automatically invalidate the cache
user.name = "New Name"
user.save()  # Updates database and refreshes cache
```

### Cache Invalidation

Proper cache invalidation is crucial to prevent stale data:

```python
from rhosocial.activerecord.cache import ModelCache

# Manually invalidate a specific model instance
ModelCache.delete(User, 1)

# Invalidate all cached instances of a model
ModelCache.clear(User)

# Automatic invalidation on model updates
user = User.objects.get(id=1)
user.update(name="New Name")  # Automatically invalidates cache
```

## Query Result Caching

Query result caching stores the results of database queries, which is particularly useful for expensive queries that are executed frequently.

### Basic Query Caching

```python
from rhosocial.activerecord.models import Article
from rhosocial.activerecord.cache import QueryCache

# Define a query
query = Article.objects.filter(status='published').order_by('-published_at').limit(10)

# Cache the query results
results = QueryCache.get_or_set('recent_articles', lambda: query.all(), ttl=300)

# Later, retrieve the cached results
cached_results = QueryCache.get('recent_articles')
if cached_results is None:
    # Cache miss, execute query and cache results
    cached_results = query.all()
    QueryCache.set('recent_articles', cached_results, ttl=300)
```

### Query Cache Considerations

1. **Cache Key Generation**: Use consistent and unique cache keys

```python
from rhosocial.activerecord.cache import generate_query_cache_key

# Generate a cache key based on the query
query = Article.objects.filter(status='published').order_by('-published_at')
cache_key = generate_query_cache_key(query)

# Use the generated key
results = QueryCache.get_or_set(cache_key, lambda: query.all(), ttl=300)
```

2. **Cache Invalidation Strategies**:

```python
# Time-based invalidation (TTL)
QueryCache.set('recent_articles', results, ttl=300)  # Expires after 5 minutes

# Manual invalidation
QueryCache.delete('recent_articles')

# Pattern-based invalidation
QueryCache.delete_pattern('article:*')  # Deletes all keys matching the pattern

# Model-based invalidation
QueryCache.invalidate_for_model(Article)  # Invalidate all caches related to Article model
```

## Relationship Caching

Relationship caching stores the results of relationship queries, which helps prevent N+1 query problems.

### Configuring Relationship Caching

rhosocial ActiveRecord provides built-in caching for model relationships:

```python
from rhosocial.activerecord.models import User, Order
from rhosocial.activerecord.relation import HasMany, CacheConfig

class User(ActiveRecord):
    __table_name__ = 'users'
    
    # Configure relationship caching
    orders: ClassVar[HasMany['Order']] = HasMany(
                    foreign_key='user_id',
                    cache_config=CacheConfig(enabled=True, ttl=300))
```

### Global Cache Configuration

You can also configure caching globally for all relationships:

```python
from rhosocial.activerecord.relation import GlobalCacheConfig

# Enable caching for all relationships
GlobalCacheConfig.enabled = True
GlobalCacheConfig.ttl = 600  # 10 minutes
```

### Relationship Cache Management

```python
# Clear cache for a specific relationship
user = User.objects.get(id=1)
user.clear_relation_cache('orders')

# Clear cache for all relationships on an instance
user.clear_relation_cache()
```

## Distributed Caching

For production applications, a distributed cache like Redis or Memcached is recommended:

```python
from rhosocial.activerecord.cache import configure_cache
import redis

# Configure Redis as the cache backend
redis_client = redis.Redis(host='localhost', port=6379, db=0)
configure_cache(backend='redis', client=redis_client)

# Now all caching operations will use Redis
ModelCache.set(User, 1, user, ttl=300)  # Stored in Redis
```

## Cache Monitoring and Management

Proper monitoring is essential for effective caching:

```python
from rhosocial.activerecord.cache import CacheStats

# Get cache statistics
stats = CacheStats.get()
print(f"Hits: {stats.hits}")
print(f"Misses: {stats.misses}")
print(f"Hit ratio: {stats.hit_ratio:.2f}")

# Clear all caches
from rhosocial.activerecord.cache import clear_all_caches
clear_all_caches()
```

## Best Practices for Caching

1. **Cache Selectively**: Cache data that is:
   - Expensive to compute or retrieve
   - Accessed frequently
   - Relatively stable (doesn't change often)

2. **Set Appropriate TTLs**: Balance freshness with performance
   - Short TTLs for frequently changing data
   - Longer TTLs for stable data

3. **Plan for Cache Invalidation**: Ensure data consistency by properly invalidating caches when data changes

4. **Monitor Cache Performance**: Regularly check hit rates and adjust caching strategies accordingly

5. **Consider Memory Usage**: Be mindful of memory consumption, especially for large datasets

6. **Use Layered Caching**: Combine different caching strategies for optimal performance

7. **Test with and without Caching**: Ensure your application works correctly even if the cache fails

## Performance Impact

Effective caching can dramatically improve application performance:

- **Reduced Database Load**: Fewer queries hitting the database
- **Lower Latency**: Faster response times for cached operations
- **Improved Scalability**: Support more concurrent users with the same resources
- **Reduced Network Traffic**: Less data transferred between application and database

## Conclusion

Caching is a powerful optimization technique that can significantly improve the performance of your rhosocial ActiveRecord applications. By implementing the appropriate caching strategies at different levels of your application, you can reduce database load, improve response times, and enhance overall application scalability.

Remember that caching introduces complexity, especially around cache invalidation. Always ensure your caching strategy maintains data consistency while providing performance benefits.