503d9aa485
Update dependencies and refactor input filter handling for consistency - Updated sientia-dataops-library dependency version from 1.10.3 to 1.10.4 in requirements.txt. - Refactored input filter handling in the Gates class to read policy and config keys in a case-insensitive manner. - Updated test cases to ensure consistency in filter key naming conventions across various scenarios.
167 lines
4.9 KiB
Python
167 lines
4.9 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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- runtime: Runtime / environment identifier (matches ``RUNTIME`` env, see ``SientiaMonitoring``)
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- model_name: Name of the ML model being used
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- workflow_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 Counter, Gauge, Histogram
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from sientia_do.observability.metrics import CORE_LABELS
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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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# 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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# Prediction total response time
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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 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', 'tag'],
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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', 'tag'],
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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_CONNECTIONS_TOTAL = Counter(
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'opc_connections_initiated_total',
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'Total connection attempts to OPC servers',
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['pod_id', 'server_name'],
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)
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OPC_CONNECTIONS_FAILED = Counter(
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'opc_connections_failed_total',
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'Total failed connection attempts to OPC servers',
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['pod_id', 'server_name'],
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)
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OPC_CONNECTION_STATUS = Gauge(
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'opc_connection_status',
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'Connection status with the OPC server (1=connected, 0=disconnected)',
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['pod_id', 'server_name', 'server_url'],
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)
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# ================== Model metrics ==================
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MODEL_READ_LAG = Histogram(
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'laborious_model_read_lag',
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'Lag between the start and read of read operations',
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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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MODEL_WRITE_LAG = Histogram(
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'laborious_model_write_lag',
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'Lag between the start and end of write operations',
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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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MODEL_READ_COUNT = Counter(
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'laborious_model_read_count',
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'Number of reads from the model',
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CORE_LABELS,
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)
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MODEL_WRITE_COUNT = Counter(
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'laborious_model_write_count',
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'Number of writes to the model',
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CORE_LABELS,
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)
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MODEL_READ_ERROR_COUNT = Counter(
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'laborious_model_read_error_count',
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'Number of errors reading from the model',
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CORE_LABELS,
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)
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MODEL_WRITE_ERROR_COUNT = Counter(
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'laborious_model_write_error_count',
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'Number of errors writing to the model',
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CORE_LABELS,
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)
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MODEL_ANALYZE_LAG = Histogram(
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'laborious_model_analyze_lag',
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'Lag between the start and end of analyze operations',
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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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MODEL_ANALYZE_COUNT = Counter(
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'laborious_model_analyze_count',
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'Number of analyze operations',
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CORE_LABELS,
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)
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MODEL_ANALYZE_ERROR_COUNT = Counter(
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'laborious_model_analyze_error_count',
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'Number of errors during analyze operations',
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CORE_LABELS,
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)
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# ================== PI Web API metrics ==================
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PI_WEB_API_LABELS = [*CORE_LABELS, 'tag_name']
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PI_WEB_API_PREDICTION_WRITTEN_COUNT = Counter(
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'laborious_pi_web_api_prediction_written_count',
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'Number of predictions written to the PI Web API',
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PI_WEB_API_LABELS,
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)
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PI_WEB_API_PREDICTION_WRITTEN_ERROR_COUNT = Counter(
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'laborious_pi_web_api_prediction_written_error_count',
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'Number of errors writing predictions to the PI Web API',
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PI_WEB_API_LABELS,
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)
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