Model Additional Levels in a Hierarchy

You can add a level to an existing hierarchy by dragging a dataset column to the desired position in the Hierarchies panel. A hierarchy should go from the most granular level on the bottom to the most general level on the top.

Procedure

1. Open the dimension editor canvas.

2. Drag a dataset column to above or below an existing level in the Hierarchies panel. Look for a thin green line, and drop in the correct position in the hierarchy.

3. Complete the requested information in the Create a Level dialog.

   Field	Description
   Name	The user-friendly caption name that users see in BI reporting and visualization tools.
   Query Name	The SQL or MDX column name for this dimension attribute in the published AtScale cube. The query name is how the AtScale engine refers to this attribute internally. This should not be changed after the cube is published, as changing the query name may break existing data sources and reports that rely on this cube.
   Dataset	The source dataset that contains the columns that this level is based on.
   Keys	The dataset column or columns that the level is based on. If the level has a compound key (a key of more than one column), click the + symbol to specify the other dataset columns that comprise the key. If the key consists of one column, the values in that column must be unique. If the key is a compound key, the columns together must provide unique values.

note

Note: In AtScale 7.0, when updating an existing level with relationships, you cannot add or remove key columns. You can still change the selected key columns of the level, but you cannot change the number of key columns. If a snowflake relationships is needed between two levels, one is automatically created and, consequently, neither level will allow adding or removing key columns.To change the number of key columns of an existing level with relationships, you have two options: Delete all of the relationships, update the key column definitions, and define new relationships that use the new key definitions. Delete the level. AtScale automatically deletes the relationships. You can then recreate the level with the desired key column definitions.

For more about selecting key columns and modeling hierarchies, see Requirements for Modeling Dimensional Hierarchies." "Unique", "Specifies whether each row in the key columns contains a value that is unique within the dataset. Selecting this check box is equivalent to declaring the key to be a primary key. The AtScale engine uses this selection as input when joining rows from this dimension level to other datasets in the cube model. If the Key Column values are unique for each row, select this option. The join behavior would then consider the first matching row at query runtime. If the Key Column values are multi-valued, then leave unselected. The join behavior would then consider all matching rows at query runtime. For more about this check box and requirements for modeling hierarchies, see Requirements for Modeling Dimensional Hierarchies." "Name", "The column whose values are used when a user selects this level in a visualization or report. For example, an ID number may be the key, but you want users to see a product name instead. For more about selecting name columns and modeling hierarchies, see Requirements for Modeling Dimensional Hierarchies." "Sort Column", "By default, sorts are on the column that you specify in the Value field. If you want to sort on a different column, select that column here. This applies to MDX queries only (i.e. queries received through the 'XMLA interface')." "Exclude from System-Generated Dimension-Only Aggregates", "Excludes this attribute from system generated dimension-only aggregates. Useful if the attribute contains a large number (e.g. millions) of distinct values that you don't want to aggregate." "Exclude from System-Generated Fact-Based Aggregates", "Excludes this attribute from system generated fact-based aggregates. Useful if the attribute contains a large number (e.g. millions) of distinct values that you don't want to aggregate." "Custom Empty Member", "If a null value is found for the level, AtScale substitutes this value, enabling joins on dimension rows where the level contains nulls. This value must match the data type of the value column in the dimension. For example, if the data type of the value column is INTEGER, the value to substitute for nulls should be an integer, such as 123. To include the column in factless queries, add a UNION to those queries. For example, suppose you want to query the Sales Reason dimension without using a measure. The value column is salesreasonkey, for which the data type is STRING. Two other columns in the query - salesreasonname and salesreasongroup - might have null values. The label for null values in salesreasonkey is no reason, while the label for null values in salesreasongroup is no group. The query would look like this: select salesreasonkey, salesreasonname, salesreasongroup from schema.dimsalesreason union select null, 'no reason', 'no group'" "Formatting", "Choose the format for the values. See Formats for Data Values for a description of the available formats." "Visible?", "Specifies whether to make the level visible to BI client software. You might want to make a level invisible if you need it for join relationships, but don't want users to be able to select it in their queries."

1. Click Save.