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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 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 support the dbt sl validate command to automatically test your semantic nodes. You can also 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.

In dbt Cloud, run MetricFlow commands directly in the dbt Cloud IDE or in the dbt Cloud CLI.

For dbt Cloud CLI users, MetricFlow commands are embedded in the dbt Cloud CLI, which 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 because your dbt Cloud account will automatically manage the versioning for you.

Note: The Defer to staging/production toggle button doesn't apply when running Semantic Layer commands in the dbt Cloud IDE. To use defer for Semantic layer commands in the IDE, toggle the button on and manually add the --defer flag to the command. This is a temporary workaround and will be available soon.

MetricFlow commands

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

You can use the dbt sl prefix before the command name to execute them in the dbt Cloud IDE or dbt Cloud CLI. For example, to list all metrics, run dbt sl list metrics.

dbt Cloud CLI users can run dbt sl --help in the terminal for a complete list of the MetricFlow commands and flags.

The following table lists the commands compatible with the dbt Cloud IDE and dbt Cloud CLI:

Command
Description
dbt Cloud IDEdbt Cloud CLI
list metricsLists metrics with dimensions.
list dimensionsLists unique dimensions for metrics.
list dimension-valuesList dimensions with metrics.
list entitiesLists all unique entities.
list saved-queriesLists available saved queries. Use the --show-exports flag to display each export listed under a saved query or --show-parameters to show the full query parameters each saved query uses.
queryQuery metrics, saved queries, and dimensions you want to see in the command line interface. Refer to query examples to help you get started.
validateValidates semantic model configurations.
exportRuns exports for a singular saved query for testing and generating exports in your development environment. You can also use the --select flag to specify particular exports from a saved query.
export-allRuns exports for multiple saved queries at once, saving time and effort.
Run dbt parse to reflect metric changes

When you make changes to metrics, make sure to run dbt parse at a minimum to update the dbt Semantic Layer. This updates the semantic_manifest.json file, reflecting your changes when querying metrics. By running dbt parse, you won't need to rebuild all the models.

 How can I query or preview metrics with the dbt Cloud CLI?

List metrics

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)
*Not available in dbt Cloud yet
--start-time TEXT Optional iso8601 timestamp to constraint the start time
of the data (inclusive)
*Not available in dbt Cloud yet
--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.

List saved queries

This command lists all available saved queries:

dbt sl list saved-queries

You can also add the --show-exports flag (or option) to show each export listed under a saved query:

dbt sl list saved-queries --show-exports

Output

dbt sl list saved-queries --show-exports

The list of available saved queries:
- new_customer_orders
exports:
- Export(new_customer_orders_table, exportAs=TABLE)
- Export(new_customer_orders_view, exportAs=VIEW)
- Export(new_customer_orders, alias=orders, schemas=customer_schema, exportAs=TABLE)

Validate

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

dbt sl validate # For dbt Cloud users
mf validate-configs # For dbt Core users

Options:
--timeout # dbt Cloud only
Optional timeout for data warehouse validation in dbt Cloud.
--dw-timeout INTEGER # dbt Core only
Optional timeout for data warehouse
validation steps. Default None.
--skip-dw # dbt Core only
Skips the data warehouse validations.
--show-all # dbt Core only
Prints warnings and future errors.
--verbose-issues # dbt Core only
Prints extra details about issues.
--semantic-validation-workers INTEGER # dbt Core only
Uses specified number of workers for 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 and execute it against your data platform. The query returns the following result:

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

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

Options:

--metrics SEQUENCE Syntax to query single metrics: --metrics metric_name
For example, --metrics bookings
To query multiple metrics, use --metrics followed by the metric names, separated by commas without spaces.
For example, --metrics bookings,messages

--group-by SEQUENCE Syntax to group by single dimension/entity: --group-by dimension_name
For example, --group-by ds
For multiple dimensions/entities, use --group-by followed by the dimension/entity names, separated by commas without spaces.
For example, --group-by ds,org


--end-time TEXT Optional iso8601 timestamp to constraint the end
time of the data (inclusive).
*Not available in dbt Cloud yet

--start-time TEXT Optional iso8601 timestamp to constraint the start
time of the data (inclusive)
*Not available in dbt Cloud yet

--where TEXT SQL-like where statement provided as a string and wrapped in quotes: --where "condition_statement"
For example, to query a single statement: --where "revenue > 100"
To query multiple statements: --where "revenue > 100 and user_count < 1000"
To add a dimension filter to a where filter, ensure 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-by SEQUENCE Specify metrics, dimension, or group bys to order by.
Add the `-` prefix to sort query in descending (DESC) order.
Leave blank for ascending (ASC) order.
For example, to sort metric_time in DESC order: --order-by -metric_time
To sort metric_time in ASC order and revenue in DESC order: --order-by metric_time,-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 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 multiple metrics by dimension and return the order_total and users_active metrics by metric_time.

Query

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

mf query --metrics order_total,users_active --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-by -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-by -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

Export

Run exports for a specific saved query. Use this command to test and generate exports in your development environment. You can also use the --select flag to specify particular exports from a saved query. Refer to exports in development for more info.

Export is available in dbt Cloud.

dbt sl export 

Export-all

Run exports for multiple saved queries at once. This command provides a convenient way to manage and execute exports for several queries simultaneously, saving time and effort. Refer to exports in development for more info.

Export is available in dbt Cloud.

dbt sl export-all 

FAQs

 How can I add a dimension filter to a where filter?
 Why is my query limited to 100 rows in the dbt Cloud CLI?
 How can I query multiple metrics, group bys, or where statements?
 How can I sort my query in ascending or descending order?
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