"""
Data Pipeline Builder Agent

Builds and manages ETL/ELT data pipelines for extracting, transforming,
and loading data across various data sources and destinations.
"""

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


class DataPipelineBuilderAgent(BaseAgent):
    """
    Builds ETL/ELT data pipelines for data integration.

    Supports:
    - Multiple data sources (databases, APIs, files, streams)
    - Transformation logic (filtering, aggregation, enrichment)
    - Multiple destinations (data warehouses, lakes, databases)
    - Scheduling and orchestration
    - Error handling and retry logic
    """

    def __init__(self):
        super().__init__(
            name='data-pipeline-builder',
            description='Build and manage ETL/ELT data pipelines',
            category='data',
            version='1.0.0',
            tags=['etl', 'elt', 'data-integration', 'pipeline', 'orchestration']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Build and execute a data pipeline.

        Args:
            params: {
                'pipeline_name': str,
                'source': {
                    'type': 'database|api|file|stream|s3|gcs',
                    'connection': {...},  # Source-specific connection details
                    'query': str,         # SQL query or API endpoint
                    'format': 'json|csv|parquet|avro'
                },
                'transformations': [
                    {
                        'type': 'filter|aggregate|join|enrich|normalize',
                        'config': {...}
                    }
                ],
                'destination': {
                    'type': 'warehouse|lake|database|file',
                    'connection': {...},
                    'table': str,
                    'mode': 'append|overwrite|upsert'
                },
                'schedule': 'cron_expression',  # Optional
                'options': {
                    'batch_size': int,
                    'parallel_workers': int,
                    'retry_attempts': int,
                    'error_handling': 'fail|skip|log'
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'pipeline_id': str,
                'records_processed': int,
                'records_failed': int,
                'execution_time_seconds': float,
                'source_rows': int,
                'destination_rows': int,
                'transformations_applied': int,
                'errors': List[str],
                'metadata': {
                    'start_time': str,
                    'end_time': str,
                    'next_scheduled_run': str
                }
            }
        """
        pipeline_name = params.get('pipeline_name', 'untitled_pipeline')
        source = params.get('source', {})
        transformations = params.get('transformations', [])
        destination = params.get('destination', {})
        options = params.get('options', {})

        self.logger.info(
            f"Building pipeline '{pipeline_name}': "
            f"{source.get('type')} -> {destination.get('type')}"
        )

        # Mock pipeline execution
        source_rows = 10000
        records_processed = source_rows - (source_rows // 100)  # 1% failure rate
        records_failed = source_rows - records_processed

        return {
            'status': 'success',
            'pipeline_id': f'pipeline_{pipeline_name}',
            'pipeline_name': pipeline_name,
            'records_processed': records_processed,
            'records_failed': records_failed,
            'execution_time_seconds': 45.3,
            'source_rows': source_rows,
            'destination_rows': records_processed,
            'transformations_applied': len(transformations),
            'source_type': source.get('type'),
            'destination_type': destination.get('type'),
            'batch_size': options.get('batch_size', 1000),
            'errors': [f'Failed to process {records_failed} records'] if records_failed > 0 else [],
            'warnings': [
                'Consider adding data quality checks',
                'Enable incremental loading for better performance'
            ],
            'metadata': {
                'start_time': '2025-11-16T10:00:00Z',
                'end_time': '2025-11-16T10:00:45Z',
                'next_scheduled_run': '2025-11-17T10:00:00Z'
            }
        }

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

        if 'destination' not in params:
            self.logger.error("Missing required field: destination")
            return False

        valid_source_types = ['database', 'api', 'file', 'stream', 's3', 'gcs']
        source_type = params.get('source', {}).get('type')

        if source_type not in valid_source_types:
            self.logger.error(f"Invalid source type: {source_type}")
            return False

        return True
