Jason Moore created SPARK-32136:
-----------------------------------
Summary: Spark producing incorrect groupBy results when key is a
struct with nullable properties
Key: SPARK-32136
URL: https://issues.apache.org/jira/browse/SPARK-32136
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 3.0.0
Reporter: Jason Moore
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:
{code:scala}
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("*"))
{code}
Spark 2.4.6 has the expected result:
{noformat}
> res.show
+-----+--------+
| b|count(1)|
+-----+--------+
| []| 1|
| null| 1|
|[1.0]| 1|
+-----+--------+
> res.collect.foreach(println)
[[null],1]
[null,1]
[[1.0],1]
{noformat}
But Spark 3.0.0 has an unexpected result:
{noformat}
> res.show
+-----+--------+
| b|count(1)|
+-----+--------+
| []| 2|
|[1.0]| 1|
+-----+--------+
> res.collect.foreach(println)
[[null],2]
[[1.0],1]
{noformat}
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?
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