caching-utilities

name	caching-utilities
description	Guide for using caching utilities in speedy_utils, including memory, disk, and hybrid caching strategies for sync and async functions.

Caching Utilities Guide

This skill provides comprehensive guidance for using the caching utilities in speedy_utils.

When to Use This Skill

Use this skill when you need to:

• Optimize performance by caching expensive function calls.
• Persist results across program runs using disk caching.
• Use memory caching for fast access within a single run.
• Handle caching for both synchronous and asynchronous functions.
• Use imemoize for persistent caching in interactive environments like Jupyter notebooks.

Prerequisites

• speedy_utils installed in your environment.

Core Capabilities

Universal Memoization (@memoize)

• Supports memory, disk, and both (hybrid) caching backends.
• Works with both sync and async functions.
• Configurable LRU cache size for memory caching.
• Custom key generation strategies.

Interactive Memoization (@imemoize)

• Designed for Jupyter notebooks and interactive sessions.
• Persists cache across module reloads (%load).
• Uses global memory cache.

Object Identification (identify)

• Generates stable, content-based identifiers for arbitrary Python objects.
• Handles complex types like DataFrames, Pydantic models, and nested structures.

Usage Examples

Example 1: Basic Hybrid Caching

Cache results in both memory and disk.

from speedy_utils import memoize
import time

@memoize(cache_type='both', size=128)
def expensive_func(x: int):
    time.sleep(1)
    return x * x

Example 2: Async Disk Caching

Cache results of an async function to disk.

from speedy_utils import memoize
import asyncio

@memoize(cache_type='disk', cache_dir='./my_cache')
async def fetch_data(url: str):
    # simulate network call
    await asyncio.sleep(1)
    return {"data": "content"}

Example 3: Custom Key Function

Use a custom key function for complex arguments.

from speedy_utils import memoize

def get_user_id(user):
    return user.id

@memoize(key=get_user_id)
def process_user(user):
    # ...
    pass

Example 4: Interactive Caching (Notebooks)

Use @imemoize to keep cache even if you reload the cell/module.

from speedy_utils import imemoize

@imemoize
def notebook_func(data):
    # ...
    return result

Guidelines

1. Choose the Right Backend:

   • Use memory for small, fast results needed frequently in one session.
   • Use disk for large results or to persist across runs.
   • Use both (default) for the best of both worlds.

2. Key Stability:

   • Ensure arguments are stable (e.g., avoid using objects with changing internal state as keys unless you provide a custom key function).
   • identify handles most common types, but be careful with custom classes without __repr__ or stable serialization.

3. Cache Directory:

   • Default disk cache is ~/.cache/speedy_cache.
   • Override cache_dir for project-specific caching.

4. Async Support:

   • The decorators automatically detect async functions and handle await correctly.
   • Do not mix sync/async usage without proper await.

Common Patterns

Pattern: Ignoring self

By default, ignore_self=True is set. This means methods on different instances of the same class will share cache if other arguments are the same. Set ignore_self=False if the instance state matters.

class Processor:
    def __init__(self, multiplier):
        self.multiplier = multiplier

    @memoize(ignore_self=False)
    def compute(self, x):
        return x * self.multiplier

Limitations

• Pickle Compatibility: Disk caching relies on pickle (or JSON). Ensure return values are serializable.
• Cache Invalidation: There is no automatic TTL (Time To Live) or expiration. You must manually clear cache files if data becomes stale.