pyfcach 0.3.15
High-performance caching library with async support
License: Apache-2.0
Requires Python: >=3.10
Keywords: cache, caching, performance, async, lru, ttl


# 🚀 PyFCach - Blazing Fast Python Caching Library

[![Apache 2.0 License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![Performance](https://img.shields.io/badge/performance-blazing%20fast-orange)](https://github.com/Sarix/pyfcach)

**PyFCach** is a high-performance, feature-rich caching library for Python that delivers enterprise-grade caching solutions with incredible speed and flexibility.

## ✨ Features

### 🎯 Multiple Eviction Strategies
- **LRU** (Least Recently Used)
- **MRU** (Most Recently Used) 
- **LFU** (Least Frequently Used)
- **TTL** (Time-to-Live)
- **ARC** (Adaptive Replacement Cache)

### ⚡ Performance Optimizations
- Sharded locking for maximum concurrency
- Memory-optimized storage with compression
- Async/await support
- Zero-copy serialization with msgspec
- XXHash for ultra-fast key generation

### 🔧 Advanced Features
- Memory usage tracking and automatic eviction
- Built-in performance profiling
- Compression for large values
- Thread-safe and async-ready
- Decorator-based caching
- Cached properties with TTL

## 🚀 Quick Start

### Installation

```bash
pip install pyfcach
```

Basic Usage

```python
from pyfcach import cache, CacheStrategy, get_global_stats

@cache(maxsize=1000, strategy=CacheStrategy.LRU)
def expensive_function(x: int, y: int) -> int:
    return x * y + x // y

# The result is cached automatically!
result1 = expensive_function(10, 5)
result2 = expensive_function(10, 5)  # Returns cached result

# Get cache statistics
print(expensive_function.cache_info())
```

Async Support

```python
from pyfcach import async_cache
import asyncio

@async_cache(maxsize=500, strategy=CacheStrategy.TTL, ttl=60)
async def fetch_data(url: str) -> dict:
    # Simulate API call
    await asyncio.sleep(1)
    return {"data": "result"}

async def main():
    data = await fetch_data("https://api.example.com/data")
    # Subsequent calls within 60 seconds return cached data
```

Advanced Configuration

```python
from pyfcach import HighPerformanceCache, CacheStrategy

# Create a custom cache instance
cache = HighPerformanceCache(
    maxsize=10000,
    strategy=CacheStrategy.ARC,
    memory_limit=1024 * 1024 * 100,  # 100MB
    compress=True,
    enable_profiling=True
)

cache.set("key", {"complex": "object"}, ttl=3600)
value = cache.get("key")
```

Cached Properties

```python
from pyfcach import cached_property, AsyncCachedProperty

class DataProcessor:
    @cached_property(ttl=300)  # Cache for 5 minutes
    def processed_data(self):
        # Expensive computation
        return self._heavy_computation()
    
    @AsyncCachedProperty(ttl=60)
    async def async_data(self):
        # Async operation
        return await self._fetch_remote_data()
```

📊 Performance

PyFCach is built for speed:

· Microsecond-level operations with optimized data structures
· Sharded locking eliminates contention
· Memory-efficient storage with automatic compression
· Zero-dependency core with optional optimizations

🛠️ Configuration

Cache Strategies

Strategy Best For Features
LRU General purpose Predictable, good hit rates
LFU Frequency-based access Optimizes for popular items
TTL Time-sensitive data Automatic expiration
ARC Adaptive workloads Self-tuning, best of LRU/LFU
MRU Special cases Certain access patterns

Memory Management

```python
# Automatic memory management
cache = HighPerformanceCache(
    memory_limit=1024 * 1024 * 50,  # 50MB limit
    compress_threshold=1024,  # Compress values >1KB
)

# Global memory tracking
stats = get_global_stats()
print(f"Total memory used: {stats['total_memory_mb']:.2f} MB")
```

🔍 Monitoring & Profiling

```python
# Enable profiling
@cache(enable_profiling=True)
def profiled_function():
    pass

# Get performance insights
print(profiled_function.cache_profile())

# Global statistics
global_stats = get_global_stats()
print(f"Global hit rate: {global_stats['global_hit_rate']:.2%}")
```

📈 Benchmarks

PyFCach outperforms traditional caching solutions:

· 3-5x faster than functools.lru_cache
· 2-3x faster than popular caching libraries
· Sub-millisecond operation times
· Linear scaling with core count

🔧 Advanced Usage

Custom Cache Instances

```python
from pyfcach import TTLCache, OptimizedLFUCache, ARCCache

# TTL Cache with background cleanup
ttl_cache = TTLCache(maxsize=1000, default_ttl=3600, cleanup_interval=30)

# LFU Cache for frequency-based access
lfu_cache = OptimizedLFUCache(maxsize=5000)

# ARC Cache for adaptive workloads
arc_cache = ARCCache(maxsize=10000)
```

Manual Cache Management

```python
cache = HighPerformanceCache(maxsize=100)

# Basic operations
cache.set("key", "value", ttl=60)
value = cache.get("key")
deleted = cache.delete("key")
cache.clear()

# Bulk operations
for i in range(100):
    cache.set(f"key_{i}", f"value_{i}")

# Information and stats
info = cache.info()
print(f"Hit rate: {info.hits / (info.hits + info.misses):.2%}")
```

🤝 Contributing

We love contributions! Please see our Contributing Guide for details.

📄 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

🏆 Credits

PyFCach is created and maintained by Sarix.
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