Martin Mauch created SPARK-23677:
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Summary: Selecting columns from joined DataFrames with the same
origin yields wrong results
Key: SPARK-23677
URL: https://issues.apache.org/jira/browse/SPARK-23677
Project: Spark
Issue Type: Bug
Components: Spark Core, SQL
Affects Versions: 2.3.0
Reporter: Martin Mauch
When trying to join two DataFrames with the same origin DataFrame and later
selecting columns from the join, Spark can't distinguish between the columns
and gives a wrong (or at least very surprising) result. One can work around
this using expr.
Here is a minimal example:
{code:java}
import spark.implicits._
val edf = Seq((1), (2), (3), (4), (5)).toDF("num")
val big = edf.where(edf("num") > 2).alias("big")
val small = edf.where(edf("num") < 4).alias("small")
small.join(big, expr("big.num == (small.num + 1)")).select(small("num"),
big("num")).show()
// +---+---+
// |num|num|
// +---+---+
// | 2| 2|
// | 3| 3|
// +—+—+
small.join(big, expr("big.num == (small.num + 1)")).select(expr("small.num"),
expr("big.num")).show()
// +---+---+
// |num|num|
// +---+---+
// | 2| 3|
// | 3| 4|
// +---+---+
{code}
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