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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, or mobile).
  • Ensure that alternative identifier fields such as national_id or passport_number are 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
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.

Correct usage" collapsible="true
O_TOTALPRICE > 1000
C_MKTSEGMENT = 'BUILDING'
Incorrect usage" collapsible="true
WHERE O_TOTALPRICE > 1000
WHERE C_MKTSEGMENT = 'BUILDING'

Combining Conditions

Combine multiple conditions using logical operators like AND and OR.

Correct usage" collapsible="true
O_ORDERPRIORITY = '1-URGENT' AND O_ORDERSTATUS = 'O'
(L_SHIPDATE = '1998-09-02' OR L_RECEIPTDATE = '1998-09-01') AND L_RETURNFLAG = 'R'
Incorrect usage" collapsible="true
WHERE O_ORDERPRIORITY = '1-URGENT' AND O_ORDERSTATUS = 'O'
O_TOTALPRICE > 1000, O_ORDERSTATUS = 'O'

Utilizing Functions

Leverage Spark SQL functions to refine and enhance your conditions.

Correct usage" collapsible="true
RIGHT(
    O_ORDERPRIORITY,
    LENGTH(O_ORDERPRIORITY) - INSTR('-', O_ORDERPRIORITY)
) = 'URGENT'
LEVENSHTEIN(C_NAME, 'Supplier#000000001') < 7
Incorrect usage" collapsible="true
RIGHT(
    O_ORDERPRIORITY,
    LENGTH(O_ORDERPRIORITY) - CHARINDEX('-', O_ORDERPRIORITY)
) = 'URGENT'
EDITDISTANCE(C_NAME, 'Supplier#000000001') < 7

Using scan-time variables

To refer to the current dataframe being analyzed, use the reserved dynamic variable {{_qualytics_self}}.

Correct usage" collapsible="true
O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM {{_qualytics_self}}
    WHERE O_TOTALPRICE > 1000
)
Incorrect usage" collapsible="true
O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM ORDERS
    WHERE O_TOTALPRICE > 1000
)

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
Shape
Flag inconsistencies in the overall patterns and distributions of a field

Next Steps

  • How It Works


    Full semantics: evaluation flow, NULL handling, filter behavior, coverage, anomaly templates, and how Any Not Null relates to other rule types.

    How It Works

  • Examples


    Three production scenarios with sample data, anomaly messages, and the SQL equivalent of what the check evaluates.

    Examples

  • API


    Payload shape and field notes for creating an Any Not Null check programmatically.

    API

  • FAQ


    Short answers to questions about NULLs, coverage, filters, and anomaly reporting.

    FAQ