Function-Based Architecture: NetApp MCP Server on Knative¶

# Cost comparison calculation
def calculate_monthly_costs():
    # Traditional deployment
    traditional_cost = {
        'vm_instance': 150,  # Always running VM
        'storage': 50,       # Persistent storage
        'networking': 30,    # Network costs
        'total': 230
    }

    # Function-based deployment
    function_cost = {
        'compute_time': 45,      # Pay per execution
        'storage': 10,           # Minimal persistent storage
        'networking': 15,        # Reduced network costs
        'knative_overhead': 5,   # Platform costs
        'total': 75
    }

    savings = traditional_cost['total'] - function_cost['total']
    savings_percentage = (savings / traditional_cost['total']) * 100

    return {
        'traditional': traditional_cost,
        'functions': function_cost,
        'monthly_savings': savings,
        'savings_percentage': savings_percentage
    }

# Result: ~67% cost reduction with function-based architecture

# Predictive scaling based on historical patterns
class PredictiveScaler:
    def __init__(self):
        self.patterns = {
            'business_hours': (9, 17),  # 9 AM to 5 PM
            'peak_days': ['monday', 'tuesday', 'wednesday'],
            'maintenance_windows': ['sunday_2am']
        }

    def predict_scaling_needs(self, current_time):
        hour = current_time.hour
        day = current_time.strftime('%A').lower()

        # Pre-scale for business hours
        if self.patterns['business_hours'][0] <= hour <= self.patterns['business_hours'][1]:
            if day in self.patterns['peak_days']:
                return {'min_scale': 2, 'max_scale': 20}
            else:
                return {'min_scale': 1, 'max_scale': 10}

        # Scale to zero during off-hours
        return {'min_scale': 0, 'max_scale': 5}

    def apply_scaling_config(self, service_name, scaling_config):
        # Update Knative service annotations
        annotations = {
            'autoscaling.knative.dev/minScale': str(scaling_config['min_scale']),
            'autoscaling.knative.dev/maxScale': str(scaling_config['max_scale'])
        }
        # Apply via Kubernetes API
        return self.update_knative_service(service_name, annotations)

# OpenTelemetry tracing for function calls
from opentelemetry import trace
from opentelemetry.instrumentation.requests import RequestsInstrumentor

tracer = trace.get_tracer(__name__)

@mcp.tool()
async def create_volume_with_tracing(volume_config: dict) -> str:
    with tracer.start_as_current_span("create_volume") as span:
        span.set_attribute("volume.size", volume_config.get("size"))
        span.set_attribute("volume.svm", volume_config.get("svm"))

        try:
            # Function execution
            result = await netapp_client.create_volume(volume_config)
            span.set_attribute("operation.status", "success")
            span.set_attribute("volume.uuid", result.get("uuid"))
            return result
        except Exception as e:
            span.set_attribute("operation.status", "error")
            span.set_attribute("error.message", str(e))
            raise

# Automated canary deployment controller
class CanaryController:
    def __init__(self):
        self.success_threshold = 0.95  # 95% success rate
        self.error_threshold = 0.05    # 5% error rate

    async def promote_canary(self, service_name, canary_revision):
        # Monitor canary metrics for 10 minutes
        metrics = await self.monitor_canary(canary_revision, duration=600)

        if metrics['success_rate'] >= self.success_threshold:
            # Promote canary to 100% traffic
            await self.update_traffic_split(service_name, {
                canary_revision: 100
            })
            return True
        else:
            # Rollback canary
            await self.rollback_canary(service_name)
            return False

    async def monitor_canary(self, revision, duration):
        # Collect metrics from Prometheus
        query = f'sum(rate(function_requests_total{{revision="{revision}"}}[5m]))'
        # Implementation details...
        return {'success_rate': 0.98, 'error_rate': 0.02}

# Connection pooling for NetApp API calls
class NetAppClientPool:
    def __init__(self):
        self.pool = asyncio.Queue(maxsize=10)
        self.initialize_pool()

    async def initialize_pool(self):
        for _ in range(5):  # Pre-create 5 connections
            client = NetAppClient()
            await client.connect()
            await self.pool.put(client)

    async def get_client(self):
        if self.pool.empty():
            # Create new client if pool is empty
            client = NetAppClient()
            await client.connect()
            return client
        return await self.pool.get()

    async def return_client(self, client):
        if not client.is_connected():
            await client.reconnect()
        await self.pool.put(client)

# Usage in function
@mcp.tool()
async def optimized_volume_query(query_params: dict) -> str:
    client = await client_pool.get_client()
    try:
        result = await client.get_volumes(query_params)
        return json.dumps(result)
    finally:
        await client_pool.return_client(client)