How Positive Checks Work

Definition

Asserts that every value in a numeric field is a positive number.

Overview

The Positive rule applies a fixed comparison to a single numeric field: every value must be a positive number. There is nothing to configure beyond the field itself. Zero is rejected. Positive requires values strictly above zero, so a row holding 0 is flagged alongside negatives.

Typical use cases:

• Guard quantities and amounts that must always be above zero.
• Catch rows where a cart, an invoice, or a measurement was saved empty.
• Detect negative values that belong in a different table, such as returns or credits.

Field Scope

Single: The rule evaluates exactly one field per check.

Accepted Types

On an array field every element is tested and the row fails as soon as one element breaks the rule. 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
)

Anomaly Types

Evaluation Flow

Every Positive 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. Apply the comparison. The row passes when the value is a positive number, and fails otherwise.
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).

How Zero Is Treated

Zero is rejected. Positive requires values strictly above zero, so a row holding 0 is flagged alongside negatives. When the other behavior is what you need, use Not Negative instead: it accepts zero and rejects only negative values.

NULL Handling

The check passes NULL values: a row with NULL in the evaluated field is not counted as a violation. Positive only asserts that present values satisfy the comparison.

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

Array Fields

On an array field, every element is tested and the row fails as soon as one element breaks the rule. Empty arrays and NULL arrays pass, since there is nothing to evaluate.

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).

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 failing. 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 failures is expected. Use coverage carefully: a 0.5% tolerance can mask a real regression that happens to fall just under the threshold.

See Also