Schema checks

Last modified on 29-Mar-23

Use a schema check to validate the presence, absence or position of columns in a dataset, or to validate the type of data column contains. Read more about SodaCL metrics and checks in general.

checks for dim_product:
  - schema:
      name: Confirm that required columns are present
      warn:
        when required column missing: [weight_unit_measure_code, product_subcategory_key, made_up_column]
      fail:
        when required column missing:
          - product_key 
          - product_alternate_key
  - schema:
      warn:
        when forbidden column present: [credit_card]
        when wrong column type:
          standard_cost: money
      fail:
        when forbidden column present: [pii*]
        when wrong column type:
          reorder_point: smallint
  - schema:
      name: Columns out of order
      warn:
        when wrong column index:
          style: 1
      fail:
        when wrong column index:
          model_name: 22
# Requires a Soda Cloud account
  - schema:
      name: Any schema changes
      warn: 
        when schema changes: any

Define schema checks
Optional check configurations
List of validation keys
Expect one check result
Go further

Define schema checks

In the context of SodaCL check types, schema checks are unique. Schema checks always employ alert configurations – specifying warn and/or fail alert conditions – with validation keys that are unique to this type of check. Refer to Add alert configurations for exhaustive alert configuration details.

The validation key:value pairs in schema 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 the when required column missing validation key to validate that specific columns are present in a dataset; if any of columns in the list are absent, the check result is fail.

checks for dim_product:
  - schema:
      fail:
        when required column missing:
          - standard_cost
          - list_price
          - weight

In the example above, the value for the validation key is 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:
  - schema:
      fail:
        when required column missing: [standard_cost, list_price, weight]

You can define a schema 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 schema 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. Note that unlike the nested list of column names in the example above, the nested key:value pairs that form the value for these validation keys are indented, but do not use a -.

checks for dim_product:
  - schema:
      warn:
        when forbidden column present: [standard_cost]
        when wrong column type:
          standard_cost: money
          weight: double precision
      fail:
        when forbidden column present: [sombrero]
        when wrong column type:
          reorder_point: smallint

Define schema evolution checks

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

This type of validation key requires a Soda Cloud account. If you have connected Soda Core to a Soda Cloud account, Soda Core pushes check results to your cloud account where Soda Cloud stores all the previously-measured, historic values for your checks in the Cloud Metric Store. SodaCL can then use these stored values to establish a relative state against which to evaluate future schema checks. Therefore, you must have a created and connected a Soda Cloud account to use schema evolution checks.

Soda Cloud must have at least two measurements to yield a check result. In other words, the first time you run a scan to execute a schema evolution check, Soda returns no results because it has nothing against which to compare; the second scan that executes the check yields a check result.

checks for dim_customer:
  - schema:
      warn:
        when schema changes: any
      fail:
        when schema changes: 
         - column delete
         - column add
         - column index change
         - column type change

Optional check configurations

Example with check name

checks for dim_product:
  - schema:
      name: Confirm that required columns are present
      warn:
        when required column missing: [weight_unit_measure_code, product_subcategory_key]

Example with alert configuration

checks for dim_product:
  - schema:
      warn:
        when forbidden column present: [standard_cost]

Example with quotes

checks for dim_product:
  - schema:
      warn:
        when wrong column type:
          standard_cost: "money"

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.

checks for dim_product:
  - schema:
      fail:
        when forbidden column present:
          - credit_card
          - obsolete_%
          - '%SALARY%'
          - pii*

Example with for each

for each dataset T:
  datasets:
    - dim_product_%
  checks:
    schema:
      warn:
        when schema changes: any

Example with dataset filter

filter CUSTOMERS [daily]:
  where: TIMESTAMP '{ts_start}' <= "ts" AND "ts" < TIMESTAMP '${ts_end}'

checks for CUSTOMERS [daily]:
  - schema:
      fail:
        when forbidden column present:
          - credit_card

List of validation keys

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.

Using the following example, Soda Core, 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 warn.

checks for dim_employee:
  - schema:
      warn:
        when required column missing: [not_so_important_column]
        when forbidden column present: [birth_date]

Soda Core 3.0.xxx
Scan summary:
1/1 check WARNED: 
    dim_employee in adventureworks
      schema [WARNED]
        missing_column_names = [not_so_important_column]
        forbidden_present_column_names = [birth_date]
        schema_measured = [employee_key integer, ...]
Only 1 warning. 0 failure. 0 errors. 0 pass.
Sending results to Soda Cloud

Adding to the example check above, 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_employee:
  - schema:
      warn:
        when required column missing: [not_so_important_column]
        when forbidden column present: [birth_date]
      fail:
        when required column missing: [very_important_column]

Soda Core 3.0.xxx
Scan summary:
1/1 check FAILED: 
    dim_employee in adventureworks
      schema [FAILED]
        missing_column_names = [very_important_column]
        schema_measured = [employee_key integer, ...]
Oops! 1 failures. 0 warnings. 0 errors. 0 pass.
Sending results to Soda Cloud

Go further

• Learn more about SodaCL metrics and checks in general.
• Use a reference check to validate matching contents between datasets.
• Need help? Join the Soda community on Slack.
• Reference tips and best practices for SodaCL.
  

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Last modified on 29-Mar-23