Group evolution checks

Use a SodaCL group evolution data quality check to validate changes to the categorical groups you defined.

This feature is not supported in Soda Core OSS. Migrate to Soda Library in minutes to start using this feature for free with a 45-day trial.

Use a group evolution check to validate the presence or absence of a group in a dataset, or to check for changes to groups in a dataset relative to their previous state.

checks for dim_customer:
  - group evolution:
      name: Marital status
      query: |
        SELECT marital_status FROM dim_employee GROUP BY marital_status
      warn:
        when required group missing: [M]
        when forbidden group present: [T]
      fail:
        when groups change: any

✖️ Requires Soda Core Scientific (included in a Soda Runner) ✖️ Supported in Soda Core ✔️ Supported in Soda Library + Soda Cloud ✔️ Supported in Soda Cloud Agreements + Soda Runner ✖️ Available as a no-code check

Define group evolution checks

In the context of SodaCL check types, group by checks are unique. Evolution checks always employ a custom SQL query and an alert configuration – specifying warn and/or fail alert conditions – with validation keys. Refer to Add alert configurations for exhaustive alert configuration details.

The validation key:value pairs in group evolution checks set the conditions for a warn or a fail check result. See a List of validation keys below.

For example, the following check uses a group by configuration to execute a check on a dataset and return check results in groups. In a group evolution check, the when required group missing validation key confirms that specific groups are present in a dataset; if any of groups in the list are absent, the check result is warn.

checks for dim_product:
  - group by:
      query: |
        SELECT style, AVG(days_to_manufacture) as rare
        FROM dim_product 
        GROUP BY style
      fields:
        - style
      checks:
        - rare > 3:
            name: Rare

  - group evolution:
      query: | 
        SELECT style FROM dim_product GROUP BY style
      warn:
        when required group missing:
          - U
          - W

In the example above, the values for the validation key are in a nested list format, but you can use an inline list of comma-separated values inside square brackets instead. The following example yields identical checks results to the example above.

checks for dim_product:
  - group evolution:
      query: | 
        SELECT style FROM dim_product GROUP BY style
      warn:
        when required group missing: [U, W]

You can define a group evolution check with both warn and fail alert conditions, each with multiple validation keys. Refer to Configure multiple alerts for details. Be aware, however, that a single group evolution check only ever produces a single check result. See Expect one check result below for details.

The following example is a single check; Soda executes each of its validations during a scan and returns a single result for the check: pass, warn, or fail.

checks for dim_employee:
  - group evolution:
      name: Marital status
      query: |
        SELECT marital_status FROM dim_employee GROUP BY marital_status
      warn:
        when required group missing: [M]
        when forbidden group present: [S]
      fail:
        when required group missing: [T]

Define group changes

Rather than specifying exact parameters for group changes, you can use the when groups change validation key to warn or fail when indistinct changes occur in a dataset.

Soda Cloud must have at least two measurements to yield a check result for group changes. In other words, the first time you run a scan to execute a group evolution check, Soda does not evaluate the check because it has nothing against which to compare; the second scan that executes the check yields a check result.

- group evolution:
    name: Rare product
    query: | 
      SELECT style FROM dim_product GROUP BY style
    warn:
      when groups change: any
    fail:
      when groups change: 
        - group delete
        - group add

Optional check configurations

Supported	Configuration	Documentation
✓	Define a name for a group evolution 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; see example.	Add alert configurations
	Apply an in-check filter to return results for a specific portion of the data in your dataset.	-
✓	Use quotes when identifying dataset or group 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; see example.	See note in example below.
	Use for each to apply group evolution checks to multiple datasets in one scan.	-
	Apply a dataset filter to partition data during a scan.	-

Example with check name

- group evolution:
    name: Rare product
    query: | 
      SELECT style FROM dim_product GROUP BY style
    warn:
      when groups change: any

Example with alert configuration

Be aware that Soda only ever returns a single check result per check. See Expect one check result for details.

- group evolution:
    name: Rare product
    query: | 
      SELECT style FROM dim_product GROUP BY style
    warn:
      when forbidden column present: [T]
    fail:
      when groups change: 
        - group delete
        - group add

Example with quotes

- group evolution:
    name: Marital status
    query: |
      SELECT marital_status FROM "dim_employee" GROUP BY marital_status
    warn:
      when required group missing: ["M"]
      when forbidden group present: ["T"]

Example with wildcards

You can use * or % as wildcard characters in a list of column names. If the column name begins with a wildcard character, add single quotes as per the example below.

- group evolution:
    name: Rare product
    query: | 
      SELECT style FROM dim_product GROUP BY style
    warn:
      when forbidden group present: [T%]

List of validation keys

Validation key	Values
when required group missing	one or more group names in an inline list of comma-separated values, or a nested list
when forbidden group present	one or more group names in an inline list of comma-separated values, or a nested list
when groups change	any as an inline value group add as a nested list item group delete as a nested list item

Expect one check result

Be aware that a check that contains one or more alert configurations only ever yields a single check result; one check yields one check result. If your check triggers both a warn and a fail, the check result only displays the more severe, failed check result. (Schema checks behave slightly differently; see Schema checks.)

Using the following example, Soda Library, during a scan, discovers that the data in the dataset triggers both alerts, but the check result is still Only 1 warning. Nonetheless, the results in the CLI still display both alerts as having both triggered a [WARNED] state.

checks for dim_customer:
  - row_count:
      warn:
        when > 2
        when < 0

Soda Library 1.0.x
Soda Core 3.0.x
Scan summary:
1/1 check WARNED: 
    dim_customer in adventureworks
      row_count warn when > 2 when > 3 [WARNED]
        check_value: 18484
Only 1 warning. 0 failure. 0 errors. 0 pass.
Sending results to Soda Cloud
Soda Cloud Trace: 42812***

The check in the example below data triggers both warn alerts and the fail alert, but only returns a single check result, the more severe Oops! 1 failures.

checks for dim_product:
  - sum(safety_stock_level):
      name: Stock levels are safe
      warn:
        when > 0
      fail:
        when > 0

Soda Library 1.0.x
Soda Core 3.0.x
Scan summary:
1/1 check FAILED: 
    dim_product in adventureworks
      Stock levels are safe [FAILED]
        check_value: 275936
Oops! 1 failures. 0 warnings. 0 errors. 0 pass.
Sending results to Soda Cloud
Soda Cloud Trace: 6016***

Go further

• Use a group by configuration to categorize your check results into groups.
• Learn more about alert configurations.
• Learn more about SodaCL metrics and checks in general.
• Reference tips and best practices for SodaCL.

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