dc-query-building

To build a CubeQuery for a semantic layer, you define measures, dimensions, timeDimensions, filters, and ordering within a structured CubeQuery object. This approach models analytics requests consistently without ad-hoc SQL development.

Querying time dimensions with granularities in Drizzle Cube involves configuring the timeDimensions property within your CubeQuery object. This enables trend analysis across days, months, or quarters directly through the semantic layer.

Executing multiple CubeQuery objects using the Cube.js API requires passing your configured query arrays to the cubeClient.load() function. This retrieves analytical results and supports rapid experimentation across multi-query scenarios.

You can preview SQL before executing filters and measures by utilizing dry-run capabilities through the CubeClient APIs. This allows rapid debugging and validation of your CubeQuery configurations without retrieving actual data.

Constructing a semantic CubeQuery does not require writing raw SQL for dashboards. Instead, you define structured objects with measures, dimensions, and filters to increase consistency across your analyses.

Structured query building for multi-query scenarios replaces ad-hoc SQL by standardizing analytical requests into reusable CubeQuery objects. This reduces development overhead and ensures consistent filtering and ordering across dashboards.