# Active checks and datasets Soda’s licensing model can include volume-based measures of active checks, or a similar model based on active datasets. An **active dataset** is one for which Soda has at least one check, excluding empty datasets. A dataset counts as active if you add configuration for: * a check * automated monitoring checks; see [Add automated monitoring checks](/soda-documentation/soda-v3/soda-cl-overview/automated-monitoring.md) * an anomaly detection dashboard (available in 2025); see [Activate anomaly dashboards](/soda-documentation/soda-v3/collaborate/anomaly-dashboard.md) An **active check** is one that Soda has executed during a scan at least once in the past 90 days. A single check, whether it has been executed during one scan, fifty scans, or five hundred scans in the last 90 days counts as one active check. A single check is identifiable as a test that yields a single result. A check with one or more [alert configurations](/soda-documentation/soda-v3/sodacl-reference/optional-config.md#add-alert-configurations) counts as a single check. The following is an example of a single active check as it only ever yields one result: pass, warn, fail, or error. Note, A check that results in an error counts as an active check. Soda executes the check during a scan in order to yield a result; if the result is an error, it is still a result. ```yaml checks for dim_reseller: - duplicate_count(phone): warn: when between 1 and 10 fail: when > 10 ``` A check that is included as part of a [for each](/soda-documentation/soda-v3/sodacl-reference/for-each.md) configuration yields a single result for *each* dataset against which it is executed. The following example produces four check results and, thus, has four active checks and four active datasets. ```yaml for each dataset T: datasets: - dim_employee - dim_customer - dim_product - dim_reseller checks: - row_count: fail: when < 5 warn: when > 10 ``` \ Similarly, a single check that is included in a scan against two data sources, or two environments such as staging and production, counts as two active checks for two active datasets. The following example `checks.yml` file contains as a single check. The scan commands that follow instruct Soda to execute the check on two different environments which counts as two active checks for two active datasets. See also: [Configure the same scan in multiple environments](/soda-documentation/soda-v3/run-a-scan.md#configure-the-same-scan-to-run-in-multiple-environments). ```yaml checks for dim_customer: - row_count > 0 ``` ```sh soda scan -d snowflake_prod -c configuration.yml -s prod_run checks.yml soda scan -d snowflake_staging -c configuration.yml -s stage_run checks.yml ``` \ A check that involves data comparison between multiple datasets in the same, or different, data sources counts as a single check. The following example has four checks, two [cross checks](/soda-documentation/soda-v3/sodacl-reference/cross-row-checks.md) and two [reference checks](/soda-documentation/soda-v3/sodacl-reference/reference.md), and counts as two active datasets. ```yaml checks for dim_customer: - row_count same as dim_department_group - row_count same as retail_customers in aws_postgres_retail checks for dim_department_group: - values in (department_group_name) must exist in dim_employee (department_name) - values in (birthdate) must not exist in dim_department_group_prod (birthdate) ``` \ Similarly, a [reconciliation check](/soda-documentation/soda-v3/sodacl-reference/recon.md) that compares data between source and target datasets in the same, or different, data sources counts as a single active check running against a single active dataset which, for this type of check, is the target dataset. The following example has five active checks for five active datasets. ```yaml reconciliation Production: datasets: source: dataset: dim_customer datasource: mysql_adventureworks target: dataset: dim_customer datasource: snowflake_retail checks: - row_count diff = 0 - duplicate_count(last_name): fail: when diff > 10% warn: when diff is between 5% and 9% - avg(total_children) diff < 10 - rows diff < 5 - schema: types: - source: bit target: boolean - source: enum target: string ``` \ Where a check involves grouping its results by category, as in a [group by](/soda-documentation/soda-v3/sodacl-reference/group-by.md) configuration, the check itself still counts as a single check. The following example has one active check for one active dataset. ```yaml checks for fact_internet_sales: - group by: group_limit: 10 query: | SELECT sales_territory_key, AVG(discount_amount) as average_discount FROM fact_internet_sales GROUP BY sales_territory_key fields: - sales_territory_key checks: - average_discount: fail: when > 40 name: Average discount percentage is less than 40% (grouped-by sales territory) ``` ## Go further * Access information about [optional configurations](/soda-documentation/soda-v3/sodacl-reference/optional-config.md) that you can use in SodaCL checks. > Need help? Join the [Soda community on Slack](https://community.soda.io/slack). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.soda.io/soda-documentation/soda-v3/learning-resources/active-check.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.