Group evolution checks
Last modified on 06-Nov-24
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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 Agent)
✖️ Supported in Soda Core
✔️ Supported in Soda Library + Soda Cloud
✔️ Supported in Soda Cloud Agreements + Soda Agent
✖️ Available as a no-code check
Define group evolution checks
Optional check configurations
List of validation keys
Expect one check result
Go further
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 valuegroup add as a nested list itemgroup 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.
- Need help? Join the Soda community on Slack.
- Reference tips and best practices for SodaCL.
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Documentation always applies to the latest version of Soda products
Last modified on 06-Nov-24