What’s new in superQuery: June 2019

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.

UI Updates

  • Error highlighting has been improved. It’s much easier now to identify errors in your SQL that are preventing your query from executing.
Improved Error Highlighting in superQuery
  • Closing query tabs became much easier. Just click the little “x” in the right corner of your tab.
Closing Tabs in SuperQuery is Easy
  • 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.

Example below:
Sequential DDL Queries in superQuery
In this case, each query will build off of the one preceding it.
  • We added a new horizontal bar graph visualization for exploring query results.
Horizontal Bar Graph in superQuery
  • Support for additional ML model accuracy metrics has been added.
superQuery DML support for ML Models


We released our Python library for Google BigQuerysuperPy. This dramatically simplifies the work of python developers accessing BigQuery data in three ways: 

    • 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.

Statistics of BigQuery data in Python
Example of query statistics available in superPy
  • Check out the superPy GitHub readme here.

Bug Fixes

  • 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.
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