Welcome to the lruheap documentation! This package provides an in-memory LRU (Least Recently Used) cache implementation backed by a min-heap priority queue, along with decorator-based caching for Python functions.

Contents

• Guides
  • Features
  • Installation
  • Import
  • Quickstart
  • Testing
• Usage
  • LRUCache class initialization
  • set method
  • get method
  • get_dict method
  • get_duration method
  • get_lru_element method
  • get_capacity method
  • get_cache method
  • get_ttl method
  • clear_all method
  • clear_cache_key method
  • is_empty method
  • ttl property
  • Special methods
  • @lru_cache decorator
  • @lru_cache_time decorator
  • Enable thread_safe parameter
• Further Examples
  • Using with Django
  • Using with Flask
  • Possible use cases
• API References
  • lru.lrucache
    • BoundedLRUCache
      • BoundedLRUCache.clear_all()
    • LRUCache
      • LRUCache.clear_all()
      • LRUCache.clear_cache_key()
      • LRUCache.get()
      • LRUCache.get_cache()
      • LRUCache.get_capacity()
      • LRUCache.get_dict()
      • LRUCache.get_duration()
      • LRUCache.get_lru_element()
      • LRUCache.get_ttl()
      • LRUCache.is_empty()
      • LRUCache.set()
      • LRUCache.ttl
  • lru.heap
    • Heap
      • Heap.add()
      • Heap.build_floyd_heap
      • Heap.remove()
      • Heap.remove_key()
      • Heap.update()
  • lru.decorators
    • lru_cache()
    • lru_cache_time()
  • lru.utils
    • BypassThreadSafe
    • generate_hash_key()
• Caveats
  • Known limitations
  • When is it suitable?
• Miscellaneous
  • Why object-level caching instead of database filtering?
  • Purpose of this package
• Roadmap
  • Completed
  • Planned / In progress
    • Contributions