Count Exact

Count Exact#

Check: row-count-exact-check

Purpose: Verifies that the dataset contains exactly the specified number of rows. Useful for enforcing strict expectations on data volume.

Python Configuration#

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

RowCountExactCheckConfig(
    check_id="validate_snapshot_size",
    expected_count=500,
    severity=Severity.ERROR
)

Declarative Configuration#

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

Typical Use Cases#

  • ✅ Validate fixed-size imports (e.g., daily exports with exactly 1,000 records).

  • ✅ Ensure integrity in snapshot-based pipelines with exact record counts.

  • ✅ Identify silent load failures or unintended duplications early in the process.

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