[SPARK-32136] Spark producing incorrect groupBy results when key is a struct with nullable properties - ASF JIRA

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Details

Type: Bug
Status: Resolved
Priority: Blocker
Resolution: Fixed
Affects Version/s: 3.0.0
Fix Version/s: 3.0.1, 3.1.0
Component/s: SQL
Labels:
- correctness

Target Version/s:

3.0.1

Description

I'm in the process of migrating from Spark 2.4.x to Spark 3.0.0 and I'm noticing a behaviour change in a particular aggregation we're doing, and I think I've tracked it down to how Spark is now treating nullable properties within the column being grouped by.

Here's a simple test I've been able to set up to repro it:

case class B(c: Option[Double])
case class A(b: Option[B])
val df = Seq(
A(None),
A(Some(B(None))),
A(Some(B(Some(1.0))))
).toDF
val res = df.groupBy("b").agg(count("*"))

Spark 2.4.6 has the expected result:

> res.show
+-----+--------+
|    b|count(1)|
+-----+--------+
|   []|       1|
| null|       1|
|[1.0]|       1|
+-----+--------+

> res.collect.foreach(println)
[[null],1]
[null,1]
[[1.0],1]

But Spark 3.0.0 has an unexpected result:

> res.show
+-----+--------+
|    b|count(1)|
+-----+--------+
|   []|       2|
|[1.0]|       1|
+-----+--------+

> res.collect.foreach(println)
[[null],2]
[[1.0],1]

Notice how it has keyed one of the values in be as `[null]`; that is, an instance of B with a null value for the `c` property instead of a null for the overall value itself.

Is this an intended change?