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Training Tursio

Tursio trains small models to infer the semantics of the underlying data. Each database connection is trained separately with its own semantic model, which is then used for query processing. Users can tune the semantic model for a better querying experience.

When to Train?

Training is needed whenever:

  • A new database is added.
  • An existing database connection is edited (e.g., name, credentials, password, or host).
  • A database is deleted.
  • New Query Tables are added.
  • Query Tables are modified or removed.
  • The semantic model is changed (dimensions, simple measures, custom measures, or join relationships).

Note

The system automatically detects changes. If retraining is needed, a message at the top of the Settings page will read: "Changes detected in connections xxx. Please re-run the training for each of these."

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Run Training

Training can be triggered from either the Databases tab or the Query Tables tab on the Settings page.

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  1. Click Run Training.
  2. A popup window appears.
  3. Select the database you want to train.
  4. Click Run to start the process.
  5. Refresh the page after the process completes (roughly 30–60 minutes depending on the database).
  6. Go to the Select Dataset dropdown and choose your newly trained dataset to start querying.

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Training Status

When training is in progress:

  • The dataset cannot be queried (the dataset dropdown will be empty).
  • Training cannot be triggered again until the current run completes.

Once training completes:

  • All updates — new databases, schema changes, or semantic feedback — are applied and available for querying.
  • Re-training is not allowed unless further changes are detected.
  • The Last Trained timestamp (UTC) is updated for the database connection.

Feedback Cycle

Training should be treated as a cyclic process:

  1. Perform initial training after connecting the database.
  2. Explore the semantic model:
    • Dimensions
    • Simple Measures
    • Custom Measures
    • Join Relationships
  3. Add or modify elements in the semantic model.
  4. Save all changes once they are finalized.
  5. Re-run training to incorporate the changes into the semantic model.

Prior Knowledge

Incremental Training

Tursio preserves user configurations from prior training runs, preventing overwrites for:

  • Aliases
  • Custom measures
  • Dimension/measure feedback
  • Accepted joins

Schema Constraints

Whenever accessible, Tursio automatically extracts referential constraints from metadata and converts them to equivalent LEFT JOINs in an Accepted state.

  • Supported databases include PostgreSQL, SQL Server, Azure SQL, Snowflake, Oracle, and Azure Databricks, with more to be added soon.
  • Composite keys are handled via conjunctive (multi-column) join conditions.