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Is Address

Definition

Asserts that the values contain the specified required elements of an address.

In-Depth Overview

This check leverages machine learning powered by the libpostal library to support multilingual street address parsing/normalization that can handle addresses all over the world. The underlying statistical NLP model was trained using data from OpenAddress and OpenStreetMap, a total of about 1.2 billion records of data from over 230 countries, in 100+ languages. The international address parser uses Conditional Random Fields, which can infer a globally optimal tag sequence instead of making local decisions at each word, and it achieves 99.45% full-parse accuracy on held-out addresses (i.e. addresses from the training set that were purposefully removed so we could evaluate the parser on addresses it hasn’t seen before).

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

Name Description
Required Labels
 The labels that must be identifiable in the value of each record

Info

The address parser can technically use any string labels that are defined in the training data, but these are the ones currently supported:

  • road: Street name(s)
  • city: Any human settlement including cities, towns, villages, hamlets, localities, etc
  • state: First-level administrative division. Scotland, Northern Ireland, Wales, and England in the UK are mapped to "state" as well (convention used in OSM, GeoPlanet, etc.)
  • country: Sovereign nations and their dependent territories, anything with an ISO-3166 code
  • postcode: Postal codes used for mail sorting

This check allows the user to define any combination of these labels as required elements of the value held in each record. Any value thse does not contain every required element will be identified as anomalous.

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 values in O_MAILING_ADDRESS include the labels "road", "city", "state", and "postcode"

Sample Data

O_ORDERKEY O_MAILING_ADDRESS
1 One-hundred twenty E 96th St, new york NY 14925
2
Quatre vingt douze R. de l'Église, 75196 cedex 04
3 781 Franklin Ave Crown Heights Brooklyn NYC NY 11216 USA

Anomaly Explanation

In the sample data above, the entry with O_ORDERKEY 2 does not satisfy the rule because the O_MAILING_ADDRESS value includes only a road and postcode which violates the business logic that city and state also be present.

graph TD
A[Start] --> B[Retrieve O_MAILING_ADDRESS]
B --> C[Infer address labels using ML]
C --> D{Are all required labels present?}
D -->|Yes| E[Move to Next Record/End]
D -->|No| F[Mark as Anomalous]
F --> E

Potential Violation Messages

Record Anomaly

The O_MAILING_ADDRESS value of Quatre vingt douze R. de l'Église, 75196 cedex 04 does not adhere to the required format.

Shape Anomaly

In O_MAILING_ADDRESS, 33.33% of 3 filtered records (1) do not adhere to the required format.

Last update: April 25, 2024