# Velithon Memory Optimization Guide

This guide explains Velithon's simplified memory optimization approach focused on practical performance gains.

## Overview

Velithon provides streamlined memory optimization features:

- **Simplified Garbage Collection**: Basic GC tuning without complex scheduling
- **Lightweight Caching**: Minimal caching for frequently used small data
- **Error-Tolerant Cleanup**: Graceful handling of memory management operations
- **No-Op Fallbacks**: Safe defaults when optimizations can't be applied

## Quick Start

### Basic Setup

```python
from velithon import Velithon
from velithon._utils import MemoryManager

# Create your app
app = Velithon()

# Basic memory management initialization
memory_manager = MemoryManager()

@app.get("/")
async def root():
    return {"message": "Optimized Velithon app!"}
```

### Simple Memory Management

```python
from velithon._utils import MemoryManager

# Initialize with basic error handling
memory_manager = MemoryManager()

# Safe cleanup (handles errors gracefully)
memory_manager.cleanup()

# Check if memory management is available
if hasattr(memory_manager, 'gc_disable'):
    memory_manager.gc_disable()
    # ... do work ...
    memory_manager.gc_enable()
```

## Current Memory Management

### Simplified Approach

Velithon now uses a simplified memory management approach:

```python
from velithon._utils import MemoryManager

class MemoryManager:
    """Simplified memory management with error-tolerant operations."""
    
    def __init__(self):
        """Initialize with basic error handling."""
        try:
            import gc
            self.gc = gc
        except ImportError:
            self.gc = None
    
    def cleanup(self):
        """Safe cleanup that handles errors gracefully."""
        if self.gc:
            try:
                self.gc.collect()
            except Exception:
                pass  # Graceful degradation
    
    def gc_disable(self):
        """Disable GC if available."""
        if self.gc:
            self.gc.disable()
    
    def gc_enable(self):
        """Enable GC if available."""
        if self.gc:
            self.gc.enable()
```

### What Changed

**Removed Complex Features:**
- ❌ Object pooling (overhead > benefit for web workloads)
- ❌ Complex memory monitoring and statistics
- ❌ Automatic memory threshold management
- ❌ Weak reference caching systems
- ❌ Specialized middleware for memory optimization

**Why Simplified?**
- Object pools added more overhead than they saved
- Complex monitoring consumed resources without clear benefit
- Automatic thresholds were difficult to tune correctly
- Python's built-in GC is quite effective for web workloads

### Best Practices

#### 1. Use Generators for Large Data

```python
@app.get("/large-dataset")
async def get_large_dataset():
    """Use generators to avoid loading everything into memory"""
    
    def data_generator():
        # Yield items one by one instead of loading all
        for i in range(100000):
            yield {"id": i, "data": f"Item {i}"}
    
    return JSONResponse(data_generator())
```

#### 2. Pagination for User-Facing APIs

```python
@app.get("/users")
async def get_users(page: int = 1, size: int = 100):
    """Use pagination to limit memory usage"""
    
    offset = (page - 1) * size
    users = fetch_users(offset=offset, limit=size)
    
    return JSONResponse({
        "users": users,
        "page": page,
        "total_pages": calculate_total_pages(size)
    })
```

#### 3. Let orjson Handle Large JSON

```python
@app.get("/analytics")
async def get_analytics():
    """orjson efficiently handles large objects"""
    
    large_data = {
        "metrics": list(range(50000)),
        "timestamps": [datetime.now() for _ in range(50000)]
    }
    
    # orjson handles this efficiently without special optimization
    return JSONResponse(large_data)
```

## Performance Monitoring

### Simple Memory Tracking

```python
import psutil

@app.get("/health/memory")
async def memory_health():
    """Simple memory usage check"""
    
    memory = psutil.virtual_memory()
    
    return JSONResponse({
        "memory_percent": memory.percent,
        "memory_available_gb": memory.available / (1024**3),
        "status": "healthy" if memory.percent < 80 else "warning"
    })
```

### Process-Level Monitoring

```python
import os
import psutil

