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Reference checks

Last modified on 31-May-23

Use a reference check to validate that column contents match between datasets in the same data source.

See also: Compare data using SodaCL

checks for dim_department_group:
  - values in (department_group_name) must exist in dim_employee (department_name)

Define reference checks
    Failed row samples
Optional check configurations
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Define reference checks

In the context of SodaCL check types, reference checks are unique. This check is limited in its syntax variation, with only a few mutable parts to specify column and dataset names.

The example below checks that the values in the source column, department_group_name, in the dim_department_group dataset exist somewhere in the destination column, department_name, in the dim_employee dataset. If the values are absent in the department_name column, the check fails.

  • Soda CL considers missing values in the source column as invalid.
  • Optionally, do not use brackets around column names. The brackets serve as visual aids to improve check readability.
checks for dim_department_group:
  - values in (department_group_name) must exist in dim_employee (department_name)

Failed row samples

Reference checks automatically collect samples of any failed rows to display Soda Cloud. The default number of failed row samples that Soda collects and displays is 100.

If you wish to limit or broaden the sample size, you can use the samples limit configuration in a reference check configuration. You can add this configuration to your checks YAML file for Soda Core, or when writing checks as part of an agreement in Soda Cloud.

checks for dim_customers:
  - values in (state_code, state_name) must exist in iso_3166-2 (code, subdivision_name):
      samples limit: 20


For security, you can add a configuration to your data source connection details to prevent Soda from collecting failed rows samples from specific columns that contain sensitive data. Refer to Disable failed rows sampling for specific columns.

Alternatively, you can set the samples limit to 0 to prevent Soda from collecting and sending failed rows samples for an individual check, as in the following example.

checks for dim_customers:
  - values in (state_code, state_name) must exist in iso_3166-2 (code, subdivision_name):
      samples limit: 0


To review the failed rows in Soda Cloud, navigate to the Checks dashboard, then click the row for a reference check. Examine failed rows in the Failed rows tab; see Examine failed rows for further details.

Optional check configurations

Supported Configuration Documentation
✓ Define a name for a reference check; see example. Customize check names
✓ Add an identity to a check. Add a check identity
  Define alert configurations to specify warn and fail alert conditions. -
  Apply an in-check filter to return results for a specific portion of the data in your dataset. -
✓ Use quotes when identifying dataset or column names; see example.
Note that the type of quotes you use must match that which your data source uses. For example, BigQuery uses a backtick (`) as a quotation mark.		 Use quotes in a check
  Use wildcard characters ( % or * ) in values in the check. -
  Use for each to apply reference checks to multiple datasets in one scan. -
✓ Apply a dataset filter to partition data during a scan; see example. Scan a portion of your dataset

Example with check name 

checks for dim_department_group:
  - values in (department_group_name) must exist in dim_employee (department_name):
      name: Compare department datasets

Example with quotes 

checks for dim_department_group:
  - values in ("department_group_name") must exist in dim_employee ("department_name")

Example with dataset filter 

filter customers_c8d90f60 [daily]:
  where: ts > TIMESTAMP '${NOW}' - interval '100y'

checks for customers_c8d90f60 [daily]:
  - values in (cat) must exist in customers_europe (cat2)

Refer to Troubleshoot SodaCL to address challenges specific to reference checks with dataset filters.


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Last modified on 31-May-23