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

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" collapsible="true

O_TOTALPRICE > 1000

C_MKTSEGMENT = 'BUILDING'

Incorrect usage" collapsible="true

WHERE O_TOTALPRICE > 1000

WHERE C_MKTSEGMENT = 'BUILDING'

Combining Conditions

Combine multiple conditions using logical operators like AND and OR.

Correct usage" collapsible="true

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" collapsible="true

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" collapsible="true

RIGHT(
    O_ORDERPRIORITY,
    LENGTH(O_ORDERPRIORITY) - INSTR('-', O_ORDERPRIORITY)
) = 'URGENT'

LEVENSHTEIN(C_NAME, 'Supplier#000000001') < 7

Incorrect usage" collapsible="true

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" collapsible="true

O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM {{ _qualytics_self }}
    WHERE O_TOTALPRICE > 1000
)

Incorrect usage" collapsible="true

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
Comparison	Specifies how the distinct count should be compared against the value.
Value	The value to compare the distinct count against.

Details

Comparison is a required property that accepts the following values:

Comparison	Description
`eq`	Equal To - Assert distinct count equals the value
`gt`	Greater Than - Assert distinct count is greater than the value
`gte`	Greater Than Or Equal To - Assert distinct count is ≥ value
`lt`	Less Than - Assert distinct count is less than the value
`lte`	Less Than Or Equal To - Assert distinct count is ≤ value

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

Payload example

{
    "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": {
        "comparison": "eq",
        "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, which is not equal to 3. The expected values were 'O' (Open), 'F' (Finished), and 'P' (In Progress), but an unexpected value 'X' was found.

FlowchartSQL

graph TD
A[Start] --> B[Retrieve all O_ORDERSTATUS entries and count distinct values]
B --> C{Does distinct count satisfy comparison condition?}
C -->|No| D[Mark as Anomalous]
C -->|Yes| E[End]

-- An illustrative SQL query demonstrating the rule applied to example dataset(s).
-- Using comparison = 'eq' (equal to)
select
    count(distinct o_orderstatus)
from orders
having count(distinct o_orderstatus) <> 3

Potential Violation Messages

Shape Anomaly (eq)

The distinct count of values in O_ORDERSTATUS is 4 which is not equal to 3.

Shape Anomaly (gte)

The distinct count of values in O_ORDERSTATUS is 2 which is not greater than or equal to 3.

Shape Anomaly (lte)

The distinct count of values in O_ORDERSTATUS is 5 which is not less than or equal to 3.