Refactor logging in Gates and MLFlow activities to use info level for key operations
- Updated logging statements in the Gates class to replace debug logs with info logs for input and output gate operations, enhancing visibility.
- Modified MLFlow class to use info logs for data transformation and prediction processes, improving clarity in the logging output.
- Adjusted OPC class to return the count of successfully written tags, providing better insight into data writing operations.
Update dependencies and refactor logging imports for observability
- Updated the sientia-dataops-library dependency version to 1.4.0 in requirements.txt.
- Changed image tag in values.yaml from 0.3.2 to 0.4.1.
- Refactored logging imports across multiple files to use the new observability module instead of the temporal.utils.logger.
- Updated retry policy imports in workflow files to reflect the new module structure.
Implement server validation and output tag management in OPC class
- Added a new method to validate the existence of OPC servers before writing data, improving error handling.
- Introduced a method to manage writing of prediction and confidence tags, streamlining the data writing process.
- Refactored the write_opc_data method to utilize the new validation and management methods for better code organization and clarity.
Refactor OPC data writing and repository initialization
- Simplified success tracking logic in the OPC class for writing prediction and confidence data.
- Removed unused pod_id attribute from OpcRepository initialization.
- Updated test cases to include pod_id for improved metrics tracking during data writing operations.
Refactor OPC data writing to improve success tracking
- Updated the OPC class to store the success status of data writing operations for both prediction and confidence tags.
- Added logging for successful and failed writes to the OPC server, enhancing traceability of data operations.
Implement OPC writing metrics and enhance OPC class initialization
- Added metrics for counting predictions written to the OPC server and monitoring their response times.
- Enhanced the OPC class initialization to include the pod ID for better tracking.
- Updated the write method to increment the prediction count and observe response times.
Enhance BaseActivity initialization across multiple activities to include error counter
- Updated the initialization of the BaseActivity in Gates, MLFlow, and OPC classes to set the error counter to True, improving error tracking and handling capabilities.
Update GITHUB_BRANCH in values.yaml to reflect new stress testing branch and modify OPC error handling to set success flag instead of continuing on error.
Enhance OPC error handling by adding detailed notifications for missing servers during write operations. The notification includes the list of available OPC servers for better debugging.
Refactor OPC connection handling to improve error notifications and update GITHUB_BRANCH in values.yaml for MongoDB integration. Change requirements.txt to point to local dataops library path.
Update notification handler references to use CoreNotificationHandler and add MongoDB configuration to connectors. Update GITHUB_BRANCH in values.yaml for optimization tasks.
Refactor notification handling in Gates and OPC activities to use send_notification method with metadata integration, enhancing error reporting and traceability.
Refactor OPC activity and OpcRepository to include metadata parameter in write_data methods, improving error handling and logging capabilities for better traceability.
SIENTIAPDE-1110 Refactor OPC activity to use server IDs instead of names, update values.yaml for OPC_ID, and enhance OpcRepository initialization for improved clarity and consistency.
Refactor and enhance the laborious workflow and utilities
- Removed outdated test file `test_predictions_batch.py` from workflows.
- Added `input_sample.json` for standardized input configuration.
- Introduced `connectors_config.py` to manage database and service configurations.
- Implemented a logging utility in `logger.py` for consistent logging across the application.
- Created `policies.py` to define retry policies for workflows.
- Developed comprehensive tests for `MLFlowRepository` in `test_model_repository.py`.
- Added extensive tests for `OpcRepository` in `test_opc_repository.py`.
- Updated `test_predictions_batch.py` to reflect new workflow structure and testing methodology.
Refactor activity methods and update requirements.txt to enhance functionality and remove deprecated filters. Added detailed docstrings for clarity and improved error handling in data processing workflows.
