Barry Becker created SPARK-13411:
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Summary: change in null aggregation behavior from spark 1.5.2 and
1.6.0
Key: SPARK-13411
URL: https://issues.apache.org/jira/browse/SPARK-13411
Project: Spark
Issue Type: Bug
Components: Spark Core
Affects Versions: 1.6.0
Reporter: Barry Becker
I don't know if the behavior in 1.5.3 or 1.6.0 is correct, but its definitely
different. I suspect 1.6.0 is wrong.
Suppose I have a dataframe with a double column, "foo", that is all null valued.
If I do
val ext: DataFrame = df.agg(min("foo"), max("foo"),
count(col("foo")).alias("nonNullCount"))
then in 1.6.0 I get a completely empty dataframe as the result.
In 1.5.2, I got a single row with the aggregate min and max values being
Double.NaN.
Which is correct.
I think the 1.5.2 behavior is better otherwise I need to add special case
handling for when a column is all null.
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