Sientia DataOps Laborious

A comprehensive, Temporal-based ML orchestration system for industrial data processing and model inference. Laborious delivers enterprise-grade batch prediction, model management, optional real-time export (OPC and PI Web API), and automated retraining with strong data quality validation and observability.

📑 Table of Contents

• Features
  • Core Functionality
  • Advanced Capabilities
  • Development & Quality Assurance
• Architecture
  • Architecture Principles
  • Key Components
  • Data Flow Architecture
  • Security Architecture
• Workflows
  • Predictions Batch Workflow
  • Prediction Process Workflow
  • Format and Export Prediction Workflow
  • Minimal Retrain Workflow
  • Drift Workflow
  • Simple Metrics Workflow
• Installation & Setup
  • Prerequisites
  • Environment Setup
  • Temporal Namespace Setup
  • Local Development Setup
• How to Run
  • Running the Laborious Application
  • Running Tests and Coverage
  • Manual Test Execution
  • Manual Application Execution
• Code Quality & Validation
  • Overview
  • Validation Tools
  • Tools Installation
  • Complete Validation
  • Automatic Fixes
  • Configuration
  • CI/CD Integration
  • Best Practices
• Testing
  • Test Structure
  • Test Execution
• Monitoring and Metrics
  • Application Health Metrics
  • Prediction Operation Metrics
  • OPC Export Metrics
  • Data Quality Metrics
• Configuration
  • Environment Variables
  • OPC Configuration
  • Workflow Configuration
• Development
  • Project Structure
  • Adding New Features
• Troubleshooting
  • Common Issues
  • Debug Mode
• Performance Tuning
  • Key Parameters
  • Scaling Considerations
• Contributing
  • Code Quality Standards
• License
• Support

Features

Core Functionality

• Batch Prediction Processing: High-throughput ML inference using MLFlow models
• Temporal Workflow Orchestration: Robust workflow management with retries and fault tolerance
• Data Quality Gates: Configurable filtering for input data and MLFlow API responses
• Multi-Model Support: Flexible model management with retention and versioning
• Optional Real-time Export: PostgreSQL persistence, OPC server integration, and PI Web API integration for industrial systems
• Comprehensive Monitoring: Prometheus metrics and structured logging for observability

Advanced Capabilities

• Incremental Data Processing: Timestamp-based loading to avoid reprocessing
• Configurable Data Retention: Model retention policies with automatic cleanup
• MinIO Payload Offload: Automatic offload of large DataFrames to MinIO with retention cleanup
• Data Drift Detection: Univariate and multivariate drift monitoring against reference data
• Regression Metrics: Automated RMSE, MSE, MAE, R² calculation and export
• Notification System: Integrated alerting via MongoDB
• Scalable Architecture: Kubernetes-ready with horizontal scaling
• Model Retraining: Automated retraining workflows with production model updates

Development & Quality Assurance

• Code Quality Tools: Ruff (lint/format), mypy (types), Bandit (security)
• Automated Validation: CI quality gates and individual tool commands
• Comprehensive Testing: pytest with async support and high coverage
• Type Safety: Static type checking with mypy
• Coverage Visualization: Coverage Gutters integration

Architecture

Laborious uses a Temporal-based architecture with strong separation of concerns and defensive error handling for production ML.

Architecture Principles

1. Separation of Concerns

• Worker Layer: Temporal workers, task queues, lifecycle
• Workflow Layer: Business orchestration and coordination
• Activity Layer: External system interactions and isolated operations
• Data Layer: Persistence, caching, connectors

2. Fault Tolerance & Resilience

• Automatic Retry Policies for transient failures
• Graceful Degradation and circuit breaking for dependencies
• Detailed Error Handling with notifications

3. Scalability & Performance

• Horizontal Scaling: Multiple worker instances for load distribution
• Task Queue Isolation: Separate queues for different workflow types
• Connection Pooling: Optimized database and external service connections
• Asynchronous Processing: Non-blocking operations for improved throughput

4. Observability & Monitoring

• Prometheus Metrics: Comprehensive system and business metrics
• Structured Logging: Consistent log format with correlation IDs
• Health Checks: Endpoint health monitoring and alerting
• Performance Tracing: Request flow tracking and bottleneck identification

Key Components

Worker (laborious/worker/worker.py)

• Temporal client setup, worker lifecycle, task queues
• Metrics server initialization, notification handler setup
• Graceful shutdown and autoscaling-friendly behavior

Workflows (laborious/workflows/)

• predictions_batch.py: Batch prediction entry point
• sub_workflows/prediction_process.py: Core prediction pipeline
• sub_workflows/format_and_export_prediction.py: Formatting and export
• minimal_retrain.py: Automated model retraining and production update
• drift.py: Data drift detection and monitoring
• simple_metrics.py: Regression metrics calculation (RMSE, MSE, MAE, R²)

Activities (laborious/activities/)

• gates.py: Data quality validation, filtering, and data formatting operations
  • Input/response/content gates for quality validation
  • Prediction and transformed data formatting
  • Retrain report formatting and metrics recording
• mlflow.py: Transform, predict, and model management operations
  • MLFlow model transformation and prediction
  • Model retraining and production updates
  • Reference data retrieval from MLflow Model Registry
• storage.py: PostgreSQL queries and MinIO-aware data loading
  • load_query_with_minio_offload: SQL load with automatic MinIO offload
  • export_payload_to_postgres: Resolve MinIO payloads and export to Postgres
  • cleanup_minio_objects_expired: Retention-based MinIO object cleanup
  • query_to_minio: Legacy parquet upload for retraining data
• model_metrics.py: Drift detection and regression metrics
  • Univariate and multivariate drift calculation
  • Simple metrics (RMSE, MSE, MAE, R²)
• opc.py: OPC UA export to industrial systems (optional)
• api.py: PI Web API export operations (optional)
  • Prediction and confidence data writing to PI Web API
  • Error handling and notification integration
• activities.py: Aggregates all activity interfaces (Storage, MLFlow, Gates, OPC, ModelMetrics, API)

Data Services (laborious/utils/)

• connectors_config.py: Env-driven configuration builders
• models/minio_dataframe_payload.py: MinIO-offloaded DataFrame payload model
• repository/model_repository.py: MLFlow operations and retraining
• repository/opc_repository.py: OPC communication and writes
• repository/minio_manager.py: MinIO object storage operations
• filters/conditional_filters.py and filters/mlflow_filters.py

Data Flow Architecture

1. Batch Prediction Pipeline

Input Data (PostgreSQL) → Data Quality Gates → MLFlow Transform →
MLFlow Prediction → Response Validation → Format & Export
  ├─→ Predictions → PostgreSQL [+ OPC] [+ PI Web API]
  └─→ Transformed Data → PostgreSQL (optional)

2. Model Retraining Pipeline

Training Data → Model Retraining → Quality Validation →
Production Update → Notification & Monitoring

3. Drift Detection Pipeline

Target Data (PostgreSQL) + Reference Data (MLFlow) →
Drift Calculation (univariate + multivariate) → PostgreSQL Export

4. Simple Metrics Pipeline

Predictions + Targets (PostgreSQL JOIN) →
Metrics Calculation (RMSE, MSE, MAE, R²) → PostgreSQL Export

Security Architecture

Authentication & Authorization

• MLFlow API Authentication: Username/password
• Database Security: Encrypted connections and credential management
• OPC Certificates (if enabled): Client/server certs
• PI Web API Authentication: Bearer token or basic authentication
• Kubernetes Secrets: Secure secret storage

Network Security

• TLS/SSL, network policies, service mesh, firewalls, VPN

Data Security

• At-rest/in-transit encryption, RBAC, audit logging, lifecycle management

Workflows

1. Predictions Batch Workflow (predictions_batch.py)

The PredictionsBatch workflow is the main entry point for batch prediction pipelines. It orchestrates the complete prediction process and implements a robust data loading and processing pattern.

Purpose

• Batch Prediction Orchestration: Coordinates data loading and prediction processing
• Data Preparation: Loads data using custom SQL queries with configurable schemas
• Workflow Delegation: Delegates actual prediction processing to the PredictionProcess workflow
• Configuration Management: Handles model configuration, filters, and retention policies

Execution Flow

1. Data Loading: Executes custom SQL query to load data from PostgreSQL
2. Input Preparation: Prepares prediction input with metadata and configuration
3. Workflow Delegation: Spawns PredictionProcess child workflow for actual processing
4. Error Handling: Implements comprehensive error handling with retry policies

Key Features

• Custom Query Support: Flexible SQL-based data loading
• Schema Configuration: Configurable data schema definitions
• Automatic Retry: Implements Temporal retry policies for fault tolerance
• Timeout Management: 60-second timeout for all activities
• Comprehensive Error Handling: Detailed error reporting and notification integration

Input Parameters

{
  "schedule_name": "hourly_predictions",
  "model_name": "temperature_prediction_model",
  "model_id": "temp_pred_001",
  "query": "SELECT * FROM sensor_data WHERE timestamp > NOW() - INTERVAL '1 hour'",
  "schema": {
    "timestamp": "datetime",
    "temperature": "float",
    "humidity": "float"
  },
  "table_name": "predictions",
  "input_filters": {
    "EMPTY_DATA": {"POLICY": "STOP"}
  },
  "mlflow_transform_filters": {
    "API_ERROR": {"POLICY": "STOP"}
  },
  "mlflow_predict_filters": {
    "API_ERROR": {"POLICY": "STOP"}
  },
  "model_retention": 60,
  "path_priority": ["STOP", "CONTINUE", "REPEAT"],
  "opc_output_config": {
    "server_id": "opc_server_1",
    "tags": ["prediction_output"]
  },
  "pi_web_api_output_config": {
    "endpoint": "https://pi-server.com/piwebapi",
    "prediction_tags": {"tag1": "web_id_1"},
    "confidence_tags": {"tag2": "web_id_2"}
  }
}

Architecture Diagram

flowchart LR
    A[1. load_custom_query] --> B[2. prediction_process 🔃]
    
    A -.-> Database[(Database)]

2. Prediction Process Workflow (prediction_process.py)

The PredictionProcess workflow implements the core prediction pipeline for ML model inference. It handles data quality validation, MLFlow model interactions, and prediction processing.

Purpose

• Data Quality Validation: Applies configurable filters for data integrity
• MLFlow Integration: Manages model transformation and prediction requests
• Response Validation: Filters MLFlow API responses for quality assurance
• Prediction Export: Delegates prediction formatting and export operations

Execution Flow

1. Input Data Gate: Applies configured filters for data quality validation
2. Path Decision: Determines processing path based on filter results
3. MLFlow Transform: Requests data transformation using MLFlow models
4. Response Validation: Filters transform responses for quality assurance
5. Content Validation: Filters transformed data content for quality check
6. MLFlow Prediction: Executes prediction using transformed data
7. Prediction Response Validation: Filters prediction responses for final quality check
8. Export Delegation: Delegates to FormatAndExportPrediction workflow
9. MinIO Cleanup: Cleans up expired offloaded payloads (if any, in finally block)

Key Features

• Configurable Quality Gates: Multiple filter types with policy-based configuration
• Flexible Path Handling: Configurable decision paths (STOP, CONTINUE, REPEAT)
• MLFlow Integration: Comprehensive model management and inference
• MinIO Cleanup: Automatic retention-based cleanup of offloaded payloads
• Comprehensive Monitoring: Detailed metrics and error reporting

Input Parameters

{
  "metadata": {
    "schedule_name": "hourly_predictions",
    "model_name": "temperature_prediction_model",
    "model_id": "temp_pred_001",
    "workflow_name": "predictions_batch"
  },
  "data": {...},
  "schema": {...},
  "table_name": "predictions",
  "model_id": "temp_pred_001",
  "model_name": "temperature_prediction_model",
  "input_filters": {
    "EMPTY_DATA": {"POLICY": "STOP"},
    "SPECIFIC_VARIABLES_NULL_VALUES": {
      "POLICY": "STOP",
      "config": {"variables": ["temperature", "humidity"]}
    }
  },
  "mlflow_transform_filters": {
    "API_ERROR": {"POLICY": "STOP"}
  },
  "mlflow_predict_filters": {
    "API_ERROR": {"POLICY": "STOP"},
    "NAN_VALUES": {"POLICY": "STOP"}
  },
  "model_retention": 60,
  "path_priority": ["STOP", "CONTINUE", "REPEAT"],
  "opc_output_config": {...},
  "pi_web_api_output_config": {
    "endpoint": "https://pi-server.com/piwebapi",
    "prediction_tags": {"tag1": "web_id_1"},
    "confidence_tags": {"tag2": "web_id_2"}
  }
}

Architecture Diagram

flowchart LR
    A[1. input_gate] --> B[2. request_transform] --> C[3. mlflow_response_gate] --> D[4. mlflow_content_gate] --> E[5. request_predict] --> F[6. mlflow_response_gate] --> G[7. format_and_export_prediction🔃]
    G --> H[8. cleanup_minio_objects_expired]
    
    B -.-> MLFlow[MLFlow]
    E -.-> MLFlow[MLFlow]
    H -.-> MinIO[(MinIO)]

3. Format and Export Prediction Workflow (format_and_export_prediction.py)

The FormatAndExportPrediction workflow handles prediction data formatting and export operations to multiple destinations.

Purpose

• Data Formatting: Formats prediction data for different output destinations
• PostgreSQL Export: Persists predictions to database with metrics
• OPC Integration: Writes predictions to OPC servers for real-time access
• PI Web API Integration: Writes predictions and confidence to PI Web API for industrial systems
• Metrics Recording: Tracks export operations and performance metrics

Execution Flow

1. Path Decision: Determines formatting path based on configuration
2. Data Formatting: Formats prediction data for specific output requirements
3. Transformed Data Processing: Optionally formats and exports transformed data separately
4. PI Web API Export: Writes predictions and confidence to PI Web API (if configured)
5. OPC Export: Writes predictions to OPC servers (if configured)
6. PostgreSQL Export: Writes formatted predictions to database
7. Metrics Recording: Records export performance and success metrics

Key Features

• Flexible Formatting: Configurable output formats for different destinations
• Multi-Destination Export: PostgreSQL, OPC server, and PI Web API integration
• Transformed Data Export: Optional separate export of MLFlow transformed data
• Performance Monitoring: Comprehensive metrics for export operations
• Error Handling: Robust error handling with notification integration

Architecture Diagram

flowchart LR
    A[1. format_prediction/format_default_prediction] --> B[2. format_transformed_data] --> C[3. write_pi_web_api_data] --> D[4. write_opc_data] --> E[5. export_data_to_postgres] --> F[6. write_metrics]
    
    A -.-> Format[Data Formatting]
    B -.-> Transform[Transformed Data]
    C -.-> PIWebAPI[PI Web API]
    D -.-> OPC[OPC Servers]
    E -.-> PostgreSQL[(PostgreSQL)]
    F -.-> Prometheus[Prometheus]

Transformed Data Export

When transformed_data is provided in the input, the workflow will:

• Format the transformed data using format_transformed_data activity
• Export it to a separate table (transform_table_name) asynchronously
• Wait for both prediction and transformed data exports to complete
• This enables separate tracking of model transformations for analysis and debugging

4. Minimal Retrain Workflow (minimal_retrain.py)

The MinimalRetrain workflow handles automated model retraining and production model updates.

Purpose

• Model Retraining: Automates ML model retraining processes
• Production Updates: Manages production model version updates
• Data Export: Exports training data for model development
• Quality Assurance: Ensures model quality before production deployment

Execution Flow

1. Data Loading: Loads training data using custom queries
2. Model Retraining: Executes model retraining process
3. Quality Validation: Validates retrained model performance
4. Production Update: Updates production model if quality criteria met
5. Data Export: Exports training data for analysis

Architecture Diagram

flowchart LR
    A[1. load_custom_query] --> B[2. retrain_model] --> C[3. update_production_model] --> D[4. export_data_to_postgres]
    
    A -.-> Database[(Database)]
    B -.-> MLFlow[MLFlow]
    C -.-> MLFlow[MLFlow]
    D -.-> PostgreSQL[(PostgreSQL)]

5. Drift Workflow (drift.py)

The Drift workflow detects data drift by comparing current data against a reference dataset from the MLflow Model Registry.

Execution Flow

1. Data Loading: Loads target data and reference data in parallel
2. Drift Calculation: Calculates univariate and multivariate drift metrics
3. Data Export: Exports drift metrics to PostgreSQL

Architecture Diagram

flowchart LR
    A[1. load_custom_query] --> C[3. calculate_drift] --> D[4. export_data_to_postgres]
    B[2. get_reference_data] --> C
    
    A -.-> Database[(Database)]
    B -.-> MLFlow[MLFlow]
    D -.-> PostgreSQL[(PostgreSQL)]

Input Parameters

{
  "schedule_name": "hourly_drift",
  "model_name": "temperature_model",
  "model_id": "temp_001",
  "schema": "sientia_data",
  "source_table_name": "laborious_data",
  "target_table_name": "drift_metrics",
  "interval": 60,
  "model_config": { "target": "temperature" },
  "drift_metrics": ["kolmogorov_smirnov", "jensen_shannon", "wasserstein"],
  "chunk_period": "min"
}

6. Simple Metrics Workflow (simple_metrics.py)

The SimpleMetrics workflow calculates regression metrics (RMSE, MSE, MAE, R²) by comparing predictions against actual target values.

Execution Flow

1. Data Loading: Loads prediction vs target data via a JOIN query
2. Metrics Calculation: Calculates configured regression metrics
3. Data Export: Exports metrics to PostgreSQL

Architecture Diagram

flowchart LR
    A[1. load_custom_query] --> B[2. calculate_simple_metrics] --> C[3. export_data_to_postgres]
    
    A -.-> Database[(Database)]
    C -.-> PostgreSQL[(PostgreSQL)]

Input Parameters

{
  "schedule_name": "hourly_metrics",
  "model_name": "temperature_model",
  "model_id": "temp_001",
  "schema": "sientia_data",
  "predictions_table_name": "predictions",
  "data_table_name": "laborious_data",
  "target_table_name": "simple_metrics",
  "interval_minutes": 60,
  "model_config": { "target": "temperature" },
  "metrics": ["rmse", "mse", "mae", "r2"]
}

📋 Prerequisites

• Python 3.11+
• Temporal server/cluster
• PostgreSQL database
• MLFlow server
• MinIO object storage (for MLFlow artifacts)
• MongoDB server (for notifications)
• OPC server(s) if using OPC export
• PI Web API server if using PI Web API export

Note: External dependencies must be available either through:

• Kubernetes cluster deployment
• Docker Compose setup
• Cloud-managed services
• Local installations

🚀 Installation

Local Development Setup

1. Clone the repository

   git clone <repository-url>
   cd sientia-dataops-laborious
   

2. Create virtual environment

   python3.11 -m venv venv
   source ./venv/bin/activate
   

3. Install dependencies

4. Install github cli bash sudo apt update sudo apt install gh -y

5. Authenticate with github bash gh auth login

6. Run the install_dependencies.sh script bash chmod +x install_dependencies.sh ./install_dependencies.sh

7. Create environment configuration file

   cp .env.example .env
   # Edit .env with your connection details
   

8. Configure external dependencies

   You'll need to set up port forwarding or connections to external services. For example:

   # Port forwarding from Kubernetes cluster
   kubectl port-forward svc/postgresql 5432:5432
   kubectl port-forward svc/mlflow 5000:5000
   kubectl port-forward svc/mongodb 27017:27017
   
   # Or connect to external services
   # Ensure services are accessible on localhost with appropriate ports
   

📦 How to Run

Running the Laborious Application

Use the provided script to run the application locally:

# Make script executable (first time only)
chmod +x run_local.sh

# Run the application
./run_local.sh

The script will:

• Activate the virtual environment
• Load environment variables from .env
• Start the laborious worker application

Running Tests and Coverage

Use the provided script to run tests with coverage:

# Make script executable (first time only)
chmod +x run_coverage.sh

# Run tests with coverage
./run_coverage.sh

The script will:

• Activate the virtual environment
• Run pytest with coverage reporting
• Generate HTML coverage report
• Open the coverage report in your browser

Manual Test Execution

You can also run tests manually:

# Activate virtual environment
source ./venv/bin/activate

# Run all tests
pytest

# Run with coverage
pytest --cov=laborious --cov-report=html

# Run specific test categories
pytest tests/laborious/activities/
pytest tests/laborious/workflows/

Manual Application Execution

For manual execution without scripts:

# Activate virtual environment
source ./venv/bin/activate

# Load environment variables (if using .env file)
if [ -f .env ]; then
    export $(cat .env | grep -v '^#' | xargs)
fi

# Start the laborious worker
python -m laborious.worker.worker

Code Quality & Validation

Overview

Since Python is not compiled, we validate quality, security, and correctness before execution.

Validation Tools

• Ruff: Linting and formatting
• mypy: Static type checking
• Bandit: Security analysis
• pytest: Unit/integration testing with coverage

Tools Installation

pip install -r requirements-dev.txt

Complete Validation

Run each validation step individually:

ruff format --check laborious/ tests/
ruff check laborious/ tests/
mypy laborious/
bandit -r laborious/ -ll
pytest tests/ --cov=laborious --cov-report=term-missing

Automatic Fixes

ruff format laborious/ tests/
ruff check --fix laborious/ tests/

Configuration

All settings reside in pyproject.toml (Ruff, mypy, pytest, Bandit).

CI/CD Integration

The workflow at .github/workflows/quality-gate.yml executes validations on each push/PR.

Best Practices

• Run all validation steps before committing
• Use ruff check --watch for continuous feedback
• Add type hints and tests for new code

🧪 Testing

Test Structure

tests/
├── conftest.py                     # Global fixtures and env setup
├── laborious/
│   ├── activities/                 # Activity implementation tests
│   │   ├── test_activities.py      # Activities aggregator tests
│   │   ├── test_gates.py           # Data quality gates and formatting tests
│   │   ├── test_mlflow.py          # MLFlow operations and reference data tests
│   │   ├── test_storage.py         # Storage and MinIO offload tests
│   │   ├── test_model_metrics.py   # Drift and simple metrics tests
│   │   ├── test_opc.py             # OPC operations tests
│   │   └── test_api.py             # PI Web API operations tests
│   ├── workflows/                  # Workflow orchestration tests
│   │   ├── test_predictions_batch.py
│   │   ├── test_minimal_retrain.py
│   │   ├── test_drift.py
│   │   ├── test_simple_metrics.py
│   │   └── subworkflows/
│   │       ├── test_prediction_process.py
│   │       └── test_format_and_export_prediction.py
│   └── utils/                      # Utility function tests
│       ├── test_connectors_config.py
│       ├── models/
│       │   └── test_minio_dataframe_payload.py
│       ├── filters/
│       │   ├── test_conditional_filters.py
│       │   └── test_mlflow_filters.py
│       └── repository/
│           ├── test_model_repository.py
│           └── test_opc_repository.py

Test Coverage

The test suite provides comprehensive coverage for:

• Data Quality Gates: Input, response, and content validation filters
• Data Formatting: Prediction, transformed data, and retrain report formatting
• MLFlow Operations: Transform, predict, retrain, and reference data retrieval
• Workflow Orchestration: Complete workflow execution paths and error handling
• Metrics Recording: Performance monitoring and OPC export metrics

Test Execution

# Install test dependencies
pip install pytest pytest-cov pytest-asyncio

# Run tests with coverage
pytest --cov=laborious --cov-report=html

# Run specific test modules
pytest tests/laborious/activities/test_gates.py
pytest tests/laborious/workflows/test_predictions_batch.py

📊 Monitoring and Metrics

The Laborious system exposes comprehensive Prometheus metrics for operational visibility and performance monitoring:

Application Health Metrics

• app_up: Application health status (1=healthy, 0=unhealthy)
  • Labels: pod_id

Prediction Operation Metrics

• laborious_predictions_written_count: Counter for successful prediction exports
  • Labels: pod_id, model_name, workflow_name
• laborious_prediction_confidence_monitor: Gauge for current prediction confidence levels
  • Labels: pod_id, model_name, workflow_name
• laborious_prediction_response_time_monitor: Histogram for prediction response times
  • Labels: pod_id, model_name, workflow_name
  • Buckets: [0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]

OPC Export Metrics

• laborious_prediction_opc_writing_count: Counter for OPC server write operations
  • Labels: pod_id, model_name, workflow_name, opc_server_id
• laborious_prediction_opc_writing_response_time_monitor: Histogram for OPC write response times
  • Labels: pod_id, model_name, workflow_name, opc_server_id
  • Buckets: [0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]

Data Quality Metrics

• Filter pass/fail rates through notification system
• MLFlow API response validation metrics
• Data quality gate performance tracking

⚙️ Configuration

Environment Variables

Variable	Description	Default	Required
TEMPORAL_HOST	Temporal server address	localhost:7233	Yes
TEMPORAL_NAMESPACE	Temporal namespace	laborious	No
POSTGRES_HOST	PostgreSQL hostname	localhost	Yes
POSTGRES_PORT	PostgreSQL port	5432	Yes
POSTGRES_USER	PostgreSQL username	sientia	Yes
POSTGRES_PASSWORD	PostgreSQL password	sientia	Yes
POSTGRES_DBNAME	PostgreSQL database	sientia	Yes
POSTGRES_MIN_CONNECTIONS	Minimum PostgreSQL connections	5	No
POSTGRES_MAX_CONNECTIONS	Maximum PostgreSQL connections	20	No
MLFLOW_HOST	MLFlow server hostname	http://localhost	Yes
MLFLOW_PORT	MLFlow server port	5080	Yes
MLFLOW_USERNAME	MLFlow username	aignosi	Yes
MLFLOW_PASSWORD	MLFlow password	aignosi	Yes
OPC_CONFIG	OPC server configuration (JSON)	{}	No
OPC_ID	OPC server identifier	1	No
OPC_URL	OPC server URL	opc.tcp://localhost:4840	No
OPC_SERVER_URI	OPC server URI	opc.tcp://localhost:4840	No
OPC_CERT_PATH	OPC client certificate path	None	No
OPC_PRIVATE_KEY_PATH	OPC private key path	None	No
OPC_SERVER_CERT_PATH	OPC server certificate path	None	No
OPC_RECONNECTION_INTERVAL	OPC reconnection interval (ms)	120	No
PI_WEB_API_BASE_URL	PI Web API server base URL	None	No
PI_WEB_API_AUTH_TYPE	PI Web API authentication type (basic/bearer)	None	No
PI_WEB_API_AUTH_TOKEN	PI Web API authentication token	None	No
MONGODB_URL	MongoDB connection URI	localhost:27018	Yes
MONGODB_USERNAME	MongoDB username	root	Yes
MONGODB_PASSWORD	MongoDB password	wKZDbMNU1c	Yes
MONGODB_DATABASE_NAME	MongoDB database name	sientia	Yes
MONGODB_TTL_INDEX_HOURS	MongoDB TTL index hours	1	No
LOG_LEVEL	Application log level	INFO	No
PROJECT_NAME	Project name for metrics	laborious	No
HTTP_METRICS_PORT	Prometheus metrics port	9090	No
HTTP_SDK_METRICS_PORT	Temporal SDK metrics port	9091	No
POD_ID	Kubernetes pod identifier	None	No
MINIO_ENDPOINT_URL	MinIO endpoint URL	http://localhost:9000	Yes
MINIO_ACCESS_KEY	MinIO access key	minioadmin	Yes
MINIO_SECRET_KEY	MinIO secret key	minioadmin	Yes
MINIO_REGION_NAME	MinIO region name	us-east-1	No
MINIO_DEFAULT_BUCKET	Default MinIO bucket	laborious	No
MINIO_RETENTION_HOURS	Retention window (hours) for offloaded MinIO objects	24	No
SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES	Offload threshold for DataFrame-derived payloads	int(1.5 * 1024 * 1024)	No

MinIO Payload Offload & Retention

Laborious uses MinIO to prevent Temporal workflow history from carrying very large in-memory payloads (pandas DataFrame-derived dicts). Whenever a payload exceeds a configurable size threshold, it is stored as a parquet file in MinIO and the workflow history only keeps a lightweight reference.

Notes:

• SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES supports:
  • Integer bytes (e.g. "1572864")
  • Float MiB (e.g. "1.5"), converted to bytes as MiB * 1024 * 1024
• Fallback behavior uses 1.5 MiB when the env var is missing or invalid.

Wire Contract: MinioDataFramePayload

The payload is implemented in laborious/utils/models/minio_dataframe_payload.py. The dataclass does not store a pandas DataFrame field. Instead, the DataFrame is only used at build time by:

• MinioDataFramePayload.from_dataframe(...)
• MinioDataFramePayload.from_dataframe_to_dict(...)

After evaluation, the payload is serialized for Temporal as a flat dict:

• Inline path: data contains df.to_dict(), and MinIO keys (object_key, bucket, ...) are absent / None.
• MinIO path: the dict contains:
  • bucket
  • object_key (full MinIO object name returned by MinioRepository.upload_file)
  • object_prefix (directory prefix used for cleanup listing; relative to the repository namespace)
  • uri (best-effort s3://<bucket>/<...> string)
  • data is omitted / set to None.

When an activity needs pandas operations, it resolves references using:

• MinioDataFramePayload.retrieve(minio_repo) — downloads from MinIO or returns inline data as a DataFrame

MinIO Object Naming (Retention Parsing)

MinIO object basename (required convention): {model_name}-{operation}-{timestamp}.parquet

Where:

• model_name: model identifier used by the pipeline
• operation: initial (SQL/query load before transform) or transform (after MLFlow transform)
• timestamp: DATETIME_FORMAT_FILENAME from sientia_do.temporal.constants

The relative object key (under the repository namespace) is always shaped as: training_datasets/{model_name}/{basename}

Retention cleanup parses timestamps from the basename using the -initial- / -transform- anchors. model_name may contain hyphens; parsing is resilient to it.

Workflows / Activities Integration

Predictions batch uses MinIO offload as follows:

1. predictions_batch calls Activities.load_query_with_minio_offload
   • On success, it puts the serialized MinioDataFramePayload dict into prediction_input["data"].
2. sub_workflows/prediction_process
   • Tracks which MinIO prefixes were referenced for offloaded payloads.
   • Runs Activities.cleanup_minio_objects_expired in a finally block (only when MinIO offload happened).
3. laborious/activities/gates.py and laborious/activities/mlflow.py
   • Resolve offloaded payloads transparently before constructing pandas DataFrame objects.

Legacy: query_to_minio (Minimal Retrain)

Storage.query_to_minio is intentionally kept with its legacy behavior for minimal_retrain. It always uploads parquet and returns {success, object_key, uri}. It is not used by predictions batch MinIO offload, and its objects are not part of the retention parser described above.

Legacy MinIO object layout (relative key): training_datasets/{model_name}/{object_prefix}_{timestamp}.parquet where object_prefix is sanitized (slashes replaced by underscores) to keep a stable model-level directory.

OPC Configuration

For multiple OPC servers, use the OPC_CONFIG environment variable:

{
  "opc_server_1": {
    "url": "opc.tcp://server1:4840",
    "name": "Server1",
    "server_uri": "urn:server1:opcua",
    "cert_path": "/path/to/cert.pem",
    "private_key_path": "/path/to/key.pem",
    "server_cert_path": "/path/to/server_cert.pem",
    "reconnection_interval": 5000
  },
  "opc_server_2": {
    "url": "opc.tcp://server2:4840",
    "name": "Server2",
    "server_uri": "urn:server2:opcua",
    "cert_path": "/path/to/cert.pem",
    "private_key_path": "/path/to/key.pem",
    "server_cert_path": "/path/to/server_cert.pem",
    "reconnection_interval": 5000
  }
}

For single OPC server, use individual environment variables:

• OPC_URL
• OPC_NAME
• OPC_SERVER_URI
• OPC_CERT_PATH
• OPC_PRIVATE_KEY_PATH
• OPC_SERVER_CERT_PATH
• OPC_RECONNECTION_INTERVAL

PI Web API Configuration

PI Web API configuration is built from environment variables using the build_api_config function from sientia_do.connectors_config. The configuration includes:

• PI_WEB_API_BASE_URL: Base URL of the PI Web API server
• PI_WEB_API_AUTH_TYPE: Authentication type ('basic' or 'bearer')
• PI_WEB_API_AUTH_TOKEN: Authentication token for API access

The PI Web API export is optional and can be configured per workflow through the pi_web_api_output_config parameter:

{
  "pi_web_api_output_config": {
    "endpoint": "https://pi-server.com/piwebapi",
    "prediction_tags": {
      "tag1": "web_id_1",
      "tag2": "web_id_2"
    },
    "confidence_tags": {
      "tag3": "web_id_3",
      "tag4": "web_id_4"
    }
  }
}

Where:

• endpoint: PI Web API endpoint URL
• prediction_tags: Dictionary mapping tag names to web IDs for prediction values
• confidence_tags: Dictionary mapping tag names to web IDs for confidence values

Workflow Configuration

MongoDB pipeline configuration:

Predictions Batch Workflow configuration sample

This is the configuration for the Predictions Batch Workflow, to be inserted into the MongoDB pipeline collection.

{
  "schedule_name": "laborious-orchestrated-pipeline",
  "model_id": "1",
  "workflow_type": "predictions_batch",
  "frequency": "30s",
  "max_retry_policy": 1,
  "query": "select * from sientia_data.laborious_data where model_id = 1 and \"timestamp\" > NOW() - INTERVAL '5 minutes' order by \"timestamp\" desc limit 30;",
  "write_tags": [
    {
      "server_id": "server1",
      "type": "prediction",
      "addr": "ns=2;i=5",
      "data_type": "double"
    },
    {
      "server_id": "server1",
      "type": "confidence",
      "addr": "ns=2;i=6",
      "data_type": "double"
    }
  ],
  "input_filters": {
    "EMPTY_DATA": {"POLICY": "STOP"},
    "SPECIFIC_VARIABLES_NULL_VALUES": {
      "POLICY": "CONTINUE",
      "config": {"variables": ["Counter"]}
    }
  },
  "mlflow_transform_filters": {
    "API_ERROR": {"POLICY": "REPEAT"},
    "NAN_VALUES": {"POLICY": "STOP"}
  },
  "mlflow_predict_filters": {
    "API_ERROR": {"POLICY": "CONTINUE"}
  },
  "path_priority": ["STOP", "CONTINUE", "REPEAT"],
  "active": true,
  "updated_at": {
    "$date": "2025-09-16T10:00:00.000Z"
  },
  "datetime_columns": ["timestamp", "created_at"],
  "predictions_storage_policy": "lts:1"
}

This is the configuration created by the Orchestrator in Temporal.

{
  "datetime_columns":["timestamp","created_at"],
  "frequency":"15m",
  "input_filters":{"EMPTY_DATA":{"config":{},"policy":"STOP"}},
  "max_retry_policy":1,
  "mlflow_predict_filters":{"API_ERROR":{"config":{},"policy":"CONTINUE"}},
  "mlflow_transform_filters":{
    "API_ERROR":{"config":{},"policy":"CONTINUE"},
    "EMPTY_DATA":{"config":{},"policy":"STOP"}
  },
  "model_config":{
    "is_compressed":true,
    "predict_flavor":"pyfunc",
    "retention_minutes":60,
    "retention_target":"artifact",
    "transform_function_keyword":"transform"
  },
  "model_id":"352",
  "model_name":"courier",
  "opc_output_config":{},
  "path_priority":["STOP","CONTINUE","REPEAT"],
  "predictions_storage_policy":"lts:1",
  "query":"select * from sientia_data.laborious_data where model_id = 352 order by \"timestamp\" desc limit 300;",
  "retention_time":3600,
  "schedule_name":"laborious-courier",
  "schema":"sientia_data",
  "table_name":"predictions",
  "updated_at":"2025-09-12 19:35:01.600000+0000",
  "workflow_type":"predictions_batch"
}

🔧 Development

Project Structure

laborious/
├── activities/              # Temporal activity implementations
│   ├── activities.py       # Main activities aggregator
│   ├── gates.py            # Data quality gates and filtering
│   ├── mlflow.py           # MLFlow model operations
│   ├── storage.py          # PostgreSQL queries and MinIO offload
│   ├── model_metrics.py    # Drift and regression metrics
│   ├── opc.py              # OPC server operations
│   └── api.py              # PI Web API operations
├── workflows/               # Temporal workflow definitions
│   ├── predictions_batch.py # Main batch prediction workflow
│   ├── minimal_retrain.py  # Model retraining workflow
│   ├── drift.py            # Data drift detection workflow
│   ├── simple_metrics.py   # Regression metrics workflow
│   └── sub_workflows/      # Sub-workflow implementations
│       ├── prediction_process.py # Core prediction workflow
│       └── format_and_export_prediction.py # Export workflow
├── worker/                  # Worker implementation
│   ├── worker.py           # Main worker orchestrator
│   └── prepare_worker.py   # Worker factory with autoscaling config
├── utils/                   # Utility functions
│   ├── connectors_config.py # Environment-driven config builders
│   ├── models/             # Data models
│   │   └── minio_dataframe_payload.py # MinIO-offloaded DataFrame payload
│   ├── filters/            # Data quality filters
│   │   ├── conditional_filters.py # Conditional data filters
│   │   └── mlflow_filters.py # MLFlow response filters
│   └── repository/         # Data access layer
│       ├── model_repository.py # MLFlow model operations
│       ├── opc_repository.py # OPC server operations
│       └── minio_manager.py # MinIO object storage operations
├── metrics.py               # Prometheus metrics definitions
└── __init__.py

Adding New Features

1. Follow Temporal patterns for new workflows and activities
2. Add comprehensive docstrings for all public methods
3. Include Prometheus metrics for monitoring
4. Add unit tests for new functionality
5. Update this README with new features and configuration

🐛 Troubleshooting

Common Issues

1. Temporal Connection Failures

   • Verify Temporal server is running and accessible
   • Check namespace configuration and permissions
   • Review server logs for connection issues

2. MLFlow Connection Issues

   • Verify MLFlow server is running and accessible
   • Check authentication credentials and permissions
   • Ensure model names and versions exist

3. Database Connection Issues

   • Verify PostgreSQL service is running
   • Check connection credentials and network access
   • Ensure proper connection pool configuration

4. OPC Connection Failures

   • Verify OPC server is accessible
   • Check certificate and key file paths
   • Review OPC server logs for connection issues

5. PI Web API Connection Failures

   • Verify PI Web API server is accessible
   • Check authentication credentials and token validity
   • Verify web IDs exist and have write permissions
   • Review PI Web API server logs for connection issues
   • Check notification system for error details

6. Workflow Execution Failures

   • Review activity error logs and notifications
   • Check data quality filter configurations
   • Verify input data format and required fields

Debug Mode

Enable debug logging by setting the log level:

export LOG_LEVEL=DEBUG

⚡ Performance Tuning

Key Parameters

• Worker Concurrency: Adjust max_concurrent_workflow_tasks and max_concurrent_activities
• Connection Pools: Optimize database connection pool sizes
• Model Retention: Configure MLFlow model retention based on requirements
• Batch Sizes: Adjust data processing batch sizes for optimal throughput

Scaling Considerations

• Horizontal Scaling: Deploy multiple worker instances
• Task Queue Distribution: Use multiple task queues for different workflow types
• Database Performance: Optimize indexes and connection pooling
• MLFlow Performance: Configure appropriate model serving resources

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes with comprehensive testing
4. Update documentation and docstrings
5. Submit a pull request

Code Quality Standards

• Follow PEP 8 style guidelines
• Include comprehensive docstrings for all public methods
• Maintain test coverage above 80%
• Use type hints where appropriate
• Follow Temporal.io best practices

📄 License

This project is licensed under the terms specified in the LICENSE file.

🆘 Support

For support and questions:

• Check the troubleshooting section above
• Review the metrics and logs for error patterns
• Open an issue in the project repository
• Contact the development team

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Note: The Laborious system is designed for production use in industrial ML environments. Ensure proper security configuration and network isolation for production deployments.