""" Laborious Worker Module This module provides the main worker implementation for the Sientia DataOps Laborious system. It orchestrates Temporal workers, manages task queues, and handles the lifecycle of prediction and retraining workflows. The worker supports two main task queues: - predictions_batch-queue: Handles batch prediction workflows - minimal_retrain-queue: Handles model retraining workflows Key Features: - Automatic scaling with PollerBehaviorAutoscaling - Prometheus metrics integration - Comprehensive error handling and logging - Graceful shutdown with cleanup - Multiple worker instances for different workflow types Environment Variables: - TEMPORAL_HOST: Temporal server address (default: localhost:7233) - TEMPORAL_NAMESPACE: Temporal namespace (default: laborious) - POD_ID: Kubernetes pod identifier for metrics - HTTP_METRICS_PORT: Prometheus metrics server port (default: 9090) - HTTP_SDK_METRICS_PORT: Temporal SDK metrics port (default: 9091) - PROJECT_NAME: Project name for notifications (default: laborious) """ from temporalio import workflow, client from temporalio.worker import Worker, PollerBehaviorAutoscaling from temporalio.runtime import Runtime, TelemetryConfig, PrometheusConfig with workflow.unsafe.imports_passed_through(): import os import sys import asyncio from laborious.workflows.minimal_retrain import MinimalRetrain from laborious.workflows.predictions_batch import PredictionsBatch from laborious.workflows.sub_workflows.prediction_process import PredictionProcess from laborious.workflows.sub_workflows.format_and_export_prediction import \ FormatAndExportPrediction from laborious.activities.activities import Activities from laborious.utils.connectors_config import ( build_postgres_config, build_mlflow_config, build_opc_config, build_mongodb_config ) from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler from sientia_do.observability.logger import get_logger from laborious import metrics from prometheus_client import start_http_server POD_ID = os.getenv('POD_ID') SDK_METRICS_PORT = int(os.getenv('HTTP_SDK_METRICS_PORT', "9091")) async def main(): """ Main entry point for the Laborious worker application. This function initializes and starts all components of the worker: 1. Sets up logging and metadata 2. Starts Prometheus metrics server 3. Initializes notification handler 4. Creates and configures activities 5. Initializes OPC connections 6. Starts Temporal client and workers 7. Manages worker lifecycle and graceful shutdown The function runs indefinitely until interrupted or an error occurs. On error, it performs cleanup and exits with a non-zero status code. Raises: Exception: Any unhandled exception during worker execution SystemExit: On graceful shutdown or error conditions """ host = os.getenv('TEMPORAL_HOST', 'localhost:7233') logger = get_logger(__name__) metadata = { 'pod_id': POD_ID, 'model_name': '-', 'model_id': '-', 'workflow_name': '-', 'schedule_name': '-', } logger.custom_info(f'Starting Worker with POD_ID: {POD_ID}', metadata) logger.custom_info("Starting prometheus client...", metadata) start_prometheus_server() logger.custom_info('Starting Notification Handler...', metadata) mongo_config = build_mongodb_config() notification_handler = NotificationHandler( connection_string=mongo_config['connection_string'], database=mongo_config['database_name'], logger=logger, project_name=os.getenv('PROJECT_NAME', 'laborious') ) logger.custom_info('Starting Activities...', metadata) activities = Activities( postgres_config=build_postgres_config(), mlflow_config=build_mlflow_config(), opc_config=build_opc_config(), logger=logger, notification_handler=notification_handler ) logger.custom_info('Initializing OPC...', metadata) await activities.init_opc() logger.custom_info( f'Starting SDK Metrics Server on port {SDK_METRICS_PORT}...', metadata) new_runtime = Runtime( telemetry=TelemetryConfig( metrics=PrometheusConfig( bind_address=f"0.0.0.0:{SDK_METRICS_PORT}") ) ) logger.custom_info(f'Starting Temporal Client at {host}...', metadata) temporal_client = await client.Client.connect( target_host=host, namespace=os.getenv('TEMPORAL_NAMESPACE', 'laborious'), runtime=new_runtime ) logger.custom_info('Starting Workers...', metadata) workers = [ Worker( temporal_client, task_queue='minimal_retrain-queue', workflows=[MinimalRetrain], activities=[ activities.load_custom_query, activities.retrain_model, activities.update_production_model, activities.export_data_to_postgres ], max_concurrent_workflow_tasks=50, max_concurrent_activities=50, max_concurrent_local_activities=50, max_cached_workflows=200, workflow_task_poller_behavior=PollerBehaviorAutoscaling(), activity_task_poller_behavior=PollerBehaviorAutoscaling() ), Worker( temporal_client, task_queue='predictions_batch-queue', workflows=[PredictionsBatch, PredictionProcess, FormatAndExportPrediction], activities=[ # MLFlow activities.request_predict, activities.request_transform, # Gates activities.input_gate, activities.mlflow_response_gate, activities.mlflow_content_gate, activities.format_prediction, activities.format_default_prediction, activities.get_last_timestamp, # OPC activities.write_opc_data, # Postgres activities.load_custom_query, activities.repeat_last_prediction, activities.export_data_to_postgres, activities.write_metrics ], max_concurrent_workflow_tasks=50, max_concurrent_activities=50, max_concurrent_local_activities=50, max_cached_workflows=200, workflow_task_poller_behavior=PollerBehaviorAutoscaling(), activity_task_poller_behavior=PollerBehaviorAutoscaling() ) ] handlers = [] for w in workers: handlers.append(w.run()) logger.custom_info('Workers started successfully', metadata) try: # This will run the workers and wait for them to complete. # If an exception occurs in any of the worker handlers, it will be propagated here. await asyncio.gather(*handlers) except BaseException as e: # NOSONAR logger.custom_error(f"An unhandled exception occurred: {e}", metadata) finally: if notification_handler: notification_handler.shutdown() if activities: await activities.shutdown() # Exit with a non-zero status code to indicate failure to Kubernetes metrics.APP_UP.labels(pod_id=POD_ID).set(0) # Mark app as DOWN sys.exit(1) def start_prometheus_server(): """ Starts the Prometheus metrics server for monitoring and observability. This function initializes the Prometheus HTTP server on the configured port and sets the application health metric to indicate the service is running. The server exposes metrics that can be scraped by Prometheus for monitoring the health and performance of the Laborious worker. Environment Variables: HTTP_METRICS_PORT: Port for the metrics server (default: 9090) POD_ID: Pod identifier for metrics labeling Raises: SystemExit: If the metrics server fails to start """ try: port = int(os.getenv("HTTP_METRICS_PORT", 9090)) start_http_server(port) print(f"Prometheus server started on port {port}.") metrics.APP_UP.labels(pod_id=POD_ID).set(1) # Mark app as UP except Exception as e: print(f"Failed to start Prometheus server: {e}") os._exit(1) if __name__ == '__main__': asyncio.run(main())