Refactor logging in worker.py to use custom_info and custom_error methods, enhancing log metadata with pod and workflow details for improved observability.
Enhance logging in Gates, MongoDB, and Redis activities by replacing debug statements with info level logs, improving observability of data processing steps. Update worker configuration to adjust concurrency settings and enable autoscaling for task polling.
Update dependencies and improve observability by changing logger imports. Bump sientia-dataops-library version to 1.4.1 and update image tag to 0.4.2 in values.yaml.
Enable SDK metrics service in values.yaml and update worker.py to initialize SDK metrics server with Prometheus configuration, enhancing observability of the application.
Update replica count in values.yaml, add application status metrics in metrics.py, and implement Prometheus server in worker.py for enhanced observability.
Update sonar-project.properties to exclude worker.py from coverage, enhance error handling in worker.py, and add new tests for error scenarios in test_redis.py and test_mongo.py.
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.
Integrate Druid activity into the Activities class, adding support for Druid configuration and initialization. Update worker and connectors configuration to accommodate Druid, enhancing data processing capabilities.
Update environment configuration and refactor activity imports
- Changed Kafka, Redis, and Temporal host configurations to use localhost.
- Updated the version reference for the sientia-dataops-library in requirements.txt.
- Refactored import paths for activities to align with new module structure.
- Removed unused base.py and postgres.py files.
- Updated logger and policies imports to reflect new module locations.
- Adjusted values.yaml for branch and log level settings.
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.
