# 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, available across the dbt Core or [dbt Fusion engine](../fusion/about-fusion.md). To upgrade to Fusion, see [Get started with Fusion](../fusion/get-started-fusion.md). MetricFlow allows you to define and query metrics in your dbt project in [dbt platform](../platform/about-develop-dbt.md) or [dbt Core](../local/install-dbt.md). To experience the power of the universal [Semantic Layer](../use-dbt-semantic-layer/dbt-sl.md) and dynamically query those metrics in downstream tools, you'll need a dbt [Starter, Enterprise, or Enterprise+](https://www.getdbt.com/pricing/) account. 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 in the dbt platform or dbt Core (open source), using either the dbt Fusion engine or dbt Core engine. ### MetricFlow in Fusion or dbt platform This section applies to dbt platform users running the dbt Fusion engine, where commands and validations execute remotely in dbt platform. * Run MetricFlow commands using the `dbt sl` prefix in the [Studio IDE](../platform/studio-ide/develop-in-studio.md) or [dbt CLI](../platform/dbt-cli-installation.md) or using the [VS Code extension](../install-dbt-extension.md). * For CLI or VS Code/Cursor users, MetricFlow commands are embedded, which means you can immediately run them once you install the [dbt CLI](../platform/dbt-cli-installation.md) or [VS Code extension](../install-dbt-extension.md) 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](../deploy/ci-jobs.md#semantic-validations-in-ci). 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 dbt Core This section applies to dbt Core users running the dbt Core engine or users running [source available](https://www.getdbt.com/dbt-fusion-engine-license-agreement) Fusion locally and aren't on dbt platform. You can install [MetricFlow](https://github.com/dbt-labs/metricflow#getting-started) from [PyPI](https://pypi.org/project/dbt-metricflow/). 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 between dbt Core, your adapter, and MetricFlow. 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 CLI or VS Code extension Fusion only | dbt Fusion engine or dbt Core engine | dbt platform | `dbt sl` | Remote execution; Platform manages versions.
VS Code extension users must have a [`dbt_cloud.yml`](../../reference/dbt_cloud.yml.md) file with a valid token to connect to dbt platform | | Open-source
(no dbt platform project) | Fusion (source available) or
dbt Core engine | Local machine | `mf` | Install and manage MetricFlow locally. | * If you’re using Fusion with dbt platform and have a [`dbt_cloud.yml`](../../reference/dbt_cloud.yml.md) 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](../platform/studio-ide/develop-in-studio.md) or [dbt CLI](../platform/dbt-cli-installation.md)). * If you’re using [Fusion CLI](../local/install-dbt.md?version=2) ([source available](https://www.getdbt.com/dbt-fusion-engine-license-agreement)) and aren't connected to dbt platform, install MetricFlow separately and use the `mf` prefix to run commands locally. * If you’re using dbt Core locally without Fusion, run MetricFlow commands using the `mf` prefix. ### Commands for dbt platform This section applies to dbt platform users running the dbt Fusion engine or dbt Core 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 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 CLI) powered by the dbt Fusion engine or dbt Core engine: | Command | Description | Studio IDE | dbt CLI | VS Code/Cursor | | ------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | ------- | -------------- | | [`list metrics`](#list-metrics) | Lists metrics with dimensions. | ✅ | ✅ | ✅ | | [`list dimensions`](#list) | Lists unique dimensions for metrics. | ✅ | ✅ | ✅ | | [`list dimension-values`](#list-dimension-values) | List dimensions with metrics. | ✅ | ✅ | ✅ | | [`list entities`](#list-entities) | Lists all unique entities. | ✅ | ✅ | ✅ | | [`list saved-queries`](#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) | Query metrics, saved queries, and dimensions you want to see in the command line interface. Refer to [query examples](#query-examples) to query metrics and dimensions (such as querying metrics, using the `where` filter, adding an `order`, and more). | ✅ | ✅ | ✅ | | [`validate`](#validate) | Validates semantic model configurations. | ✅ | ✅ | ✅ | | [`export`](#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`](#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 CLI: ### Commands for dbt Core This section applies to dbt Core users running the dbt Core engine or users running [source available](https://www.getdbt.com/dbt-fusion-engine-license-agreement) Fusion locally and not a dbt platform user. 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`](#list-metrics) — Lists metrics with dimensions. * [`list dimensions`](#list) — Lists unique dimensions for metrics. * [`list dimension-values`](#list-dimension-values) — List dimensions with metrics. * [`list entities`](#list-entities) — Lists all unique entities. * [`validate-configs`](#validate-configs) — Validates semantic model configurations. * [`health-checks`](#health-checks) — Performs data platform health check. * [`tutorial`](#tutorial) — Dedicated MetricFlow tutorial to help get you started. * [`query`](#query) — Query metrics and dimensions you want to see in the command line interface. Refer to [query examples](#query-examples) to help you get started. ## List metrics This command lists the metrics with their available dimensions: ```bash dbt sl list metrics # For dbt platform users (Core or Fusion engine) mf list metrics # For open-source users (Core or Fusion source available) 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: ```bash dbt sl list dimensions --metrics # For dbt platform users (Core or Fusion engine) mf list dimensions --metrics # For open-source users (Core or Fusion source available) 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: ```bash dbt sl list dimension-values --metrics --dimension # For dbt platform users (Core or Fusion engine) mf list dimension-values --metrics --dimension # For open-source users (Core or Fusion source available) 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/Fusion yet --start-time TEXT Optional iso8601 timestamp to constraint the start time of the data (inclusive) *Not available in in the dbt platform/Fusion yet --help Show this message and exit. ``` ## List entities This command lists all unique entities: ```bash dbt sl list entities --metrics # For dbt platform users (Core or Fusion engine) mf list entities --metrics # For open-source users (Core or Fusion source available) 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: ```bash dbt sl list saved-queries # For dbt platform users (Core or Fusion engine) ``` You can also add the `--show-exports` flag (or option) to show each export listed under a saved query: ```bash dbt sl list saved-queries --show-exports # For dbt platform users (Core or Fusion engine) ``` **Output** ```bash 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 Fusion and dbt users in the dbt platform CLI or locally with a valid [`dbt_cloud.yml`](../../reference/dbt_cloud.yml.md): ```bash dbt sl validate ``` * For open-source users (Core or Fusion source available): ```bash mf validate-configs ``` ```bash Options: --timeout # dbt platform only Optional timeout for data warehouse validation in dbt platform. --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, the `health-checks` command isn't required since it uses dbt's credentials to perform the health check. ```bash mf health-checks # For open-source users (Core or Fusion source available) ``` ## Tutorial Follow the dedicated MetricFlow tutorial to help you get started: ```bash mf tutorial # For open-source users (Core or Fusion source available) ``` ## Query Create a new query with MetricFlow and execute it against your data platform. The query returns the following result: ```bash dbt sl query --metrics --group-by # For dbt platform users (Core or Fusion engine) dbt sl query --saved-query # For dbt platform users (Core or Fusion engine) mf query --metrics --group-by # For open-source users (Core or Fusion source available) 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/Fusion yet --start-time TEXT Optional iso8601 timestamp to constraint the start time of the data (inclusive) *Not available in the dbt platform/Fusion 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/Fusion) 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 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-metrics) * [Query dimensions](#query-dimensions) * [Add `order`/`limit` function](#add-orderlimit) * [Add `where` clause](#add-where-clause) * [Filter by time](#filter-by-time) * [Query saved queries](#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** ```bash dbt sl query --metrics order_total,users_active --group-by metric_time # For dbt platform users (Core or Fusion engine) mf query --metrics order_total,users_active --group-by metric_time # For open-source users (Core or Fusion source available) ``` **Result** ```bash ✔ 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** ```bash dbt sl query --metrics order_total --group-by order_id__is_food_order # For dbt platform users (Core or Fusion engine) mf query --metrics order_total --group-by order_id__is_food_order # For open-source users (Core or Fusion source available) ``` **Result** ```bash 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** ```bash # For dbt platform users (Core or Fusion engine) dbt sl query --metrics order_total --group-by order_id__is_food_order --limit 10 --order-by -metric_time # For open-source users (Core or Fusion source available) mf query --metrics order_total --group-by order_id__is_food_order --limit 10 --order-by -metric_time ``` **Result** ```bash ✔ 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** ```bash # For dbt platform users (Core or Fusion engine) 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 open-source users (Core or Fusion source available) 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** ```bash ✔ 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** ```bash # For open-source users (Core or Fusion source available) 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** ```bash ✔ 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 `` with the name of your [saved query](./saved-queries.md). **Query** ```bash dbt sl query --saved-query # For dbt platform users (Core or Fusion engine) mf query --saved-query # For open-source users (Core or Fusion source available) ``` 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](./saved-queries.md), 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 dbt Core users) to your query to view the SQL generated by MetricFlow. **Query** ```bash # For dbt platform users (Core or Fusion engine) 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 open-source users (Core or Fusion source available) 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** ```bash ✔ 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 in dbt Core only and not supported in dbt. **Query** ```bash # For open-source users (Core or Fusion source available) 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** ```bash ✔ 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: ```bash dbt sl query --metrics revenue --group-by metric_time__month # For dbt platform users (Core or Fusion engine) mf query --metrics revenue --group-by metric_time__month # For open-source users (Core or Fusion source available) ``` ## Export Run [exports for a specific saved query](../use-dbt-semantic-layer/exports.md#exports-for-single-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](../use-dbt-semantic-layer/exports.md#exports-in-development) for more info. Export is available in dbt. ```bash dbt sl export # For dbt platform users (Core or Fusion engine) ``` ## Export-all Run [exports for multiple saved queries](../use-dbt-semantic-layer/exports.md#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](../use-dbt-semantic-layer/exports.md#exports-in-development) for more info. Export is available in dbt. ```bash dbt sl export-all # For dbt platform users (Core or Fusion engine) ``` ## 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 `.zshrc`profile 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`: ```bash nano ~/.zshrc ``` 3. Add the following line to the file: ```bash 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: ```bash 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 CLI is 100 rows. We set this default to prevent returning unnecessarily large data sets as the dbt 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? YesNo [Privacy policy](https://www.getdbt.com/cloud/privacy-policy)[Create a GitHub issue](https://github.com/dbt-labs/docs.getdbt.com/issues) This site is protected by reCAPTCHA and the Google [Privacy Policy](https://policies.google.com/privacy) and [Terms of Service](https://policies.google.com/terms) apply.