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Between

Definition

Asserts that values are equal to or between two numbers.

Field Scope

Single: The rule evaluates a single specified field.

Accepted Types

Type
Integral
Fractional

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 both minimum and maximum boundaries, and determine if these boundaries should be inclusive.

Name Explanation
Max
 The upper boundary of the range.
Inclusive (Max)
 If true, the upper boundary is considered a valid value within the range. Otherwise, it's exclusive.
Min
 The lower boundary of the range.
Inclusive (Min)
 If true, the lower boundary is considered a valid value within the range. Otherwise, it's exclusive.

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 L_QUANTITY entries in the LINEITEM table are between 5 and 20 (inclusive).

Sample Data

L_ORDERKEY L_QUANTITY
1 4
2 15
3 21 
{
    "description": "Ensure that all L_QUANTITY entries in the LINEITEM table are between 5 and 20 (inclusive)",
    "coverage": 1,
    "properties": {
        "min":5
        "inclusive_min":true,
        "max":20,
        "inclusive_max":true,
    },
    "tags": [],
    "fields": ["L_QUANTITY"],
    "additional_metadata": {"key 1": "value 1", "key 2": "value 2"},
    "rule": "between",
    "container_id": {container_id},
    "template_id": {template_id},
    "filter": "1=1"
}

Anomaly Explanation

In the sample data above, the entries with L_ORDERKEY 1 and 3 do not satisfy the rule because their L_QUANTITY values are not between 5 and 20 inclusive.

graph TD
A[Start] --> B[Retrieve L_QUANTITY]
B --> C{Is 5 <= L_QUANTITY <= 20?}
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
    l_orderkey
    , l_quantity
from lineitem 
where
    l_quantity < 5
    or l_quantity > 20

Potential Violation Messages

Record Anomaly

The value for L_QUANTITY of 4 is not between 5.000 and 20.000.

Shape Anomaly

In L_QUANTITY, 66.67% of 3 filtered records (2) are not between 5.000 and 20.000.