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chart-components.mdchart-plugins.mddata-transformation.mdform-data.mdindex.mdquery-building.md
tile.json

rubric.jsonevals/scenario-3/

{
  "context": "This evaluation assesses how well the engineer uses Apache Superset's SQL Lab capabilities to implement SQL query validation and formatting functionality. The focus is on proper usage of Superset's SQL validation and formatting APIs.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Superset module imports",
      "description": "Imports necessary Superset modules such as superset.sql_lab, superset.sqllab.api.SqlLabRestApi, or superset.models.core.Database for SQL validation and formatting",
      "max_score": 15
    },
    {
      "name": "SQL validation logic",
      "description": "Implements SQL validation using Superset's query validation functionality from superset.sql_lab.py or database engine specs to verify SQL syntax correctness",
      "max_score": 35
    },
    {
      "name": "SQL formatting logic",
      "description": "Implements SQL formatting using Superset's SQL formatting capabilities or query processing utilities to format queries with proper spacing, indentation, and keyword casing",
      "max_score": 30
    },
    {
      "name": "Database integration",
      "description": "Uses Superset's database connection models (superset.models.core.Database) or SQLAlchemy URI handling to establish database context for validation",
      "max_score": 15
    },
    {
      "name": "Result structure",
      "description": "Returns validation results in the specified format (dictionary with 'valid' boolean and 'errors' list) consistent with Superset's error handling patterns",
      "max_score": 5
    }
  ]
}