Numeric Min

Numeric Min#

Check: numeric-min-check

Purpose: Checks whether values in the specified numeric columns are strictly greater than a defined minimum value (min_value). A row fails the check if any of the selected columns contains a value less than the minimum, or equal to it if inclusive=False.

You can control inclusivity with the inclusive parameter:

  • inclusive = False (default): value > min_value

  • inclusive = True: value >= min_value

Python Configuration#

from sparkdq.checks import NumericMinCheckConfig
from sparkdq.core import Severity

NumericMinCheckConfig(
    check_id="minimum_allowed_price",
    columns=["price", "discount"],
    min_value=0.0,
    inclusive=True,
    severity=Severity.CRITICAL
)

Declarative Configuration#

- check: numeric-min-check
  check-id: minimum_allowed_price
  columns:
    - price
    - discount
  min-value: 0.0
  inclusive: true
  severity: critical

Typical Use Cases#

  • ✅ Ensure that sales amounts are not negative.

  • ✅ Validate sensor readings against physical minimum thresholds.

  • ✅ Check minimum allowed values for KPIs or metrics.

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