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MetricFlow commands

Once you define metrics in your dbt project, you can query metrics, dimensions, and dimension values, and validate your configs using the MetricFlow commands.

MetricFlow allows you to define and query metrics in your dbt project in the dbt Cloud CLI, dbt Cloud IDE, or dbt Core. To experience the power of the universal dbt Semantic Layer and dynamically query those metrics in downstream tools, you'll need a dbt Cloud Team or Enterprise account.

MetricFlow is compatible with Python versions 3.8, 3.9, 3.10, and 3.11.

MetricFlow​

MetricFlow is a dbt package that allows you to define and query metrics in your dbt project. You can use MetricFlow to query metrics in your dbt project in the dbt Cloud CLI, dbt Cloud IDE, or dbt Core.

Using MetricFlow with dbt Cloud means you won't need to manage versioning — your dbt Cloud account will automatically manage the versioning.

dbt Cloud jobs — MetricFlow commands aren't supported in dbt Cloud jobs yet. However, you can add MetricFlow validations with your git provider (such as GitHub Actions) by installing MetricFlow (python -m pip install metricflow). This allows you to run MetricFlow commands as part of your continuous integration checks on PRs.

  • MetricFlow commands are embedded in the dbt Cloud CLI. This means you can immediately run them once you install the dbt Cloud CLI and don't need to install MetricFlow separately.
  • You don't need to manage versioning — your dbt Cloud account will automatically manage the versioning for you.

Something to note, MetricFlow mf commands return an error if you have a Metafont latex package installed. To run mf commands, uninstall the package.

MetricFlow commands​

MetricFlow provides the following commands to retrieve metadata and query metrics.

Use the dbt sl prefix before the command name to execute them in dbt Cloud. For example, to list all metrics, run dbt sl list metrics.

List​

This command retrieves metadata values related to Metrics, Dimensions, and Entities values.

List metrics​

dbt sl list # In dbt Cloud
mf list # In dbt Core

This command lists the metrics with their available dimensions:

dbt sl list metrics <metric_name> # In dbt Cloud

mf list metrics <metric_name> # In dbt Core

Options:
  --search TEXT          Filter available metrics by this search term
  --show-all-dimensions  Show all dimensions associated with a metric.
  --help                 Show this message and exit.

List dimensions​

This command lists all unique dimensions for a metric or multiple metrics. It displays only common dimensions when querying multiple metrics:

dbt sl list dimensions --metrics <metric_name> # In dbt Cloud

mf list dimensions --metrics <metric_name> # In dbt Core

Options:
  --metrics SEQUENCE  List dimensions by given metrics (intersection). Ex. --metrics bookings,messages
  --help              Show this message and exit.

List dimension-values​

This command lists all dimension values with the corresponding metric:

dbt sl list dimension-values --metrics <metric_name> --dimension <dimension_name> # In dbt Cloud

mf list dimension-values --metrics <metric_name> --dimension <dimension_name> # In dbt Core

Options:
  --dimension TEXT    Dimension to query values from  [required]
  --metrics SEQUENCE  Metrics that are associated with the dimension
                      [required]
  --end-time TEXT     Optional iso8601 timestamp to constraint the end time of
                      the data (inclusive)
  --start-time TEXT   Optional iso8601 timestamp to constraint the start time
                      of the data (inclusive)
  --help              Show this message and exit.

List entities​

This command lists all unique entities:

dbt sl list entities --metrics <metric_name> # In dbt Cloud 

mf list entities --metrics <metric_name> # In dbt Core

Options:
  --metrics SEQUENCE  List entities by given metrics (intersection). Ex. --metrics bookings,messages
  --help              Show this message and exit.

Validate-configs​

The following command performs validations against the defined semantic model configurations.

Note, in dbt Cloud you don't need to validate the Semantic Layer config separately. Running a dbt command (such as dbt parse, dbt build, dbt compile, dbt run) automatically checks it.


mf validate-configs # In dbt Core

Options:
  --dw-timeout INTEGER            Optional timeout for data warehouse
                                  validation steps. Default None.
  --skip-dw                       If specified, skips the data warehouse
                                  validations
  --show-all                      If specified, prints warnings and future-
                                  errors
  --verbose-issues                If specified, prints any extra details
                                  issues might have
  --semantic-validation-workers INTEGER
                                  Optional. Uses the number of workers
                                  specified to run the semantic validations.
                                  Should only be used for exceptionally large
                                  configs
  --help                          Show this message and exit.

Health checks​

The following command performs a health check against the data platform you provided in the configs.

Note, in dbt Cloud the health-checks command isn't required since it uses dbt Cloud's credentials to perform the health check.

mf health-checks # In dbt Core

Tutorial​

Follow the dedicated MetricFlow tutorial to help you get started:

mf tutorial # In dbt Core

Query​

Create a new query with MetricFlow, execute that query against the user's data platform, and return the result:

dbt sl query --metrics <metric_name> --group-by <dimension_name> # In dbt Cloud 

mf query --metrics <metric_name> --group-by <dimension_name> # In dbt Core

Options:

  --metrics SEQUENCE       Metrics to query for: syntax is --metrics bookings
                           or for multiple metrics --metrics bookings, messages.

  --group-by SEQUENCE      Dimensions and/or entities to group by: syntax is
                           --group-by ds or for multiple group bys --group-by
                           ds, org.

  --end-time TEXT          Optional iso8601 timestamp to constraint the end
                           time of the data (inclusive)

  --start-time TEXT        Optional iso8601 timestamp to constraint the start
                           time of the data (inclusive)

  --where TEXT             SQL-like where statement provided as a string. For
                           example: --where "revenue > 100". To add a dimension filter to 
                           a where filter, you have to indicate that the filter item is part of your model. 
                           Refer to the FAQ for more info on how to do this using a template wrapper.

  --limit TEXT             Limit the number of rows out using an int or leave
                           blank for no limit. For example: --limit 100

  --order SEQUENCE         Metrics or group bys to order by ("-" prefix for
                           DESC). For example: --order -ds or --order
                           ds,-revenue

  --csv FILENAME           Provide filepath for data frame output to csv

 --compile (dbt Cloud)    In the query output, show the query that was
 --explain (dbt Core)     executed against the data warehouse         
                           

  --show-dataflow-plan     Display dataflow plan in explain output

  --display-plans          Display plans (such as metric dataflow) in the browser

  --decimals INTEGER       Choose the number of decimal places to round for
                           the numerical values

  --show-sql-descriptions  Shows inline descriptions of nodes in displayed SQL

  --help                   Show this message and exit.

Query examples​

The following tabs present various different types of query examples that you can use to query metrics and dimensions. Select the tab that best suits your needs:

Use the example to query metrics by dimension and return the order_total metric by metric_time.

Query

dbt sl query --metrics order_total --group-by metric_time # In dbt Cloud

mf query --metrics order_total --group-by metric_time # In dbt Core

Result

✔ Success 🦄 - query completed after 1.24 seconds
| METRIC_TIME   |   ORDER_TOTAL |
|:--------------|---------------:|
| 2017-06-16    |         792.17 |
| 2017-06-17    |         458.35 |
| 2017-06-18    |         490.69 |
| 2017-06-19    |         749.09 |
| 2017-06-20    |         712.51 |
| 2017-06-21    |         541.65 |

Additional query examples​

The following tabs present additional query examples, like exporting to a CSV. Select the tab that best suits your needs:

Add --compile (or --explain for dbt Core users) to your query to view the SQL generated by MetricFlow.

Query

# In dbt Cloud
dbt sl query --metrics order_total --group-by metric_time,is_food_order --limit 10 --order -metric_time --where "is_food_order = True" --start-time '2017-08-22' --end-time '2017-08-27' --compile

# In dbt Core
mf query --metrics order_total --group-by metric_time,is_food_order --limit 10 --order -metric_time --where "is_food_order = True" --start-time '2017-08-22' --end-time '2017-08-27' --explain

Result

✔ Success 🦄 - query completed after 0.28 seconds
🔎 SQL (remove --compile to see data or add --show-dataflow-plan to see the generated dataflow plan):
SELECT
 metric_time
 , is_food_order
 , SUM(order_cost) AS order_total
FROM (
 SELECT
   cast(ordered_at as date) AS metric_time
   , is_food_order
   , order_cost
 FROM ANALYTICS.js_dbt_sl_demo.orders orders_src_1
 WHERE cast(ordered_at as date) BETWEEN CAST('2017-08-22' AS TIMESTAMP) AND CAST('2017-08-27' AS TIMESTAMP)
) subq_3
WHERE is_food_order = True
GROUP BY
 metric_time
 , is_food_order
ORDER BY metric_time DESC
LIMIT 10

Time granularity​

Optionally, you can specify the time granularity you want your data to be aggregated at by appending two underscores and the unit of granularity you want to metric_time, the global time dimension. You can group the granularity by: day, week, month, quarter, and year.

Below is an example for querying metric data at a monthly grain:

dbt sl query --metrics revenue --group-by metric_time__month # In dbt Cloud

mf query --metrics revenue --group-by metric_time__month # In dbt Core

FAQs​

How can I add a dimension filter to a where filter?

To add a dimension filter to a where filter, you have to indicate that the filter item is part of your model and use a template wrapper: {{Dimension('primary_entity__dimension_name')}}.

Here's an example query: dbt sl query --metrics order_total --group-by metric_time --where "{{Dimension('order_id__is_food_order')}} = True".

Before using the template wrapper, however, set up your terminal to escape curly braces for the filter template to work.

How to set up your terminal to escape curly braces?
To configure your .zshrcprofile to escape curly braces, you can use the setopt command to enable the BRACECCL option. This option will cause the shell to treat curly braces as literals and prevent brace expansion. Refer to the following steps to set it up:
  1. Open your terminal.
  2. Open your .zshrc file using a text editor like nano, vim, or any other text editor you prefer. You can use the following command to open it with nano:
nano ~/.zshrc
  1. Add the following line to the file:
setopt BRACECCL

  1. Save and exit the text editor (in nano, press Ctrl + O to save, and Ctrl + X to exit).

  2. Source your .zshrc file to apply the changes:

source ~/.zshrc
  1. After making these changes, your Zsh shell will treat curly braces as literal characters and will not perform brace expansion. This means that you can use curly braces without worrying about unintended expansions.

Keep in mind that modifying your shell configuration files can have an impact on how your shell behaves. If you're not familiar with shell configuration, it's a good idea to make a backup of your .zshrc file before making any changes. If you encounter any issues or unexpected behavior, you can revert to the backup.

Why is my query limited to 100 rows in the dbt Cloud CLI?
The default limit for query issues from the dbt Cloud CLI is 100 rows. We set this default to prevent returning unnecessarily large data sets as the dbt Cloud CLI is typically used to query the dbt Semantic Layer during the development process, not for production reporting or to access large data sets. For most workflows, you only need to return a subset of the data.

However, you can change this limit if needed by setting the --limit option in your query. For example, to return 1000 rows, you can run dbt sl list metrics --limit 1000.
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