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Numeric Max Check

Check name: numeric-max-check · Type: row-level · Config: NumericMaxCheckConfig

Flags any record whose value in one of the configured columns exceeds a maximum threshold. Use it to enforce upper bounds such as a percentage capped at 100, a maximum order quantity, or a plausible measurement ceiling.

Parameters

Parameter Type Required Default Description
check_id str yes — Unique identifier for this check within the CheckSet.
columns list[str] yes — Numeric columns to compare against the threshold. YAML key: columns.
max_value float \| int yes — The upper bound. 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 NumericMaxCheckConfig
from sparkdq.core import Severity

NumericMaxCheckConfig(
    check_id="max-score",
    columns=["score"],
    max_value=100,
    inclusive=True,
    severity=Severity.CRITICAL,
)

- check: numeric-max-check
  check-id: max-score
  columns:
    - score
  max-value: 100
  inclusive: true
  severity: critical

Behavior

  • inclusive controls the boundary, and defaults to False. With inclusive=True, a value equal to max_value passes (value <= max_value is required). With the default inclusive=False, the boundary value itself fails (value < max_value is required). Set inclusive=True when the threshold should be an allowed value — this is the most common intent.
  • OR semantics across columns. With multiple columns, a record fails if any of them exceeds 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 score <= 100 (inclusive=True), both styles produce the same result.

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

spark = SparkSession.builder.getOrCreate()

df = spark.createDataFrame([
    {"id": 1, "score": 90},
    {"id": 2, "score": 105},
])

check_set = CheckSet().add_check(
    NumericMaxCheckConfig(check_id="max-score", columns=["score"], max_value=100, inclusive=True)
)
result = BatchDQEngine(check_set).run_batch(df)
result.fail_df().show(truncate=False)

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, "score": 90},
    {"id": 2, "score": 105},
])

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 out-of-range row fails:

+---+-----+----------------------------------------+----------+--------------------------+
|id |score|_dq_errors                              |_dq_passed|_dq_validation_ts         |
+---+-----+----------------------------------------+----------+--------------------------+
|2  |105  |[{NumericMaxCheck, max-score, critical}]|false     |2026-01-01 00:00:00.000000|
+---+-----+----------------------------------------+----------+--------------------------+

Typical use cases

  • Cap percentages or scores at a known ceiling (e.g. 100).
  • Enforce a maximum order quantity or transaction amount.
  • Detect data entry errors or anomalies producing implausibly large values.

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