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Exists In

Definition

Asserts that values assigned to a field exist as values in another field.

In-Depth Overview

The ExistsIn rule allows you to cross-validate data between different sources, whether it’s object storage systems or databases.

Traditionally, databases might utilize foreign key constraints (if available) to enforce data integrity between related tables. The ExistsIn rule extends this concept in two powerful ways:

  1. Cross-System Integrity: it allows for integrity checks to span across different databases or even entirely separate systems. This is particularly advantageous in scenarios where data sources are fragmented across diverse platforms.
  2. Flexible Data Formats: Beyond just databases, this rule can validate values against various data formats, such as ensuring values in a file align with those in a table.

These enhancements enable businesses to maintain data integrity even in complex, multi-system environments.

Field Scope

Single: The rule evaluates a single specified field.

Accepted Types

Type
Date
Timestamp
Integral
Fractional
String
Boolean

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 word _qualytics_${containerId} where ${containerId} should be replaced with the numeric identifier of the container.

Correct usage 
O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM qualytics_42
    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_${containerId}

The _qualytics_${containerId} keyword refers to the dataframe that's currently under examination. In the context of a full scan, this variable represents the entire target container. However, during incremental scans, it only reflects a subset of the target container, capturing just the incremental data. It's crucial to recognize that in such scenarios, using _qualytics_${containerId} may not encompass all entries from the target container.

Specific Properties

Define the datastore, table/file, and field where the rule should look for matching values.

Name Description
Datastore
 The source datastore where the profile of the reference field is located.
Table/file
 The profile (e.g. table, view or file) containing the reference field.
Field
 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

Objective: Ensure that all NATION_NAME entries in the NATION table match entries under the COUNTRY_NAME column in an external lookup file listing official country names.

Sample Data

N_NATIONKEY N_NATIONNAME
1 Algeria
2 Argentina
3 Atlantida

Lookup File Sample

COUNTRY_NAME
Algeria
Argentina
Brazil
Canada
...
Zimbabwe

Anomaly Explanation

In the sample data above, the entry with N_NATIONKEY 3 does not satisfy the rule because the N_NATIONNAME "Atlantida" does not match any COUNTRY_NAME in the official country names lookup file.

graph TD
A[Start] --> B[Retrieve COUNTRY_NAME]
B --> C[Retrieve N_NATIONNAME]
C --> D{Does N_NATIONNAME exists in COUNTRY_NAME?}
D -->|Yes| E[Move to Next Record/End]
D -->|No| F[Mark as Anomalous]
F --> E
-- An illustrative SQL query demonstrating the rule applied to example dataset(s).
select
    n_nationkey
    , n_nationname
from nation 
where
    n_nationname not in ('Algeria', 'Argentina', ... /* other valid countries */)

Potential Violation Messages

Record Anomaly

The N_NATIONNAME value of 'Atlantida' does not exist in COUNTRY_NAME.

Shape Anomaly

In N_NATIONNAME, 33.333% of 3 filtered records (1) do not match any COUNTRY_NAME.

Last update: December 1, 2023