mistercrunch opened a new pull request, #34639:
URL: https://github.com/apache/superset/pull/34639
## Summary
Introduces dataset-level chart defaults that automatically populate when
users create new charts, significantly improving the user experience by
reducing repetitive configuration steps and guiding users toward optimal
visualizations.
## Motivation
When creating charts from datasets in Apache Superset, users previously had
to manually configure all settings from scratch every time. This led to:
- Repetitive work for common configurations
- Confusion for new users about which metrics/dimensions to use
- Inconsistent chart configurations across teams
- Slower time-to-insight when exploring data
## Changes
### Backend (`superset/connectors/sqla/models.py`)
- Added properties to `SqlaTable` model for managing chart defaults:
- `default_chart_metadata`: Retrieves chart defaults from the `extra` JSON
field
- `default_metric`, `default_dimension`, `default_time_grain`,
`default_time_range`, `default_row_limit`: Individual default accessors
- Added `set_default_chart_metadata()` method for updating defaults
- Stores configuration in existing `extra` field to avoid schema changes
### Frontend - Dataset Editor
(`superset-frontend/src/components/Datasource/DatasourceEditor.jsx`)
- Added "Chart Defaults" configuration section with controls for:
- Default metric selection
- Default dimension/groupby column
- Time grain and time range settings
- Row limit configuration
- Adhoc filter defaults
- Implemented `onChartDefaultChange()` handler for updates
- Added `cleanupChartDefaults()` to handle column deletions during metadata
sync
- Created `parseExtra()` helper for robust JSON parsing
### Frontend - Chart Creation
(`superset-frontend/src/explore/actions/hydrateExplore.ts`)
- Implemented `applyDatasetChartDefaults()` function that:
- Applies defaults only to new charts (preserves existing charts)
- Validates that referenced columns/metrics still exist
- Gracefully handles JSON parsing errors
- Respects user overrides and existing settings
- Integrated into explore hydration flow for automatic application
## User Experience
### For Dataset Administrators
1. Navigate to Dataset Editor
2. Configure desired defaults in the new "Chart Defaults" section
3. Save dataset with configured defaults
### For Chart Creators
1. Click "Create Chart" from a dataset with defaults
2. Chart opens with pre-populated settings
3. Can immediately visualize data or adjust as needed
## Benefits
- ✨ **Improved onboarding**: New users get sensible starting configurations
- ⚡ **Faster chart creation**: Common settings are pre-populated
- 🎯 **Better consistency**: Encourages standard configurations across teams
- 🔄 **Flexible**: Defaults can be overridden, not enforced
- 📊 **Discoverability**: Helps users understand recommended
metrics/dimensions
## Technical Decisions
- **Used existing `extra` field**: Avoids database migration, maintains
backward compatibility
- **New charts only**: Preserves existing chart configurations
- **Silent failure handling**: Doesn't block users if defaults are invalid
- **Native Explore components**: Ensures UI consistency with chart builder
## Testing Considerations
- Verify defaults apply correctly to new charts
- Ensure existing charts remain unaffected
- Test cleanup when referenced columns are deleted
- Validate graceful handling of malformed JSON
- Confirm defaults can be overridden by users
## Screenshots
[Dataset Editor - Chart Defaults Section]
![Chart Defaults Configuration UI]
[Explore View - Pre-populated Settings]
![New Chart with Applied Defaults]
## Future Enhancements
- Viz-type specific defaults
- Multiple named configurations
- API endpoints for programmatic access
- Validation for incompatible default combinations
## Breaking Changes
None - Feature is backward compatible and opt-in.
## Related Issues
Closes #[ISSUE_NUMBER]
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