Using Notebooks
  • 27 Apr 2022
  • 2 Minutes to read
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Using Notebooks

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Notebooks is the Sisense coding interface that enables data analysts to be write ad-hoc queries and easily transform them into visualizations, for rapid and agile exploration. Using a live connector, Notebooks enables you to test your data or get preliminary results from an ad-hoc SQL, Python or R query before visualizing the data with dashboards. This means that you can enter and execute a query to immediately view the results.
For example, you can run a SQL, Python or R query to find your company’s best selling items. You can join results from queries on different tables to find the top selling product with the highest profit margin and then use Python to make sophisticated profit predictions using advanced code libraries. The result is a table that can then be easily converted to one of several types of graphs. By default, the results are also rendered in a Statistics view as a basic column chart.

To make writing your queries easier, the Schema Browser on the left shows all of the schemas and tables that exist in your database, and the columns within them.
notebook_main.png

Watch this video for an introduction to Notebooks:

Limitations

When you connect to a Live data source, new changes are not automatically updated in the Notebooks schema.

To refresh the data, in the Schema Browser, mouse-over the data source and click Refresh.
refresh_button.png

For On-Prem instances, access to the internet is required to use Notebooks. If the On-prem instance has blocked off internet access, Notebooks cannot be utilized in Sisense. Access to the internet is required as the query and code outputs are stored in the Sisense Cloud for up to 30 days. After 30 days, the output table expires until the query or code is rerun from the editor.

Code Limitations

  • Timeout limits

    • SQL query timeout is 4 minutes
    • Code execution timeout is 1 minute
  • Browser memory

    • Most browsers can only handle visualizations of up to 5MB, so to keep notebooks running smoothly, visualization results are capped.The Limit Rows bar of the SQL editor can be used to control the amount of data visualized, while allowing CSV downloads to include full unlimited result sets. To adjust the number of rows visualized, change the number in the limit box in the query editor.
    • Once a code block for Python is added to a chart, the “Limit Preview” setting for charts will only apply to the SQL Output preview. The “Limit Preview” setting will not affect the rows or size of the dataframe sent to the Python script, allowing the code to run on the full result set within a 500MB limit. If the Python code returns over 5MB of data, an error will be surfaced and a hard-coded limit or aggregation, outside of the “Limit Preview” setting, should be added to the SQL or code blocks.

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