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

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

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 1: Satisfies Expression Using a `CASE` Statement

Let's assume you want to ensure that for orders with a priority of '1-URGENT' or '2-HIGH', the orderstatus must be 'O' (for open), and for orders with a priority of '3-MEDIUM', the orderstatus must be either 'O' or 'P' (for pending).

 CASE
    WHEN o_orderpriority IN ('1-URGENT', '2-HIGH') AND o_orderstatus != 'O' THEN FALSE
    WHEN o_orderpriority = '3-MEDIUM' AND o_orderstatus NOT IN ('O', 'P') THEN FALSE
    ELSE TRUE
 END

Example 2: Satisfies Expression Using a `Relatively Complex CTE` Statement

Objective:: To ensure that the overall effect of discounts on item prices remains within acceptable limits, we validate whether the average discounted price of all items is greater than the maximum discount applied to any single item.

Background:

In pricing analysis, it’s important to monitor how discounts affect the final prices of products. By comparing the average price after discounts with the maximum discount applied, we can assess whether the discounts are having an overly significant impact or if they are within a reasonable range.

CASE 
        WHEN (SELECT AVG(l_extendedprice * (1 - l_discount)) FROM lineitem) > 
             (SELECT MAX(l_discount) FROM {{ _qualytics_self }}) 
        THEN TRUE 
        ELSE FALSE 
END AS is_discount_within_limits

Use Case

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

Payload example

{
    "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.

FlowchartSQL

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

Satisfies Expression

Definition

In-Depth Overview

Field Scope

General Properties

Specific Properties

Anomaly Types

Example 1: Satisfies Expression Using a CASE Statement

Example 2: Satisfies Expression Using a Relatively Complex CTE Statement

Use Case

Example 1: Satisfies Expression Using a `CASE` Statement

Example 2: Satisfies Expression Using a `Relatively Complex CTE` Statement