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Less Than Field

Definition

Asserts that the field is less than another field.

Field Scope

Single: The rule evaluates a single specified field.

Accepted Types

Type
Date
Timestamp
Integral
Fractional

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
O_TOTALPRICE > 1000
C_MKTSEGMENT = 'BUILDING'
Incorrect usage
WHERE O_TOTALPRICE > 1000
WHERE C_MKTSEGMENT = 'BUILDING'

Combining Conditions

Combine multiple conditions using logical operators like AND and OR.

Correct usage
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
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
RIGHT(
    O_ORDERPRIORITY,
    LENGTH(O_ORDERPRIORITY) - INSTR('-', O_ORDERPRIORITY)
) = 'URGENT'
LEVENSHTEIN(C_NAME, 'Supplier#000000001') < 7
Incorrect usage
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
O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM {{_qualytics_self}}
    WHERE O_TOTALPRICE > 1000
)
Incorrect usage
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

Allows specifying another field against which the value comparison will be performed.

Name Description
Field to compare
Specifies the name of the field against which the value will be compared.
Inclusive
If true, the comparison will also allow values equal to the value of the other field. Otherwise, it's exclusive.

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 all O_DISCOUNT entries in the ORDERS table are less than their respective O_TOTALPRICE.

Sample Data

O_ORDERKEY O_TOTALPRICE O_DISCOUNT
1 105 100
2 500 10
3 121 125

Anomaly Explanation

In the sample data above, the entry with O_ORDERKEY 3 does not satisfy the rule because its O_DISCOUNT value is not less than its respective O_TOTALPRICE value.

graph TD
A[Start] --> B[Retrieve O_TOTALPRICE and O_DISCOUNT]
B --> C{Is O_DISCOUNT < O_TOTALPRICE?}
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_totalprice,
    o_discount
from orders 
where
    o_discount >= o_totalprice;

Potential Violation Messages

Record Anomaly

The O_DISCOUNT value of 125 is not less than the value of O_TOTALPRICE.

Shape Anomaly

In O_DISCOUNT, 33.333% of 3 filtered records (1) is not less than O_TOTALPRICE.


Last update: February 27, 2024