[SPARK-35108] Pickle produces incorrect key labels for GenericRowWithSchema (data corruption)

• Type: Bug
• Status: Resolved
• Priority: Blocker
• Resolution: Duplicate
• Affects Version/s: 3.0.1, 3.0.2
• Component/s: SQL
• Labels: correctness
• Assignee: Unassigned
• Reporter: Robert Joseph Evans
• Created: 16/Apr/21 21:37
• Updated: 12/Dec/22 18:10
• Resolved: 04/May/21 12:46

Description

I think this also shows up for all versions of Spark that pickle the data when doing a collect from python.

When you do a collect in python java will do a collect and convert the UnsafeRows into GenericRowWithSchema instances before it sends them to the Pickler. The Pickler, by default, will try to dedupe objects using hashCode and .equals for the object. But .equals and .hashCode for GenericRowWithSchema only looks at the data, not the schema. But when we pickle the row the keys from the schema are written out.

This can result in data corruption, sort of, in a few cases where a row has the same number of elements as a struct within the row does, or a sub-struct within another struct.

If the data happens to be the same, the keys for the resulting row or struct can be wrong.

My repro case is a bit convoluted, but it does happen.

Issue Links

• duplicates: SPARK-34545 PySpark Python UDF return inconsistent results when applying 2 UDFs with different return type to 2 columns together