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Numeric Max

Check: numeric-max-check

Purpose: Validates that values in numeric columns do not exceed a defined maximum. A row fails if any selected column contains a value greater than max_value.

Use the inclusive parameter to control boundary behavior:

  • inclusive: false (default) — value < max_value
  • inclusive: true — value <= max_value
from sparkdq.checks import NumericMaxCheckConfig
from sparkdq.core import Severity

NumericMaxCheckConfig(
    check_id="discount-not-above-100",
    columns=["discount"],
    max_value=100.0,
    inclusive=True,
    severity=Severity.CRITICAL
)

- check: numeric-max-check
  check-id: discount-not-above-100
  columns:
    - discount
  max-value: 100.0
  inclusive: true
  severity: critical

Typical Use Cases

  • Ensure prices, quantities, or KPI values do not exceed defined upper limits.
  • Validate that percentages or scores stay at or below 100.
  • Detect data entry errors or system anomalies producing out-of-range values.

← Row-Level Checks