Skip to content

Matches Pattern

Definition

Asserts that a field must match a pattern.

In-Depth Overview

Patterns, typically expressed as regular expressions, allow for the enforcement of custom structural norms for data fields. For complex patterns, regular expressions offer a powerful tool to ensure conformity to the expected format.

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

Allows specifying a pattern against which the field will be checked.

Name Description
Pattern
 Specifies the regular expression pattern the field must match.

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 P_SERIAL entries in the PART table match the pattern for product serial numbers: TPCH-XXXX-####, where XXXX are uppercase alphabetic characters and #### are numbers.

Sample Data

P_PARTKEY P_SERIAL
1 TPCH-ABCD-1234
2 TPCH-1234-ABCD
3 TPCH-WXYZ-9876 
{
    "description": "Ensure that all P_SERIAL entries in the PART table match the pattern for product serial numbers: `TPCH-XXXX-####`, where `XXXX` are uppercase alphabetic characters and `####` are numbers",
    "coverage": 1,
    "properties": {
        "pattern":"^tpch-[a-z]{4}-[0-9]{4}$"
    },
    "tags": [],
    "fields": ["P_SERIAL"],
    "additional_metadata": {"key 1": "value 1", "key 2": "value 2"},
    "rule": "matchesPattern",
    "container_id": {container_id},
    "template_id": {template_id},
    "filter": "1=1"
}

Anomaly Explanation

In the sample data above, the entry with P_PARTKEY 2 does not satisfy the rule because its P_SERIAL does not match the required pattern.

graph TD
A[Start] --> B[Retrieve P_SERIAL]
B --> C{Does P_SERIAL match TPCH-XXXX-#### format?}
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
    p_partkey,
    p_serial
from part 
where
    not regexp_like(p_serial, '^tpch-[a-z]{4}-[0-9]{4}$')

Potential Violation Messages

Record Anomaly

The P_SERIAL value of TPCH-1234-ABCD does not match the pattern TPCH-XXXX-####.

Shape Anomaly

In P_SERIAL, 33.333% of 3 filtered records (1) do not match the pattern TPCH-XXXX-####.