Enabling AQL for Legacy Users

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This guide is only for users who signed up before April 19, 2024. Users who signed up on or after this date have AQL enabled by default and can skip this guide.

Enabling AQL

For legacy users, enabling AQL for your dataset involves a simple process of adding version flagging at the dataset level. You just need to incorporate a version flag in your dataset definition as: __engine__: 'aql'.

This will activate AQL within your specified dataset while other datasets continue to operate using the prior engine.

Dataset aql_dataset {
  __engine__: 'aql' //turn this Dataset to using AQL Engine
  data_source_name: 'your_datasource_name'
  models: [ ]
  relationships: [ ]
}

Important Note

We suggest creating a duplicate of the dataset you wish to apply AQL to for backward compatibility. You can then add version flags to this cloned dataset. Once you've tested AQL on the clone, you are free to enable AQL for your main dataset.

If you find that the new AQL engine impacts your existing reports, you can conveniently remove the version flags from the concerned dataset.