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Satisfies Expression

Definition

Evaluates the given expression (any valid Spark SQL) for each record.

In-Depth Overview

The Satisfies Expression rule allows for a wide range of custom validations on the dataset. By defining a Spark SQL expression, you can create customized conditions that the data should meet.

This rule will evaluate an expression against each record, marking those that do not satisfy the condition as anomalies. It provides the flexibility to create complex validation logic without being restricted to predefined rule structures.

Field Scope

Calculated: The rule automatically identifies the fields involved, without requiring explicit field selection.

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

Evaluates each record against a specified Spark SQL expression to ensure it meets custom validation conditions.

Name Description
Expression
Defines the Spark SQL expression that each record should meet.

Info

Refers to the Filter Guide in the General Properties topic for examples of valid Spark SQL expressions.

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 the total tax applied to each item in the LINEITEM table is not more than 10% of the extended price.

Sample Data

L_ORDERKEY L_LINENUMBER L_EXTENDEDPRICE L_TAX
1 1 10000 900
2 1 15000 2000
3 1 20000 1800
4 1 10000 1500
Inputs
  • Expression: L_TAX <= L_EXTENDEDPRICE * 0.10
{
    "description": "Ensure that the total tax applied to each item in the LINEITEM table is not more than 10% of the extended price",
    "coverage": 1,
    "properties": {
        "expression":"L_TAX <= L_EXTENDEDPRICE * 0.10"
        },
    "tags": [],
    "fields": ["L_TAX", "L_EXTENDEDPRICE"],
    "additional_metadata": {"key 1": "value 1", "key 2": "value 2"},
    "rule": "satisfiesExpression",
    "container_id": {container_id},
    "template_id": {template_id},
    "filter": "1=1"
}

Anomaly Explanation

In the sample data above, the entries with L_ORDERKEY 2 and 4 do not satisfy the rule because the L_TAX values are more than 10% of their respective L_EXTENDEDPRICE values.

graph TD
A[Start] --> B[Retrieve L_EXTENDEDPRICE and L_TAX]
B --> C{Is L_TAX <= L_EXTENDEDPRICE * 0.10?}
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
    l_orderkey,
    l_linenumber,
    l_extendedprice,
    l_tax
from
    lineitem 
where
    l_tax > l_extendedprice * 0.10;

Potential Violation Messages

Record Anomaly

The record does not satisfy the expression: L_TAX <= L_EXTENDEDPRICE * 0.10

Shape Anomaly

50.000% of 4 filtered records (2) do not satisfy the expression: L_TAX <= L_EXTENDEDPRICE * 0.10


Last update: June 14, 2024