Dimensions

Dimensions represent the non-aggregatable columns in your data set, which are the attributes, features, or characteristics that describe or categorize data. In the context of the dbt Semantic Layer, dimensions are part of a larger structure called a semantic model. They are created along with other elements like entities and measures and used to add more details to your data. In SQL, dimensions are typically included in the group by clause of your SQL query.

All dimensions require a name, type, and can optionally include an expr parameter. The name for your Dimension must be unique wihtin the same semantic model.

Parameter	Description	Type
name	Refers to the name of the group that will be visible to the user in downstream tools. It can also serve as an alias if the column name or SQL query reference is different and provided in the expr parameter. Dimension names should be unique within a semantic model, but they can be non-unique across different models as MetricFlow uses joins to identify the right dimension.	Required
type	Specifies the type of group created in the semantic model. There are two types: - Categorical: Describe attributes or features like geography or sales region. - Time: Time-based dimensions like timestamps or dates.	Required
type_params	Specific type params such as if the time is primary or used as a partition	Required
description	A clear description of the dimension	Optional
expr	Defines the underlying column or SQL query for a dimension. If no expr is specified, MetricFlow will use the column with the same name as the group. You can use the column name itself to input a SQL expression.	Optional
label	A recommended string that defines the display value in downstream tools. Accepts plain text, spaces, and quotes (such as orders_total or "orders_total").	Optional

Refer to the following for the complete specification for dimensions:

dimensions:
  - name: Name of the group that will be visible to the user in downstream tools # Required
    type: Categorical or Time # Required
    label: Recommended adding a string that defines the display value in downstream tools. # Optional
    type_params: Specific type params such as if the time is primary or used as a partition # Required
    description: Same as always # Optional
    expr: The column name or expression. If not provided the default is the dimension name # Optional

Refer to the following example to see how dimensions are used in a semantic model:

semantic_models:
  - name: transactions
    description: A record for every transaction that takes place. Carts are considered multiple transactions for each SKU. 
    model: {{ ref("fact_transactions") }}
    defaults:
      agg_time_dimension: order_date
# --- entities --- 
  entities: 
    - name: transaction
      type: primary
      ...
# --- measures --- 
  measures: 
      ... 
# --- dimensions ---
  dimensions:
    - name: order_date
      type: time
      type_params:
        time_granularity: day
      label: "Date of transaction" # Recommend adding a label to provide more context to users consuming the data
      expr: ts
    - name: is_bulk
      type: categorical
      expr: case when quantity > 10 then true else false end
    - name: type
      type: categorical

Dimensions are bound to the primary entity of the semantic model they are defined in. For example the dimensoin type is defined in a model that has transaction as a primary entity. type is scoped to the transaction entity, and to reference this dimension you would use the fully qualified dimension name i.e transaction__type.

MetricFlow requires that all semantic models have a primary entity. This is to guarantee unique dimension names. If your data source doesn't have a primary entity, you need to assign the entity a name using the primary_entity key. It doesn't necessarily have to map to a column in that table and assigning the name doesn't affect query generation. We recommend making these "virtual primary entities" unique across your semantic model. An example of defining a primary entity for a data source that doesn't have a primary entity column is below:

semantic_model:
  name: bookings_monthly_source
  description: bookings_monthly_source
  defaults:
    agg_time_dimension: ds
  model: ref('bookings_monthly_source')
  measures:
    - name: bookings_monthly
      agg: sum
      create_metric: true
  primary_entity: booking_id

Dimensions types

This section further explains the dimension definitions, along with examples. Dimensions have the following types:

• Dimensions types
• Categorical
• Time
  • SCD Type II
    • Basic structure
    • Semantic model parameters and keys
    • Implementation
    • SCD examples

Categorical

Categorical dimensions are used to group metrics by different attributes, features, or characteristics such as product type. They can refer to existing columns in your dbt model or be calculated using a SQL expression with the expr parameter. An example of a categorical dimension is is_bulk_transaction, which is a group created by applying a case statement to the underlying column quantity. This allows users to group or filter the data based on bulk transactions.

dimensions: 
  - name: is_bulk_transaction
    type: categorical
    expr: case when quantity > 10 then true else false end

Time

Time has additional parameters specified under the type_params section. When you query one or more metrics, the default time dimension for each metric is the aggregation time dimension, which you can refer to as metric_time or use the dimension's name.

You can use multiple time groups in separate metrics. For example, the users_created metric uses created_at, and the users_deleted metric uses deleted_at:

# dbt Cloud users
dbt sl query --metrics users_created,users_deleted --group-by metric_time__year --order-by metric_time__year

# dbt Core users
mf query --metrics users_created,users_deleted --group-by metric_time__year --order-by metric_time__year

You can set is_partition for time to define specific time spans. Additionally, use the type_params section to set time_granularity to adjust aggregation details (hourly, daily, weekly, and so on).

is_partition

Use is_partition: True to show that a dimension exists over a specific time window. For example, a date-partitioned dimensional table. When you query metrics from different tables, the dbt Semantic Layer uses this parameter to ensure that the correct dimensional values are joined to measures.

dimensions: 
  - name: created_at
    type: time
    label: "Date of creation"
    expr: ts_created # ts_created is the underlying column name from the table 
    is_partition: True
    type_params:
      time_granularity: day
  - name: deleted_at
    type: time
    label: "Date of deletion"
    expr: ts_deleted # ts_deleted is the underlying column name from the table
    is_partition: True 
    type_params:
      time_granularity: day

measures:
  - name: users_deleted
    expr: 1
    agg: sum
    agg_time_dimension: deleted_at
  - name: users_created
    expr: 1
    agg: sum

time_granularity

time_granularity specifies the grain of a time dimension. MetricFlow will transform the underlying column to the specified granularity. For example, if you add hourly granularity to a time dimension column, MetricFlow will run a date_trunc function to convert the timestamp to hourly. You can easily change the time grain at query time and aggregate it to a coarser grain, for example, from hourly to monthly. However, you can't go from a coarser grain to a finer grain (monthly to hourly).

Our supported granularities are:

• nanosecond (Snowflake only)
• microsecond
• millisecond
• second
• minute
• hour
• day
• week
• quarter
• year

Aggregation between metrics with different granularities is possible, with the Semantic Layer returning results at the coarsest granularity by default. For example, when querying two metrics with daily and monthly granularity, the resulting aggregation will be at the monthly level.

dimensions: 
  - name: created_at
    type: time
    label: "Date of creation"
    expr: ts_created # ts_created is the underlying column name from the table 
    is_partition: True 
    type_params:
      time_granularity: hour 
  - name: deleted_at
    type: time
    label: "Date of deletion"
    expr: ts_deleted # ts_deleted is the underlying column name from the table 
    is_partition: True 
    type_params:
      time_granularity: day 

measures:
  - name: users_deleted
    expr: 1
    agg: sum 
    agg_time_dimension: deleted_at
  - name: users_created
    expr: 1
    agg: sum

SCD Type II

caution

Currently, semantic models with SCD Type II dimensions cannot contain measures.

MetricFlow supports joins against dimensions values in a semantic model built on top of a slowly changing dimension (SCD) Type II table. This is useful when you need a particular metric sliced by a group that changes over time, such as the historical trends of sales by a customer's country.

Basic structure

SCD Type II are groups that change values at a coarser time granularity. SCD Type II tables typically have two time columns that indicate the validity period of a dimension: valid_from (or tier_start) and valid_to (or tier_end). This creates a range of valid rows with different dimension values for a metric or measure.

MetricFlow associates the metric with the earliest available dimension value within a coarser time window, such as a month. By default, it uses the group valid at the start of this time granularity.

MetricFlow supports the following basic structure of an SCD Type II data platform table:

entity_key	dimensions_1	dimensions_2	...	dimensions_x	valid_from	valid_to

• entity_key (required): A unique identifier for each row in the table, such as a primary key or another unique identifier specific to the entity.
• valid_from (required): Start date timestamp for when the dimension is valid. Use validity_params: is_start: True in the semantic model to specify this.
• valid_to (required): End date timestamp for when the dimension is valid. Use validity_params: is_end: True in the semantic model to specify this.

Semantic model parameters and keys

When configuring an SCD Type II table in a semantic model, use validity_params to specify the start (valid_from) and end (valid_to) of the validity window for each dimension.

• validity_params: Parameters that define the validity window.
  • is_start: True: Indicates the start of the validity period. Displayed as valid_from in the SCD table.
  • is_end: True: Indicates the end of the validity period. Displayed as valid_to in the SCD table.

Here’s an example configuration:

- name: tier_start #  The name of the dimension.
  type: time # The type of dimension (such as time)
  label: "Start date of tier" # A readable label for the dimension
  expr: start_date # Expression or column name the dimension represents
  type_params: # Additional parameters for the dimension type
    time_granularity: day # Specifies the granularity of the time dimension (such as day)
    validity_params: # Defines the validity window
      is_start: True # Indicates the start of the validity period. 
- name: tier_end 
  type: time
  label: "End date of tier"
  expr: end_date
  type_params:
    time_granularity: day
    validity_params:
      is_end: True # Indicates the end of the validity period.

SCD Type II tables have a specific dimension with a start and end date. To join tables:

• Set the additional entity type parameter to the natural key.
• Use a natural key as an entity type, which means you don't need a primary key.
• In most instances, SCD tables don't have a logically usable primary key because natural keys map to multiple rows.

Implementation

Here are some guidelines to follow when implementing SCD Type II tables:

• The SCD table must have valid_to and valid_from time dimensions, which are logical constructs.
• The valid_from and valid_to properties must be specified exactly once per SCD table configuration.
• The valid_from and valid_to properties shouldn't be used or specified on the same time dimension.
• The valid_from and valid_to time dimensions must cover a non-overlapping period where one row matches each natural key value (meaning they must not overlap and should be distinct).
• We recommend defining the underlying dbt model with dbt snapshots. This supports the SCD Type II table layout and ensures that the table is updated with the latest data.

This is an example of SQL code that shows how a sample metric called num_events is joined with versioned dimensions data (stored in a table called scd_dimensions) using a primary key made up of the entity_key and timestamp columns.

select metric_time, dimensions_1, sum(1) as num_events
from events a
left outer join scd_dimensions b
on 
  a.entity_key = b.entity_key 
  and a.metric_time >= b.valid_from 
  and (a.metric_time < b. valid_to or b.valid_to is null)
group by 1, 2

SCD examples

The following are examples of how to use SCD Type II tables in a semantic model:

 SCD dimensions for sales tiers and the time length of that tier.

This example shows how to create slowly changing dimensions (SCD) using a semantic model. The SCD table contains information about salespersons' tier and the time length of that tier. Suppose you have the underlying SCD table:

sales_person_id	tier	start_date	end_date
111	1	2019-02-03	2020-01-05
111	2	2020-01-05	2048-01-01
222	2	2020-03-05	2048-01-01
333	2	2020-08-19	2021-10-22
333	3	2021-10-22	2048-01-01

As mentioned earlier, the validity_params include two important arguments that specify the columns in the SCD table that mark the start and end dates (or timestamps) for each tier or dimension:

• is_start
• is_end

Additionally, the entity is tagged as natural to differentiate it from a primary entity. In a primary entity, each entity value has one row. In contrast, a natural entity has one row for each combination of entity value and its validity period.

semantic_models:
  - name: sales_person_tiers
    description: SCD Type II table of tiers for salespeople 
    model: {{ref(sales_person_tiers)}}
    defaults:
      agg_time_dimension: tier_start

    dimensions:
      - name: tier_start
        type: time
        label: "Start date of tier"
        expr: start_date
        type_params:
          time_granularity: day
          validity_params:
            is_start: True
      - name: tier_end 
        type: time
        label: "End date of tier"
        expr: end_date
        type_params:
          time_granularity: day
          validity_params:
            is_end: True
      - name: tier
        type: categorical

    primary_entity: sales_person

    entities:
      - name: sales_person
        type: natural 
        expr: sales_person_id

The following code represents a separate semantic model that holds a fact table for transactions:

semantic_models: 
  - name: transactions 
    description: |
      Each row represents one transaction.
      There is a transaction, product, sales_person, and customer id for 
      every transaction. There is only one transaction id per 
      transaction. The `metric_time` or date is reflected in UTC.
    model: {{ ref(fact_transactions) }}
    defaults:
      agg_time_dimension: metric_time

    entities:
      - name: transaction_id
        type: primary
      - name: customer
        type: foreign
        expr: customer_id
      - name: product
        type: foreign
        expr: product_id
      - name: sales_person
        type: foreign
        expr: sales_person_id

    measures:
      - name: transactions
        expr: 1
        agg: sum
      - name: gross_sales
        expr: sales_price
        agg: sum
      - name: sales_persons_with_a_sale
        expr: sales_person_id
        agg: count_distinct

    dimensions:
      - name: metric_time
        type: time
        label: "Date of transaction"
        is_partition: true
        type_params:
          time_granularity: day
      - name: sales_geo
        type: categorical

You can now access the metrics in the transactions semantic model organized by the slowly changing dimension of tier.

In the sales tier example, For instance, if a salesperson was Tier 1 from 2022-03-01 to 2022-03-12, and gets promoted to Tier 2 from 2022-03-12 onwards, all transactions from March would be categorized under Tier 1 since the dimensions value of Tier 1 comes earlier (and is the default starting point), even though the salesperson was promoted to Tier 2 on 2022-03-12.

 SCD dimensions with sales tiers and group transactions by month when tiers are missing

This example shows how to create slowly changing dimensions (SCD) using a semantic model. The SCD table contains information about salespersons' tier and the time length of that tier. Suppose you have the underlying SCD table:

sales_person_id	tier	start_date	end_date
111	1	2019-02-03	2020-01-05
111	2	2020-01-05	2048-01-01
222	2	2020-03-05	2048-01-01
333	2	2020-08-19	2021-10-22
333	3	2021-10-22	2048-01-01

In the sales tier example, if sales_person_id 456 is Tier 2 from 2022-03-08 onwards, but there is no associated tier level dimension for this person from 2022-03-01 to 2022-03-08, then all transactions associated with sales_person_id 456 for the month of March will be grouped under 'NA' since no tier is present prior to Tier 2.

The following command or code represents how to return the count of transactions generated by each sales tier per month:

# dbt Cloud users
dbt sl query --metrics transactions --group-by metric_time__month,sales_person__tier --order-by metric_time__month,sales_person__tier

# dbt Core users
mf query --metrics transactions --group-by metric_time__month,sales_person__tier --order-by metric_time__month,sales_person__tier