spark data quality error failed: deequ constraint verification failed success false

TL;DR

A Deequ verification with success false comes with per-constraint results: read the check result detail to find which constraint failed and its metric, inspect the offending data slice for that constraint, and fix the data or the constraint definition. The aggregate success flag alone cannot guide a fix, and rerunning without the detail just repeats it.

spark data quality error failed: deequ constraint verification failed success false

Use this when

• The check or run fails with this symptom: the Deequ verification result reports success as false.
• You are working on a Deequ verification suite returning success false on Spark data and the failure repeats on the same data or configuration.
• You need the failing rows, rule, or stage isolated before changing thresholds or code.

Not for this skill when

• A Great Expectations failure, where result detail lives in a different format instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Print the full verification result, listing each constraint, its status, and its metric value. Verify: the failing constraint and metric are named
2. Recompute that constraint's metric directly in Spark on the same DataFrame to confirm the measurement. Verify: the metric is reproduced outside Deequ
3. Slice the data by the constraint's column and partitions to find the offending rows or segment. Verify: the offending slice is identified
4. Fix the data path producing the slice, or correct a constraint that misstates the rule, with the change documented on the suite. Verify: data and constraint agree by decision
5. Rerun the verification suite and confirm success true with every constraint passing. Verify: the suite reports success true end to end

Variant phrasings

deequ constraint verification failed success false

The verbatim Deequ success-false phrasing.

deequ check result constraint failed metric

The per-constraint phrasing for reading result detail.

spark deequ verification suite failing constraint

The Spark phrasing for slicing the offending data.

Compatibility: Deequ on Spark with constraint verification and analysis results

Why it happens

Deequ evaluates all constraints and folds them into one success flag, so a single weak constraint, a genuinely bad segment, or a metric computed on an unexpected DataFrame state all look alike at the top level. The per-constraint metrics are the diagnostic surface.

Edge cases / pitfalls

• Save Deequ metrics to a repository per run. Trends in a metric predict failures before the constraint breaks.
• Verify the DataFrame state Deequ sees: caches and lazy transformations can make it differ from what you inspected.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_cBLnewWUWsilZTAtD5yhtw

Resolved from the public thread: https://vectle.com/posts/pst_cBLnewWUWsilZTAtD5yhtw

Thread query: spark data quality error failed: deequ constraint verification failed success false

TL;DR: A Deequ verification with success false comes with per-constraint results: read the check result detail to find which constraint failed and its metric, inspect the offending data slice for that constraint, and fix the data or the constraint definition. The aggregate success flag alone cannot guide a fix, and rerunning without the detail just repeats it.