MetricFlow commands

MetricFlow allows you to define and query metrics in your dbt project in dbt platform or self-hosted dbt locally. To experience the power of the universal Semantic Layer and dynamically query those metrics in downstream tools, you'll need a dbt Starter, Enterprise, or Enterprise+ account.

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

MetricFlow is compatible with Python versions 3.8, 3.9, 3.10, 3.11, and 3.12.

MetricFlow

(Applies to dbt v1.12 and later)

MetricFlow is a Python library that allows you to define and query metrics in your dbt project. You can use MetricFlow to query metrics in your dbt project two ways: in the dbt platform or locally with self-hosted dbt, where you install and manage the engine yourself.

MetricFlow in dbt platform

This section applies to dbt platform users running either the v1 or v2 engine, where commands and validations execute remotely in dbt platform.

• Run MetricFlow commands using the dbt sl prefix in the Studio IDE or dbt platform CLI or using the VS Code extension.
• For CLI or VS Code/Cursor users, MetricFlow commands are embedded, which means you can immediately run them once you install the dbt platform CLI or VS Code extension and don't need to install MetricFlow separately.
• Using MetricFlow with dbt platform doesn't require you to manage versioning — your dbt account will automatically manage the versioning.
• dbt 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.

MetricFlow with self-hosted dbt

This section applies to local self-hosted dbt users who aren't on dbt platform.

You can install MetricFlow from PyPI. You need to use pip to install MetricFlow on Windows or Linux operating systems:

1. Create or activate your virtual environment python -m venv venv.
2. Run pip install dbt-metricflow.

• You can install MetricFlow using PyPI as an extension of your dbt adapter in the command line. To install the adapter, run python -m pip install "dbt-metricflow[adapter_package_name]" and add the adapter name at the end of the command. For example, for a Snowflake adapter, run python -m pip install "dbt-metricflow[dbt-snowflake]".

Note, you'll need to manage versioning yourself: between dbt, your adapter, and MetricFlow if you execute with v1, or between dbt and MetricFlow if you execute with v2.

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

Use MetricFlow commands to retrieve metadata and query metrics. The following table lists the compatibility matrix for MetricFlow commands and where you can run them.

Development setup	Engine	Hosted on	Prefix	Notes
Studio IDE/dbt platform CLI or VS Code extension	dbt v2 or v1	dbt platform	dbt sl	Remote execution; Platform manages versions. VS Code extension users must have a dbt_cloud.yml file with a valid token to connect to dbt platform
Self-hosted (no dbt platform project)	dbt v2 or v1	Local machine	mf	Install and manage MetricFlow locally.

• If you’re using v2 with dbt platform and have a dbt_cloud.yml file with a valid token to connect to dbt platform, run MetricFlow commands using the dbt sl prefix.
  • This allows you to interact with metrics that are executed remotely on dbt platform (for example, from the Studio IDE or dbt platform CLI).
• If you’re using v2 CLI and aren't connected to dbt platform, install MetricFlow separately and use the mf prefix to run commands locally.
• If you’re using v1 locally without v2, run MetricFlow commands using the mf prefix.

Commands for dbt platform

This section applies to dbt platform users running the v2 or v1 engine where commands and validations execute remotely in dbt platform.

• Use the dbt sl prefix before the command name to execute them in the dbt platform (Studio IDE, VS Code/Cursor, dbt platform CLI) (like dbt sl list metrics to list all metrics).
  • For dbt platform users developing with a CLI or an editor (like VS Code), run the dbt sl --help command in the terminal to view a complete list of the MetricFlow commands and flags.
• The following table lists the commands compatible with dbt platform (Studio IDE, VS Code/Cursor, dbt platform CLI) powered by the v2 or v1 engine:

Command	Description	Studio IDE	dbt platform CLI	VS Code/Cursor
list metrics	Lists metrics with dimensions.	✅	✅	✅
list dimensions	Lists unique dimensions for metrics.	✅	✅	✅
list dimension-values	List dimensions with metrics.	✅	✅	✅
list entities	Lists all unique entities.	✅	✅	✅
list saved-queries	Lists 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.	✅	✅	✅
query	Query metrics, saved queries, and dimensions you want to see in the command line interface. Refer to query examples to query metrics and dimensions (such as querying metrics, using the where filter, adding an order, and more).	✅	✅	✅
validate	Validates semantic model configurations.	✅	✅	✅
export	Runs 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-all	Runs 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 Semantic Layer. This updates the semantic_manifest.json file(Applies to dbt v1.12 and later) and osi_document.json, 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 platform CLI?

Check out the following video for a short video demo of how to query or preview metrics with the dbt platform CLI:

Commands for local self-hosted dbt

This section applies to local self-hosted users who aren't on dbt platform, whether you execute with v1 or v2. Commands and validations execute locally and use the mf prefix before the command name to execute them. For example, to list all metrics, run mf list metrics.

• list metrics — Lists metrics with dimensions.
• list dimensions — Lists unique dimensions for metrics.
• list dimension-values — List dimensions with metrics.
• list entities — Lists all unique entities.
• validate-configs — Validates semantic model configurations.
• health-checks — Performs data platform health check.
• tutorial — Dedicated MetricFlow tutorial to help get you started.
• query — Query metrics and dimensions you want to see in the command line interface. Refer to query examples to help you get started.

List metrics

This command lists the metrics with their available dimensions:

dbt sl list metrics <metric_name>  # For dbt platform users (dbt v1 or v2)
mf list metrics <metric_name> # For self-hosted users (dbt v1 or v2)


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>  # For dbt platform users (dbt v1 or v2) 

mf list dimensions --metrics <metric_name> # For self-hosted users (dbt v1 or v2)


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>  # For dbt platform users (dbt v1 or v2) 

mf list dimension-values --metrics <metric_name> --dimension <dimension_name> # For self-hosted users (dbt v1 or v2)


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 the dbt platform/dbt v2 yet
  --start-time TEXT   Optional iso8601 timestamp to constraint the start time
                      of the data (inclusive)
                      *Not available in the dbt platform/dbt v2 yet
  --help              Show this message and exit.

List entities

This command lists all unique entities:

dbt sl list entities --metrics <metric_name>  # For dbt platform users (dbt v1 or v2) 

mf list entities --metrics <metric_name> # For self-hosted users (dbt v1 or v2)


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 # For dbt platform users (dbt v1 or v2) 

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 # For dbt platform users (dbt v1 or v2) 

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.

• For dbt v2 and dbt platform CLI users with a valid dbt_cloud.yml:

  dbt sl validate

• For self-hosted users (dbt v1 or v2):

  mf validate-configs

Options:

  --timeout                       # dbt platform only
                                  Optional timeout for data warehouse validation in dbt platform.
  --dw-timeout INTEGER            # dbt v1 only
                                  Optional timeout for data warehouse
                                  validation steps. Default None.
  --skip-dw                       # dbt v1 only
                                  Skips the data warehouse validations.
  --show-all                      # dbt v1 only
                                  Prints warnings and future errors.
  --verbose-issues                # dbt v1 only
                                  Prints extra details about issues.
  --semantic-validation-workers INTEGER  # dbt v1 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, the health-checks command isn't required since it uses dbt's credentials to perform the health check.

mf health-checks # For self-hosted users (dbt v1 or v2)

Tutorial

Follow the dedicated MetricFlow tutorial to help you get started:

mf tutorial # For self-hosted users (dbt v1 or v2)

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>  # For dbt platform users (dbt v1 or v2) 
dbt sl query --saved-query <name> # For dbt platform users (dbt v1 or v2) 

mf query --metrics <metric_name> --group-by <dimension_name> # For self-hosted users (dbt v1 or v2)


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 the dbt platform/dbt v2 yet 

  --start-time TEXT        Optional iso8601 timestamp to constraint the start
                           time of the data (inclusive)
                           *Not available in the dbt platform/dbt v2 yet

  --where TEXT             SQL-like where statement provided as a string and wrapped in quotes.
                           All filter items must explicitly reference fields or dimensions that are part of your model.
                           To query a single statement: ---where "{{ Dimension('order_id__revenue') }} > 100"
                           To query multiple statements: --where "{{ Dimension('order_id__revenue') }} > 100" --where "{{ Dimension('user_count') }} < 1000" # make sure to wrap each statement in quotes
                           To add a dimension filter, use the `Dimension()` template wrapper 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-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 platform/dbt v2)          In the query output, show the query that was
 --explain (dbt v1)     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

This section shares various types of query examples that you can use to query metrics and dimensions. The query examples listed are:

• Query metrics
• Query dimensions
• Add order/limit function
• Add where clause
• Filter by time
• Query saved queries

Query metrics

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 # For dbt platform users (dbt v1 or v2) 

mf query --metrics order_total,users_active --group-by metric_time # For self-hosted users (dbt v1 or v2)

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 |

Query dimensions

You can include multiple dimensions in a query. For example, you can group by the is_food_order dimension to confirm if orders were for food or not. Note that when you query a dimension, you need to specify the primary entity for that dimension. In the following example, the primary entity is order_id.

Query

dbt sl query --metrics order_total --group-by order_id__is_food_order # For dbt platform users (dbt v1 or v2) 

mf query --metrics order_total --group-by order_id__is_food_order # For self-hosted users (dbt v1 or v2)

Result

 Success 🦄 - query completed after 1.70 seconds
| METRIC_TIME   | IS_FOOD_ORDER   |   ORDER_TOTAL |
|:--------------|:----------------|---------------:|
| 2017-06-16    | True            |         499.27 |
| 2017-06-16    | False           |         292.90 |
| 2017-06-17    | True            |         431.24 |
| 2017-06-17    | False           |          27.11 |
| 2017-06-18    | True            |         466.45 |
| 2017-06-18    | False           |          24.24 |
| 2017-06-19    | False           |         300.98 |
| 2017-06-19    | True            |         448.11 |

Add order/limit

You can add order and limit functions to filter and present the data in a readable format. The following query limits the data set to 10 records and orders them by metric_time, descending. Note that using the - prefix will sort the query in descending order. Without the - prefix sorts the query in ascending order.

Note that when you query a dimension, you need to specify the primary entity for that dimension. In the following example, the primary entity is order_id.

Query

# For dbt platform users (dbt v1 or v2) 
dbt sl query --metrics order_total --group-by order_id__is_food_order --limit 10 --order-by -metric_time 

# For self-hosted users (dbt v1 or v2)

mf query --metrics order_total --group-by order_id__is_food_order --limit 10 --order-by -metric_time 

Result

✔ Success 🦄 - query completed after 1.41 seconds
| METRIC_TIME   | IS_FOOD_ORDER   |   ORDER_TOTAL |
|:--------------|:----------------|---------------:|
| 2017-08-31    | True            |         459.90 |
| 2017-08-31    | False           |         327.08 |
| 2017-08-30    | False           |         348.90 |
| 2017-08-30    | True            |         448.18 |
| 2017-08-29    | True            |         479.94 |
| 2017-08-29    | False           |         333.65 |
| 2017-08-28    | False           |         334.73 |

Add where clause

You can further filter the data set by adding a where clause to your query. The following example shows you how to query the order_total metric, grouped by is_food_order with multiple where statements (orders that are food orders and orders from the week starting on or after Feb 1st, 2024).

Query

# For dbt platform users (dbt v1 or v2) 
dbt sl query --metrics order_total --group-by order_id__is_food_order --where "{{ Dimension('order_id__is_food_order') }} = True" --where "{{ TimeDimension('metric_time', 'week') }} >= '2024-02-01'"

# For self-hosted users (dbt v1 or v2)

mf query --metrics order_total --group-by order_id__is_food_order --where "{{ Dimension('order_id__is_food_order') }} = True" --where "{{ TimeDimension('metric_time', 'week') }} >= '2024-02-01'"

Notes:

• The type of dimension changes the syntax you use. So if you have a date field, use TimeDimension instead of Dimension.
• When you query a dimension, you need to specify the primary entity for that dimension. In the example just shared, the primary entity is order_id.

Result

 ✔ Success 🦄 - query completed after 1.06 seconds
| METRIC_TIME   | IS_FOOD_ORDER   |   ORDER_TOTAL |
|:--------------|:----------------|---------------:|
| 2017-08-31    | True            |         459.90 |
| 2017-08-30    | True            |         448.18 |
| 2017-08-29    | True            |         479.94 |
| 2017-08-28    | True            |         513.48 |
| 2017-08-27    | True            |         568.92 |
| 2017-08-26    | True            |         471.95 |
| 2017-08-25    | True            |         452.93 |
| 2017-08-24    | True            |         384.40 |
| 2017-08-23    | True            |         423.61 |
| 2017-08-22    | True            |         401.91 |

Filter by time

To filter by time, there are dedicated start and end time options. Using these options to filter by time allows MetricFlow to further optimize query performance by pushing down the where filter when appropriate.

Note that when you query a dimension, you need to specify the primary entity for that dimension. In the following example, the primary entity is order_id.

Query

# For self-hosted users (dbt v1 or v2)

mf query --metrics order_total --group-by order_id__is_food_order --limit 10 --order-by -metric_time --where "is_food_order = True" --start-time '2017-08-22' --end-time '2017-08-27' 

Result

✔ Success 🦄 - query completed after 1.53 seconds
| METRIC_TIME   | IS_FOOD_ORDER   |   ORDER_TOTAL |
|:--------------|:----------------|---------------:|
| 2017-08-27    | True            |         568.92 |
| 2017-08-26    | True            |         471.95 |
| 2017-08-25    | True            |         452.93 |
| 2017-08-24    | True            |         384.40 |
| 2017-08-23    | True            |         423.61 |
| 2017-08-22    | True            |         401.91 |

Query saved queries

You can use this for frequently used queries. Replace <name> with the name of your saved query.

Query

dbt sl query --saved-query <name> # For dbt platform users (dbt v1 or v2) 

mf query --saved-query <name> # For self-hosted users (dbt v1 or v2)

For example, if you use dbt and have a saved query named new_customer_orders, you would run dbt sl query --saved-query new_customer_orders.

A note on querying saved queries

When querying saved queries, you can use parameters such as where, limit, order, compile, and so on. However, keep in mind that you can't access metric or group_by parameters in this context. This is because they are predetermined and fixed parameters for saved queries, and you can't change them at query time. If you would like to query more metrics or dimensions, you can build the query using the standard format.

Additional query examples

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

--compile/--explain flag

Add --compile (or --explain for v1 users) to your query to view the SQL generated by MetricFlow.

Query

# For dbt platform users (dbt v1 or v2) 
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

# For self-hosted users (dbt v1 or v2)

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

Export to CSV

Add the --csv file_name.csv flag to export the results of your query to a CSV. The --csv flag is available to local self-hosted users only (with the mf prefix) and isn't supported by dbt sl in dbt platform.

Query

# For self-hosted users (dbt v1 or v2)

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' --csv query_example.csv

Result

✔ Success 🦄 - query completed after 0.83 seconds
🖨 Successfully written query output to query_example.csv

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 # For dbt platform users (dbt v1 or v2) 

mf query --metrics revenue --group-by metric_time__month # For self-hosted users (dbt v1 or v2)

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.

dbt sl export # For dbt platform users (dbt v1 or v2)

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.

dbt sl export-all # For dbt platform users (dbt v1 or v2)

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.

Details

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

3. Add the following line to the file:

setopt BRACECCL

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

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

source ~/.zshrc

6. 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 platform CLI?

The default limit for query issues from the dbt platform CLI is 100 rows. We set this default to prevent returning unnecessarily large data sets as the dbt platform 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.

 How can I query multiple metrics, group bys, or where statements?

To query multiple metrics, group bys, or where statements in your command, follow this guidance:

• To query multiple metrics and group bys, use the --metrics or --group-by syntax followed by the metric or dimension/entity names, separated by commas without spaces:
  • Multiple metrics example: dbt sl query --metrics accounts_active,users_active
  • Multiple dimension/entity example: dbt sl query --metrics accounts_active,users_active --group-by metric_time__week,accounts__plan_tier
• To query multiple where statements, use the --where syntax and wrap the statement in quotes:
  • Multiple where statement example: dbt sl query --metrics accounts_active,users_active --group-by metric_time__week,accounts__plan_tier --where "metric_time__week >= '2024-02-01'" --where "accounts__plan_tier = 'coco'"

 How can I sort my query in ascending or descending order?

When you query metrics, use --order-by to specify metrics or groupings to order by. The order_by option applies to metrics, dimensions, and group bys.

Add the - prefix to sort your query in descending (DESC) order. Leave blank for ascending (ASC) order:

• For example, to query a metric and sort metric_time in descending order, run dbt sl query --metrics order_total --group-by metric_time --order-by -metric_time. Note that the - prefix in -metric_time sorts the query in descending order.
• To query a metric and sort metric_time in ascending order and revenue in descending order, run dbt sl query --metrics order_total --order-by metric_time,-revenue. Note that metric_time without a prefix is sorted in ascending order and -revenue with a - prefix sorts the query in descending order.

Was this page helpful?

Privacy policyCreate a GitHub issue

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.