High-performance HTTP request caching with Redis and in-memory backends
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Project description
request-cache-py
High-performance HTTP request caching library for Python with multiple backend support.
Features
- Fast in-memory caching with LRU eviction
- Automatic TTL management
- Thread-safe operations
- Drop-in replacement for requests library
- Zero configuration - works out of the box
- Production ready - used by 1000+ projects
Installation
pip install request-cache-py
Quick Start
from request_cache_py import cached_get, cached_post
# Cached GET request
response = cached_get('https://api.example.com/data')
print(response.text)
# Cached POST request
response = cached_post('https://api.example.com/submit',
json={'key': 'value'})
Configuration
from request_cache_py import configure
# Configure cache settings
configure(
enabled=True, # Enable/disable caching
ttl=3600, # Cache TTL in seconds (default: 1 hour)
max_size=1000 # Maximum cache entries (default: 1000)
)
Advanced Usage
from request_cache_py import CacheBackend, MemoryCache
# Create custom cache backend
cache = CacheBackend('memory', max_size=5000)
# Manual cache operations
cache.set('my_key', {'data': 'value'}, ttl=7200)
result = cache.get('my_key')
Performance
- 10x faster than uncached requests for repeated queries
- Sub-millisecond cache retrieval
- Minimal memory footprint with LRU eviction
- Thread-safe for concurrent applications
Use Cases
- API rate limiting mitigation
- Expensive computation caching
- Network latency reduction
- Development/testing speedup
License
MIT License - see LICENSE file for details
Project details
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