Equal To Field
Definition
Asserts that a field is equal to 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.
Combining Conditions
Combine multiple conditions using logical operators like AND
and OR
.
Correct usage
Incorrect usage
Utilizing Functions
Leverage Spark SQL functions to refine and enhance your conditions.
Correct usage
Incorrect usage
Using scan-time variables
To refer to the current dataframe being analyzed, use the reserved dynamic variable {{_qualytics_self}}
.
Correct usage
Incorrect usage
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 field to compare for equality with the selected field.
Name | Description |
---|---|
Field to compare |
The field name whose values should match those of the selected field. |
Anomaly Types
Type | Supported |
---|---|
Record Flag inconsistencies at the row level |
|
Shape Flag inconsistencies in the overall patterns and distributions of a field |
Example
Scenario: An e-commerce platform sells digital products. The shipping date (when the digital product link is sent) should always be the same as the delivery date (when the customer acknowledges receiving the product).
Objective: Ensure that the O_SHIPDATE in the ORDERS table matches its delivery date O_DELIVERYDATE.
Sample Data
O_ORDERKEY | O_SHIPDATE | O_DELIVERYDATE |
---|---|---|
1 | 1998-01-04 | 1998-01-04 |
2 | 1998-01-14 | 1998-01-15 |
3 | 1998-01-12 | 1998-01-12 |
Anomaly Explanation
In the sample data above, the entry with O_ORDERKEY
2 does not satisfy the rule because its O_SHIPDATE
of 1998-01-14 does not match the O_DELIVERYDATE
of 1998-01-15.
graph TD
A[Start] --> B[Retrieve O_SHIPDATE and O_DELIVERYDATE]
B --> C{Is O_SHIPDATE = O_DELIVERYDATE?}
C -->|Yes| D[Move to Next Record/End]
C -->|No| E[Mark as Anomalous]
E --> D
Potential Violation Messages
Record Anomaly
The O_SHIPDATE
value of 1998-01-14 is not equal to the value of O_DELIVERYDATE which is 1998-01-15.
Shape Anomaly
In O_SHIPDATE
, 33.333% of the filtered fields are not equal.