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

DetailsFilter Guide

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.
Comparators	Specifies how variations are handled, allowing for slight deviations within a defined margin of error.

Details

Comparators

The Comparators allow you to set margins of error, accommodating slight variations in data validation. This flexibility is crucial for maintaining data integrity, especially when working with different data types such as numeric values, durations, and strings. Here's an overview of how each type of comparator can be beneficial for you:

Numeric

Numeric comparators enable you to compare numbers with a specified margin, which can be a fixed absolute value or a percentage. This allows for minor numerical differences that are often acceptable in real-world data.

Comparison Type

Absolute Value: Uses a fixed threshold for determining equality. It's ideal when you need consistent precision across measurements.
Percentage Value: Uses a percentage of the original value as the threshold for equality comparisons. It's suitable for floating point numbers where precision varies.

Threshold

The threshold is the value you set to define the margin of error:

When using Absolute Value, the threshold represents the maximum allowable difference between two values for them to be considered equal.
For Percentage Value, the threshold is the percentage that describes how much a value can deviate from a reference value and still be considered equal.

Illustration using Absolute Value

In this example, it is being compared Value A and Value B according to the defined Threshold of 50.

Value A	Value B	Difference	Are equal?
100	150	50	True
100	90	10	True
100	155	55	False
100	49	51	False

Illustration using Percentage Value

In this example, it is being compared Value A and Value B according to the defined Threshold of 10%.

Percentage Change Formula: [ (Value B - Value A) / Value A ] * 100

Value A	Value B	Percentage Change	Are equal?
120	132	10%	True
150	135	10%	True
200	180	10%	True
160	150	6.25%	True
180	200	11.11%	False

Duration

Duration comparators support time-based comparisons, allowing for flexibility in how duration differences are managed. This flexibility is crucial for datasets where time measurements are essential but can vary slightly.

Unit

The unit of time you select determines how granular the comparison is:

Millis: Measures time in milliseconds, ideal for high-precision needs.
Seconds: Suitable for most general purposes where precision is important but doesn't need to be to the millisecond.
Days: Best for longer durations.

Value

Value sets the maximum acceptable difference in time to consider two values as equal. It serves to define the margin of error, accommodating small discrepancies that naturally occur over time.

Illustration using Duration Comparator

Unit	Value A	Value B	Difference	Threshold	Are equal?
Millis	500 ms	520 ms	20 ms	25 ms	True
Seconds	30 sec	31 sec	1 sec	2 sec	True
Days	5 days	7 days	2 days	1 day	False
Millis	1000 ms	1040 ms	40 ms	25 ms	False
Seconds	45 sec	48 sec	3 sec	2 sec	False

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

Payload example

{
    "description": "Ensure that all O_DISCOUNT entries in the ORDERS table are less than their respective O_TOTALPRICE",
    "coverage": 1,
    "properties": {
        "field_name": "O_TOTALPRICE",
        "inclusive":true
    },
    "tags": [],
    "fields": ["O_DISCOUNT"],
    "additional_metadata": {"key 1": "value 1", "key 2": "value 2"},
    "rule": "lessThanField",
    "container_id": {container_id},
    "template_id": {template_id},
    "filter": "1=1"
}

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.

FlowchartSQL

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.