# Feature Specification: Feature Flags System

## Overview
A lightweight, flexible feature flag system for Python applications supporting gradual rollouts, A/B testing, and runtime configuration.

## User Stories

### As a Developer
- I want to wrap new features in flags so I can deploy safely
- I want to gradually roll out features to minimize risk
- I want to disable features quickly without redeploying

### As a DevOps Engineer
- I want to control feature states via environment variables
- I want to monitor feature flag usage and impact
- I want emergency kill switches for critical features

### As a Product Manager
- I want to A/B test new features with specific user segments
- I want to schedule feature launches for specific times
- I want to see adoption metrics for new features

## Technical Requirements

### Core Functionality
1. **Flag States**: enabled, disabled, percentage, conditional
2. **Configuration Sources**: Environment vars, JSON files, runtime API
3. **Consistent Hashing**: Deterministic percentage rollouts per user
4. **Thread Safety**: Safe concurrent access to flag states
5. **Performance**: <1ms overhead for flag checks

### API Design
```python
# Basic usage
flags = FeatureFlags()
flags.register("new_feature", default="disabled")

if flags.is_enabled("new_feature", context={"user_id": "123"}):
    new_behavior()
else:
    old_behavior()

# Decorator pattern
@feature_flag("experimental_api", fallback=legacy_handler)
def new_api_handler(request):
    return new_response()

# Runtime updates
flags.set_state("new_feature", "percentage:25")
```

### CLI Interface
```bash
augint flags list                      # Show all flags
augint flags set <name> <state>        # Update flag state
augint flags test <name> --context k=v # Test evaluation
```

## Implementation Notes

### State Machine
```
disabled -> percentage:N -> enabled
    ^            |            |
    |____________|____________|
         (rollback path)
```

### Percentage Algorithm
- Use MD5 hash of flag_name + user_id
- Take first 8 hex chars as integer
- Modulo 100 gives user bucket (0-99)
- User in feature if bucket < percentage

### Context Schema
```python
{
    "user_id": str,       # For consistent rollout
    "user_type": str,     # For segmentation
    "environment": str,   # dev/staging/prod
    "region": str,        # Geographic targeting
    "timestamp": datetime # Time-based flags
}
```

## Testing Strategy

### Unit Tests
- All state transitions
- Percentage distribution (statistical)
- Configuration loading priority
- Thread safety under concurrent access
- Error handling for invalid states

### Integration Tests
- Environment variable loading
- JSON file parsing
- CLI commands
- Decorator behavior
- Context evaluation

### Performance Tests
- Flag evaluation < 1ms
- Minimal memory overhead
- Efficient percentage calculation

## Success Metrics
- Zero production incidents from new features
- 50% reduction in rollback time
- 90% of features use gradual rollout
- <1ms performance overhead

## Future Enhancements
- Web UI for flag management
- Audit log for flag changes
- Integration with APM tools
- Feature flag analytics dashboard
- Webhook notifications for state changes
