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Greater Than Field

Definition

Asserts that the field is greater 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.
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_TOTALPRICE entries in the ORDERS table are greater than their respective O_DISCOUNT.

Sample Data

O_ORDERKEY O_TOTALPRICE O_DISCOUNT
1 100 105
2 500 10
3 120 121
{
    "description": "Ensure that all O_TOTALPRICE entries in the ORDERS table are greater than their respective O_DISCOUNT",
    "coverage": 1,
    "properties": {
        "field_name": "O_DISCOUNT",
        "inclusive": true
    },
    "tags": [],
    "fields": ["O_TOTALPRICE"],
    "additional_metadata": {"key 1": "value 1", "key 2": "value 2"},
    "rule": "greaterThanField",
    "container_id": {container_id},
    "template_id": {template_id},
    "filter": "1=1"
}

Anomaly Explanation

In the sample data above, the entries with O_ORDERKEY 1 and 3 do not satisfy the rule because their O_TOTALPRICE values are not greater than their respective O_DISCOUNT values.

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

Potential Violation Messages

Record Anomaly

The O_TOTALPRICE value of 100 is not greater than the value of O_DISCOUNT.

Shape Anomaly

In O_TOTALPRICE, 66.667% of 3 filtered records (2) are not greater than O_DISCOUNT.


Last update: June 14, 2024