SIENTIAPDE-1646
Refactor worker task queue management and update README - Introduced runtime-scoped task queues for workflows, replacing legacy queue names. - Updated worker implementation to utilize `sientia_do.temporal.worker.prepare_worker`. - Added `RUNTIME` environment variable to configure task queue suffixes. - Enhanced README documentation to reflect changes in task queue structure and worker setup.
This commit is contained in:
15
README.md
15
README.md
@@ -126,10 +126,15 @@ Laborious uses a Temporal-based architecture with strong separation of concerns
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### Key Components
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#### **Worker (`laborious/worker/worker.py`)**
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- Temporal client setup, worker lifecycle, task queues
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- Temporal client setup, four workers via `sientia_do.temporal.worker.prepare_worker`
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- Runtime-scoped task queues: `{workflow}-{RUNTIME}-queue` for all workflows
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- Metrics server initialization, notification handler setup
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- Graceful shutdown and autoscaling-friendly behavior
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**Breaking (schedulers):** drift and simple_metrics queues are no longer `drift-queue` /
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`simple_metrics-queue`. Use `drift-{RUNTIME}-queue` and `simple_metrics-{RUNTIME}-queue`
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matching the worker pod `RUNTIME` env (same as `predictions_batch` / `minimal_retrain`).
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#### **Workflows (`laborious/workflows/`)**
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- `predictions_batch.py`: Batch prediction entry point
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- `sub_workflows/prediction_process.py`: Core prediction pipeline
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@@ -802,6 +807,7 @@ See [OPC UA Communication](#opc-ua-communication) for semantics, concurrency, an
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|----------|-------------|---------|----------|
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| `TEMPORAL_HOST` | Temporal server address | `localhost:7233` | Yes |
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| `TEMPORAL_NAMESPACE` | Temporal namespace | `laborious` | No |
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| `RUNTIME` | Task queue suffix for all workflows (`{workflow}-{RUNTIME}-queue`) | _(none)_ | Yes |
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| `POSTGRES_HOST` | PostgreSQL hostname | `localhost` | Yes |
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| `POSTGRES_PORT` | PostgreSQL port | `5432` | Yes |
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| `POSTGRES_USER` | PostgreSQL username | `sientia` | Yes |
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@@ -1097,8 +1103,7 @@ laborious/
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│ ├── prediction_process.py # Core prediction workflow
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│ └── format_and_export_prediction.py # Export workflow
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├── worker/ # Worker implementation
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│ ├── worker.py # Main worker orchestrator
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│ └── prepare_worker.py # Worker factory with autoscaling config
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│ └── worker.py # Main worker orchestrator (uses sientia_do prepare_worker)
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├── utils/ # Utility functions
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│ ├── connectors_config.py # Environment-driven config builders
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│ ├── models/ # Data models
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@@ -1179,7 +1184,9 @@ export LOG_LEVEL=DEBUG
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### Scaling Considerations
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- **Horizontal Scaling**: Deploy multiple worker instances
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- **Task Queue Distribution**: Use multiple task queues for different workflow types
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- **Task Queue Distribution**: One worker pod per `RUNTIME`; queues are
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`predictions_batch-{RUNTIME}-queue`, `minimal_retrain-{RUNTIME}-queue`,
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`drift-{RUNTIME}-queue`, `simple_metrics-{RUNTIME}-queue`
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- **Database Performance**: Optimize indexes and connection pooling
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- **MLFlow Performance**: Configure appropriate model serving resources
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@@ -1,73 +0,0 @@
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import os
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import re
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from collections.abc import Sequence
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from typing import Any
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from sientia_do.observability.logger import Logger
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from temporalio.client import Client
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from temporalio.worker import PollerBehaviorAutoscaling, Worker
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parameters = [
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('MAX_CONCURRENT_WORKFLOW_TASKS', '200'),
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('MAX_CONCURRENT_ACTIVITIES', '200'),
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('MAX_CONCURRENT_LOCAL_ACTIVITIES', '200'),
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('MAX_CACHED_WORKFLOWS', '200'),
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('WORKFLOW_POLLER_BEHAVIOUR_MINIMUM', '10'),
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('WORKFLOW_POLLER_BEHAVIOUR_INITIAL', '100'),
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('WORKFLOW_POLLER_BEHAVIOUR_MAXIMUM', '200'),
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('ACTIVITY_POLLER_BEHAVIOUR_MINIMUM', '10'),
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('ACTIVITY_POLLER_BEHAVIOUR_INITIAL', '100'),
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('ACTIVITY_POLLER_BEHAVIOUR_MAXIMUM', '200'),
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]
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def camel_to_snake(text: str) -> str:
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"""Convert camelCase or PascalCase to snake_case."""
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text = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', text)
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text = re.sub('([a-z0-9])([A-Z])', r'\1_\2', text)
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return text.lower()
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def prepare_worker(
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main_workflow: type,
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other_workflows: Sequence[type],
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activities: Sequence[Any],
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temporal_client: Client,
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logger: Logger,
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) -> Worker:
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main_workflow_name = main_workflow.__name__.upper()
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queue_name = f'{camel_to_snake(main_workflow.__name__)}-queue'
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local_workflow_parameters = {}
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for parameter in parameters:
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local_workflow_parameters[parameter[0]] = int(
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os.getenv(main_workflow_name + '_' + parameter[0], parameter[1])
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)
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logger.info(f'Preparing worker for {main_workflow_name} with queue {queue_name}')
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logger.info(f'Worker runtime config: {local_workflow_parameters}')
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return Worker(
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temporal_client,
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task_queue=queue_name,
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workflows=[main_workflow, *other_workflows],
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activities=[*activities],
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max_concurrent_workflow_tasks=local_workflow_parameters['MAX_CONCURRENT_WORKFLOW_TASKS'],
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max_concurrent_activities=local_workflow_parameters['MAX_CONCURRENT_ACTIVITIES'],
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max_concurrent_local_activities=local_workflow_parameters[
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'MAX_CONCURRENT_LOCAL_ACTIVITIES'
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],
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max_cached_workflows=local_workflow_parameters['MAX_CACHED_WORKFLOWS'],
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workflow_task_poller_behavior=PollerBehaviorAutoscaling(
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minimum=local_workflow_parameters['WORKFLOW_POLLER_BEHAVIOUR_MINIMUM'],
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initial=local_workflow_parameters['WORKFLOW_POLLER_BEHAVIOUR_INITIAL'],
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maximum=local_workflow_parameters['WORKFLOW_POLLER_BEHAVIOUR_MAXIMUM'],
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),
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activity_task_poller_behavior=PollerBehaviorAutoscaling(
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minimum=local_workflow_parameters['ACTIVITY_POLLER_BEHAVIOUR_MINIMUM'],
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initial=local_workflow_parameters['ACTIVITY_POLLER_BEHAVIOUR_INITIAL'],
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maximum=local_workflow_parameters['ACTIVITY_POLLER_BEHAVIOUR_MAXIMUM'],
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),
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)
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@@ -5,12 +5,14 @@ This module provides the main worker implementation for the Sientia DataOps Labo
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It orchestrates Temporal workers, manages task queues, and handles the lifecycle of
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prediction and retraining workflows.
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The worker supports multiple task queues:
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- predictions_batch-queue: Handles batch prediction workflows (heavy workload)
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Includes activities for MLFlow, data quality gates, OPC export, PI Web API export, and PostgreSQL
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- minimal_retrain-queue: Handles model retraining workflows
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- drift-queue: Handles drift detection workflows
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- simple_metrics-queue: Handles simple metrics calculation workflows
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The worker supports multiple runtime-scoped task queues (via ``sientia_do.temporal.worker.prepare_worker``):
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- predictions_batch-{runtime}-queue: Batch prediction workflows (heavy workload)
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- minimal_retrain-{runtime}-queue: Model retraining workflows
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- drift-{runtime}-queue: Drift detection workflows
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- simple_metrics-{runtime}-queue: Simple metrics workflows
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``RUNTIME`` must be set; it is passed to every ``prepare_worker`` call. Schedulers must use the
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same queue names (breaking change vs legacy ``drift-queue`` / ``simple_metrics-queue``).
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Key Features:
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- Resource-based scaling with WorkerTuner (CPU and memory aware)
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@@ -21,6 +23,7 @@ Key Features:
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- Multiple worker instances for different workflow types
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Environment Variables:
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- RUNTIME: Required non-empty string; suffix for all task queue names
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- TEMPORAL_HOST: Temporal server address (default: localhost:7233)
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- TEMPORAL_NAMESPACE: Temporal namespace (default: laborious)
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- POD_ID: Kubernetes pod identifier for metrics
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@@ -40,6 +43,7 @@ with workflow.unsafe.imports_passed_through():
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from prometheus_client import start_http_server
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from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
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from sientia_do.observability.logger import get_logger
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from sientia_do.temporal.worker.prepare_worker import prepare_worker
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from sientia_do.utils.connectors_config import (
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build_api_config,
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build_mongodb_config,
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@@ -53,7 +57,6 @@ with workflow.unsafe.imports_passed_through():
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build_mlflow_config,
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build_opc_config,
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)
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from laborious.worker.prepare_worker import prepare_worker
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from laborious.workflows.drift import Drift
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from laborious.workflows.minimal_retrain import MinimalRetrain
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from laborious.workflows.predictions_batch import PredictionsBatch
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@@ -100,10 +103,21 @@ async def main():
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logger.custom_info(f'Starting Worker with POD_ID: {POD_ID}', metadata)
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logger.custom_info('Starting prometheus client...', metadata)
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runtime = os.getenv('RUNTIME', '').strip()
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if not runtime:
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logger.custom_critical(
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'RUNTIME environment variable is required and must be non-empty',
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metadata,
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)
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metrics.APP_UP.labels(pod_id=POD_ID).set(0)
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sys.exit(1)
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metadata_runtime = {**metadata, 'runtime': runtime}
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logger.custom_info('Starting prometheus client...', metadata_runtime)
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start_prometheus_server()
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logger.custom_info('Starting Notification Handler...', metadata)
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logger.custom_info('Starting Notification Handler...', metadata_runtime)
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mongo_config = build_mongodb_config()
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notification_handler = NotificationHandler(
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@@ -113,7 +127,7 @@ async def main():
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project_name=os.getenv('PROJECT_NAME', 'laborious'),
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)
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logger.custom_info('Starting Activities...', metadata)
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logger.custom_info('Starting Activities...', metadata_runtime)
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activities = Activities(
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postgres_config=build_postgres_config(),
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@@ -125,10 +139,13 @@ async def main():
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notification_handler=notification_handler,
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)
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logger.custom_info('Initializing OPC...', metadata)
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logger.custom_info('Initializing OPC...', metadata_runtime)
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await activities.init_opc()
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logger.custom_info(f'Starting SDK Metrics Server on port {SDK_METRICS_PORT}...', metadata)
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logger.custom_info(
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f'Starting SDK Metrics Server on port {SDK_METRICS_PORT}...',
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metadata_runtime,
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)
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new_runtime = Runtime(
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telemetry=TelemetryConfig(
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@@ -136,7 +153,7 @@ async def main():
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)
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)
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logger.custom_info(f'Starting Temporal Client at {host}...', metadata)
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logger.custom_info(f'Starting Temporal Client at {host}...', metadata_runtime)
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temporal_client = await client.Client.connect(
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target_host=host,
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@@ -144,7 +161,7 @@ async def main():
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runtime=new_runtime,
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)
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logger.custom_info('Starting Workers...', metadata)
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logger.custom_info(f'Starting Workers (runtime={runtime})...', metadata_runtime)
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workers = [
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prepare_worker(
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@@ -159,6 +176,7 @@ async def main():
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activities.export_data_to_postgres,
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],
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logger=logger,
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runtime=runtime,
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),
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prepare_worker(
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temporal_client=temporal_client,
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@@ -170,6 +188,7 @@ async def main():
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activities.export_data_to_postgres,
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],
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logger=logger,
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runtime=runtime,
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),
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prepare_worker(
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temporal_client=temporal_client,
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@@ -182,6 +201,7 @@ async def main():
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activities.export_data_to_postgres,
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],
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logger=logger,
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runtime=runtime,
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),
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prepare_worker(
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temporal_client=temporal_client,
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@@ -211,6 +231,7 @@ async def main():
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activities.write_pi_web_api_data,
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],
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logger=logger,
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runtime=runtime,
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),
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]
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@@ -218,7 +239,7 @@ async def main():
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for w in workers:
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handlers.append(w.run())
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logger.custom_info('Workers started successfully', metadata)
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logger.custom_info('Workers started successfully', metadata_runtime)
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exit_code = 0
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try:
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14
tests/laborious/worker/test_runtime_task_queues.py
Normal file
14
tests/laborious/worker/test_runtime_task_queues.py
Normal file
@@ -0,0 +1,14 @@
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from sientia_do.temporal.worker.prepare_worker import build_queue_name
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from laborious.workflows.drift import Drift
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from laborious.workflows.minimal_retrain import MinimalRetrain
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from laborious.workflows.predictions_batch import PredictionsBatch
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from laborious.workflows.simple_metrics import SimpleMetrics
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def test_runtime_scoped_queue_names():
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runtime = 'prod-a'
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assert build_queue_name(PredictionsBatch.__name__, runtime) == 'predictions_batch-prod-a-queue'
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assert build_queue_name(MinimalRetrain.__name__, runtime) == 'minimal_retrain-prod-a-queue'
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assert build_queue_name(Drift.__name__, runtime) == 'drift-prod-a-queue'
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assert build_queue_name(SimpleMetrics.__name__, runtime) == 'simple_metrics-prod-a-queue'
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@@ -209,6 +209,9 @@ env:
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value: "temporal-frontend.temporal.svc.cluster.local:7233"
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- name: TEMPORAL_NAMESPACE
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value: "laborious"
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# Suffix for all Temporal task queues: {workflow}-{RUNTIME}-queue
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- name: RUNTIME
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value: "legacy"
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- name: MONGODB_USERNAME
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value: "root"
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