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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

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