🔧 Operations Documentation¶

# capacity_planning.py
import pandas as pd
from datetime import datetime, timedelta
from azure.monitor.query import MetricsQueryClient

def forecast_capacity(metric_name, days_ahead=30):
    """Forecast capacity requirements."""
    client = MetricsQueryClient(credential)

    # Get historical data
    end_time = datetime.now()
    start_time = end_time - timedelta(days=90)

    response = client.query_resource(
        resource_uri=resource_id,
        metric_names=[metric_name],
        timespan=(start_time, end_time),
        granularity=timedelta(hours=1)
    )

    # Create forecast
    df = pd.DataFrame(response.metrics[0].timeseries[0].data)
    df['timestamp'] = pd.to_datetime(df['timestamp'])
    df.set_index('timestamp', inplace=True)

    # Simple linear regression forecast
    from sklearn.linear_model import LinearRegression

    X = df.index.astype(int).values.reshape(-1, 1)
    y = df['average'].values

    model = LinearRegression()
    model.fit(X, y)

    # Predict future
    future_dates = pd.date_range(
        start=end_time,
        periods=days_ahead,
        freq='D'
    )

    predictions = model.predict(
        future_dates.astype(int).values.reshape(-1, 1)
    )

    return {
        'current': y[-1],
        'predicted_30d': predictions[-1],
        'growth_rate': (predictions[-1] - y[-1]) / y[-1] * 100
    }

# service_recovery.py
import asyncio
from typing import List, Dict

class DisasterRecoveryOrchestrator:
    def __init__(self):
        self.services = [
            "storage",
            "databricks",
            "kafka",
            "powerbi"
        ]

    async def failover_to_secondary(self):
        """Execute failover to secondary region."""
        tasks = []

        for service in self.services:
            tasks.append(self.failover_service(service))

        results = await asyncio.gather(*tasks)
        return results

    async def failover_service(self, service: str) -> Dict:
        """Failover individual service."""
        try:
            # Update DNS
            await self.update_dns(service)

            # Start secondary instance
            await self.start_secondary(service)

            # Verify health
            is_healthy = await self.health_check(service)

            return {
                "service": service,
                "status": "success" if is_healthy else "failed",
                "timestamp": datetime.utcnow()
            }
        except Exception as e:
            return {
                "service": service,
                "status": "error",
                "error": str(e)
            }

# cluster_maintenance.py
from databricks.sdk import WorkspaceClient
import time

def perform_cluster_maintenance(cluster_id: str):
    """Perform rolling cluster maintenance."""
    w = WorkspaceClient()

    # Step 1: Create temporary cluster
    temp_cluster = w.clusters.create(
        cluster_name=f"temp-{cluster_id}",
        spark_version="13.3.x-scala2.12",
        node_type_id="Standard_D16s_v3",
        num_workers=10
    )

    # Step 2: Redirect traffic
    update_load_balancer(temp_cluster.cluster_id)

    # Step 3: Restart original cluster
    w.clusters.restart(cluster_id)

    # Step 4: Wait for healthy state
    while True:
        state = w.clusters.get(cluster_id).state
        if state == "RUNNING":
            break
        time.sleep(30)

    # Step 5: Redirect traffic back
    update_load_balancer(cluster_id)

    # Step 6: Terminate temporary cluster
    w.clusters.delete(temp_cluster.cluster_id)

# diagnostics.py
import pandas as pd
from typing import Dict, List

class SystemDiagnostics:
    def __init__(self):
        self.checks = [
            self.check_cluster_health,
            self.check_storage_health,
            self.check_streaming_health,
            self.check_network_health
        ]

    def run_diagnostics(self) -> Dict:
        """Run all diagnostic checks."""
        results = {}

        for check in self.checks:
            check_name = check.__name__
            try:
                results[check_name] = check()
            except Exception as e:
                results[check_name] = {
                    "status": "error",
                    "error": str(e)
                }

        return results

    def check_cluster_health(self) -> Dict:
        """Check Databricks cluster health."""
        # Implementation
        pass

    def check_storage_health(self) -> Dict:
        """Check storage account health."""
        # Implementation
        pass