ORMBridge Caching

ORMBridge provides automatic caching to improve performance for API operations. This document explains how caching works in ORMBridge and how to configure it for your application.

How ORMBridge Caching Works

Automatic Instance-Level Caching

ORMBridge uses instance-level caching rather than query-level caching:

• Individual model instances and their serialized representations are cached
• Dependencies between objects are automatically tracked
• When an object changes, all cached items that depend on it are invalidated

The Serialization Process

1. When a client requests data, ORMBridge generates a unique cache key for each instance
2. If a cached representation exists, it's used immediately
3. Otherwise, ORMBridge serializes the instance and stores it in the cache
4. Dependencies are tracked so when an object changes, related cache entries are invalidated

Configuring Caching

ORMBridge integrates with Django's caching framework by default:

# settings.py
CACHES = {
    'default': {
        'BACKEND': 'django.core.cache.backends.redis.RedisCache',
        'LOCATION': 'redis://127.0.0.1:6379/1',
    }
}

# ORMBridge cache settings
ORMBRIDGE_CACHE = {
    'NAME': 'default',      # Which Django cache to use
    'DEFAULT_TTL': 3600,    # Default time-to-live in seconds (optional)
}

If no Django cache is configured, ORMBridge falls back to using fakeredis for development environments.

Important: Bulk Operations and Signals

Since cache invalidation relies on Django's signals, you must manually trigger signals after bulk backend operations:

# After bulk operations, manually trigger signals to ensure cache invalidation
from django.db.models.signals import post_save

# After a bulk update
Product.objects.filter(category='electronics').update(discount=10)
for product in Product.objects.filter(category='electronics'):
    post_save.send(sender=Product, instance=product, created=False)

This ensures that cache entries are properly invalidated when performing bulk operations that bypass Django's normal signal system.

Performance Considerations

• ORMBridge's caching system works best for data with high read-to-write ratios
• Configure appropriate TTL values to prevent stale data
• For high-traffic applications, consider using a dedicated Redis instance for caching

Debugging Cache

To verify caching is working correctly, enable debug logging:

# settings.py
LOGGING = {
    'loggers': {
        'ormbridge.caching': {
            'level': 'DEBUG',
        },
    },
}

This will show cache hits, misses, and invalidations in your logs.