rowLevelResultsAsDataFrame returns all True on multi-partition DataFrames

[billpratt]

Bug

VerificationResult.rowLevelResultsAsDataFrame() returns all true Boolean values for row-level check columns when the input DataFrame has multiple partitions (e.g., read from a Delta table with spark.table()).

Reproduction

1. Read a DataFrame from a persisted Delta table (~200K rows, multiple partitions)
2. Run a VerificationSuite with isUnique constraint — aggregate result correctly reports uniqueness < 1.0
3. Call VerificationResult.rowLevelResultsAsDataFrame(spark, result, data)
4. The Boolean column is true for all rows — no rows are flagged as failures

A minimal in-memory DataFrame (e.g., 4 rows with spark.createDataFrame) works correctly.

Expected behavior

Duplicate rows should have false in the row-level Boolean column.

Workaround

Calling df.repartition(1) before passing to the VerificationSuite produces correct row-level results. This suggests a row ordering / partition alignment issue in the underlying Deequ JVM method.

Related

• Root cause issue filed on awslabs/deequ: rowLevelResultsAsDataFrame returns all True on multi-partition DataFrames deequ#753

Environment

• Spark 3.5 (Microsoft Fabric)
• pydeequ (latest)
• Delta Lake table source

[github-actions]

Thanks for the detailed report. This is a known issue in the underlying Deequ JVM library — the rowLevelResultsAsDataFrame method doesn't handle multi-partition DataFrames correctly because it relies on row ordering that isn't guaranteed across partitions. You've correctly identified the root cause and already filed it upstream at awslabs/deequ#753.

Since PyDeequ's rowLevelResultsAsDataFrame is a thin wrapper that delegates directly to the Scala VerificationResult.rowLevelResultsAsDataFrame method, there's nothing we can fix on the Python side — the fix needs to land in the Deequ JVM library itself.

In the meantime, your workaround of .repartition(1) (or .coalesce(1)) before passing the DataFrame to the VerificationSuite is the correct mitigation. For large datasets where a single partition isn't practical, you could also try .repartition(1) only for the row-level results call while keeping the verification itself on the original partitioning.

We'll track the upstream fix and update PyDeequ if any wrapper-level changes are needed once it's resolved.

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[billpratt]

Update: After further investigation, I found that the original reproduction of this issue was actually caused by a different bug in my use case - I was creating two Checks (Error and Warning level) with the same description string, which caused rowLevelResultsAsDataFrame to produce duplicate column names. The warning check's all-True column was overwriting the error check's column, making it appear that all rows passed.

After fixing the duplicate check names, I was unable to reproduce the partition alignment issue. Closing this issue as the original reproduction was invalid. Apologies for the noise.