Core Features
Charts & Results
Dory helps you inspect SQL results and convert useful result sets into charts. Chart quality depends on the shape of the SQL output.
Result Tables
After running SQL, first review the result table:
| Check | Why It Matters |
|---|---|
| Column names | Clear names make chart configuration easier. |
| Row count | Very large result sets are harder to chart directly. |
| Data types | Dates, numbers, and categories should be shaped correctly. |
| Null values | Missing values can distort charts. |
| Aggregation level | Chart data usually works best after SQL aggregation. |
Recommended Sequence
- Write or generate SQL.
- Run the query.
- Review result columns and sample rows.
- Aggregate or reshape the result if needed.
- Open charting from the result area.
- Choose a chart type that matches the data.
Good Candidates for Charts
- Time series with date and metric columns.
- Category comparison with category and value columns.
- Status distribution with status and count columns.
- Top-N lists with name and metric columns.
Common Chart Data Shapes
| Chart Goal | Recommended Columns |
|---|---|
| Line chart | date, metric |
| Bar chart | category, metric |
| Pie chart | category, value |
| Table summary | dimension columns plus metric columns |
Let AI Help Shape Results
If the current result is hard to chart, ask AI to rewrite the SQL into chart-friendly output. Be specific about the chart type and required columns.
Example: ask AI to return daily order count and total revenue for the last 30 days, with one row per day.