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Date Min Check

Check name: date-min-check · Type: row-level · Config: DateMinCheckConfig

Flags any record whose value in one of the configured date columns is earlier than a minimum date. Use it to reject records before an operational start date or system go-live.

Parameters

Parameter Type Required Default Description
check_id str yes — Unique identifier for this check within the CheckSet.
columns list[str] yes — Date columns to compare against the threshold. YAML key: columns.
min_value str yes — Lower bound as YYYY-MM-DD. YAML key: min-value.
inclusive bool no False Whether min_value itself is allowed (see Behavior).
severity Severity no CRITICAL CRITICAL fails the row; WARNING only records it.

Usage

from sparkdq.checks import DateMinCheckConfig
from sparkdq.core import Severity

DateMinCheckConfig(
    check_id="min-date",
    columns=["d"],
    min_value="2024-01-01",
    inclusive=True,
    severity=Severity.CRITICAL,
)

- check: date-min-check
  check-id: min-date
  columns:
    - d
  min-value: "2024-01-01"
  inclusive: true
  severity: critical

Behavior

  • min_value is a YYYY-MM-DD string. The column is cast to date before comparison.
  • inclusive controls the boundary, and defaults to False. With inclusive=True, a value equal to min_value passes (value >= min_value). With the default, the boundary date itself fails (value > min_value).
  • OR semantics across columns. With multiple columns, a record fails if any of them is before the threshold.
  • Failure is row-level. Each failing row is annotated in _dq_errors; with the default severity, a failure sets _dq_passed = False.
  • Missing columns raise. If a configured column does not exist, the check raises MissingColumnError at validation time.

Example

Requiring d >= 2024-01-01 (inclusive=True), both styles produce the same result.

import datetime
from pyspark.sql import SparkSession
from sparkdq.checks import DateMinCheckConfig
from sparkdq.engine import BatchDQEngine
from sparkdq.management import CheckSet

spark = SparkSession.builder.getOrCreate()

df = spark.createDataFrame([
    {"id": 1, "d": datetime.date(2024, 6, 1)},
    {"id": 2, "d": datetime.date(2023, 12, 31)},
])

check_set = CheckSet().add_check(
    DateMinCheckConfig(check_id="min-date", columns=["d"], min_value="2024-01-01", inclusive=True)
)
result = BatchDQEngine(check_set).run_batch(df)
result.fail_df().show(truncate=False)

import datetime
import yaml
from pyspark.sql import SparkSession
from sparkdq.engine import BatchDQEngine
from sparkdq.management import CheckSet

spark = SparkSession.builder.getOrCreate()

df = spark.createDataFrame([
    {"id": 1, "d": datetime.date(2024, 6, 1)},
    {"id": 2, "d": datetime.date(2023, 12, 31)},
])

with open("checks.yml") as f:
    config = yaml.safe_load(f)

check_set = CheckSet()
check_set.add_checks_from_dicts(config)
result = BatchDQEngine(check_set).run_batch(df)
result.fail_df().show(truncate=False)

Only the too-early row fails:

+----------+---+------------------------------------+----------+--------------------------+
|d         |id |_dq_errors                          |_dq_passed|_dq_validation_ts         |
+----------+---+------------------------------------+----------+--------------------------+
|2023-12-31|2  |[{DateMinCheck, min-date, critical}]|false     |2026-01-01 00:00:00.000000|
+----------+---+------------------------------------+----------+--------------------------+

Typical use cases

  • Ensure event or transaction dates are not before an operational start date.
  • Reject historically stale records outside the valid business period.
  • Align entries with a go-live or migration cutover date.

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