We’re excited to share some of the superQuery UI and performance updates released in June!
If there is anything you feel we should be prioritizing next, please let us know here.
- Error highlighting has been improved. It’s much easier now to identify errors in your SQL that are preventing your query from executing.
- Closing query tabs became much easier. Just click the little “x” in the right corner of your tab.
- We improved auto-detection for queries in cases when users run multiple queries in a tab.
Now, when you place DDL commands in your SQL, we will run your queries sequentially.
- We added a new horizontal bar graph visualization for exploring query results.
- Support for additional ML model accuracy metrics has been added.
- Lazy loading of data: Build ML models from a subset of your data, instead of waiting for an entire dataset to upload.
- Cost control & statistics: Get rich query statistics in your return object, including the cost of your query. Statistics like query cost can be returned even without running a query.
- Managed Interface: Work with a seamless interface between Python and superQuery, managed by us.
This means if you have any requests for additional capabilities, we can quickly add them to the library. Let us know here what you’d like added.
- After creating a new table, some users wouldn’t see the table in their dataset in the Resource Panel.
- Some users experienced issues exporting query results to Google Sheets.
- Destination Table tags weren’t being updated when users would edit a destination project or dataset.
- Other minor bug fixes & improvements.