Positive Check Examples

The situation: Every line in the order_lines table must carry a quantity above zero. Rows with 0 come from a cart bug that saves an emptied line, and negative rows come from returns that were written into the wrong table.

Check configuration

Payload

{
    "description": "Order line quantity must be a positive number.",
    "rule": "positive",
    "fields": ["quantity"],
    "container_id": 145,
    "coverage": 1,
    "filter": null,
    "properties": {},
    "tags": ["orders", "range"],
    "additional_metadata": {"jira": "DATA-8201"},
    "anomaly_message_field": null,
    "template_id": null,
    "status": "Active",
    "owner_id": 7,
    "default_anomaly_assignee_id": 12
}

Sample Data

What gets flagged

Line L-002 holds zero and L-003 holds a negative quantity; both fail, because Positive requires values strictly above zero. L-004 is NULL and passes without firing an anomaly. Coverage is 100%, so each failing row is reported as a Record Anomaly.

Flowchart

graph TD
    A["No filter, evaluate all rows"] --> B["Read quantity"]
    B --> C{"Is value NULL?"}
    C -->|Yes| D["Row passes"]
    C -->|No| E{"Is quantity > 0?"}
    E -->|Yes| D
    E -->|No| F["Flag row.<br/>Record Anomaly per failing row."]

Equivalent SQL

-- Rows the Positive check would flag.
SELECT l.*
FROM order_lines l
WHERE l.quantity IS NOT NULL
  AND l.quantity <= 0;

The situation: Invoice lines marked as billable must carry an amount above zero. Credit lines are negative by design and live in the same table, so they are excluded with a filter.

Check configuration

Payload

{
    "description": "Billable invoice lines must carry a positive amount.",
    "rule": "positive",
    "fields": ["amount"],
    "container_id": 512,
    "coverage": 1,
    "filter": "line_type = 'billable'",
    "properties": {},
    "tags": ["billing", "range"],
    "additional_metadata": {"jira": "DATA-8233"},
    "anomaly_message_field": null,
    "template_id": null,
    "status": "Active",
    "owner_id": 7,
    "default_anomaly_assignee_id": 29
}

Sample Data (filtered to line_type = 'billable')

Why the filter matters

The filter runs before the comparison, so credit lines, which are negative by design, are never tested. Only billable lines are evaluated.

What gets flagged

I-02 carries a zero amount, which is not a valid billable line. Coverage is 100%, so the failure is reported as a Record Anomaly, and the message ends with the filter that scoped the evaluation.

Flowchart

graph TD
    A["Apply filter: line_type = 'billable'"] --> B["Read amount"]
    B --> C{"Is amount > 0?"}
    C -->|Yes| D["Row passes"]
    C -->|No| E["Flag row.<br/>Anomaly message ends with<br/>[filter: line_type = 'billable']"]

Equivalent SQL

-- Rows the check would flag among billable lines.
SELECT i.*
FROM invoice_lines i
WHERE i.line_type = 'billable'
  AND i.amount IS NOT NULL
  AND i.amount <= 0;

The situation: A legacy feed writes weight_kg with occasional zeros and negatives from a broken unit conversion. A fix is rolling out, so a small fraction of bad rows is expected and the check tolerates up to 0.5% failures.

Check configuration

Payload

{
    "description": "Shipment weight must be a positive number.",
    "rule": "positive",
    "fields": ["weight_kg"],
    "container_id": 733,
    "coverage": 0.995,
    "filter": null,
    "properties": {},
    "tags": ["logistics", "plausibility"],
    "additional_metadata": {"jira": "DATA-8266"},
    "anomaly_message_field": null,
    "template_id": null,
    "status": "Active",
    "owner_id": 7,
    "default_anomaly_assignee_id": 52
}

Sample Data

What gets flagged

Shipments S-02 and S-03 come from the legacy feed with a zero and a negative weight. S-04 is NULL, so it passes without firing an anomaly but still counts in the scanned total. Only 50% of the rows pass, which is below the 99.5% coverage threshold, so the check reports a single Shape Anomaly for the dataset with a sample of the offending rows. Coverage below 100% does not produce Record Anomalies.

Flowchart

graph TD
    A["No filter, evaluate all rows"] --> B["Read weight_kg"]
    B --> C{"Is value NULL?"}
    C -->|Yes| D["Row passes"]
    C -->|No| E{"Is weight_kg > 0?"}
    E -->|Yes| D
    E -->|No| F["Flag row.<br/>Passing rate falls below the 99.5%<br/>coverage, so one Shape Anomaly is reported."]

Equivalent SQL

-- Rows the check would flag.
SELECT s.*
FROM shipments s
WHERE s.weight_kg IS NOT NULL
  AND s.weight_kg <= 0;