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Date Max Check

Check name: date-max-check · Type: row-level · Config: DateMaxCheckConfig

Flags any record whose value in one of the configured date columns is later than a maximum date. Use it to reject future-dated records or dates beyond a valid reporting window.

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.
max_value str yes — Upper bound as YYYY-MM-DD. YAML key: max-value.
inclusive bool no False Whether max_value itself is allowed (see Behavior).
severity Severity no CRITICAL CRITICAL fails the row; WARNING only records it.

Usage

from sparkdq.checks import DateMaxCheckConfig
from sparkdq.core import Severity

DateMaxCheckConfig(
    check_id="max-date",
    columns=["d"],
    max_value="2024-12-31",
    inclusive=True,
    severity=Severity.CRITICAL,
)

- check: date-max-check
  check-id: max-date
  columns:
    - d
  max-value: "2024-12-31"
  inclusive: true
  severity: critical

Behavior

  • max_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 max_value passes (value <= max_value). With the default, the boundary date itself fails (value < max_value).
  • OR semantics across columns. With multiple columns, a record fails if any of them is after 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 <= 2024-12-31 (inclusive=True), both styles produce the same result.

import datetime
from pyspark.sql import SparkSession
from sparkdq.checks import DateMaxCheckConfig
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(2025, 2, 1)},
])

check_set = CheckSet().add_check(
    DateMaxCheckConfig(check_id="max-date", columns=["d"], max_value="2024-12-31", 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(2025, 2, 1)},
])

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-late row fails:

+----------+---+------------------------------------+----------+--------------------------+
|d         |id |_dq_errors                          |_dq_passed|_dq_validation_ts         |
+----------+---+------------------------------------+----------+--------------------------+
|2025-02-01|2  |[{DateMaxCheck, max-date, critical}]|false     |2026-01-01 00:00:00.000000|
+----------+---+------------------------------------+----------+--------------------------+

Typical use cases

  • Reject future-dated records where only past dates are valid.
  • Enforce an upper bound aligned with a reporting or snapshot window.
  • Detect clock or timezone errors producing out-of-range dates.

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