"""
DataLineagePy Phase 9: Orchestration Integration Demo

This demo showcases the comprehensive orchestration integration capabilities including:
- Apache Airflow: Native operators, hooks, and DAG lineage
- dbt: Model dependencies, manifest parsing, and impact analysis  
- Prefect: Flow and task lineage tracking
- Dagster: Asset lineage and job tracking
- Universal Orchestration: Cross-platform workflow management
- Testing Framework: Orchestration-specific testing capabilities

Run this demo to see Phase 9 orchestration lineage in action!
"""

from lineagepy.orchestration import (
    UniversalOrchestrationManager,
    CrossPlatformWorkflow,
    OrchestrationPlatform,
    print_orchestration_status,
    get_orchestration_info
)
import sys
import os
from datetime import datetime, timedelta
from pathlib import Path

# Add the project root to Python path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))


# Mock platform trackers for demo

class MockAirflowTracker:
    def __init__(self):
        self.name = "Airflow Mock Tracker"

    def track_dag_execution(self, dag_id, context):
        return f"airflow_dag_{dag_id}_execution"


class MockDbtTracker:
    def __init__(self):
        self.name = "dbt Mock Tracker"

    def track_model_lineage(self, model_name):
        return f"dbt_model_{model_name}_lineage"


class MockPrefectTracker:
    def __init__(self):
        self.name = "Prefect Mock Tracker"

    def track_flow_run(self, flow_name):
        return f"prefect_flow_{flow_name}_run"


class MockDagsterTracker:
    def __init__(self):
        self.name = "Dagster Mock Tracker"

    def track_asset_materialization(self, asset_name):
        return f"dagster_asset_{asset_name}_materialization"


def demo_platform_availability():
    """Demo: Check orchestration platform availability."""
    print("🔧 DataLineagePy Phase 9: Orchestration Platform Availability")
    print("=" * 70)

    # Print platform status
    print_orchestration_status()

    # Get detailed info
    info = get_orchestration_info()
    print(f"\nTotal platforms available: {info['total_platforms']}")
    print(f"Enterprise ready: {info['enterprise_ready']}")

    print("\nRecommendations:")
    for recommendation in info['recommendations']:
        print(f"  • {recommendation}")

    print("\n✅ Platform availability check completed!\n")


def demo_airflow_integration():
    """Demo: Apache Airflow lineage integration."""
    print("✈️ Demo: Apache Airflow Integration")
    print("-" * 40)

    try:
        from lineagepy.orchestration.airflow_lineage import (
            AirflowLineageTracker, LineageOperator, lineage_tracked
        )

        # Initialize Airflow tracker
        airflow_tracker = AirflowLineageTracker()

        # Mock DAG execution
        dag_context = {
            'dag': type('MockDAG', (), {'dag_id': 'user_analytics_dag'})(),
            'task': type('MockTask', (), {'task_id': 'extract_users'})(),
            'execution_date': datetime.now()
        }

        # Track DAG execution
        dag_lineage_id = airflow_tracker.track_dag_execution(
            dag_id='user_analytics_dag',
            dag_run_id='manual_2024_01_15',
            context=dag_context
        )

        # Track task execution
        task_lineage_id = airflow_tracker.track_task_execution(
            task_id='extract_users',
            dag_id='user_analytics_dag',
            context=dag_context,
            operation_type='data_extraction'
        )

        # Track task dependencies
        airflow_tracker.track_task_dependency(
            upstream_task='extract_users',
            downstream_task='transform_users',
            dag_id='user_analytics_dag'
        )

        # Track connection usage
        connection_id = airflow_tracker.track_connection_usage(
            conn_id='postgres_default',
            task_id='extract_users',
            dag_id='user_analytics_dag',
            operation='SELECT',
            query='SELECT * FROM users'
        )

        # Analyze DAG lineage
        dag_analysis = airflow_tracker.analyze_dag_lineage(
            'user_analytics_dag')

        print(f"  ✅ DAG lineage tracked: {dag_lineage_id[:8]}...")
        print(f"  ✅ Task lineage tracked: {task_lineage_id[:8]}...")
        print(f"  ✅ Connection usage tracked: {connection_id[:8]}...")
        print(f"  📊 DAG Analysis:")
        print(f"    - Total tasks: {dag_analysis['total_tasks']}")
        print(f"    - Total connections: {dag_analysis['total_connections']}")
        print(
            f"    - Task dependencies: {len(dag_analysis['task_dependencies'])}")
        print(
            f"    - Lineage complexity: {dag_analysis['lineage_complexity']}")

        # Demo lineage decorator
        @lineage_tracked(operation_type="data_processing")
        def process_user_data(context):
            return {"processed": True, "records": 1500}

        # Mock execution with context
        result = process_user_data(dag_context)
        print(f"  🎯 Lineage decorator executed: {result}")

        print("  ✅ Airflow integration demo completed!")

    except ImportError:
        print("  ❌ Airflow not available - using mock implementation")

        # Mock implementation
        mock_tracker = MockAirflowTracker()
        dag_id = mock_tracker.track_dag_execution('user_analytics_dag', {})
        print(f"  ✅ Mock DAG tracking: {dag_id}")

    print()


def demo_dbt_integration():
    """Demo: dbt model lineage integration."""
    print("🔄 Demo: dbt Integration")
    print("-" * 40)

    try:
        from lineagepy.orchestration.dbt_lineage import (
            DbtLineageTracker, DbtManifestParser, dbt_lineage_macro
        )

        # Initialize dbt tracker with mock project
        dbt_tracker = DbtLineageTracker(project_dir="./mock_dbt_project")

        # Mock model configuration
        mock_model_config = {
            'name': 'user_analytics',
            'schema': 'analytics',
            'database': 'warehouse',
            'description': 'User analytics aggregation model',
            'columns': {'user_id': {}, 'total_orders': {}, 'total_spent': {}},
            'config': {'materialized': 'table'},
            'tags': ['analytics', 'users']
        }

        # Track model lineage
        model_lineage_id = dbt_tracker.track_model_lineage(
            model_name='user_analytics',
            model_config=mock_model_config
        )

        # Track dbt run
        run_lineage_id = dbt_tracker.track_dbt_run(
            command='dbt run --models user_analytics',
            target='prod',
            models=['user_analytics', 'user_segments']
        )

        # Complete the run
        dbt_tracker.complete_dbt_run(
            lineage_id=run_lineage_id,
            status='success',
            results={'models_built': 2, 'duration': '45s'}
        )

        # Track dbt test
        test_lineage_id = dbt_tracker.track_dbt_test(
            test_name='not_null_user_id',
            model_name='user_analytics',
            test_type='schema_test'
        )

        # Mock impact analysis
        impact_analysis = dbt_tracker.analyze_model_impact('users')

        print(f"  ✅ Model lineage tracked: {model_lineage_id[:8]}...")
        print(f"  ✅ dbt run tracked: {run_lineage_id[:8]}...")
        print(f"  ✅ dbt test tracked: {test_lineage_id[:8]}...")
        print(f"  📊 Impact Analysis for 'users':")
        print(f"    - Impact scope: {impact_analysis['impact_scope']} models")
        print(
            f"    - Affected tests: {len(impact_analysis['affected_tests'])}")
        print(
            f"    - Recommendations: {len(impact_analysis['recommendations'])}")

        # Generate dbt macro
        macro_code = dbt_lineage_macro()
        print(f"  🎯 Generated dbt macro ({len(macro_code)} characters)")

        print("  ✅ dbt integration demo completed!")

    except ImportError:
        print("  ❌ dbt not available - using mock implementation")

        # Mock implementation
        mock_tracker = MockDbtTracker()
        model_id = mock_tracker.track_model_lineage('user_analytics')
        print(f"  ✅ Mock model tracking: {model_id}")

    print()


def demo_prefect_integration():
    """Demo: Prefect flow lineage integration."""
    print("🌊 Demo: Prefect Integration")
    print("-" * 40)

    try:
        from lineagepy.orchestration.prefect_lineage import (
            PrefectLineageTracker, lineage_tracked_flow, lineage_tracked_task
        )

        # Initialize Prefect tracker
        prefect_tracker = PrefectLineageTracker()

        # Mock flow execution
        flow_lineage_id = prefect_tracker.track_flow_execution(
            flow_name='user_analytics_flow',
            deployment_name='production',
            run_id='flow_run_123',
            parameters={'env': 'prod', 'batch_size': 1000}
        )

        # Mock task execution
        task_lineage_id = prefect_tracker.track_task_execution(
            task_name='extract_users',
            flow_name='user_analytics_flow',
            run_id='task_run_456',
            task_inputs=['database_connection'],
            task_outputs=['users_dataframe']
        )

        # Track flow completion
        prefect_tracker.complete_flow_run(
            lineage_id=flow_lineage_id,
            status='Completed',
            final_state='SUCCESS',
            artifacts=['users_analytics.parquet']
        )

        # Mock deployment tracking
        deployment_id = prefect_tracker.track_deployment(
            deployment_name='user_analytics_prod',
            flow_name='user_analytics_flow',
            work_pool='production-pool',
            schedule='0 2 * * *'
        )

        print(f"  ✅ Flow lineage tracked: {flow_lineage_id[:8]}...")
        print(f"  ✅ Task lineage tracked: {task_lineage_id[:8]}...")
        print(f"  ✅ Deployment tracked: {deployment_id[:8]}...")

        # Demo lineage decorators
        print("  🎯 Testing Prefect lineage decorators:")

        @lineage_tracked_flow
        def mock_analytics_flow():
            return "Flow executed successfully"

        @lineage_tracked_task
        def mock_extract_task():
            return {"records": 5000, "format": "parquet"}

        flow_result = mock_analytics_flow()
        task_result = mock_extract_task()

        print(f"    - Flow result: {flow_result}")
        print(f"    - Task result: {task_result}")

        print("  ✅ Prefect integration demo completed!")

    except ImportError:
        print("  ❌ Prefect not available - using mock implementation")

        # Mock implementation
        mock_tracker = MockPrefectTracker()
        flow_id = mock_tracker.track_flow_run('user_analytics_flow')
        print(f"  ✅ Mock flow tracking: {flow_id}")

    print()


def demo_dagster_integration():
    """Demo: Dagster asset lineage integration."""
    print("🏭 Demo: Dagster Integration")
    print("-" * 40)

    try:
        from lineagepy.orchestration.dagster_lineage import (
            DagsterLineageTracker, lineage_tracked_asset
        )

        # Initialize Dagster tracker
        dagster_tracker = DagsterLineageTracker()

        # Mock asset materialization
        asset_lineage_id = dagster_tracker.track_asset_materialization(
            asset_name='user_analytics',
            job_name='analytics_job',
            run_id='materialization_789',
            partition_key='2024-01-15',
            metadata={'rows': 15000, 'size_mb': 45.2}
        )

        # Mock job execution
        job_lineage_id = dagster_tracker.track_job_execution(
            job_name='analytics_job',
            run_id='job_run_321',
            assets_materialized=['users_raw',
                                 'users_cleaned', 'user_analytics'],
            status='SUCCESS'
        )

        # Track asset dependencies
        dagster_tracker.track_asset_dependency(
            upstream_asset='users_raw',
            downstream_asset='users_cleaned',
            dependency_type='transformation'
        )

        # Mock partition tracking
        partition_id = dagster_tracker.track_partition_execution(
            asset_name='user_analytics',
            partition_key='2024-01-15',
            status='SUCCESS',
            execution_time=120.5
        )

        print(f"  ✅ Asset lineage tracked: {asset_lineage_id[:8]}...")
        print(f"  ✅ Job execution tracked: {job_lineage_id[:8]}...")
        print(f"  ✅ Partition tracked: {partition_id[:8]}...")

        # Demo asset lineage decorator
        @lineage_tracked_asset
        def mock_user_analytics_asset():
            return {"materialized": True, "records": 10000}

        asset_result = mock_user_analytics_asset()
        print(f"  🎯 Asset decorator result: {asset_result}")

        print("  ✅ Dagster integration demo completed!")

    except ImportError:
        print("  ❌ Dagster not available - using mock implementation")

        # Mock implementation
        mock_tracker = MockDagsterTracker()
        asset_id = mock_tracker.track_asset_materialization('user_analytics')
        print(f"  ✅ Mock asset tracking: {asset_id}")

    print()


def demo_cross_platform_orchestration():
    """Demo: Cross-platform workflow orchestration."""
    print("🌍 Demo: Cross-Platform Orchestration")
    print("-" * 40)

    # Initialize Universal Orchestration Manager
    orchestration_manager = UniversalOrchestrationManager({
        'airflow': MockAirflowTracker(),
        'dbt': MockDbtTracker(),
        'prefect': MockPrefectTracker(),
        'dagster': MockDagsterTracker()
    })

    # Create cross-platform workflow
    workflow = orchestration_manager.create_cross_platform_workflow(
        workflow_id='user_analytics_cross_platform',
        name='User Analytics Cross-Platform Pipeline',
        description='Complete user analytics pipeline spanning multiple orchestration platforms'
    )

    # Add stages across different platforms

    # Stage 1: Airflow extracts data
    airflow_stage = orchestration_manager.add_airflow_stage(
        workflow_id=workflow.workflow_id,
        stage_id='extract_data',
        dag_id='data_extraction',
        task_id='extract_users',
        connection='postgres_prod'
    )

    # Stage 2: dbt transforms data
    dbt_stage = orchestration_manager.add_dbt_stage(
        workflow_id=workflow.workflow_id,
        stage_id='transform_data',
        project='analytics_project',
        model='user_analytics'
    )

    # Stage 3: Prefect processes results
    prefect_stage = orchestration_manager.add_prefect_stage(
        workflow_id=workflow.workflow_id,
        stage_id='process_results',
        flow_name='analytics_processing',
        deployment='production'
    )

    # Stage 4: Dagster materializes final assets
    dagster_stage = orchestration_manager.add_dagster_stage(
        workflow_id=workflow.workflow_id,
        stage_id='materialize_assets',
        asset_name='user_insights',
        job_name='insights_job'
    )

    # Add dependencies between stages
    orchestration_manager.add_stage_dependency(
        workflow_id=workflow.workflow_id,
        upstream_stage='extract_data',
        downstream_stage='transform_data'
    )

    orchestration_manager.add_stage_dependency(
        workflow_id=workflow.workflow_id,
        upstream_stage='transform_data',
        downstream_stage='process_results'
    )

    orchestration_manager.add_stage_dependency(
        workflow_id=workflow.workflow_id,
        upstream_stage='process_results',
        downstream_stage='materialize_assets'
    )

    # Execute the workflow
    print(f"  📋 Created workflow with {len(workflow.stages)} stages")
    print(f"  🔧 Platforms involved: {[p.value for p in workflow.platforms]}")

    # Execute workflow (parallel by default)
    execution_results = orchestration_manager.execute_workflow(
        workflow_id=workflow.workflow_id,
        parallel=True
    )

    print(f"  ✅ Workflow execution completed!")
    print(f"    - Status: {execution_results['status']}")
    print(f"    - Total stages: {execution_results['total_stages']}")
    print(f"    - Completed stages: {execution_results['completed_stages']}")
    print(
        f"    - Execution duration: {execution_results['execution_duration']}")
    print(f"    - Platforms used: {execution_results['platforms_used']}")

    # Analyze cross-platform dependencies
    dependencies = orchestration_manager.analyze_cross_platform_dependencies()
    print(f"  📊 Cross-Platform Analysis:")
    print(f"    - Total workflows: {dependencies['total_workflows']}")
    print(
        f"    - Cross-platform connections: {len(dependencies['cross_platform_connections'])}")
    print(f"    - Complexity score: {dependencies['complexity_score']}")
    print(
        f"    - Most used platforms: {list(dependencies['most_used_platforms'].keys())[:3]}")

    print("  ✅ Cross-platform orchestration demo completed!")
    print()


def demo_orchestration_testing():
    """Demo: Orchestration testing framework."""
    print("🧪 Demo: Orchestration Testing Framework")
    print("-" * 40)

    try:
        from lineagepy.orchestration.testing import (
            OrchestrationTestFramework, WorkflowTester
        )

        # Initialize test framework
        test_framework = OrchestrationTestFramework()

        # Create workflow tester
        workflow_tester = WorkflowTester()

        # Mock workflow for testing
        test_workflow = {
            'name': 'test_user_analytics',
            'stages': [
                {'id': 'extract', 'platform': 'airflow', 'type': 'data_extraction'},
                {'id': 'transform', 'platform': 'dbt', 'type': 'model_run'},
                {'id': 'load', 'platform': 'prefect', 'type': 'data_loading'}
            ],
            'dependencies': [
                {'upstream': 'extract', 'downstream': 'transform'},
                {'upstream': 'transform', 'downstream': 'load'}
            ]
        }

        # Test workflow structure
        structure_test = workflow_tester.test_workflow_structure(test_workflow)
        print(f"  ✅ Workflow structure test: {structure_test['status']}")
        print(f"    - Stages validated: {structure_test['stages_validated']}")
        print(
            f"    - Dependencies validated: {structure_test['dependencies_validated']}")

        # Test workflow execution simulation
        execution_test = workflow_tester.simulate_workflow_execution(
            test_workflow)
        print(f"  ✅ Workflow execution simulation: {execution_test['status']}")
        print(f"    - Execution order: {execution_test['execution_order']}")
        print(
            f"    - Estimated duration: {execution_test['estimated_duration']}")

        # Test lineage assertions
        lineage_test = workflow_tester.test_lineage_completeness(test_workflow)
        print(f"  ✅ Lineage completeness test: {lineage_test['status']}")
        print(f"    - Coverage: {lineage_test['coverage_percentage']}%")
        print(
            f"    - Missing lineage: {len(lineage_test['missing_lineage'])} items")

        print("  ✅ Orchestration testing demo completed!")

    except ImportError:
        print("  ❌ Testing framework not available - using mock implementation")

        # Mock testing results
        print("  ✅ Mock workflow structure test: PASSED")
        print("  ✅ Mock execution simulation: PASSED")
        print("  ✅ Mock lineage test: PASSED (95% coverage)")

    print()


def demo_performance_analytics():
    """Demo: Orchestration performance analytics."""
    print("📊 Demo: Performance Analytics")
    print("-" * 40)

    # Mock performance data
    performance_metrics = {
        'workflow_executions': 145,
        'avg_execution_time': '8.5 minutes',
        'success_rate': 94.5,
        'platform_usage': {
            'airflow': 45,
            'dbt': 38,
            'prefect': 32,
            'dagster': 30
        },
        'optimization_opportunities': [
            'Parallelize dbt model execution in stage 2',
            'Optimize Airflow task dependencies for better resource usage',
            'Consider Prefect work pool optimization',
            'Implement Dagster asset partitioning for better performance'
        ]
    }

    print(f"  📈 Performance Metrics:")
    print(
        f"    - Total workflow executions: {performance_metrics['workflow_executions']}")
    print(
        f"    - Average execution time: {performance_metrics['avg_execution_time']}")
    print(f"    - Success rate: {performance_metrics['success_rate']}%")

    print(f"  🔧 Platform Usage:")
    for platform, count in performance_metrics['platform_usage'].items():
        print(f"    - {platform.capitalize()}: {count} executions")

    print(f"  🎯 Optimization Opportunities:")
    for i, opportunity in enumerate(performance_metrics['optimization_opportunities'], 1):
        print(f"    {i}. {opportunity}")

    print("  ✅ Performance analytics demo completed!")
    print()


def main():
    """Run the complete Phase 9 orchestration demo."""
    print("🎉 DataLineagePy Phase 9: Orchestration Integration Demo")
    print("=" * 70)
    print("Demonstrating native integration with major orchestration platforms!")
    print()

    # Run all demo sections
    demo_platform_availability()
    demo_airflow_integration()
    demo_dbt_integration()
    demo_prefect_integration()
    demo_dagster_integration()
    demo_cross_platform_orchestration()
    demo_orchestration_testing()
    demo_performance_analytics()

    # Final summary
    print("🎉 Phase 9 Orchestration Demo Completed!")
    print("=" * 70)
    print("✅ Apache Airflow: Native operators and DAG lineage tracking")
    print("✅ dbt: Model dependencies and manifest integration")
    print("✅ Prefect: Flow and task lineage with deployment tracking")
    print("✅ Dagster: Asset lineage and job execution tracking")
    print("✅ Cross-Platform: Universal workflow orchestration")
    print("✅ Testing: Comprehensive orchestration testing framework")
    print("✅ Analytics: Performance monitoring and optimization")
    print()
    print("🚀 DataLineagePy Phase 9 establishes universal orchestration lineage!")
    print("Ready to orchestrate the future of workflow governance! 🔧⚡")


if __name__ == "__main__":
    main()
