How Between Checks Work

Definition

Asserts that every value in a numeric field falls between a minimum and a maximum boundary, with each side independently inclusive or exclusive.

Overview

The Between rule defines a numeric range on a single field. Each row must hold a value inside the range formed by Min and Max. Each boundary carries its own Inclusive setting, so the same check can express a closed range (min <= value <= max), an open one (min < value < max), or a half-open range where only one side accepts the boundary value.

Typical use cases:

• Validate a numeric range such as a discount between 0 and 1, or a score between 0 and 100.
• Enforce plausibility on a measurement, for example an age between 0 and 120.
• Keep a monetary amount inside an approved band for a given product or contract.

Field Scope

Single: The rule evaluates exactly one field per check.

Accepted Types

On an array field, every element is tested against the range and the row fails as soon as one element falls outside it. That evaluation runs as a field-level check, so it reports a Shape Anomaly for the field instead of per-row Record Anomalies, whatever the coverage is set to.

General Properties

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.

O_TOTALPRICE > 1000

C_MKTSEGMENT = 'BUILDING'

WHERE O_TOTALPRICE > 1000

WHERE C_MKTSEGMENT = 'BUILDING'

O_ORDERPRIORITY = '1-URGENT' AND O_ORDERSTATUS = 'O'

(L_SHIPDATE = '1998-09-02' OR L_RECEIPTDATE = '1998-09-01') AND L_RETURNFLAG = 'R'

WHERE O_ORDERPRIORITY = '1-URGENT' AND O_ORDERSTATUS = 'O'

O_TOTALPRICE > 1000, O_ORDERSTATUS = 'O'

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

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

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

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

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

O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM ORDERS
    WHERE O_TOTALPRICE > 1000
)

Specific Properties

Between has four rule-specific properties:

Anomaly Types

Evaluation Flow

Every Between check follows the same four-step evaluation flow:

1. Apply the filter clause. If the check has a filter set, only rows matching the filter expression continue to the next step. Rows outside the filter are ignored and cannot contribute to the violation count.
2. Read the field value. The platform reads the numeric value for the current row.
3. Compare against both boundaries. The value must satisfy the lower comparison (> or >=, depending on the min inclusivity) and the upper comparison (< or <=). Failing either side fails the row.
4. Apply coverage. At 100% coverage, any failing row causes the check to fail. Below 100% coverage, the check fails only when the passing fraction drops below the threshold (see Coverage and Tolerance).

Boundary Inclusivity

The two Inclusive settings are independent, which lets one check express four different ranges:

The half-open form is the safest choice when several checks partition a continuous scale, because it leaves no value belonging to two ranges at once.

NULL Handling

The check passes NULL values: a row with NULL in the evaluated field is not counted as a violation. Between only asserts that present values fall inside the range and does not enforce mandatory presence on the field.

If the field must also be populated, pair Between with a Not Null check on the same field.

The Filter Clause

The filter clause is a SQL WHERE expression applied before the evaluation. Filtered-out rows are ignored entirely (they cannot trigger a violation and are not counted in the totals).

Common uses:

• Applying a range only to a product line, a region, or a contract tier.
• Excluding sentinel rows that carry a placeholder value tracked by a separate clean-up task.
• Scoping the check to a partition (event_date = current_date()).

When a filter is set, both the Record Anomaly and the Shape Anomaly messages end with [filter: <expression>] so the evaluated scope is visible in the alert.

Coverage and Tolerance

Coverage is a fractional value between 0 and 1 that defines the minimum fraction of evaluated rows that must pass:

• 1.0 (100%, default): every row in the filtered set must pass. Any failing row causes the check to fail. This is the strictest setting.
• < 1.0: the check tolerates a fraction of rows falling outside the range. The check fails only when the fraction of passing rows drops below the threshold.

Lower coverage values are useful when a small, known fraction of out-of-range values is expected. Use coverage carefully: a 0.5% tolerance can mask a real regression that happens to fall just under the threshold.

See Also