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User Defined Function

Definition

Asserts that the given user-defined function (as scala script) evaluates to true over the field's value.

In-Depth Overview

The User Defined Function rule enables the application of a custom Scala function on a specified field, allowing for highly customizable and flexible validation based on user-defined logic.

Field Scope

Single: The rule evaluates a single specified field.

Accepted Types

Type
String

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

Implements a user-defined scala script.

Name Description
Scala Script
 The custom scala script to evaluate each record.
Note

The Scala script must contain a function that should return a boolean value, determining the validity of the record based on the field's value.

Below is a scaffold to guide the creation of the Scala function:

(field: String) => {
  // Your custom logic goes here
}

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: Validate that each record in the LINEITEM table has a well-structured JSON in the L_ATTRIBUTES column by ensuring the presence of essential keys: "color", "weight", and "dimensions".

Sample Data

L_ORDERKEY L_LINENUMBER L_ATTRIBUTES
1 1 {"color": "red", "weight": 15, "dimensions": "10x20x15"}
2 2 {"color": "blue", "weight": 20}
3 1 {"color": "green", "dimensions": "5x5x5"}
4 3 {"weight": 10, "dimensions": "20x20x20"}
Inputs

Scala Script

(lAttributes: String) => {
  import play.api.libs.json._

  try {
    val json = Json.parse(lAttributes)

    // Define the keys we expect to find in the JSON
    val expectedKeys = List("color", "weight", "dimensions")

    // Check if the expected keys are present in the JSON
    expectedKeys.forall(key => (json \ key).toOption.isDefined)
  } catch {
    case e: Exception => false // Return false if parsing fails
  }
}

Anomaly Explanation

In the sample data above, the entries with L_ORDERKEY 2, 3, and 4 do not satisfy the rule because they lack at least one of the essential keys ("color", "weight", "dimensions") in the L_ATTRIBUTES column.

graph TD
A[Start] --> B[Retrieve L_ATTRIBUTES]
B --> C{Does L_ATTRIBUTES contain all essential keys?}
C -->|Yes| D[Move to Next Record/End]
C -->|No| E[Mark as Anomalous]
E --> D

Potential Violation Messages

Record Anomaly

The L_ATTRIBUTES value of {"color": "blue", "weight": 20} does not evaluate true as a parameter to the given UDF.

Shape Anomaly

In L_ATTRIBUTES, 75.000% of 4 filtered records (3) do not evaluate true as a parameter to the given UDF.

Last update: April 7, 2024