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Any Not Null

Use the Any Not Null rule when business logic requires at least one field in a group to contain a value. This rule is ideal for optional-but-required data scenarios where multiple fields exist, but at least one must be populated.

What is Any Not Null?

Think of Any Not Null as a “minimum information required” check for your data.

It makes sure that at least one important field in a record is filled.
If all selected fields are empty, that record is considered invalid and gets flagged.

In simple terms: At least one of these fields must have a value.

This rule is especially useful when multiple optional fields exist, but having none of them makes the record unusable.

Add Any Not Null Check

Use the Any Not Null check when you want to ensure that records are not completely blank across a group of related fields.

This helps detect:

  • Incomplete records
  • Broken data ingestion pipelines
  • UI or API issues where optional fields are skipped entirely

What Does Any Not Null Do?

It answers questions like:

  • “Did this record capture any meaningful information at all?”
  • “Are users submitting forms without filling any contact details?”
  • “Is my system creating placeholder rows with no real data?”

In short: It prevents empty or useless records from silently entering your system.

How Does Any Not Null Work?

Step 1: Select Multiple Fields

You choose a set of related fields, such as:

  • Email
  • Phone number
  • Username

Step 2: Rule Evaluation

For each record:

  • If at least one field has a value → ✅ Pass
  • If all selected fields are NULL → 🚨 Anomaly

Step 3: Anomaly Reporting

Any record that fails the rule is flagged and appears in the anomaly results.

Why Should You Use Any Not Null?

1. Stop Empty Records Early

Empty rows can:

  • Break downstream analytics
  • Inflate row counts
  • Cause confusion during audits

This rule blocks them immediately.

2. Improve Data Quality at the Source

If data is missing here, it’s usually a form, API, or ingestion issue.
Any Not Null helps you catch it where it starts.

3. Protect Reporting & Automation

Automations, alerts, and reports rely on at least one usable field.
This check ensures records are worth processing.

Real-Life Example: Orders Missing Required Context After System Update

The Situation

SunriseMart is an online retail company that processes thousands of customer orders every day.
Each order is stored in the ORDERS table and is used by multiple teams:

  • Order fulfillment
  • Customer support
  • Sales and revenue reporting

For every order, SunriseMart expects at least one of the following fields to be present:

  • O_COMMENT – customer or system notes
  • O_ORDERSTATUS – order state such as Pending, Shipped, or Cancelled

Individually, these fields are optional — but having both missing makes the order unusable.

The Problem They Faced

After deploying a backend update, the operations team noticed something unusual:

  • Some orders were appearing in reports
  • But fulfillment teams could not process them
  • Customer support could not identify their status

On investigation, they discovered that:

  • A background job was creating order records
  • The job populated technical fields like O_ORDERKEY, timestamps, and metadata
  • But failed to populate both O_COMMENT and O_ORDERSTATUS

This issue went unnoticed at first because:

  • The table contained millions of rows
  • The problematic records were mixed with valid ones
  • Manually checking each record was not feasible

Why Manual Checking Didn’t Work

Without an automated rule, the team had to:

  • Write manual SQL queries
  • Scan large result sets
  • Re-run checks repeatedly as new data arrived

This approach was:

  • Time-consuming
  • Easy to miss edge cases
  • Not scalable as data volume increased

By the time an issue was found, downstream systems had already consumed the bad data.

The Solution: Any Not Null

To solve this, the data team implemented an Any Not Null check on:

  • O_COMMENT
  • O_ORDERSTATUS

The rule enforces a simple requirement:

At least one of these fields must contain a value for every order record.

What the Check Detected

When the check ran, it immediately flagged anomalous records where:

  • O_COMMENT = NULL
  • O_ORDERSTATUS = NULL

Example anomalous record:

O_COMMENT O_ORDERSTATUS O_ORDERKEY
NULL NULL 1034599

These records appeared under Failed Checks with a clear violation message:

There is no value set for any of O_COMMENT and O_ORDERSTATUS

anomaly-detail

What This Confirmed

The Any Not Null check confirmed that:

  • Orders were being created without any meaningful context
  • The issue originated from the ingestion layer
  • The problem was systematic, not a one-off error

The Outcome

Immediate Benefits

  • Invalid order records were detected automatically
  • No manual scanning or ad-hoc queries were required
  • Engineers quickly identified and fixed the faulty job

Long-Term Benefits

  • Every order now contains at least one usable field
  • Fulfillment and support workflows work reliably
  • Data quality issues are caught early instead of downstream
  • Trust in reporting and analytics was restored

Key Takeaway

Any Not Null acts as a safety net that prevents contextless records from silently entering the system, replacing slow and unreliable manual validation with automated enforcement.

Field Scope

Multiple: The rule evaluates multiple specified fields.

Accepted Types

Type
Date
Timestamp
Integral
Fractional
String
Boolean

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

Example

Objective: Ensure that for every record in the ORDERS table, at least one of the fields (O_COMMENT, O_ORDERSTATUS) isn't null.

Sample Data

O_ORDERKEY O_COMMENT O_ORDERSTATUS
1 NULL NULL
2 Good product NULL
3 NULL Shipped 
{
    "description": "Ensure that for every record in the ORDERS table, at least one of the fields (O_COMMENT, O_ORDERSTATUS) isn't null",
    "coverage": 1,
    "properties": {},
    "tags": [],
    "fields": ["O_ORDERSTATUS","O_COMMENT"],
    "additional_metadata": {"key 1": "value 1", "key 2": "value 2"},
    "rule": "anyNotNull",
    "container_id": {container_id},
    "template_id": {template_id},
    "filter": "_PARITY = 'odd'"
}

Anomaly Explanation

In the sample data above, the entry with O_ORDERKEY 1 does not satisfy the rule because both O_COMMENT and O_ORDERSTATUS do not hold a value.

graph TD
A[Start] --> B[Retrieve O_COMMENT and O_ORDERSTATUS]
B --> C{Is Either Field Not Null?}
C -->|Yes| D[Move to Next Record/End]
C -->|No| E[Mark as Anomalous]
E --> D
-- An illustrative SQL query demonstrating the rule applied to example dataset(s).
select
    o_orderkey
    , o_comment
    , o_orderstatus
from orders 
where
    o_comment is null
    and o_orderstatus is null

Potential Violation Messages

Record Anomaly

There is no value set for any of O_COMMENT, O_ORDERSTATUS

Shape Anomaly

In O_COMMENT, O_ORDERSTATUS, 33.333% of 3 filtered records (1) have no value set for any of O_COMMENT, O_ORDERSTATUS