# Event-Driven Framework Architecture

## Overview
The event-driven framework provides high-performance event processing with:
- Throughput of 100+ events per second
- Thread-safe operation
- AES-256 encryption compliance
- Tight integration with scheduler system

## Core Components

```mermaid
classDiagram
    class EventSystem {
        +publish(event, priority)
        +subscribe(event_type, handler)
    }
    
    class EventDispatcher {
        +register_handler(event_type, handler)
        +dispatch(event)
        +start()
        +stop()
    }
    
    class EventQueue {
        +push(priority, event)
        +pop() event
    }
    
    EventSystem --> EventDispatcher
    EventDispatcher --> EventQueue
    EventDispatcher --> Scheduler
```

### EventQueue
- Priority-based processing (min-heap)
- Thread-safe operations using RLock
- Efficient wakeup signaling with Event objects
- FIFO ordering for same-priority events

### EventDispatcher
- Maintains handler registry
- Routes events to appropriate handlers
- Manages worker thread lifecycle
- Integrates with scheduler for delayed events

### EventSystem
- Public API for publishing/subscribing
- Handles encryption/decryption
- Wraps dispatcher functionality

## Performance Characteristics

| Metric | Value | Test Case |
|--------|-------|-----------|
| Throughput | ≥100 events/sec | test_event_throughput |
| Concurrent Publishers | 10 threads | test_concurrent_publishers |
| Latency | <10ms per event | test_scheduled_events |

## Security Implementation
- All events encrypted with AES-256 in transit
- Encryption can be disabled for debugging
- Thread-safe operations prevent race conditions
- Error handling prevents crashes from bad events

## Scheduler Integration
The event system integrates with the scheduler through:
1. Delayed event execution via `schedule_event`
2. Shared thread pool resources
3. Common encryption implementation

```mermaid
sequenceDiagram
    participant Publisher
    participant EventSystem
    participant Scheduler
    participant Handler
    
    Publisher->>EventSystem: publish(event)
    EventSystem->>Scheduler: schedule_event(delayed)
    Scheduler->>EventSystem: execute delayed
    EventSystem->>Handler: dispatch(event)
```

## Scaling Considerations
- Queue size monitoring recommended
- Handler execution time critical for throughput
- Consider dedicated thread pools for slow handlers
- Horizontal scaling possible with distributed queue
## NLP Processing Module

### Security Architecture
- **Encryption**: All model data encrypted at rest using AES-256
- **Access Control**: RBAC enforced via `@requires_permission` decorators
- **Audit Trail**: All operations logged via security/audit.py

### Integration Points
1. **Security Subsystem**:
   - Uses RBAC engine for permission checks
   - Writes audit logs for all NLP operations

2. **Event Processing**:
   - Intent analysis available as a service
   - Secure decorator for custom NLP operations

### Implementation Notes
- Base class: `nlp/intent.IntentRecognizer`
- Tests: `nlp/tests/test_intent.py`
- Follows security requirements from `symphony-core.md`