/**
 * Enterprise Data Pipeline Loading & Storage Engine
 * Phase 6: Handles destination optimization, storage strategies, and data loading
 */

import { Logger } from '../../../core/logger.js';
import { readFile, writeFile, mkdir } from 'fs/promises';
import { join, dirname } from 'path';
import { existsSync } from 'fs';

export interface LoadingStrategy {
  strategy: 'batch' | 'streaming' | 'micro-batch' | 'bulk' | 'incremental' | 'upsert';
  batchSize?: number;
  parallelism?: number;
  compressionType?: 'gzip' | 'snappy' | 'lz4' | 'zstd';
  errorHandling: 'abort' | 'skip' | 'retry' | 'dead-letter';
  retryConfig?: {
    maxRetries: number;
    backoffStrategy: 'exponential' | 'linear' | 'fixed';
    initialDelay: number;
  };
}

export interface DestinationConfig {
  type: 'snowflake' | 'redshift' | 's3' | 'databricks' | 'bigquery' | 'postgres' | 'mysql' | 'mongodb';
  connectionParams: Record<string, any>;
  schema?: string;
  table?: string;
  partitioning?: {
    columns: string[];
    strategy: 'range' | 'hash' | 'list';
  };
  clustering?: {
    columns: string[];
  };
  indexing?: {
    primary: string[];
    secondary: Array<{
      name: string;
      columns: string[];
      type: 'btree' | 'hash' | 'gin' | 'gist';
    }>;
  };
}

export interface PerformanceOptimization {
  memoryAllocation: string;
  diskIO: {
    readBuffer: string;
    writeBuffer: string;
    ioThreads: number;
  };
  networking: {
    connectionPool: number;
    keepAlive: boolean;
    timeout: number;
  };
  caching: {
    enabled: boolean;
    size: string;
    ttl: number;
  };
  compression: {
    enabled: boolean;
    algorithm: string;
    level: number;
  };
}

export interface DataGovernance {
  encryption: {
    atRest: boolean;
    inTransit: boolean;
    keyManagement: 'aws-kms' | 'azure-keyvault' | 'gcp-kms' | 'vault';
  };
  access: {
    rbac: boolean;
    columnLevel: boolean;
    rowLevel: boolean;
    masking: string[];
  };
  compliance: {
    gdpr: boolean;
    hipaa: boolean;
    sox: boolean;
    dataRetention: number; // days
  };
  auditing: {
    enabled: boolean;
    logLevel: 'basic' | 'detailed' | 'comprehensive';
    destination: string;
  };
}

export interface LoadingPlan {
  id: string;
  timestamp: string;
  metadata: {
    title: string;
    description: string;
    version: string;
    author: string;
  };
  destinations: DestinationConfig[];
  loadingStrategy: LoadingStrategy;
  performance: PerformanceOptimization;
  governance: DataGovernance;
  orchestration: {
    framework: 'airflow' | 'dagster' | 'prefect' | 'argo';
    schedule: string;
    dependencies: string[];
    notifications: {
      success: string[];
      failure: string[];
      sla: string[];
    };
  };
  monitoring: {
    metrics: string[];
    alerts: Array<{
      condition: string;
      threshold: number;
      action: string;
    }>;
    dashboards: string[];
  };
  implementation: {
    infrastructure: string;
    deployment: string;
    testing: string;
    documentation: string;
  };
}

export class PipelineLoadingEngine {
  private logger: Logger;

  constructor() {
    this.logger = new Logger('PipelineLoadingEngine');
  }

  /**
   * Generate comprehensive loading plan from natural language requirements
   */
  async generateLoadingPlan(requirements: string, options: {
    validationPlan?: string;
    transformationPlan?: string;
    compliance?: boolean;
    generateCode?: boolean;
    outputPath?: string;
  } = {}): Promise<LoadingPlan> {
    try {
      this.logger.info('Generating loading plan', { requirements, options });

      // Parse previous plans if provided
      let validationContext = null;
      let transformationContext = null;

      if (options.validationPlan) {
        validationContext = await this.loadPreviousPlan(options.validationPlan);
      }

      if (options.transformationPlan) {
        transformationContext = await this.loadPreviousPlan(options.transformationPlan);
      }

      // Analyze requirements for destination optimization
      const destinations = this.analyzeDestinationRequirements(requirements, validationContext, transformationContext);
      const loadingStrategy = this.optimizeLoadingStrategy(requirements, destinations);
      const performance = this.generatePerformanceOptimization(requirements, destinations);
      const governance = this.configureDataGovernance(requirements, options.compliance || false);

      // Generate comprehensive loading plan
      const plan: LoadingPlan = {
        id: `pipeline-loading-${Date.now()}`,
        timestamp: new Date().toISOString(),
        metadata: {
          title: `Loading Plan: ${this.extractTitle(requirements)}`,
          description: requirements,
          version: '1.0.0',
          author: 'Claude Flow Pipeline Engine'
        },
        destinations,
        loadingStrategy,
        performance,
        governance,
        orchestration: this.generateOrchestration(requirements, destinations),
        monitoring: this.generateMonitoring(requirements, destinations),
        implementation: {
          infrastructure: this.generateInfrastructure(destinations, performance),
          deployment: this.generateDeployment(destinations, loadingStrategy),
          testing: this.generateTesting(destinations, loadingStrategy),
          documentation: this.generateDocumentation(requirements, destinations)
        }
      };

      // Save plan if output path specified
      if (options.outputPath) {
        await this.savePlan(plan, options.outputPath);
      }

      // Generate implementation code if requested
      if (options.generateCode) {
        await this.generateImplementationCode(plan, options.outputPath || './pipeline-loading');
      }

      this.logger.info('Loading plan generated successfully', { planId: plan.id });
      return plan;

    } catch (error) {
      this.logger.error('Failed to generate loading plan', { error, requirements });
      throw error;
    }
  }

  /**
   * Analyze destination requirements and optimize configuration
   */
  private analyzeDestinationRequirements(
    requirements: string, 
    validationContext: any, 
    transformationContext: any
  ): DestinationConfig[] {
    const destinations: DestinationConfig[] = [];
    const reqLower = requirements.toLowerCase();

    // Analyze for different destination types
    if (reqLower.includes('snowflake')) {
      destinations.push({
        type: 'snowflake',
        connectionParams: {
          account: '${SNOWFLAKE_ACCOUNT}',
          user: '${SNOWFLAKE_USER}',
          password: '${SNOWFLAKE_PASSWORD}',
          warehouse: 'COMPUTE_WH',
          database: 'ANALYTICS_DB',
          schema: 'PUBLIC',
          role: 'ACCOUNTADMIN'
        },
        schema: 'analytics',
        table: this.inferTableName(requirements),
        partitioning: {
          columns: ['created_date'],
          strategy: 'range'
        },
        clustering: {
          columns: ['customer_id', 'region']
        },
        indexing: {
          primary: ['id'],
          secondary: [
            { name: 'idx_customer', columns: ['customer_id'], type: 'btree' },
            { name: 'idx_date', columns: ['created_date'], type: 'btree' }
          ]
        }
      });
    }

    if (reqLower.includes('redshift')) {
      destinations.push({
        type: 'redshift',
        connectionParams: {
          host: '${REDSHIFT_HOST}',
          port: 5439,
          database: 'analytics',
          user: '${REDSHIFT_USER}',
          password: '${REDSHIFT_PASSWORD}',
          sslmode: 'require'
        },
        schema: 'public',
        table: this.inferTableName(requirements),
        partitioning: {
          columns: ['event_date'],
          strategy: 'range'
        },
        clustering: {
          columns: ['customer_id']
        }
      });
    }

    if (reqLower.includes('s3') || reqLower.includes('data lake')) {
      destinations.push({
        type: 's3',
        connectionParams: {
          bucket: '${S3_BUCKET}',
          region: 'us-east-1',
          accessKey: '${AWS_ACCESS_KEY}',
          secretKey: '${AWS_SECRET_KEY}',
          prefix: 'analytics/processed/'
        },
        partitioning: {
          columns: ['year', 'month', 'day'],
          strategy: 'range'
        }
      });
    }

    if (reqLower.includes('databricks') || reqLower.includes('delta')) {
      destinations.push({
        type: 'databricks',
        connectionParams: {
          host: '${DATABRICKS_HOST}',
          token: '${DATABRICKS_TOKEN}',
          cluster: '${DATABRICKS_CLUSTER}',
          catalog: 'main',
          schema: 'analytics'
        },
        table: this.inferTableName(requirements),
        partitioning: {
          columns: ['date'],
          strategy: 'range'
        }
      });
    }

    // Add BigQuery if mentioned
    if (reqLower.includes('bigquery') || reqLower.includes('gcp')) {
      destinations.push({
        type: 'bigquery',
        connectionParams: {
          projectId: '${GCP_PROJECT_ID}',
          dataset: 'analytics',
          keyFile: '${GCP_KEY_FILE}',
          location: 'US'
        },
        table: this.inferTableName(requirements),
        partitioning: {
          columns: ['date'],
          strategy: 'range'
        },
        clustering: {
          columns: ['customer_id', 'product_id']
        }
      });
    }

    // Add MongoDB if mentioned
    if (reqLower.includes('mongodb') || reqLower.includes('mongo') || reqLower.includes('document')) {
      destinations.push({
        type: 'mongodb',
        connectionParams: {
          uri: '${MONGODB_URI}',
          database: 'analytics',
          authSource: 'admin',
          ssl: true,
          retryWrites: true,
          w: 'majority'
        },
        table: this.inferTableName(requirements),
        indexing: {
          primary: ['_id'],
          secondary: [
            { name: 'idx_created_at', columns: ['created_at'], type: 'btree' },
            { name: 'idx_user_id', columns: ['user_id'], type: 'btree' }
          ]
        }
      });
    }

    // Check for generic data warehouse mentions
    if (destinations.length === 0 && (reqLower.includes('data warehouse') || reqLower.includes('warehouse'))) {
      // Default to Redshift for generic data warehouse mentions
      destinations.push({
        type: 'redshift',
        connectionParams: {
          host: '${REDSHIFT_HOST}',
          port: 5439,
          database: 'analytics',
          user: '${REDSHIFT_USER}',
          password: '${REDSHIFT_PASSWORD}',
          sslmode: 'require'
        },
        schema: 'public',
        table: this.inferTableName(requirements),
        partitioning: {
          columns: ['event_date'],
          strategy: 'range'
        },
        clustering: {
          columns: ['customer_id']
        }
      });
    }

    // Default to PostgreSQL if no specific destination mentioned
    if (destinations.length === 0) {
      destinations.push({
        type: 'postgres',
        connectionParams: {
          host: '${DB_HOST}',
          port: 5432,
          database: '${DB_NAME}',
          user: '${DB_USER}',
          password: '${DB_PASSWORD}',
          ssl: true
        },
        schema: 'public',
        table: this.inferTableName(requirements),
        indexing: {
          primary: ['id'],
          secondary: [
            { name: 'idx_created_at', columns: ['created_at'], type: 'btree' }
          ]
        }
      });
    }

    return destinations;
  }

  /**
   * Optimize loading strategy based on requirements and destinations
   */
  private optimizeLoadingStrategy(requirements: string, destinations: DestinationConfig[]): LoadingStrategy {
    const reqLower = requirements.toLowerCase();

    // Determine optimal strategy
    let strategy: LoadingStrategy['strategy'] = 'batch';
    
    if (reqLower.includes('micro-batch') || reqLower.includes('near real-time')) {
      strategy = 'micro-batch';
    } else if (reqLower.includes('real-time') || reqLower.includes('streaming')) {
      strategy = 'streaming';
    } else if (reqLower.includes('bulk') || reqLower.includes('large')) {
      strategy = 'bulk';
    } else if (reqLower.includes('incremental') || reqLower.includes('delta')) {
      strategy = 'incremental';
    } else if (reqLower.includes('upsert') || reqLower.includes('merge')) {
      strategy = 'upsert';
    }

    // Optimize batch size based on destination and strategy
    let batchSize = 10000;
    const hasDataWarehouse = destinations.some(d => ['snowflake', 'redshift', 'bigquery'].includes(d.type));
    
    if (strategy === 'bulk' || reqLower.includes('large')) {
      batchSize = 100000; // Large batches for bulk operations
    } else if (hasDataWarehouse) {
      batchSize = 50000; // Medium-large batches for data warehouses
    } else if (strategy === 'streaming') {
      batchSize = 1000; // Small batches for streaming
    }

    // Set parallelism based on strategy
    let parallelism = 4;
    if (strategy === 'streaming') {
      parallelism = 8;
    } else if (strategy === 'bulk') {
      parallelism = 16;
    }

    return {
      strategy,
      batchSize,
      parallelism,
      compressionType: 'gzip',
      errorHandling: 'retry',
      retryConfig: {
        maxRetries: 3,
        backoffStrategy: 'exponential',
        initialDelay: 1000
      }
    };
  }

  /**
   * Generate performance optimization configuration
   */
  private generatePerformanceOptimization(requirements: string, destinations: DestinationConfig[]): PerformanceOptimization {
    const hasCloudWarehouse = destinations.some(d => ['snowflake', 'redshift', 'bigquery', 'databricks'].includes(d.type));
    
    return {
      memoryAllocation: hasCloudWarehouse ? '8GB' : '4GB',
      diskIO: {
        readBuffer: '64MB',
        writeBuffer: '128MB',
        ioThreads: 8
      },
      networking: {
        connectionPool: 20,
        keepAlive: true,
        timeout: 300
      },
      caching: {
        enabled: true,
        size: '2GB',
        ttl: 3600
      },
      compression: {
        enabled: true,
        algorithm: 'gzip',
        level: 6
      }
    };
  }

  /**
   * Configure data governance and compliance
   */
  private configureDataGovernance(requirements: string, compliance: boolean): DataGovernance {
    const reqLower = requirements.toLowerCase();
    
    return {
      encryption: {
        atRest: true,
        inTransit: true,
        keyManagement: 'aws-kms'
      },
      access: {
        rbac: true,
        columnLevel: compliance || reqLower.includes('sensitive'),
        rowLevel: compliance || reqLower.includes('privacy'),
        masking: compliance ? ['email', 'phone', 'ssn', 'credit_card'] : []
      },
      compliance: {
        gdpr: compliance || reqLower.includes('gdpr') || reqLower.includes('europe'),
        hipaa: compliance || reqLower.includes('hipaa') || reqLower.includes('health'),
        sox: compliance || reqLower.includes('sox') || reqLower.includes('financial'),
        dataRetention: 2555 // 7 years default
      },
      auditing: {
        enabled: true,
        logLevel: compliance ? 'comprehensive' : 'detailed',
        destination: 'cloudwatch'
      }
    };
  }

  /**
   * Generate orchestration configuration
   */
  private generateOrchestration(requirements: string, destinations: DestinationConfig[]): LoadingPlan['orchestration'] {
    const reqLower = requirements.toLowerCase();
    
    let framework: 'airflow' | 'dagster' | 'prefect' | 'argo' = 'airflow';
    if (reqLower.includes('dagster')) framework = 'dagster';
    else if (reqLower.includes('prefect')) framework = 'prefect';
    else if (reqLower.includes('argo')) framework = 'argo';

    return {
      framework,
      schedule: reqLower.includes('hourly') ? '0 * * * *' : '0 2 * * *', // daily at 2 AM
      dependencies: ['data_validation', 'data_transformation'],
      notifications: {
        success: ['data-team@company.com'],
        failure: ['data-team@company.com', 'on-call@company.com'],
        sla: ['data-team@company.com']
      }
    };
  }

  /**
   * Generate monitoring configuration
   */
  private generateMonitoring(requirements: string, destinations: DestinationConfig[]): LoadingPlan['monitoring'] {
    return {
      metrics: [
        'loading_duration',
        'records_loaded',
        'error_rate',
        'throughput',
        'data_freshness',
        'destination_availability'
      ],
      alerts: [
        {
          condition: 'loading_duration > 1800',
          threshold: 1800,
          action: 'notify_team'
        },
        {
          condition: 'error_rate > 0.05',
          threshold: 0.05,
          action: 'stop_pipeline'
        },
        {
          condition: 'data_freshness > 7200',
          threshold: 7200,
          action: 'escalate'
        }
      ],
      dashboards: [
        'Loading Performance Dashboard',
        'Data Quality Dashboard',
        'System Health Dashboard'
      ]
    };
  }

  /**
   * Generate infrastructure code
   */
  private generateInfrastructure(destinations: DestinationConfig[], performance: PerformanceOptimization): string {
    const hasSnowflake = destinations.some(d => d.type === 'snowflake');
    const hasRedshift = destinations.some(d => d.type === 'redshift');
    const hasS3 = destinations.some(d => d.type === 's3');

    return `
# Terraform Infrastructure for Pipeline Loading
terraform {
  required_version = ">= 1.0"
  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }${hasSnowflake ? `
    snowflake = {
      source  = "Snowflake-Labs/snowflake"
      version = "~> 0.90"
    }` : ''}
  }
}

# ECS Task Definition for Loading Jobs
resource "aws_ecs_task_definition" "loading_task" {
  family                   = "pipeline-loading"
  network_mode             = "awsvpc"
  requires_compatibilities = ["FARGATE"]
  cpu                      = 2048
  memory                   = ${parseInt(performance.memoryAllocation) * 1024}

  container_definitions = jsonencode([
    {
      name  = "pipeline-loader"
      image = "pipeline-loader:latest"
      
      environment = [
        {
          name  = "BATCH_SIZE"
          value = "10000"
        },
        {
          name  = "PARALLELISM"
          value = "${performance.diskIO.ioThreads}"
        }
      ]
      
      logConfiguration = {
        logDriver = "awslogs"
        options = {
          awslogs-group         = "/ecs/pipeline-loading"
          awslogs-region        = "us-east-1"
          awslogs-stream-prefix = "ecs"
        }
      }
    }
  ])
}

# CloudWatch Log Group
resource "aws_cloudwatch_log_group" "loading_logs" {
  name              = "/ecs/pipeline-loading"
  retention_in_days = 30
}

# S3 Bucket for intermediate storage
resource "aws_s3_bucket" "pipeline_storage" {
  bucket = "pipeline-loading-storage"
}

# IAM Role for ECS tasks
resource "aws_iam_role" "loading_execution_role" {
  name = "pipeline-loading-execution-role"

  assume_role_policy = jsonencode({
    Version = "2012-10-17"
    Statement = [
      {
        Action = "sts:AssumeRole"
        Effect = "Allow"
        Principal = {
          Service = "ecs-tasks.amazonaws.com"
        }
      }
    ]
  })
}`;
  }

  /**
   * Generate deployment strategy
   */
  private generateDeployment(destinations: DestinationConfig[], strategy: LoadingStrategy): string {
    return `
# Deployment Strategy for Pipeline Loading

## Container Strategy
- **Base Image**: python:3.11-slim
- **Runtime**: AWS ECS Fargate
- **Scaling**: Auto-scaling based on queue depth
- **Health Checks**: Application-level health endpoints

## Environment Configuration
- **Development**: Single instance, reduced parallelism
- **Staging**: Production-like setup with test data
- **Production**: Full scale with ${strategy.parallelism} parallel workers

## Deployment Process
1. Build container image with loading logic
2. Run integration tests against staging destinations
3. Deploy to ECS with blue-green strategy
4. Monitor metrics for 15 minutes
5. Complete deployment or rollback

## Configuration Management
- Environment variables for connection parameters
- AWS Systems Manager Parameter Store for secrets
- Configuration validation on startup
`;
  }

  /**
   * Generate testing strategy
   */
  private generateTesting(destinations: DestinationConfig[], strategy: LoadingStrategy): string {
    return `
# Testing Strategy for Pipeline Loading

## Unit Tests
- ✅ Connection validation for each destination type
- ✅ Data transformation logic
- ✅ Error handling scenarios
- ✅ Retry mechanism validation

## Integration Tests
- ✅ End-to-end loading workflow
- ✅ Performance benchmarks
- ✅ Failure recovery testing
- ✅ Data consistency validation

## Load Tests
- ✅ ${strategy.batchSize} records per batch performance
- ✅ ${strategy.parallelism} concurrent workers
- ✅ Memory usage under load
- ✅ Network throughput optimization

## Data Quality Tests
- ✅ Schema validation on load
- ✅ Duplicate detection
- ✅ Data freshness verification
- ✅ Referential integrity checks
`;
  }

  /**
   * Generate comprehensive documentation
   */
  private generateDocumentation(requirements: string, destinations: DestinationConfig[]): string {
    return `
# Pipeline Loading Documentation

## Overview
This document describes the data loading implementation for: ${requirements}

## Architecture
- **Destinations**: ${destinations.map(d => d.type).join(', ')}
- **Strategy**: Optimized for high throughput and reliability
- **Monitoring**: Comprehensive observability and alerting

## Configuration
Detailed configuration parameters and environment setup instructions.

## Operations
- Deployment procedures
- Monitoring and alerting
- Troubleshooting guide
- Performance tuning

## Data Governance
- Encryption standards
- Access controls
- Compliance requirements
- Audit logging
`;
  }

  /**
   * Generate implementation code for the loading pipeline
   */
  private async generateImplementationCode(plan: LoadingPlan, outputPath: string): Promise<void> {
    const codeDir = join(outputPath, 'code');
    await mkdir(codeDir, { recursive: true });

    // Generate Python loading script
    const pythonCode = this.generatePythonCode(plan);
    await writeFile(join(codeDir, 'pipeline_loader.py'), pythonCode);

    // Generate Docker configuration
    const dockerFile = this.generateDockerfile(plan);
    await writeFile(join(codeDir, 'Dockerfile'), dockerFile);

    // Generate requirements.txt
    const requirements = this.generateRequirements(plan);
    await writeFile(join(codeDir, 'requirements.txt'), requirements);

    // Generate Airflow DAG
    const airflowDAG = this.generateAirflowDAG(plan);
    await writeFile(join(codeDir, 'loading_dag.py'), airflowDAG);

    this.logger.info('Implementation code generated', { outputPath: codeDir });
  }

  /**
   * Generate Python loading implementation
   */
  private generatePythonCode(plan: LoadingPlan): string {
    const destinations = plan.destinations.map(d => d.type).join(', ');
    
    return `#!/usr/bin/env python3
"""
Enterprise Data Pipeline Loader
Generated by Claude Flow Pipeline Engine

Supports: ${destinations}
Strategy: ${plan.loadingStrategy.strategy}
"""

import asyncio
import logging
import time
from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from contextlib import asynccontextmanager

import pandas as pd
import asyncpg  # PostgreSQL
import snowflake.connector  # Snowflake
import boto3  # AWS services
from sqlalchemy import create_engine
from tenacity import retry, stop_after_attempt, wait_exponential

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

@dataclass
class LoadingMetrics:
    """Metrics for monitoring loading performance"""
    start_time: float
    end_time: Optional[float] = None
    records_processed: int = 0
    records_failed: int = 0
    batches_completed: int = 0
    
    @property
    def duration(self) -> float:
        return (self.end_time or time.time()) - self.start_time
    
    @property
    def throughput(self) -> float:
        return self.records_processed / max(self.duration, 0.001)

class PipelineLoader:
    """Enterprise-grade data pipeline loader"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.metrics = LoadingMetrics(start_time=time.time())
        self.batch_size = ${plan.loadingStrategy.batchSize}
        self.parallelism = ${plan.loadingStrategy.parallelism}
        
    async def load_data(self, data_source: str, destination: str) -> LoadingMetrics:
        """Load data with comprehensive error handling and monitoring"""
        try:
            logger.info(f"Starting data loading: {data_source} -> {destination}")
            
            # Initialize connections
            async with self._get_connections() as connections:
                # Process data in batches
                async for batch in self._read_batches(data_source):
                    await self._process_batch(batch, destination, connections)
                    self.metrics.batches_completed += 1
                    
            self.metrics.end_time = time.time()
            logger.info(f"Loading completed: {self.metrics.records_processed} records in {self.metrics.duration:.2f}s")
            
            return self.metrics
            
        except Exception as e:
            logger.error(f"Loading failed: {e}")
            raise
    
    @asynccontextmanager
    async def _get_connections(self):
        """Manage database connections with proper cleanup"""
        connections = {}
        try:
            # Initialize destination connections based on configuration
            for dest in self.config['destinations']:
                if dest['type'] == 'postgres':
                    connections['postgres'] = await asyncpg.connect(**dest['connectionParams'])
                elif dest['type'] == 'snowflake':
                    connections['snowflake'] = snowflake.connector.connect(**dest['connectionParams'])
                # Add other destination types as needed
                
            yield connections
            
        finally:
            # Cleanup connections
            for conn in connections.values():
                if hasattr(conn, 'close'):
                    if asyncio.iscoroutinefunction(conn.close):
                        await conn.close()
                    else:
                        conn.close()
    
    async def _read_batches(self, data_source: str):
        """Read data in optimized batches"""
        # Implementation depends on data source type
        # This is a placeholder for the actual implementation
        for i in range(0, 100000, self.batch_size):
            # Simulate batch reading
            batch = pd.DataFrame({
                'id': range(i, min(i + self.batch_size, 100000)),
                'data': [f'record_{j}' for j in range(i, min(i + self.batch_size, 100000))]
            })
            yield batch
    
    @retry(
        stop=stop_after_attempt(${plan.loadingStrategy.retryConfig?.maxRetries || 3}),
        wait=wait_exponential(multiplier=1, min=4, max=10)
    )
    async def _process_batch(self, batch: pd.DataFrame, destination: str, connections: Dict):
        """Process a single batch with retry logic"""
        try:
            batch_size = len(batch)
            logger.debug(f"Processing batch of {batch_size} records")
            
            # Apply transformations if needed
            processed_batch = self._transform_batch(batch)
            
            # Load to destination
            await self._load_to_destination(processed_batch, destination, connections)
            
            self.metrics.records_processed += batch_size
            
        except Exception as e:
            self.metrics.records_failed += len(batch)
            logger.error(f"Batch processing failed: {e}")
            raise
    
    def _transform_batch(self, batch: pd.DataFrame) -> pd.DataFrame:
        """Apply any necessary transformations to the batch"""
        # Add transformations as needed
        batch['loaded_at'] = pd.Timestamp.now()
        return batch
    
    async def _load_to_destination(self, batch: pd.DataFrame, destination: str, connections: Dict):
        """Load batch to the specified destination"""
        # Implementation based on destination type
        if destination == 'postgres':
            await self._load_to_postgres(batch, connections['postgres'])
        elif destination == 'snowflake':
            await self._load_to_snowflake(batch, connections['snowflake'])
        # Add other destinations as needed
    
    async def _load_to_postgres(self, batch: pd.DataFrame, connection):
        """Load batch to PostgreSQL"""
        # Convert DataFrame to records and insert
        records = batch.to_dict('records')
        query = "INSERT INTO target_table (id, data, loaded_at) VALUES ($1, $2, $3)"
        
        async with connection.transaction():
            await connection.executemany(query, [
                (record['id'], record['data'], record['loaded_at']) 
                for record in records
            ])
    
    async def _load_to_snowflake(self, batch: pd.DataFrame, connection):
        """Load batch to Snowflake"""
        # Use Snowflake's bulk loading capabilities
        # This would typically involve staging data to S3 first
        pass

async def main():
    """Main execution function"""
    config = {
        'destinations': ${JSON.stringify(plan.destinations, null, 8)}
    }
    
    loader = PipelineLoader(config)
    metrics = await loader.load_data('source_data', 'target_destination')
    
    print(f"Loading completed:")
    print(f"  Records processed: {metrics.records_processed}")
    print(f"  Duration: {metrics.duration:.2f} seconds")
    print(f"  Throughput: {metrics.throughput:.2f} records/second")

if __name__ == "__main__":
    asyncio.run(main())
`;
  }

  /**
   * Generate Dockerfile for the loading service
   */
  private generateDockerfile(plan: LoadingPlan): string {
    return `FROM python:3.11-slim

# Set working directory
WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \\
    gcc \\
    g++ \\
    && rm -rf /var/lib/apt/lists/*

# Copy requirements and install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY pipeline_loader.py .

# Set environment variables
ENV PYTHONUNBUFFERED=1
ENV BATCH_SIZE=${plan.loadingStrategy.batchSize}
ENV PARALLELISM=${plan.loadingStrategy.parallelism}

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \\
    CMD python -c "import sys; sys.exit(0)"

# Run the application
CMD ["python", "pipeline_loader.py"]
`;
  }

  /**
   * Generate requirements.txt for Python dependencies
   */
  private generateRequirements(plan: LoadingPlan): string {
    const hasSnowflake = plan.destinations.some(d => d.type === 'snowflake');
    const hasRedshift = plan.destinations.some(d => d.type === 'redshift');
    const hasBigQuery = plan.destinations.some(d => d.type === 'bigquery');
    const hasMongo = plan.destinations.some(d => d.type === 'mongodb');

    let requirements = `# Core dependencies
pandas>=2.0.0
sqlalchemy>=2.0.0
asyncpg>=0.28.0
tenacity>=8.2.0
boto3>=1.26.0
aiofiles>=23.0.0

# Monitoring and logging
prometheus-client>=0.16.0
structlog>=23.0.0

# Data processing
pyarrow>=12.0.0
`;

    if (hasSnowflake) {
      requirements += `\n# Snowflake connector
snowflake-connector-python>=3.0.0
snowflake-sqlalchemy>=1.4.0
`;
    }

    if (hasRedshift) {
      requirements += `\n# Redshift connector
redshift-connector>=2.0.0
psycopg2-binary>=2.9.0
`;
    }

    if (hasBigQuery) {
      requirements += `\n# BigQuery connector
google-cloud-bigquery>=3.10.0
google-auth>=2.17.0
`;
    }

    if (hasMongo) {
      requirements += `\n# MongoDB connector
motor>=3.1.0
pymongo>=4.3.0
`;
    }

    return requirements;
  }

  /**
   * Generate Airflow DAG for orchestration
   */
  private generateAirflowDAG(plan: LoadingPlan): string {
    return `"""
Airflow DAG for Pipeline Loading
Generated by Claude Flow Pipeline Engine
"""

from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.operators.bash import BashOperator
from airflow.providers.postgres.hooks.postgres import PostgresHook
from airflow.providers.amazon.aws.operators.ecs import ECSOperator

# Default arguments
default_args = {
    'owner': 'data-team',
    'depends_on_past': False,
    'start_date': datetime(2024, 1, 1),
    'email_on_failure': True,
    'email_on_retry': False,
    'retries': ${plan.loadingStrategy.retryConfig?.maxRetries || 3},
    'retry_delay': timedelta(minutes=5),
    'sla': timedelta(hours=2)
}

# Create DAG
dag = DAG(
    'pipeline_loading',
    default_args=default_args,
    description='Enterprise data pipeline loading',
    schedule_interval='${plan.orchestration.schedule}',
    catchup=False,
    tags=['data-pipeline', 'loading', 'enterprise']
)

def validate_prerequisites(**context):
    """Validate that prerequisites are met before loading"""
    # Check data quality metrics
    # Verify source data availability
    # Validate schema compatibility
    return True

def monitor_loading_progress(**context):
    """Monitor loading progress and send alerts if needed"""
    # Check loading metrics
    # Send notifications for SLA breaches
    # Update monitoring dashboards
    return True

# Task definitions
validate_task = PythonOperator(
    task_id='validate_prerequisites',
    python_callable=validate_prerequisites,
    dag=dag
)

loading_task = ECSOperator(
    task_id='execute_loading',
    task_definition='pipeline-loading',
    cluster='data-pipeline-cluster',
    overrides={
        'containerOverrides': [
            {
                'name': 'pipeline-loader',
                'environment': [
                    {'name': 'BATCH_SIZE', 'value': '${plan.loadingStrategy.batchSize}'},
                    {'name': 'PARALLELISM', 'value': '${plan.loadingStrategy.parallelism}'}
                ]
            }
        ]
    },
    network_configuration={
        'awsvpcConfiguration': {
            'subnets': ['subnet-12345', 'subnet-67890'],
            'securityGroups': ['sg-abcdef'],
            'assignPublicIp': 'ENABLED'
        }
    },
    dag=dag
)

monitor_task = PythonOperator(
    task_id='monitor_progress',
    python_callable=monitor_loading_progress,
    dag=dag
)

# Task dependencies
validate_task >> loading_task >> monitor_task
`;
  }

  /**
   * Helper methods
   */
  private extractTitle(requirements: string): string {
    // Extract meaningful title from requirements
    const words = requirements.split(' ').slice(0, 6);
    return words.join(' ').replace(/[^\w\s]/g, '');
  }

  private inferTableName(requirements: string): string {
    const reqLower = requirements.toLowerCase();
    
    if (reqLower.includes('customer')) return 'customers';
    if (reqLower.includes('order')) return 'orders';
    if (reqLower.includes('product')) return 'products';
    if (reqLower.includes('user')) return 'users';
    if (reqLower.includes('event')) return 'events';
    if (reqLower.includes('transaction')) return 'transactions';
    
    return 'pipeline_data';
  }

  private async loadPreviousPlan(planPath: string): Promise<any> {
    try {
      if (existsSync(planPath)) {
        const content = await readFile(planPath, 'utf-8');
        return JSON.parse(content);
      }
    } catch (error) {
      this.logger.warn('Could not load previous plan', { planPath, error });
    }
    return null;
  }

  private async savePlan(plan: LoadingPlan, outputPath: string): Promise<void> {
    const planDir = dirname(outputPath);
    if (!existsSync(planDir)) {
      await mkdir(planDir, { recursive: true });
    }
    
    const filename = outputPath.endsWith('.json') ? outputPath : `${outputPath}/pipeline-loading-plan-${new Date().toISOString().split('T')[0]}.json`;
    await writeFile(filename, JSON.stringify(plan, null, 2));
    this.logger.info('Loading plan saved', { filename });
  }
}