memoshelve 1.0.9

A persistent memoization decorator using Python's shelve with two-tier caching (memory + disk).

memoshelve

A persistent memoization decorator using Python's shelve with two-tier caching (memory + disk).

Features

• Two-tier caching: in-memory + persistent disk storage
• Async and sync function support
• Cache inspection and management
• Optional enhanced serialization with dill and stablehash

Installation

pip install memoshelve

# For enhanced serialization
pip install memoshelve[robust]

Usage

Basic Decorator

from memoshelve import cache

@cache(filename="cache.db")
def expensive_function(x, y):
    return x * y + 42

result = expensive_function(10, 20)  # Computed and cached
result = expensive_function(10, 20)  # Retrieved from cache

Context Manager

from memoshelve import memoshelve

with memoshelve(expensive_function, "cache.db") as cached_fn:
    result = cached_fn(10, 20)

Async Functions

@cache(filename="async_cache.db")
async def async_function(data):
    return len(data) * 42

API

Cache Methods

@cache(filename="example.db")
def compute(x, y):
    return x ** y

# Check if cached
compute.__contains__(2, 3)

# Get without computing
compute.get(2, 3)  # Raises KeyError if not cached

# Get with status
result, status = compute.__call_with_status__(2, 3)
# status: "cached (mem)", "cached (disk)", or "miss"

# Manual operations
compute.put(2, 3, 8)      # Store value
compute.uncache(2, 3)     # Remove from cache

Configuration

@cache(
    filename="cache.db",
    ignore=["debug"],         # Ignore parameters in cache key
    get_hash=custom_hash,     # Custom hash function
    disable=False,            # Toggle caching
    print_cache_miss=True,    # Log cache misses
)
def my_function(data, debug=False):
    return process(data)

Cache Management

from memoshelve import compact

# Compact cache file
compact("cache.db", backup=True)

# Access metadata
metadata = my_function.memoshelve
metadata.disk_keys()     # Keys in disk cache
metadata.mem_keys()      # Keys in memory cache
metadata.compact()       # Compact this cache

Storage

Default cache location: ~/.cache/memoshelve/ (configurable via XDG_CACHE_HOME)

Cache files use Python's shelve module and may create multiple files (.db, .dir, .dat).

License

MIT License