"""
Data Warehouse Manager Agent

Manages data warehouse operations including schema design,
data loading, optimization, and maintenance tasks.
"""

from typing import Any, Dict, List
from agents.base import BaseAgent


class DataWarehouseManagerAgent(BaseAgent):
    """
    Manages data warehouse operations and maintenance.

    Supports:
    - Schema design and management
    - Data loading and synchronization
    - Partitioning and indexing strategies
    - Query optimization
    - Storage management
    - ETL job coordination
    """

    def __init__(self):
        super().__init__(
            name='data-warehouse-manager',
            description='Manage data warehouse operations and optimization',
            category='data',
            version='1.0.0',
            tags=['data-warehouse', 'etl', 'optimization', 'schema-management']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Execute data warehouse management tasks.

        Args:
            params: {
                'operation': 'create_schema|load_data|optimize|maintain|backup',
                'warehouse': {
                    'type': 'snowflake|redshift|bigquery|synapse|databricks',
                    'connection': {...}
                },
                'config': {
                    'schema_name': str,
                    'tables': List[Dict[str, Any]],
                    'partition_strategy': 'date|hash|range',
                    'compression': 'gzip|snappy|lzo|zstd',
                    'distribution_key': str,
                    'sort_key': List[str]
                },
                'options': {
                    'auto_vacuum': bool,
                    'analyze_tables': bool,
                    'optimize_storage': bool,
                    'create_indexes': bool
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'operation': str,
                'warehouse_type': str,
                'tables_affected': int,
                'storage_size_gb': float,
                'storage_optimized_gb': float,
                'compression_ratio': float,
                'indexes_created': int,
                'partitions_created': int,
                'execution_time_seconds': float,
                'cost_estimate': float,
                'performance_improvement': float,
                'recommendations': List[str]
            }
        """
        operation = params.get('operation', 'optimize')
        warehouse = params.get('warehouse', {})
        config = params.get('config', {})
        options = params.get('options', {})

        warehouse_type = warehouse.get('type', 'snowflake')

        self.logger.info(
            f"Executing {operation} on {warehouse_type} data warehouse"
        )

        # Mock warehouse management results
        storage_before = 150.5
        storage_after = 98.3

        return {
            'status': 'success',
            'operation': operation,
            'warehouse_type': warehouse_type,
            'schema_name': config.get('schema_name', 'analytics'),
            'tables_affected': 15,
            'storage_size_gb': storage_before,
            'storage_optimized_gb': storage_after,
            'storage_saved_gb': storage_before - storage_after,
            'compression_ratio': 0.65,
            'indexes_created': 8,
            'partitions_created': 12,
            'execution_time_seconds': 180.5,
            'cost_estimate': 45.50,
            'performance_improvement': 0.35,  # 35% faster queries
            'query_performance': {
                'avg_query_time_before': 4.5,
                'avg_query_time_after': 2.9,
                'improvement_percentage': 35.5
            },
            'storage_details': {
                'fact_tables_gb': 78.2,
                'dimension_tables_gb': 12.5,
                'staging_tables_gb': 7.6,
                'total_rows': 125000000
            },
            'recommendations': [
                'Consider incremental loads instead of full refreshes',
                'Implement slowly changing dimensions (SCD Type 2)',
                'Add materialized views for frequently accessed aggregations',
                'Review retention policy for historical data',
                'Enable automatic clustering for large tables'
            ],
            'next_maintenance': '2025-11-23T10:00:00Z'
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate warehouse management parameters."""
        if 'warehouse' not in params:
            self.logger.error("Missing required field: warehouse")
            return False

        valid_operations = ['create_schema', 'load_data', 'optimize', 'maintain', 'backup']
        operation = params.get('operation', 'optimize')

        if operation not in valid_operations:
            self.logger.error(f"Invalid operation: {operation}")
            return False

        valid_warehouses = ['snowflake', 'redshift', 'bigquery', 'synapse', 'databricks']
        warehouse_type = params.get('warehouse', {}).get('type')

        if warehouse_type not in valid_warehouses:
            self.logger.error(f"Invalid warehouse type: {warehouse_type}")
            return False

        return True
