cachetic 0.5.0

Simple cache with pydantic.

cachetic

A simple, type-safe caching library supporting Redis and disk storage with automatic Pydantic serialization.

Features

• Type-safe: Full type checking with generic support
• Flexible backends: Local disk cache (diskcache) or Redis
• Pydantic integration: Automatic serialization for any type via TypeAdapter
• Compression support: Optional zstd/zlib compression with automatic detection
• Simple API: Just get() and set() with optional TTL

Installation

pip install cachetic

Quick Start

Basic Usage

import pydantic
from cachetic import Cachetic

# Define your model
class Person(pydantic.BaseModel):
    name: str
    age: int

# Create cache instance
cache = Cachetic[Person](
    object_type=pydantic.TypeAdapter(Person),
    cache_url=".cache"  # Local disk cache
)

# Store and retrieve
person = Person(name="Alice", age=30)
cache.set("user:1", person)

result = cache.get("user:1")
print(result.name)  # "Alice"

Redis Backend

cache = Cachetic[Person](
    object_type=pydantic.TypeAdapter(Person),
    cache_url="redis://localhost:6379/0"
)

Primitive Types

# String cache
str_cache = Cachetic[str](
    object_type=pydantic.TypeAdapter(str),
    cache_url=".cache"
)

str_cache.set("greeting", "Hello, World!")
print(str_cache.get("greeting"))  # "Hello, World!"

# List cache
list_cache = Cachetic[list[str]](
    object_type=pydantic.TypeAdapter(list[str]),
    cache_url=".cache"
)

list_cache.set("items", ["apple", "banana", "cherry"])

Complex Types

from typing import Dict, List

# Dictionary cache
data = {"users": [{"id": 1, "name": "Alice"}], "total": 1}
dict_cache = Cachetic[Dict](
    object_type=pydantic.TypeAdapter(Dict),
    cache_url=".cache"
)

dict_cache.set("user_data", data)

# List of models
people_cache = Cachetic[List[Person]](
    object_type=pydantic.TypeAdapter(List[Person]),
    cache_url=".cache"
)

people = [Person(name="Alice", age=30), Person(name="Bob", age=25)]
people_cache.set("team", people)

Compression Support

Enable compression to reduce storage space and bandwidth usage:

# Enable compression (auto-selects best algorithm)
cache = Cachetic[Person](
    object_type=pydantic.TypeAdapter(Person),
    cache_url=".cache",
    compression=True  # New in v0.5.0
)

person = Person(name="Alice", age=30)
cache.set("user:1", person)  # Automatically compressed
result = cache.get("user:1")  # Automatically decompressed

Compression Algorithms:

• zstd (preferred): Used if zstandard package is installed
• zlib (fallback): Built-in Python standard library

Automatic Detection:

• Caches with compression=False can still read compressed data
• Automatic decompression occurs when compressed data is detected
• Seamless migration between compressed and uncompressed caches

Installation with zstd support:

pip install cachetic zstandard

Configuration

Constructor Parameters

• object_type: pydantic.TypeAdapter[T] - Required type adapter for serialization
• cache_url: Cache backend - file path for disk cache or redis://... for Redis
• default_ttl: Default expiration in seconds (-1 = no expiration, 0 = disabled)
• prefix: Key prefix for all cache operations
• compression: Enable compression for cached values (default: False)

TTL Examples

# No expiration (default)
cache = Cachetic[str](
    object_type=pydantic.TypeAdapter(str),
    default_ttl=-1
)

# 1 hour expiration
cache = Cachetic[str](
    object_type=pydantic.TypeAdapter(str),
    default_ttl=3600
)

# Per-operation TTL
cache.set("key", "value", ex=300)  # 5 minutes

Environment Variables

Use CACHETIC_ prefix:

export CACHETIC_CACHE_URL="redis://localhost:6379/0"
export CACHETIC_DEFAULT_TTL=3600
export CACHETIC_PREFIX="myapp"
export CACHETIC_COMPRESSION=true

Error Handling

from cachetic import CacheNotFoundError

# get() returns None for missing keys
result = cache.get("nonexistent")  # None

# get_or_raise() throws exception
try:
    result = cache.get_or_raise("nonexistent")
except CacheNotFoundError:
    print("Key not found")

License

MIT License