CacheMaster 0.0.3

A flexible caching system with in-memory and Redis support

🚀 CacheMaster - High-Performance Caching Library

CacheMaster is a high-performance caching library that supports both in-memory and Redis-based caching. It provides a simple, flexible API for efficiently storing, retrieving, and managing cache data.

✨ Features

• ✅ Supports In-Memory & Redis Caching
• 🔒 Thread-safe local caching
• 🛠 Customizable serialization (Pickle & JSON for Redis)
• ⏳ Automatic expiration & TTL management
• ⚡ Batch operations for performance optimization
• 🎯 Decorator support for function caching

📂 Project Structure

CacheMaster/
│── src/
│   ├── cache/
│   │   ├── __init__.py
│   │   ├── base_cache.py
│   │   ├── core_cache.py
│   │   ├── mem_cache.py
│   │   ├── redis_cache.py
│   ├── config/
│   │   ├── __init__.py
│   │   ├── cache_config.py
│── tests/
│── pyproject.toml
│── setup.cfg
│── setup.py
│── README.md
│── LICENSE
│── .gitignore

📦 Installation

CacheMaster requires Python 3.7+ and the following dependencies:

🔧 Dependencies:

• redis>=4.5.0 – Required for Redis-based caching
• pytest>=7.0.0 – For testing
• pytest-mock>=3.10 – Mocks for testing
• setuptools>=65.0.0, wheel>=0.40.0 – For packaging

📥 Install via pip

pip install CacheMaster

🚀 Running the Cache Application

To start using CacheMaster, initialize a cache instance:

from cachemaster.core_cache import CoreCache, CacheType

# Local (In-Memory) Cache
local_cache = CoreCache(app_name="my_app", cache_type=CacheType.LOCAL_CACHE)

# Redis Cache
redis_cache = CoreCache(app_name="my_app", cache_type=CacheType.REDIS_CACHE, redis_url="redis://localhost")

🔥 Usage

Basic Cache Operations

# Set and Get
local_cache.set("user_123", {"name": "John"}, timeout=600)
user = local_cache.get("user_123")  # Returns: {"name": "John"}

# Delete
local_cache.delete("user_123")

# Check if key exists
exists = local_cache.has_key("user_123")  # Returns: False

Function Caching with Decorators

@redis_cache.decorator_cache(namespace="users", timeout=300, keys=["user_id"])
def get_user_data(user_id):
    return expensive_database_call(user_id)

Batch Operations

# Set multiple values
local_cache.set_many({"key1": "value1", "key2": "value2"}, timeout=300)

# Get multiple values
values = local_cache.get_many(["key1", "key2"])  # Returns: {"key1": "value1", "key2": "value2"}

# Delete multiple values
local_cache.delete_many(["key1", "key2"]) 

Incrementing & Decrementing

local_cache.set("counter", 10)
local_cache.incr("counter", 2)  # 12
local_cache.decr("counter", 1)  # 11

🛠 Running Tests

To ensure everything works correctly, install the development dependencies and run:

pip install -e .[dev]
pytest tests/

If using requirements.txt:

pip install -r requirements.txt
pytest tests/

To ensure everything works correctly, run:

pytest tests/

🎯 How CacheMaster Helps You

• 🚀 Boosts performance by reducing redundant database queries.
• 💾 Efficient memory management with TTL-based expiration.
• 🔄 Scalable & flexible with both local and distributed caching.
• 📌 Easy to integrate with existing applications.

📜 License

MIT License