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Distinct Count

Definition

Asserts on the approximate count distinct of the given column.

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 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

Specify the distinct count expectation for the values in the field.

Name Description
Value
 The exact count of distinct values expected in 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 there are exactly 3 distinct O_ORDERSTATUS in the ORDERS table: 'O' (Open), 'F' (Finished), and 'P' (In Progress).

Sample Data

O_ORDERKEY O_ORDERSTATUS
1 O
2 F
... ...
20 X
21 O 
{
    "description": "Ensure that there are exactly 3 distinct O_ORDERSTATUS in the ORDERS table: 'O' (Open), 'F' (Finished), and 'P' (In Progress)",
    "coverage": 1,
    "properties": {
        "value":3
    },
    "tags": [],
    "fields": ["O_ORDERSTATUS"],
    "additional_metadata": {"key 1": "value 1", "key 2": "value 2"},
    "rule": "distinctCount",
    "container_id": {container_id},
    "template_id": {template_id},
    "filter": "1=1"
}

Anomaly Explanation

In the sample data above, the rule is violated because the O_ORDERSTATUS contains 4 distinct values and not 3: 'O' (Open), 'F' (Finished), and 'P' (In Progress).

graph TD
A[Start] --> B[Retrieve all O_ORDERSTATUS entries and count distinct values]
B --> C{Is distinct count of O_ORDERSTATUS != 3?}
C -->|Yes| D[Mark as Anomalous]
C -->|No| E[End]
-- An illustrative SQL query demonstrating the rule applied to example dataset(s).
select
    count(distinct o_orderstatus)
from orders
having count(distinct o_orderstatus) <> 3

Potential Violation Messages

Shape Anomaly

In O_ORDERSTATUS, the distinct count of the records is not 3.