93d0849c80
This commit introduces the initial project structure, including: - .env.example: Example environment configuration. - .github/workflows/quality-gate.yml: CI workflow for quality checks. - .gitignore: Specifies intentionally untracked files that Git should ignore. - Makefile: Automation of tasks like docker builds. - README.md: Project documentation. - Source code for model management, activities, utils, worker and workflows. - Test suite. - Dockerfile for the simulator. - sonar-project.properties: SonarQube configuration file. - values.yaml: Helm chart values for deployment.
73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
"""
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Laborious Metrics Module
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This module defines all Prometheus metrics used by the Sientia DataOps Laborious system
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for monitoring and observability. The metrics provide insights into system performance,
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prediction quality, and operational health.
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The metrics are designed to be scraped by Prometheus and can be visualized in
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Grafana or other monitoring dashboards to provide real-time visibility into
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the system's operation.
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Key Metric Categories:
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- Application Health: Overall system status and availability
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- Prediction Operations: Count and performance of prediction operations
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- Data Quality: Confidence levels and validation results
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- Export Operations: Database and OPC export performance
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- Response Times: Performance monitoring for various operations
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Metric Labels:
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- pod_id: Kubernetes pod identifier for multi-instance deployments
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- model_name: Name of the ML model being used
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- pipeline_name: Name of the prediction pipeline
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- opc_server_id: Identifier for OPC server operations
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"""
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from prometheus_client import Gauge, Counter, Histogram
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# Application health metric
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APP_UP = Gauge(
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"app_up",
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"Indicates if the application is running (1) or shutting down (0)",
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["pod_id"],
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)
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# Core labels used across multiple metrics
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CORE_LABELS = ["pod_id", "model_name", "pipeline_name"]
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# Prediction operation metrics
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PREDICTIONS_WRITTEN_COUNT = Counter(
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"laborious_predictions_written_count",
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"Number of predictions written to the database table predictions",
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CORE_LABELS,
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)
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# Prediction quality metrics
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PREDICTION_CONFIDENCE_MONITOR = Gauge(
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"laborious_prediction_confidence_monitor",
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"Current confidence of each prediction",
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CORE_LABELS,
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)
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# Performance monitoring metrics
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PREDICTION_RESPONSE_TIME_MONITOR = Histogram(
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"laborious_prediction_response_time_monitor",
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"Current response time of each prediction",
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CORE_LABELS,
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buckets=[0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]
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)
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# OPC export metrics
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PREDICTION_OPC_WRITING_COUNT = Counter(
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"laborious_prediction_opc_writing_count",
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"Number of predictions written to the OPC server",
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[*CORE_LABELS, "opc_server_id"],
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)
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PREDICTION_OPC_WRITING_RESPONSE_TIME_MONITOR = Histogram(
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"laborious_prediction_opc_writing_response_time_monitor",
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"Current response time of each prediction written to the OPC server",
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[*CORE_LABELS, "opc_server_id"],
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buckets=[0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]
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)
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