Advanced Modelling Use Cases

Every database is different, and sometimes one or more of the following edge case scenarios may need to be considered when modelling the data.

Multiple Datasets Mapped to the Same Table

This is typically needed when one table represents multiple logical entities, and each entity should be represented by a separate dataset in the LDM.

While the LDM Modeler supports mapping of multiple datasets to the same table, publishing an LDM with such mapping fails.

To avoid this issue, create multiple views on top of the table and map each dataset to a separate view.

For example, you have two tables, users and tickets.

• The users table contains ticket creators and assignees.
• The tickets table contains the assignee_id and creator_id columns.

To avoid mapping multiple datasets to the users table, do the following:

1. In the database, create two views on top of the users table: v_users_assignees and v_users_creators.
2. In the LDM, create three datasets: assignees, creators, and tickets.
3. Map the tickets dataset to the tickets table.
4. Map the assignees dataset to the v_users_assignees view.
5. Map the creators dataset to the v_users_creators view.
6. Create a relationship from the assignees dataset to the the tickets dataset using the assignee_id column as a primary key in the assignees dataset.
7. Create a relationship from the creators dataset to the tickets dataset using the creator_id column as a primary key in the creators dataset.

Using Multiple Data Sources in an LDM

• You can use datasets from different data sources in an LDM, but they can only be connected using common date dimension datasets. This allows applying a common date filter to them.
• Each visualization or metric can use only a single data source, but you can place visualizations from multiple data sources on a dashboard.
• Attempting to execute a visualization requiring data from multiple sources will not succeed.
• The list of data sources prioritizes those already used in the current model. You can change the data source in dataset mapping.

No Single-column Primary Key in Tables

While the LDM Modeler supports setting multi-column primary keys in datasets, publishing an LDM with multi-column primary keys fails.

To avoid this issue, create one-attribute primary keys in your database:

1. Concatenate the table columns that comprise the primary key in the table.
2. Calculate a hash value for the concatenated columns.
3. Use the hash value as a one-attribute primary key.