secondary_attributes

• Type: Array
• Required: N

Secondary attributes are dimensional attributes that are not the dimension's key, and are not part of a hierarchy.

AtScale supports the following types of secondary attributes:

• Dimensional: Provides an independent "dimensional" attribute for grouping metric data. This is the default type of secondary attribute.
• Level alias: Enables the creation of tabular reports that select hierarchical expressions without forcing the user to drill down a hierarchy.

Note: Secondary attributes cannot be used to create relationships between datasets and dimensions.

For more information on secondary attributes, see About Dimension Attributes.

The secondary_attributes property supports the following properties:

• unique_name: String, required. The unique name of the secondary attribute. This must be unique within the dimension.

• label: String, required. The name of the secondary attribute, as it appears in AtScale. This value does not need to be unique.

• dataset: String, required. The dataset that contains the key_columns the secondary attribute is based on.

• name_column: String, required. The dataset column that the attribute is based on.

• key_columns: Array, required. A list of the key columns that the attribute is based on. If the attribute has a compound key, you should specify all columns that make up the key as a list.

• sort_column: String, optional. The column used to sort the attribute's values in result sets. (This only applies to MDX queries.)

• allowed_calcs_for_dma: Array, optional. A list of the calculation types that can be used to create dimensionally modified aggregates for the secondary attribute. Note that when working with a time dimension, you can only define calculation types if the time_unit property for the level is set to day or longer. For more information on dimensionally modified aggregates, see Dimensionally Modified Aggregates.

• exclude_from_dim_agg: Boolean, optional. Excludes this attribute from system generated dimension-only aggregates. This is useful if the attribute contains a large number (millions) of distinct values that you don't want to aggregate.

• exclude_from_fact_agg: Boolean, optional. Excludes this attribute from system generated fact-based aggregates. This is useful if the attribute contains a large number (millions) of distinct values that you don't want to aggregate.

• custom_empty_member: Array, optional. Defines a custom empty member for the attribute.
  This feature allows fact data with missing or invalid foreign key values to be isolated and independently aggregated from those with valid foreign key values. Because fact records with invalid foreign keys are aggregated separately from records referencing valid dimension members, analysts can easily spot data integrity problems and further investigate them.
  Use this feature to ensure that un-joinable values are included in query results and aggregated under a specially designated dimension member called the Custom Empty Member.
  Supported properties:

  • key: Array, required. A list of the empty member values to use for key fields.
  • name: String, required. The empty member value to use for name fields.
  • sort: String, optional. The empty member value to use for the attribute's sort column, if one is specified.

• description: String, optional. A description of the secondary attribute.

• is_hidden: Boolean, optional. Determines whether the attribute is visible in BI tools.
  Supported values:

  • false (default)
  • true

• folder: String, optional. The name of the folder in which the attribute is displayed in BI tools.

• contains_unique_names: Boolean, optional. Determines whether each member of this attribute has a unique name. Do not enable this functionality if two members have different keys but the same name.
  Supported values:

  • true
  • false

aliases

• Type: array
• Required: N

The aliases property defines secondary attributes to use as aliases for specific levels within a hierarchy. These are useful in BI tools, as they enable the user to select a specific level without having to navigate through the hierarchy it belongs to. You can include as many aliases as needed in the list.

For more information on aliases in AtScale, see About Dimension Attributes.

The aliases property within a dimension hierarchy level supports the following properties:

• unique_name: String, required. The unique name of the alias. This must be unique within the dimension.

• label: String, required. The name of the alias, as it appears in AtScale and BI tools. This value does not need to be unique.

• dataset: String, required. The source dataset that contains the column that the alias is based on.

• name_column: String, required. The dataset column that the alias is based on.

• sort_column: String, required. The column to use to sort the values in result sets. This applies to MDX queries only (queries received through the XMLA interface).

• description: String, optional. A description of the alias.

• is_hidden: Boolean, optional. Determines whether the alias is visible in BI tools.
  Supported values:

  • true
  • false

• exclude_from_dim_agg: Boolean, optional. Excludes this alias from system generated dimension-only aggregates. This is useful if the alias contains a large number (millions) of distinct values that you don't want to aggregate.
  Supported values:

  • true
  • false

• exclude_from_fact_agg: Boolean, optional. Excludes this alias from system generated fact-based aggregates. This is useful if the alias contains a large number (millions) of distinct values that you don't want to aggregate.
  Supported values:

  • true
  • false

• custom_empty_member: Object, optional. Defines custom empty member values for the alias.
  This feature allows fact data with missing or invalid foreign key values to be isolated and independently aggregated from those with valid foreign key values. Because fact records with invalid foreign keys are aggregated separately from records referencing valid dimension members, analysts can easily spot data integrity problems and further investigate them.
  Use this feature to ensure that un-joinable values are included in query results and aggregated under a specially designated dimension member called the Custom Empty Member.
  Supported properties:

  • key: Array, required. A list of the empty member values to use for key fields.
  • name: String, required. The empty member value to use for name fields.
  • sort_name: String, optional. The empty member value to use for the alias's sort_column, if one is specified.

• format: String, optional. The format in which query results are returned. You can use one of AtScale's built-in named formats or a custom format string.
  Supported named formats: fixed, general number, none, percent, scientific, standard
  Custom format strings should be in quotes and contain one to four sections, separated by semicolons. For more information on defining custom format strings, see Number Format Strings.

• folder: String, optional. The name of the folder in which the alias appears in BI tools.

metrics

• Type: array
• Required: N

The metrics property of a dimension level defines secondary metrical attributes for the dimension, which behave like metrics in a very limited context of the data model.

Note: This feature is experimental and must be enabled by an AtScale admin.

For more information on secondary metrical attributes, see Add Or Edit A Metric Within A Dimension.

The metrics property within a dimension hierarchy level supports the following properties:

• label: String, required. The name of the secondary metrical attribute, as it appears in AtScale. This value does not need to be unique.

• unique_name: String, required. The unique name of the secondary metrical attribute. This must be unique within the dimension.

• dataset: String, required. The source dataset that contains the column that the secondary metrical attribute is based on. This should be the dimension dataset name.

• column: String, required. The column within the dataset that the secondary metrical attribute is based on.

• calculation_method: String, required. The calculation to apply to the data.
  Supported values: average, count distinct, count non-null, estimated count distinct, maximum, minimum, percentile, stddev_pop, stddev_samp, sum, var_pop, var_samp

• description: String, optional. A description of the secondary metrical attribute.

• is_hidden: Boolean, optional. Determines whether the secondary metrical attribute is visible in BI tools.
  Supported values:

  • true
  • false

• folder: String, optional. The name of the folder in which this secondary metrical attribute appears in BI tools.

• format: String, optional. The format of the data in the column that the measure is based on. You can use one of AtScale's built-in named formats or a custom format string.
  Supported values: fixed, general number, none, percent, scientific, standard
  Custom format strings should be in quotes and contain one to four sections, separated by semicolons. For more information on defining custom format strings, see Number Format Strings.

• exclude_from_dim_agg: Boolean, optional. Excludes this secondary metrical attribute from system generated dimension-only aggregates. This is useful if the secondary metrical attribute contains a large number (millions) of distinct values that you don't want to aggregate.
  Supported values:

  • true
  • false

• exclude_from_fact_agg: Boolean, optional. Excludes this secondary metrical attribute from system generated fact-based aggregates. This is useful if the secondary metrical attribute contains a large number (millions) of distinct values that you don't want to aggregate.
  Supported values:

  • true
  • false

• custom_empty_member: Object, optional. Defines custom empty member values for the secondary metrical attribute.
  This feature allows fact data with missing or invalid foreign key values to be isolated and independently aggregated from those with valid foreign key values. Because fact records with invalid foreign keys are aggregated separately from records referencing valid dimension members, analysts can easily spot data integrity problems and further investigate them.
  Use this feature to ensure that un-joinable values are included in query results and aggregated under a specially designated dimension member called the Custom Empty Member.
  Supported properties:

  • key: Array, required. A list of the empty member values to use for key fields.
  • name: String, required. The empty member value to use for name fields.
  • sort: String, optional. The empty member value to use for the secondary metrical attribute's sort column, if one is specified.

• unrelated_dimensions_handling: Enum, optional. Determines how the AtScale engine behaves when all of the following conditions are true:

  • A client queries a model that contains multiple fact datasets.
  • The data in each fact dataset are at a different level of granularity than the data in the other fact datasets.
  • The query references dimensions that are not related to the metrics being queried.

  Supported values:

  • error: AtScale rejects the query and returns an error message.
  • empty: AtScale displays empty cells in the query results.
  • repeat: In the query results, AtScale repeats the values for the secondary metrical attribute at a level of aggregation that is determined from the shared dimensions in the query.

unique_name

• Type: string
• Required: Y

The unique name of the level attribute. This must be unique within the dimension.

label

• Type: string
• Required: Y

The name of the level attribute, as it appears in AtScale. This value does not need to be unique.

dataset

• Type: string
• Required: Y

The source dataset that contains the columns that this level attribute is based on.

name_column

• Type: string
• Required: Y

The column whose values appear for this level in BI tools. For example, the key may be a product ID number, but you want users to see product names instead.

key_columns

• Type: array
• Required: Y

The dataset column that the level attribute is based on. If the level has a compound key, list all columns that make up 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.

description

• Type: string
• Required: N

A description of the level attribute.

is_hidden

• Type: boolean
• Required: N

Determines whether the level attribute is visible in BI tools.

Supported values:

• true
• false

is_unique_key

• Type: boolean
• Required: N

Determines whether the key_columns values are unique for each row.

Supported values:

• true: The key column values are unique for each row. The join behavior considers the first matching row at query runtime.
• false: The key column values are multi-valued. The join behavior considers all matching rows at query runtime.

Note: Setting this value to true is equivalent to declaring the key to be a primary key. The AtScale engine uses this property as input when joining rows from this dimension level to other datasets in the model.

contains_unique_names

• Type: boolean
• Required: N

Determines whether each member of this level attribute has a unique name. Do not enable this functionality if two members have different keys but the same name.

Supported values:

• true
• false

exclude_from_dim_agg

• Type: boolean
• Required: N

Excludes this level attribute from system generated dimension-only aggregates. This is useful if the attribute contains a large number (millions) of distinct values that you don't want to aggregate.

Supported values:

• true
• false

exclude_from_fact_agg

• Type: boolean
• Required: N

Excludes this level attribute from system generated fact-based aggregates. This is useful if the attribute contains a large number (millions) of distinct values that you don't want to aggregate.

Supported values:

• true
• false

sort_column

• Type: string
• Required: N

Defines the column to sort query results on. By default, this is the name_column; however, you can optionally use this property to specify a different column.

Note: This only applies to MDX queries (queries received through the XMLA interface).

allowed_calcs_for_dma

• Type: array
• Required: N

A list of the calculations that can be used when creating dimensionally modified aggregates for the level attribute.

folder

• Type: string
• Required: N

The name of the folder in which this level attribute appears in BI tools.

time_unit

• Type: string
• Required: N

For time dimensions only. The unit of time to use.

Supported values: year, halfyear, trimester, quarter, month, week, day, hour, minute, second, undefined

from

• Type: object
• Required: Y

Defines the side of the relationship that contains the physical dataset that you want to connect to another dimension.

Supported properties:

• dataset: String, required. The physical dataset you want to link to a dimension.
• join_columns: Array, required. The column(s) within the dataset that you want to use for the join.
• hierarchy: String, optional. The hierarchy within the dimension from which the relationship should originate.
• level: String, optional. The level within the hierarchy from which the relationship should originate.

For snowflake relationships (as defined by the type property), you only need to define dataset and join_columns.

to

• Type: object
• Required: Y

Defines the dimension that the from dataset is linked to.

Supported properties:

• dimension: String. The name of the dimension the from dataset is linked to.
• level: String, required if row_security is undefined. The key level within the dimension to use for the relationship.
• row_security: String, required if level is undefined. For security relationships, the row security object that the from dataset is linked to.

For snowflake relationships (as defined by the type property), you only need to define level.

type

• Type: string
• Required: Y

Defines the relationship as either embedded or snowflake.

Supported values:

• embedded: A secondary relationship, or one that connects a primary dimension to a secondary dimension.
• snowflake: A relationship that connects one of several underlying physical datasets together to create a snowflake dimension.

role_play

• Type: string
• Required: N

For role-playing relationships only. Defines the role-playing template for the relationship.

The role-playing template is the prefix or suffix that is added to every attribute in the role-played dimension. You can also specify both a prefix and a suffix.

This value must be in one of the following formats (including quotation marks):

• Prefix: "<prefix> {0}"
• Suffix: "{0} <suffix>"
• Prefix and suffix: "<prefix> {0} <suffix>"

For example, if you wanted to use the prefix Order, you would set role_play to "Order {0}".

For more information on role-playing relationships in AtScale, see Role-Playing Relationships.

unique_name

• Type: string
• Required: N

The unique name of the relationship. This must be unique within the dimension.

unique_name

• Type: string
• Required: Y

The name of the calculation group. This must be unique within the dimension.

description

• Type: string
• Required: Y

A description of the calculation group.

calculated_members

• Type: array
• Required: Y

Defines the individual calculations in the group.

Supported properties:

• unique_name: String, required. The name of the calculation. This must be unique within the dimension.

• description: String, required. A description of the calculation.

• expression: String, required. The MDX expression for the calculation. This must be in quotes.

  If you plan on referencing a calculated metric via the Aggregate MDX function in your calculation, ensure that the calculated metric has an aggregation function set. You can do this by including the mdx_aggregation_function property in the calculation file. If you do not set an aggregation function, you may encounter errors at query time. For more information, see Calculations.

• format: String, optional. The format for the calculation results. You can use one of AtScale's built-in named formats or a custom format string:

  • Supported named formats: fixed, general number, none, percent, scientific, standard
  • Custom format strings should be in one of the formats described in Formats for Data Values.

folder

• Type: string
• Required: N

The name of the folder in which the calculation group appears in BI tools.