pydantic-typed-cache 1.0.1

Async cache library for Pydantic models without FastAPI dependencies

Pydantic Typed Cache

An async cache library for Pydantic models without FastAPI dependencies. This library provides a simple decorator-based caching mechanism for async functions that return Pydantic models or other Python objects.

Note: This project was inspired by fastapi-cache but designed to work independently of FastAPI/Starlette, making it suitable for any async Python application.

Features

• 🚀 Simple decorator-based caching for async functions
• 🔄 Support for both async and sync functions (sync functions run in thread pool)
• 📦 Multiple backends: Redis, In-Memory
• 🎯 Type-safe with Pydantic model support
• 🔑 Customizable cache key generation
• 📝 Multiple serialization options (JSON, Pickle)
• ⚡ Zero FastAPI/Starlette dependencies
• 🔧 Flexible type conversion with model parameter
• ✅ Support for nullable/optional types with proper None handling
• 🎭 Support for Union types and complex type hints

Installation

# Basic installation
pip install pydantic-typed-cache

# With orjson for faster JSON serialization
pip install pydantic-typed-cache[orjson]

# With development dependencies
pip install pydantic-typed-cache[dev]

Quick Start

import asyncio
from pydantic import BaseModel
from pydantic_cache import PydanticCache, cache
from pydantic_cache.backends.inmemory import InMemoryBackend

# Define a Pydantic model
class User(BaseModel):
    id: int
    name: str
    email: str

# Initialize cache
backend = InMemoryBackend()
PydanticCache.init(backend, prefix="myapp", expire=60)

# Cache a function
@cache(expire=120, namespace="users")
async def get_user(user_id: int) -> User:
    # Expensive operation (e.g., database query)
    return User(id=user_id, name="John", email="[e-mail]")

# Use the cached function
async def main():
    user = await get_user(1)  # First call - cache miss
    user = await get_user(1)  # Second call - cache hit
    
asyncio.run(main())

Advanced Features

Type Conversion with model Parameter

The model parameter allows you to force type conversion of cached values. This works with any Python type, not just Pydantic models:

# Convert string to int
@cache(model=int)
async def get_count() -> str:
    return "42"  # Will be converted to int 42

# Convert dict to Pydantic model
class UserResponse(BaseModel):
    id: int
    name: str

@cache(model=UserResponse)
async def get_user_data(user_id: int) -> dict:
    # Returns dict that will be converted to UserResponse
    return {"id": user_id, "name": "Alice"}

# Work with collections
@cache(model=list[int])
async def get_scores() -> list[str]:
    return ["95", "87", "92"]  # Converted to [95, 87, 92]

# Support Union types
@cache(model=int | str)
async def get_flexible_value(use_number: bool) -> any:
    return 123 if use_number else "hello"

# Support Optional types
@cache(model=int | None)
async def get_optional_value(value: str | None) -> str | None:
    return value  # "123" becomes 123, None stays None

Nullable/Optional Type Support

The library properly handles None values in optional types, distinguishing between cached None values and cache misses:

# Function returning Optional types
@cache(namespace="users")
async def get_user(user_id: int) -> User | None:
    if user_id < 0:
        return None  # This None will be cached
    return User(id=user_id, name="John", email="[e-mail]")

# First call with -1
user = await get_user(-1)  # Returns None (cache miss, stores None)
user = await get_user(-1)  # Returns None (cache hit, retrieves None)

# Pydantic models with optional fields
class Profile(BaseModel):
    id: int
    bio: str | None = None
    age: int | None = None

@cache(namespace="profiles")
async def get_profile(user_id: int) -> Profile:
    return Profile(id=user_id, bio=None, age=None)  # None fields are properly cached

Sync Function Support

Sync functions are automatically wrapped and run in a thread pool:

@cache(namespace="compute")
def expensive_computation(x: int, y: int) -> int:
    # This sync function will be run in a thread pool
    import time
    time.sleep(1)
    return x * y

# Can be called as async
result = await expensive_computation(10, 20)

Backends

In-Memory Backend

Perfect for development and testing:

from pydantic_cache.backends.inmemory import InMemoryBackend

backend = InMemoryBackend()
PydanticCache.init(backend)

Redis Backend

For production use with persistence:

from redis.asyncio import Redis
from pydantic_cache.backends.redis import RedisBackend

redis = Redis(host="localhost", port=6379)
backend = RedisBackend(redis)
PydanticCache.init(backend)

Configuration

Global Configuration

PydanticCache.init(
    backend=backend,
    prefix="myapp",        # Prefix for all cache keys
    expire=300,            # Default expiration in seconds
    coder=JsonCoder,       # Serialization method (JsonCoder or PickleCoder)
    key_builder=my_key_builder,  # Custom key builder function
    enable=True            # Enable/disable caching globally
)

Per-Decorator Configuration

@cache(
    expire=120,           # Override default expiration
    namespace="users",    # Namespace for this function
    coder=PickleCoder,   # Override default coder
    key_builder=custom_key_builder,  # Custom key builder
    model=UserModel      # Force type conversion
)
async def cached_function():
    pass

Serialization

JsonCoder (Default)

• Human-readable cache values
• Good for debugging
• Supports most Python types and Pydantic models
• Moderate performance

OrjsonCoder (Recommended for performance)

• 2-3x faster than standard JSON
• Efficient datetime handling
• Better performance with large datasets
• Requires: pip install pydantic-typed-cache[orjson]

from pydantic_cache import OrjsonCoder

PydanticCache.init(backend, coder=OrjsonCoder)

PickleCoder

• Supports all Python objects
• Fast serialization
• Binary format (not human-readable)
• Better for complex nested structures

from pydantic_cache.coder import JsonCoder, OrjsonCoder, PickleCoder

# Set globally
PydanticCache.init(backend, coder=OrjsonCoder)  # Recommended

# Or per decorator
@cache(coder=JsonCoder)
async def my_function():
    pass

Performance Comparison

Coder	Speed	Human Readable	Size	Use Case
JsonCoder	Moderate	✅	Small	Debugging, small data
OrjsonCoder	Fast	✅	Small	Production, large data
PickleCoder	Fast	❌	Medium	Complex objects

Cache Management

# Clear specific key
await PydanticCache.clear(key="specific_key")

# Clear entire namespace
await PydanticCache.clear(namespace="users")

# Clear all cache
await PydanticCache.clear()

# Disable caching temporarily
PydanticCache.set_enable(False)

# Re-enable caching
PydanticCache.set_enable(True)

# Check if caching is enabled
is_enabled = PydanticCache.get_enable()

Custom Key Builder

Create custom cache key generation logic:

from pydantic_cache.types import KeyBuilder

def custom_key_builder(
    func,
    namespace: str,
    args: tuple,
    kwargs: dict
) -> str:
    # Custom logic to generate cache key
    func_name = func.__name__
    args_str = "_".join(str(arg) for arg in args)
    return f"{namespace}:{func_name}:{args_str}"

# Use globally
PydanticCache.init(backend, key_builder=custom_key_builder)

# Or per decorator
@cache(key_builder=custom_key_builder)
async def my_function():
    pass

Examples

Complex Type Conversions

# Nested structures
@cache(model=list[list[int]])
async def get_matrix() -> list[list[str]]:
    return [["1", "2"], ["3", "4"]]  # Converted to [[1, 2], [3, 4]]

# List of Pydantic models
class Item(BaseModel):
    id: int
    name: str
    price: float

@cache(model=list[Item])
async def get_items() -> list[dict]:
    return [
        {"id": "1", "name": "Item 1", "price": "9.99"},
        {"id": "2", "name": "Item 2", "price": "19.99"}
    ]

# Complex Union types
@cache(model=User | dict | None)
async def get_flexible_data(data_type: str) -> any:
    if data_type == "user":
        return {"id": 1, "name": "Alice", "email": "[e-mail]"}
    elif data_type == "dict":
        return {"key": "value"}
    else:
        return None

Model-to-Model Conversion

class DetailedUser(BaseModel):
    id: int
    name: str
    email: str
    age: int
    bio: str

class SimpleUser(BaseModel):
    id: int
    name: str

# Convert DetailedUser to SimpleUser
@cache(model=SimpleUser)
async def get_simple_user(user_id: int) -> DetailedUser:
    return DetailedUser(
        id=user_id,
        name="Alice",
        email="[e-mail]",
        age=30,
        bio="Developer"
    )
    # Result will be SimpleUser with only id and name

Working with External APIs

class APIResponse(BaseModel):
    status: str
    data: dict
    timestamp: str | None = None

# Force API responses to be validated as Pydantic models
@cache(model=APIResponse, expire=300, namespace="api")
async def fetch_from_api(endpoint: str) -> dict:
    # Make actual API call here
    return {
        "status": "success",
        "data": {"result": "some data"},
        "timestamp": "2024-01-01T12:00:00Z"
    }

# Result is always validated as APIResponse model
response = await fetch_from_api("/users")
print(response.status)  # Type-safe access

Testing

import pytest
from pydantic_cache import PydanticCache
from pydantic_cache.backends.inmemory import InMemoryBackend

@pytest.fixture
async def cache_setup():
    backend = InMemoryBackend()
    PydanticCache.init(backend, prefix="test", expire=60)
    yield
    await backend.clear()

async def test_caching(cache_setup):
    call_count = 0
    
    @cache(namespace="test")
    async def get_value():
        nonlocal call_count
        call_count += 1
        return "result"
    
    result1 = await get_value()
    result2 = await get_value()
    
    assert result1 == result2
    assert call_count == 1  # Called only once due to caching

Acknowledgments

This project was inspired by fastapi-cache by @long2ice. While fastapi-cache provides excellent caching capabilities for FastAPI applications, pydantic-typed-cache was created to offer similar functionality for general async Python applications without the FastAPI/Starlette dependency.

License

MIT