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MetricFlow time spine

It's common in analytics engineering to have a date dimension or "time-spine" table as a base table for different types of time-based joins and aggregations. The structure of this table is typically a base column of daily or hourly dates, with additional columns for other time grains, like fiscal quarters, defined based on the base column. You can join other tables to the time spine on the base column to calculate metrics like revenue at a point in time, or to aggregate to a specific time grain.

MetricFlow requires you to define a time-spine table as a model-level configuration in the Semantic Layer for time-based joins and aggregations, such as cumulative metrics. This configuration informs dbt which model should be used for time range joins. It is especially useful for cumulative metrics or calculating time-based offsets. The time-spine model is joined to other tables when calculating certain types of metrics or dimensions. MetricFlow will join the time-spine model in the compiled SQL for the following types of metrics and dimensions:

To see the generated SQL for the metric and dimension types that use time-spine joins, refer to the respective documentation or add the compile=True flag when querying the Semantic Layer to return the compiled SQL.

Configuring time-spine in YAML​

  • The time spine is a special model that tells dbt and MetricFlow how to use specific columns by defining their properties.

  • The models key for the time spine must be in your models/ directory.

  • You only need to configure time-spine models that the Semantic Layer should recognize.

  • At a minimum, define a time-spine table for a daily grain.

  • You can optionally define a time-spine table for a different granularity, like hourly.

  • Note that if you don’t have a date or calendar model in your project, you'll need to create one.

  • If you're looking to specify the grain of a time dimension so that MetricFlow can transform the underlying column to the required granularity, refer to the Time granularity documentation

If you already have a date dimension or time-spine table in your dbt project, you can point MetricFlow to this table by updating the model configuration to use this table in the Semantic Layer. This is a model-level configuration that tells dbt to use the model for time range joins in the Semantic Layer.

For example, given the following directory structure, you can create two time spine configurations, time_spine_hourly and time_spine_daily. MetricFlow supports granularities ranging from milliseconds to years. Refer to the Dimensions page (time_granularity tab) to find the full list of supported granularities.

tip

Previously, you had to create a model called metricflow_time_spine in your dbt project. Now, if your project already includes a date dimension or time spine table, you can simply configure MetricFlow to use that table by updating the model setting in the Semantic Layer.

If you don’t have a date dimension table, you can still create one by using the code snippet below to build your time spine model.

Time spine directory structureTime spine directory structure
models/_models.yml
models:
  - name: time_spine_hourly
    time_spine:
      standard_granularity_column: date_hour # column for the standard grain of your table
    columns:
      - name: date_hour
        granularity: hour # set granularity at column-level for standard_granularity_column
  - name: time_spine_daily
    time_spine:
      standard_granularity_column: date_day # column for the standard grain of your table
    columns:
      - name: date_day
        granularity: day # set granularity at column-level for standard_granularity_column

For an example project, refer to our Jaffle shop example. Note that the models key in the time spine configuration must be placed in your models/ directory.

Now, break down the configuration above. It's pointing to a model called time_spine_daily. It sets the time spine configurations under the time_spine key. The standard_granularity_column is the lowest grain of the table, in this case, it's hourly. It needs to reference a column defined under the columns key, in this case, date_hour. Use the standard_granularity_column as the join key for the time spine table when joining tables in MetricFlow. Here, the granularity of the standard_granularity_column is set at the column level, in this case, hour.

If you need to create a time spine table from scratch, you can do so by adding the following code to your dbt project. The example creates a time spine at a daily grain and an hourly grain. A few things to note when creating time spine models:

  • MetricFlow will use the time spine with the largest compatible granularity for a given query to ensure the most efficient query possible. For example, if you have a time spine at a monthly grain, and query a dimension at a monthly grain, MetricFlow will use the monthly time spine. If you only have a daily time spine, MetricFlow will use the daily time spine and date_trunc to month.
  • You can add a time spine for each granularity you intend to use if query efficiency is more important to you than configuration time, or storage constraints. For most engines, the query performance difference should be minimal and transforming your time spine to a coarser grain at query time shouldn't add significant overhead to your queries.
  • We recommend having a time spine at the finest grain used in any of your dimensions to avoid unexpected errors. i.e., if you have dimensions at an hourly grain, you should have a time spine at an hourly grain.

Example time-spine tables​

Daily​

metricflow_time_spine.sql

Daily (BigQuery)​

Use this model if you're using BigQuery. BigQuery supports DATE() instead of TO_DATE():

Hourly​

time_spine_hourly.sql
{{
    config(
        materialized = 'table',
    )
}}

with hours as (

    {{
        dbt.date_spine(
            'hour',
            "to_date('01/01/2000','mm/dd/yyyy')",
            "to_date('01/01/2025','mm/dd/yyyy')"
        )
    }}

),

final as (
    select cast(date_hour as timestamp) as date_hour
    from hours
)

select * from final
-- filter the time spine to a specific range
where date_day > dateadd(year, -4, current_timestamp()) 
and date_hour < dateadd(day, 30, current_timestamp())
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