Timestamp Between¶

Check: timestamp-between-check

Purpose: Validates that values in timestamp columns fall within a defined range. A row fails if any selected column contains a timestamp before min_value or after max_value.

Use the inclusive parameter to control boundary behavior:

Value	Behavior
`[false, false]` (default)	strictly between: `min < value < max`
`[true, false]`	include lower bound: `min <= value < max`
`[false, true]`	include upper bound: `min < value <= max`
`[true, true]`	include both bounds: `min <= value <= max`

PythonYAML

from sparkdq.checks import TimestampBetweenCheckConfig
from sparkdq.core import Severity

TimestampBetweenCheckConfig(
    check_id="event-time-within-range",
    columns=["event_time"],
    min_value="2020-01-01 00:00:00",
    max_value="2023-12-31 23:59:59",
    inclusive=(True, True),
    severity=Severity.CRITICAL
)

- check: timestamp-between-check
  check-id: event-time-within-range
  columns:
    - event_time
  min-value: "2020-01-01 00:00:00"
  max-value: "2023-12-31 23:59:59"
  inclusive: [true, true]
  severity: critical

Typical Use Cases¶

Ensure log entries or event timestamps fall within a defined processing or reporting window.
Validate that measurement or sensor data was collected within the expected observation period.
Reject future-dated or historically stale records before they reach downstream consumers.

← Row-Level Checks