# Architecture

LogXide leverages Rust's performance and safety with Python's ease of use through a sophisticated async architecture.

## Core Architecture

```
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Python API    │    │   Rust Core      │    │  Tokio Runtime  │
│                 │    │                  │    │                 │
│ ┌─────────────┐ │    │ ┌──────────────┐ │    │ ┌─────────────┐ │
│ │ PyLogger    │ │───▶│ │ LogRecord    │ │───▶│ │ Async       │ │
│ │ Methods     │ │    │ │ Creation     │ │    │ │ Handlers    │ │
│ └─────────────┘ │    │ └──────────────┘ │    │ └─────────────┘ │
│                 │    │                  │    │                 │
│ ┌─────────────┐ │    │ ┌──────────────┐ │    │ ┌─────────────┐ │
│ │ basicConfig │ │───▶│ │ Channel      │ │───▶│ │ Concurrent  │ │
│ │ flush()     │ │    │ │ Management   │ │    │ │ Processing  │ │
│ └─────────────┘ │    │ └──────────────┘ │    │ └─────────────┘ │
└─────────────────┘    └──────────────────┘    └─────────────────┘
```

## Message Processing Flow

1. **Python Call** → LogXide PyLogger methods
2. **Record Creation** → Rust LogRecord with full metadata
3. **Async Channel** → Non-blocking message passing to Tokio runtime
4. **Concurrent Processing** → Multiple handlers execute in parallel
5. **Output** → Formatted messages to files/streams/handlers

## Key Components

### PyO3 Integration
- **Zero-copy data transfer** between Python and Rust
- **Native Python objects** for seamless integration
- **Exception handling** that preserves Python semantics

### Async Message Processing
- **Non-blocking channels** with 1024-capacity buffers
- **Tokio runtime** with dedicated thread pool
- **Backpressure handling** for high-throughput scenarios

### Concurrent Handlers
- **Parallel execution** for maximum throughput
- **Handler isolation** prevents one handler from blocking others
- **Error resilience** ensures continued operation

### Memory Management
- **Rust's ownership system** prevents memory leaks
- **Arc-based sharing** for efficient data sharing
- **Minimal allocations** in hot paths

## Performance Optimizations

### Lock-Free Design
- **Atomic operations** for shared state
- **Channel-based communication** eliminates locks
- **Thread-local buffers** where possible

### Efficient String Handling
- **Rust's formatter** for fast string processing
- **String interning** for repeated logger names
- **Copy-on-write** semantics for format strings

### Async I/O
- **Non-blocking file operations**
- **Batched writes** for improved throughput
- **Async flush** operations

## Thread Safety

LogXide is designed from the ground up for multi-threaded applications:

- **Thread-safe logger instances** can be shared across threads
- **Async runtime** handles concurrent access automatically
- **No global locks** that could cause contention
- **Per-thread optimization** where beneficial

## Handler Architecture

```
┌─────────────────┐
│ Handler Registry│
├─────────────────┤
│ ┌─────────────┐ │
│ │FileHandler  │ │───┐
│ └─────────────┘ │   │
│ ┌─────────────┐ │   │    ┌──────────────┐
│ │StreamHandler│ │───┼───▶│ Concurrent   │
│ └─────────────┘ │   │    │ Execution    │
│ ┌─────────────┐ │   │    └──────────────┘
│ │CustomHandler│ │───┘
│ └─────────────┘ │
└─────────────────┘
```

### Handler Types

- **ConsoleHandler**: Optimized console output
- **FileHandler**: Async file writing with buffering
- **RotatingFileHandler**: Automatic log rotation
- **PythonHandler**: Bridge to Python-based handlers

## Memory Layout

### Log Record Structure
```rust
pub struct LogRecord {
    pub logger_name: String,
    pub level: LogLevel,
    pub message: String,
    pub timestamp: SystemTime,
    pub thread_id: u64,
    pub thread_name: Option<String>,
    // ... additional metadata
}
```

### Channel Architecture
```rust
// Unbounded channel for maximum throughput
crossbeam::channel::unbounded::<LogMessage>()

// Message types
enum LogMessage {
    Record(Box<LogRecord>),
    Flush(oneshot::Sender<()>),
}
```

## Comparison with Standard Logging

| Aspect | Python logging | LogXide |
|--------|---------------|---------|
| **Threading** | Global locks | Lock-free |
| **I/O** | Synchronous | Asynchronous |
| **Memory** | GC overhead | Zero-copy where possible |
| **Error handling** | Exceptions can block | Isolated error handling |
| **Performance** | Single-threaded bottlenecks | Concurrent processing |

## Future Architecture Plans

### Planned Optimizations
- **SIMD string processing** for format operations
- **Memory pooling** for log record allocation
- **Adaptive batching** based on throughput
- **Custom allocators** for specific use cases

### Plugin Architecture
- **Handler plugins** for custom output formats
- **Filter plugins** for advanced message filtering
- **Formatter plugins** for domain-specific formatting

## Best Practices

### For High Performance
1. Use async handlers when possible
2. Avoid frequent logger creation
3. Use appropriate buffer sizes
4. Consider message batching for extreme throughput

### For Reliability
1. Handle handler errors gracefully
2. Monitor channel capacity
3. Use flush() for critical messages
4. Test under load conditions

### For Memory Efficiency
1. Reuse logger instances
2. Avoid large format strings
3. Use string interning for repeated values
4. Monitor memory usage in long-running processes
