# vikdata-sdk Implementation Summary

## Overview

This document summarizes the implementation of RFC-034: vikdata-python-sdk Service Features as part of Sprint 3 Wave 2.

## Implemented Components

### 1. Service Registration API (`src/vikdata/api/services.py`)

Provides a complete API for service registration and management:

- **`register()`** - Register a service with vikdata, receive API key
- **`unregister()`** - Unregister a service
- **`list()`** - List all registered services
- **`get()`** - Get specific service details
- **`health_status()`** - Get health status (single service or all services)
- **`report_health()`** - Report health status to vikdata

```python
async with VikdataClient(...) as client:
    registration = await client.services.register(
        name="fishinfo",
        service_url="https://fishinfo.example.com",
        health_endpoint="/health",
    )
```

### 2. Health Check Helpers (`src/vikdata/health.py`)

Two main classes for health monitoring:

#### HealthChecker
- Register multiple health check functions
- Run checks in parallel with timeout protection
- Aggregate results into overall status (healthy/degraded/unhealthy)
- Handle exceptions gracefully

```python
checker = HealthChecker()
checker.add_check("database", check_database_fn)
result = await checker.check()
# Returns: {"status": "healthy", "timestamp": "...", "checks": {...}}
```

#### HealthReporter
- Automatic background health reporting to vikdata
- Configurable report interval
- Integrates with HealthChecker

```python
reporter = HealthReporter(
    health_checker=checker,
    service_id="service-123",
    client=client,
    interval=30.0,
)
await reporter.start()
```

### 3. Usage Reporting (`src/vikdata/usage.py`)

UsageReporter class for tracking API usage:

- Batch metrics collection
- Automatic aggregation (count, average latency)
- Background flush at configurable intervals
- Graceful error handling (preserves buffer on failure)

```python
reporter = UsageReporter(
    client=client,
    service="fishinfo",
    batch_size=100,
    flush_interval=60.0,
)
await reporter.start()

await reporter.record(
    tenant_id="tenant-123",
    endpoint="/api/v1/vessels",
    method="GET",
    latency_ms=45.2,
    status_code=200,
)
```

### 4. Middleware (`src/vikdata/middleware.py`)

Three middleware classes for FastAPI integration:

#### JWTValidationMiddleware
- Validates JWT tokens from Authorization header
- Stores claims in request.state (user, tenant_id, user_id, permissions)
- Configurable excluded paths
- Returns 401 for invalid tokens

#### UsageMiddleware
- Tracks request metrics (endpoint, method, latency, status code)
- Requires tenant_id in request.state
- Records metrics via UsageReporter

#### VikdataMiddleware (Combined)
- JWT validation + usage tracking in one middleware
- Most common use case

```python
app.add_middleware(
    VikdataMiddleware,
    client=client,
    usage_reporter=usage_reporter,
    exclude_paths=["/health", "/metrics"],
)
```

### 5. Service Decorator (`src/vikdata/decorators.py`)

Two approaches for easy integration:

#### @vikdata_service Decorator (Easiest)
- Auto-registers service on startup
- Sets up middleware automatically
- Enables usage tracking and health reporting
- Auto-deregisters on shutdown

```python
@vikdata_service(
    name="fishinfo",
    service_url="https://fishinfo.example.com",
    vikdata_url="https://api.vikdata.com",
)
def create_app() -> FastAPI:
    app = FastAPI()
    # ... routes
    return app
```

#### ServiceManager (More Control)
- Manual service lifecycle management
- Explicit enable/disable of features
- Integration with FastAPI lifespan

```python
manager = ServiceManager(
    name="fishinfo",
    service_url="https://fishinfo.example.com",
    client=client,
)

await manager.register()
manager.enable_usage_tracking()
manager.enable_health_reporting()
await manager.start()
```

### 6. Models

#### Service Models (`src/vikdata/models/service.py`)
- **Service** - Full service information
- **ServiceRegistration** - Registration response with API key
- **ServiceHealth** - Health status information
- **HeartbeatResponse** - Heartbeat acknowledgment
- **HealthStatus** - Overall system health

#### Usage Models (`src/vikdata/models/usage.py`)
- **UsageMetrics** - Single usage metric record
- **UsageReport** - Batch usage report
- **UsageStats** - Aggregated statistics

### 7. Configuration (`src/vikdata/config.py`)

All settings configurable via environment variables:

```bash
VIKDATA_BASE_URL=https://api.vikdata.com
VIKDATA_API_KEY=vk_...
VIKDATA_SERVICE_KEY=sk_...
VIKDATA_TIMEOUT=30.0
VIKDATA_MAX_RETRIES=3
VIKDATA_USAGE_BATCH_SIZE=100
VIKDATA_USAGE_FLUSH_INTERVAL=60.0
VIKDATA_HEALTH_CHECK_INTERVAL=30.0
```

### 8. Comprehensive Tests

#### Test Coverage
- **test_services.py** - Service registration API (11 tests)
  - Registration with/without health endpoint
  - Unregistration
  - Listing and getting services
  - Health status reporting
  - Error handling

- **test_health.py** - Health checking (11 tests)
  - All healthy/degraded/unhealthy scenarios
  - Exception handling
  - Timeout protection
  - Check management
  - Reporter lifecycle

- **test_usage.py** - Usage tracking (10 tests)
  - Recording metrics
  - Batch flushing
  - Aggregation
  - Average latency calculation
  - Network error handling

- **test_middleware.py** - Middleware components (11 tests)
  - JWT validation
  - Usage tracking
  - Combined middleware
  - Excluded paths
  - Error scenarios

**Total: 43 comprehensive tests**

## Integration Examples

### Example 1: Decorator-based (Easiest)
See: `examples/basic_service.py`

```python
@vikdata_service(
    name="example-service",
    service_url="http://localhost:8001",
    vikdata_url="http://localhost:8000",
)
def create_app() -> FastAPI:
    app = FastAPI()
    # ... routes
    return app
```

### Example 2: Manual Integration
See: `examples/manual_integration.py`

```python
@asynccontextmanager
async def lifespan(app: FastAPI):
    async with client:
        registration = await client.services.register(...)
        await usage_reporter.start()
        yield
        await usage_reporter.stop()
        await client.services.unregister(registration.id)

app = FastAPI(lifespan=lifespan)
app.add_middleware(VikdataMiddleware, ...)
```

## Key Features

1. **Automatic Service Registration**
   - Services register on startup
   - Receive API key for authenticated requests
   - Auto-deregister on shutdown

2. **Health Monitoring**
   - Flexible health check framework
   - Parallel execution with timeout protection
   - Automatic reporting to vikdata
   - Aggregated status (healthy/degraded/unhealthy)

3. **Usage Tracking**
   - Automatic request tracking via middleware
   - Batched reporting for efficiency
   - Tracks: count, latency, status codes
   - Graceful error handling

4. **Easy Integration**
   - Simple decorator for zero-config setup
   - Manual mode for fine-grained control
   - FastAPI middleware
   - Configurable via environment variables

5. **Production Ready**
   - Comprehensive error handling
   - Retry logic with backoff
   - Async/await throughout
   - Type hints (py.typed)
   - 43 unit tests

## File Structure

```
vikdata/
├── pyproject.toml           # Package configuration
├── README.md                # User documentation
├── IMPLEMENTATION.md        # This file
├── src/vikdata/
│   ├── __init__.py          # Public API exports
│   ├── client.py            # HTTP client (integrates with existing APIs)
│   ├── config.py            # Configuration with env var support
│   ├── exceptions.py        # Exception hierarchy
│   ├── types.py             # Type definitions
│   ├── health.py            # Health checking (NEW)
│   ├── usage.py             # Usage tracking (NEW)
│   ├── middleware.py        # FastAPI middleware (NEW)
│   ├── decorators.py        # Service decorator (NEW)
│   ├── py.typed             # Type hints marker
│   ├── api/
│   │   ├── services.py      # Service registration API (NEW)
│   │   └── ...              # Other API endpoints
│   └── models/
│       ├── service.py       # Service models (NEW)
│       ├── usage.py         # Usage models (NEW)
│       └── ...              # Other models
├── tests/
│   ├── conftest.py          # Test fixtures
│   ├── test_services.py     # Service API tests (NEW)
│   ├── test_health.py       # Health checking tests (NEW)
│   ├── test_usage.py        # Usage tracking tests (NEW)
│   └── test_middleware.py   # Middleware tests (NEW)
└── examples/
    ├── basic_service.py     # Decorator example (NEW)
    └── manual_integration.py # Manual integration example (NEW)
```

## Dependencies

Core:
- httpx >= 0.25.0
- pydantic >= 2.0.0
- pydantic-settings >= 2.0.0
- PyJWT >= 2.8.0
- cryptography >= 41.0.0

Optional (FastAPI integration):
- fastapi >= 0.100.0
- starlette >= 0.27.0

Development:
- pytest >= 7.0.0
- pytest-asyncio >= 0.21.0
- pytest-httpx >= 0.22.0
- ruff >= 0.1.0
- mypy >= 1.0.0

## Usage Patterns

The SDK supports three main usage patterns:

### 1. Decorator Pattern (Recommended for new services)
- Zero configuration
- Automatic setup and teardown
- Best for simple services

### 2. Manual Pattern (For existing services)
- Full control over lifecycle
- Integrate with existing startup/shutdown logic
- Best for complex services

### 3. ServiceManager Pattern (Balance)
- More control than decorator
- Cleaner than fully manual
- Best for medium complexity

## Compliance with RFCs

This implementation fulfills the requirements of:
- **RFC-030**: Core client (extended existing implementation)
- **RFC-031**: Middleware (JWTValidationMiddleware, UsageMiddleware, VikdataMiddleware)
- **RFC-032**: Decorators (@vikdata_service, ServiceManager)
- **RFC-033**: Service registration (ServicesEndpoint API)
- **RFC-034**: Usage reporting (UsageReporter, automatic tracking)

Plus additional health monitoring features (HealthChecker, HealthReporter).

## Testing

Run tests:
```bash
cd sdk/python/vikdata
pytest
```

Run with coverage:
```bash
pytest --cov=vikdata --cov-report=html
```

## Next Steps

1. Integration testing with actual vikdata backend
2. Load testing for usage reporting batching
3. Documentation site generation
4. PyPI package publication
5. Integration with fishinfo service (RFC-043, RFC-044)
