Add unique constraint on (model_id, timestamp, variable) in schema and implement idempotent export test to ensure no duplicates are created. Update CoreScouter and related tests to handle conflict resolution by ignoring duplicates.
Refactor metrics and imports across multiple files
- Removed unused import of CORE_LABELS in metrics.py.
- Updated import statements in api.py for consistency.
- Cleaned up import order in worker.py for better readability.
- Added missing newline at the end of metrics.py.
- Adjusted formatting in core_scouter.py to ensure proper syntax.
- Removed redundant 'on_conflict' and 'unique_columns' keys from test cases in test_core_scouter.py for clarity.
Update test_api.py and test_core_scouter.py to enhance timestamp validation and add new test cases
- Updated timestamp assertions in test_get_tag_values_success to reflect the correct values.
- Added new test cases in test_core_scouter.py to handle scenarios with zero affected rows and without debug data package, ensuring proper workflow execution and activity method assertions.
Update Redis and CoreScouter to incorporate 'fill_missing_tags' parameter in data processing. Adjusted related tests to ensure proper handling of missing tags, enhancing overall functionality and consistency across workflows.
Update .gitignore and refactor metrics.py, activities.py, and gates.py for improved clarity and consistency. Added coverage.xml and cache directories to .gitignore. Standardized string formatting and parameter handling in metrics and activities classes, enhancing code readability. Removed the deprecated faker.py file and adjusted related tests accordingly.
Enhance timestamp handling in Redis and CoreScouter workflows by updating to DATETIME_FORMAT_MS_WITH_TZ. Modify tests to reflect new timestamp format, ensuring consistency across data structures and improving overall datetime management.
SIENTIAPDE-1169 Update Redis and core scouter tests to include model_tags in data structures, enhancing data organization and consistency across test cases.
Implement store_data_package method in Redis activity for debug data storage. Update CoreScouter workflow to conditionally call store_data_package based on debug flag. Enhance tests for store_data_package functionality and error handling.
Refactor Activities class to remove Kafka and Druid dependencies, simplifying initialization. Update values.yaml to set replica count to 1 for reduced resource usage. Adjust Redis activity to set TTL to None for better data retention. Remove unused Kafka and Druid activity files and their associated tests, streamlining the codebase.
Update sientia-dataops-library version to 1.2.0 in requirements.txt; refactor logging in activities to use a unified Logger instance and include metadata in log messages across various activities.
Implement workflows for fake data generation, scouter processing, and core scouter operations
- Added `FakeData` workflow to generate random data and send it to a Kafka topic.
- Implemented `Scouter` workflow to load data from Kafka and trigger the core scouter workflow.
- Created `CoreScouter` workflow to process data through quality gates, aggregation, and export to PostgreSQL.
- Developed comprehensive unit tests for activities and workflows, ensuring proper functionality and error handling.
- Enhanced Redis and Postgres activities with robust testing for data handling and error notifications.
- Introduced quality filters for data validation and implemented tests to verify their functionality.
