- GW1: write commands (C2) carry only the server_id; ServerConfigResolver
resolves the connection (command override, live pooled connection,
connectors, opc-servers) with a 60 s cache and a 10 s negative cache.
- GW2: write_status keeps the worst outcome (ok < error < session_bad,
pending/NULL replaced), same rule as the Laborious, built with SQLAlchemy
Core (no SQL formatting).
- GW3: update_write_status never raises; outcome (updated, kept,
row_not_found, invalid, error) logged and counted
(opc_write_status_update_total); runs off the event loop.
- GW4: the reason of a failed write is logged; opc_write_status_by_model
shows the last outcome; a bad timestamp is a failed write, not an
exception.
- GW5: the NATS first connect gives up after 5 s (nats-py's defaults block
the main loop for about 4 minutes while NATS is down); reconnection after
it is unbounded; idle_sweep restarts a connection closed for good; stop()
is safe on a closed connection.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The help text said subscribe only; _unsubscribe_reader now increments it too.
Series name and labels are unchanged.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Enhance Ingestor Class with Asynchronous Metrics and Monitoring Integration
- Refactored Ingestor class to inherit from SientiaMonitoring for improved observability.
- Updated methods to utilize asynchronous operations for declaring active ingestors and managing slots.
- Integrated pod_id label into various metrics for better tracking.
- Adjusted unit tests to reflect changes in the Ingestor initialization and asynchronous behavior.
Refactor Ingestor and Metrics Classes for Enhanced Asynchronous Operations
- Updated IngestorManager, OpcManager, and ResourceManager to utilize asynchronous methods for improved performance.
- Integrated MetricsController into various classes for better observability and monitoring.
- Adjusted unit tests to accommodate the new asynchronous behavior, ensuring proper mocking of async methods.
- Removed deprecated Redis metrics and streamlined resource management logic.
SIENTIAPDE-1325: Refactor Ingestor and Manager Classes for Enhanced Asynchronous Operations
- Introduced asynchronous methods across Ingestor, IngestorManager, DataManager, and OpcManager to improve performance and responsiveness.
- Integrated MetricsController into various classes for better observability and monitoring.
- Updated Redis and MongoDB interactions to support asynchronous operations, enhancing data handling efficiency.
- Removed deprecated Redis metrics and streamlined resource management logic.
- Adjusted unit tests to accommodate the new asynchronous behavior and ensure proper mocking of async methods.
- Updated the `sientia-dataops-library` dependency version from 1.4.3 to 1.4.6 in `requirements.txt`.
- Modified the GitHub Actions workflow to install development and runtime dependencies separately, improving clarity and organization.
- Added code formatting and linting checks using Ruff, along with type checking using mypy, to ensure code quality.
- Updated `.gitignore` to include additional cache directories and log files.
- Refactored code in various files for consistency in string formatting and improved logging messages.
Remove deprecated files and enhance documentation
- Deleted `coverage.sh`, `docker-compose.yaml`, `Dockerfile`, and simulator-related files to streamline the project structure.
- Updated `README.md` to provide a comprehensive overview of the OPC Ingestor, including features, architecture, installation, usage, and troubleshooting.
- Enhanced docstrings across various classes and methods in the `ingestor` module for better clarity and maintainability.
- Improved Prometheus metrics documentation in `metrics.py` to ensure proper monitoring and observability of the system.
Update requirements and environment configurations
- Changed the sientia-dataops-library dependency to a relative path in requirements.txt.
- Updated image tag to 0.3.0 in values.yaml.
- Renamed HTTP_SERVER_PORT to HTTP_METRICS_PORT in app.py and updated its default value to 9090.
- Added TAG_WRITTEN_COUNT metric in metrics.py to track writing processes.
- Incremented TAG_WRITTEN_COUNT in DataManager upon successful data insertion.
