- Implemented inner atomic blocks to ensure that failures during the creation of NarratorLayer, TransmitterReliability, OpinionStatus, Transmitters, and related models do not affect the overall transaction.
- Improved logging to provide warnings for skipped records due to errors, enhancing feedback during the seeding process.
- Streamlined the creation process for authors and books, ensuring that existing records are utilized effectively while maintaining data integrity.
- Improved the process for identifying and fixing empty slugs in NarratorLayer by utilizing raw SQL for more reliable detection.
- Enhanced logging to provide clear feedback on the number of records found and fixed, including error handling for any issues encountered during the slug update.
- Streamlined the slug fixing logic to directly address problematic entries, improving overall data integrity in the seeding process.
- Enhanced the process for fixing empty slugs in NarratorLayer by checking all layers and providing detailed logging for problematic entries.
- Improved slug generation to ensure uniqueness and fallback options when names are not available.
- Streamlined the creation of narrator layers with better error handling and defensive programming practices to avoid skipping layers.
- Added warnings for insufficient narrator layers, prompting retrieval of existing records if necessary.
- Introduced a new phase to the seeding process to address existing data integrity issues before creating new records.
- Added functionality to fix empty and duplicate slugs for NarratorLayer, TransmitterReliability, and OpinionStatus models, ensuring unique identifiers.
- Enhanced logging to provide detailed feedback on the fixes applied during the seeding process, improving overall data quality.
- Refactored the slug generation process for NarratorLayer, TransmitterReliability, and OpinionStatus models to handle empty or invalid slugs more effectively.
- Implemented checks for existing records to prevent duplicates during the seeding process, improving data integrity.
- Enhanced error handling during the creation of authors and books, ensuring existing records are utilized when available.
- Updated logging to provide clearer feedback on the seeding process, including details on existing records and errors encountered.
- Enhanced the slug generation process in the TransmitterReliability and OpinionStatus models to include robust error handling, ensuring unique slugs are created even when the title is invalid or empty.
- Implemented a mechanism to fix duplicate slugs during the seeding process, ensuring data integrity and preventing conflicts.
- Updated the seeding command to handle existing records more efficiently, improving overall reliability and opinion status creation.
- Enhanced the narrator layer creation process to include error handling, ensuring that existing layers are retrieved if creation fails.
- Introduced a new migration to fix NarratorLayer records with empty or invalid slugs, generating slugs based on the name field or defaulting to a structured format.
- Improved slug conflict resolution to prevent duplicates during the migration process.
- Updated tag creation to check for existing tags before creating new ones, enhancing efficiency.
- Improved transmitter name generation by deriving the father's name from a random selection of names.
- Added safeguards to ensure reliability status is only assigned if available.
- Adjusted tag assignment to ensure a valid number of tags is added to each hadis entry.
- Added 'explanations' and 'address_details' JSON fields to the Hadis model for improved data structure.
- Updated HadisAdminForm to include new fields in the admin interface.
- Introduced a new management command for comprehensive seeding of Hadis data, including Russian language support.
- Enhanced serializers to process and return structured explanations and address details based on request language.
- Updated entrypoint script to include the new data seeding command during initialization.
- Reduced the number of Gunicorn workers in the production Docker configuration from 32 to 6 for improved resource management.
- Enhanced the hadis data seeding command to calculate and distribute hadis entries more effectively across categories, ensuring balanced creation while respecting maximum limits.
- Added a new list of Russian hadis titles for random selection during seeding.
- Refactored category creation to use batch operations for improved performance.
- Implemented bulk creation of hadis records to optimize database interactions.
- Updated command output messages to reflect optimizations and progress during execution.
- Introduced a new management command to seed Russian language data for Hadis, HadisCategory, and HadisSect.
- Updated entrypoint script to include the new command for seeding Russian data during initialization.
