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Timestamp Min Check

Check name: timestamp-min-check · Type: row-level · Config: TimestampMinCheckConfig

Flags any record whose value in one of the configured timestamp columns is earlier than a minimum timestamp. Use it to reject events before a valid start instant.

Parameters

Parameter Type Required Default Description
check_id str yes — Unique identifier for this check within the CheckSet.
columns list[str] yes — Timestamp columns to compare against the threshold. YAML key: columns.
min_value str yes — Lower bound as an ISO timestamp. 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 TimestampMinCheckConfig
from sparkdq.core import Severity

TimestampMinCheckConfig(
    check_id="min-ts",
    columns=["ts"],
    min_value="2024-01-01 00:00:00",
    inclusive=True,
    severity=Severity.CRITICAL,
)

- check: timestamp-min-check
  check-id: min-ts
  columns:
    - ts
  min-value: "2024-01-01 00:00:00"
  inclusive: true
  severity: critical

Behavior

  • min_value is an ISO timestamp string. The column is cast to timestamp 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 instant 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 ts >= 2024-01-01 00:00:00 (inclusive=True), both styles produce the same result.

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

spark = SparkSession.builder.getOrCreate()

df = spark.createDataFrame([
    {"id": 1, "ts": datetime.datetime(2024, 6, 1, 12, 0, 0)},
    {"id": 2, "ts": datetime.datetime(2023, 1, 1, 0, 0, 0)},
])

check_set = CheckSet().add_check(
    TimestampMinCheckConfig(
        check_id="min-ts", columns=["ts"], min_value="2024-01-01 00:00:00", 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, "ts": datetime.datetime(2024, 6, 1, 12, 0, 0)},
    {"id": 2, "ts": datetime.datetime(2023, 1, 1, 0, 0, 0)},
])

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:

+---+-------------------+---------------------------------------+----------+--------------------------+
|id |ts                 |_dq_errors                             |_dq_passed|_dq_validation_ts         |
+---+-------------------+---------------------------------------+----------+--------------------------+
|2  |2023-01-01 00:00:00|[{TimestampMinCheck, min-ts, critical}]|false     |2026-01-01 00:00:00.000000|
+---+-------------------+---------------------------------------+----------+--------------------------+

Typical use cases

  • Reject events recorded before a valid start instant.
  • Enforce a lower bound aligned with a system launch time.
  • Detect clock or timezone errors producing out-of-range timestamps.

← Row-Level Checks