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Expected Values Check

Definition

Asserts that the value in the selected field is contained in a predefined list of accepted values.

Overview

The Expected Values rule enforces a closed vocabulary on a single column. The check compares each row's value against a list configured on the rule and flags every row whose value is not in the list. Typical use cases include:

  • Enforcing status enums (order_status, payment_state).
  • Restricting a country, currency, or locale code to an allowed set.
  • Validating short categorical fields (tier, channel, segment).
  • Asserting that an array column contains only elements from a known vocabulary.

NULL values pass the check. The rule only evaluates non-NULL values against the list, so an Expected Values check on a nullable field never fires on missing values. Pair it with a Not Null check on the same field when missingness is also a violation.

Field Scope

Single: The rule evaluates one field per check.

Accepted Types

Type Supported
Date
Timestamp
Integral
Fractional
String
Boolean

For Date and Timestamp, the list is compared as strings against the column's rendered value. See How It Works for details and recommended formats.

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.

Specific Properties

Specify the predefined list of values that the field is allowed to contain.

Name Description
List
 The predefined set of accepted values. Every non-NULL value in the selected field must match one of these entries exactly (case-sensitive, no whitespace trimming).

Info

The List input flags entries with leading or trailing whitespace. A clean value like ship renders as a normal chip; values like ship or ship get a yellow chip background so the extra space stands out at a glance. A warning icon also appears next to the field title. Hover the icon to see the full list of values with extra spaces.

The chip is still saved exactly as typed. Re-enter the value without the surrounding spaces if the engine is meant to match it literally, since the comparison is exact and does not trim.

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 on NULL handling, type coercion, array support, filter and coverage behavior, and resulting anomalies.

    How It Works

  • Examples

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

    Examples

  • API

    Payload shape and field notes for creating an Expected Values check programmatically.

    API

  • FAQ

    Short answers to questions about case-sensitivity, NULL handling, array support, and anomaly reporting.

    FAQ