Skip to content

Count Exact

Check: row-count-exact-check

Purpose: Validates that the dataset contains exactly the expected number of rows. Use this to enforce strict volume contracts, particularly for fixed-size exports, snapshots, or reference datasets.

from sparkdq.checks import RowCountExactCheckConfig
from sparkdq.core import Severity

RowCountExactCheckConfig(
    check_id="snapshot-row-count",
    expected_count=500,
    severity=Severity.ERROR
)

- check: row-count-exact-check
  check-id: snapshot-row-count
  expected-count: 500
  severity: error

Typical Use Cases

  • Validate fixed-size imports where exactly a known number of records is expected.
  • Ensure integrity of snapshot-based pipelines with predictable record counts.
  • Detect silent load failures or unintended duplications early in the process.

← Aggregate Checks