🚀 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/
│── CHANGELOG.md
│── poetry.lock
│── pyproject.toml
│── README.md
│── LICENSE
│── .bumpver.toml
│── .gitignore

📦 Installation

CacheMaster requires Python 3.7+ and the following dependencies:

🔧 Dependencies:

• redis>=4.0.0 – Required for Redis-based caching
• pytest>=7.0.0 – For testing
• pytest-mock>=3.10 – Mocks for testing
• poetry>=2.1.2 – For packaging

📥 Installation

pip install CacheMaster

Or using Poetry:

poetry add dict2objects

🚀 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

🛠 Development & Contribution

1. Clone the repository:

   git clone git@github.com:JanardhanSingh98/CacheMaster.git
   cd CacheMaster

2. Install dependencies:

   poetry install

3. Run tests:

   poetry run coverage run --omit="tests*" -m pytest

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🎯 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

This project is licensed under the MIT License. See the LICENSE file for details.

🌟 Like this project? Give it a star ⭐ on GitHub!