Min Length Check Examples

The situation: Product codes in the catalog are always at least six characters. Shorter values come from a partner feed that sends internal abbreviations, and they never match anything in the master catalog.

Check configuration

Payload

{
    "description": "Product code must be at least 6 characters.",
    "rule": "minLength",
    "fields": ["product_code"],
    "container_id": 145,
    "coverage": 1,
    "filter": null,
    "properties": {"value": 6},
    "tags": ["catalog", "format"],
    "additional_metadata": {"jira": "DATA-8701"},
    "anomaly_message_field": null,
    "template_id": null,
    "status": "Active",
    "owner_id": 7,
    "default_anomaly_assignee_id": 12
}

Sample Data

What gets flagged

Item I-002 holds a three-character abbreviation from the partner feed. I-004 is NULL and passes without firing an anomaly. Coverage is 100%, so the failing row is reported as a Record Anomaly, and the message echoes the measured length.

Flowchart

graph TD
    A["No filter, evaluate all rows"] --> B["Read product_code"]
    B --> C{"Is value NULL?"}
    C -->|Yes| D["Row passes"]
    C -->|No| E{"Is the length >= 6?"}
    E -->|Yes| D
    E -->|No| F["Flag row.<br/>Record Anomaly per failing row."]

Equivalent SQL

-- Rows the Min Length check would flag.
SELECT i.*
FROM items i
WHERE i.product_code IS NOT NULL
  AND length(i.product_code) < 6;

The situation: Refunds above the approval threshold require a written justification of at least 20 characters. Entries like ok or - satisfy a Not Null check but tell a reviewer nothing. Refunds below the threshold do not require one.

Check configuration

Payload

{
    "description": "High-value refunds must carry a usable justification.",
    "rule": "minLength",
    "fields": ["justification"],
    "container_id": 512,
    "coverage": 1,
    "filter": "amount > 1000",
    "properties": {"value": 20},
    "tags": ["finance", "governance"],
    "additional_metadata": {"jira": "DATA-8744"},
    "anomaly_message_field": null,
    "template_id": null,
    "status": "Active",
    "owner_id": 7,
    "default_anomaly_assignee_id": 29
}

Sample Data (filtered to amount > 1000)

Why the filter matters

The filter runs before the measurement, so small refunds are never tested against the justification rule. Only high-value refunds are evaluated.

What gets flagged

RF-02 carries a two-character placeholder, which passes a presence check but not a usability one. 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: amount > 1000"] --> B["Read justification"]
    B --> C{"Is the length >= 20?"}
    C -->|Yes| D["Row passes"]
    C -->|No| E["Flag row.<br/>Anomaly message ends with<br/>[filter: amount > 1000]"]

Equivalent SQL

-- Rows the check would flag among high-value refunds.
SELECT r.*
FROM refunds r
WHERE r.amount > 1000
  AND r.justification IS NOT NULL
  AND length(r.justification) < 20;

The situation: A legacy external_ref column holds truncated identifiers from an old import. A backfill is restoring the full values, so a small fraction of short rows is expected and the check tolerates up to 0.5% failures.

Check configuration

Payload

{
    "description": "External reference must be at least 10 characters.",
    "rule": "minLength",
    "fields": ["external_ref"],
    "container_id": 733,
    "coverage": 0.995,
    "filter": null,
    "properties": {"value": 10},
    "tags": ["legacy", "format"],
    "additional_metadata": {"jira": "DATA-8788"},
    "anomaly_message_field": null,
    "template_id": null,
    "status": "Active",
    "owner_id": 7,
    "default_anomaly_assignee_id": 41
}

Sample Data

What gets flagged

Records X-02 and X-03 still hold truncated identifiers. X-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 external_ref"]
    B --> C{"Is value NULL?"}
    C -->|Yes| D["Row passes"]
    C -->|No| E{"Is the length >= 10?"}
    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 x.*
FROM external_records x
WHERE x.external_ref IS NOT NULL
  AND length(x.external_ref) < 10;