Any Not Null Check
Definition
Asserts that at least one of the selected fields must hold a value.
Overview
The Any Not Null rule evaluates a set of fields together and requires that, on every row, at least one of those fields is populated. A row passes as soon as any selected field carries a non-NULL value, and fails only when every selected field is NULL. The rule is useful when several fields are individually optional but the row is considered incomplete if none of them are present.
Typical use cases:
- Require at least one contact method (
phone,email, ormobile). - Ensure that alternative identifier fields such as
national_idorpassport_numberare not all missing. - Guarantee that at least one fallback value column is populated when a primary source is optional.
Field Scope
Multiple: The rule is designed for two or more fields per check. A row passes if at least one of them holds a non-NULL value.
Accepted Types
Any Not Null accepts every field type. NULL is treated the same across types, so the check does not restrict which columns can be included.
General Properties
| Name | Supported |
|---|---|
Filter Allows the targeting of specific data based on conditions |
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Coverage Customization Allows adjusting the percentage of records that must meet the rule's conditions |
The filter allows you to define a subset of data upon which the rule will operate.
It requires a valid Spark SQL expression that determines the criteria rows in the DataFrame should meet. This means the expression specifies which rows the DataFrame should include based on those criteria. Since it's applied directly to the Spark DataFrame, traditional SQL constructs like WHERE clauses are not supported.
Examples
Direct Conditions
Simply specify the condition you want to be met.
Combining Conditions
Combine multiple conditions using logical operators like AND and OR.
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Utilizing Functions
Leverage Spark SQL functions to refine and enhance your conditions.
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Using scan-time variables
To refer to the current dataframe being analyzed, use the reserved dynamic variable {{_qualytics_self}}.
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While subqueries can be useful, their application within filters in our context has limitations. For example, directly referencing other containers or the broader target container in such subqueries is not supported. Attempting to do so will result in an error.
Important Note on {{_qualytics_self}}
The {{_qualytics_self}} keyword refers to the dataframe that's currently under examination. In the context of a full scan, this variable represents the entire target container. However, during incremental scans, it only reflects a subset of the target container, capturing just the incremental data. It's crucial to recognize that in such scenarios, using {{_qualytics_self}} may not encompass all entries from the target container.
Anomaly Types
| Type | Supported |
|---|---|
| Record Flag inconsistencies at the row level |
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| Shape Flag inconsistencies in the overall patterns and distributions of a field |
Next Steps
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How It Works
Full semantics: evaluation flow, NULL handling, filter behavior, coverage, anomaly templates, and how Any Not Null relates to other rule types.
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Examples
Three production scenarios with sample data, anomaly messages, and the SQL equivalent of what the check evaluates.
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API
Payload shape and field notes for creating an Any Not Null check programmatically.
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FAQ
Short answers to questions about NULLs, coverage, filters, and anomaly reporting.