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Dimension properties

Dimensions let you drill down a model by filtering, grouping, and pivoting. They're basically SQL expressions that are referenced in the GROUP BY clause.

By default, if you don't have columns property in your YML definitions, metriql creates dimensions from all your columns by converting their column names to dimension names. If your dimension names doesn't conform the convention, we lowercase all the characters and strip the special ones from the name. For example, if your column name is Country ISO, the dimension name becomes country_iso. The best practice is to define the columns property under the dbt resources since automatically converting columns to dimensions may cause inconsistencies and break your existing reports in your BI tools.

You can define the dimensions using meta.metriql.dimensions and columns.meta. Here is an example that use both methods:

models:
- name: events
meta:
metriql:
dimensions:
computed_dimension:
sql: "{TABLE}.col1 * 2" # useful in case you want to define dimensions as SQL
type: string
columns:
- name: col1
meta:
metriql.dimension:
- type: string

Defining type under metriql.dimension is optional but recommended. If you don't define the type, metriql discovers the type by running a metadata query in your database.

Here is the list of properties that you can use to define dimensions:

column: | sql:#

You should set either column or sql in order to define the dimensions under model.meta.metriql.dimensions. If your data is already modeled and clean, a model usually points to a table in your database, and dimensions point to the columns under the table. If you're using complex types such as JSON, you can define dimensions by making use of sql.

models:
- name: events
meta:
metriql:
dimensions:
location:
sql: CONCAT({TABLE}.country, {TABLE}.city)
country:
column: country

Please note that these fields are not required under column.meta as they point to the relevant column.

sql: lets you define the complex expressions, you can compose a dimension with a combination of columns semantically. column: references the columns in the model target.

primary:#

Marks the dimensions as the primary key. We make use of primary keys when joining other models.

primary: true

pivot:#

Enables the pivoting for the dimension in the segmentation reporting type.

pivot: true

timeframes:#

You can let the end user see the date for different date intervals and buckets. By default, all the applicable timeframe values are enabled for date, timestamp, and time dimensions.

timeframes: [week, month, year, hour_of_day, day_of_month]
TypeAvailable Timeframes
timestamphour, day, week, month, year, hour_of_day, day_of_month, week_of_year, month_of_year, quarter_of_year, day_of_week
dateday, week, month, year, day_of_month, week_of_year, month_of_year, quarter_of_year, day_of_week
timeminute, hour