kanika dhuria created SPARK-32551:
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Summary: Ambiguous self join error in non self join with window
Key: SPARK-32551
URL: https://issues.apache.org/jira/browse/SPARK-32551
Project: Spark
Issue Type: Bug
Components: Spark Core
Affects Versions: 3.0.0
Reporter: kanika dhuria
Following code hits ambiguous join error even when it doesn't have self join
val v1 = spark.range(3).toDF("m")
val v2 = spark.range(3).toDF("d")
val v3 = v1.join(v2, v1("m").===(v2("d")))
val v4 = v3("d");
val w1 = Window.partitionBy(v4)
val out = v3.select(v4.as("a"), sum(v4).over(w1).as("b"))
org.apache.spark.sql.AnalysisException: Column a#45L are ambiguous. It's
probably because you joined several Datasets together, and some of these
Datasets are the same. This column points to one of the Datasets but Spark is
unable to figure out which one. Please alias the Datasets with different names
via `Dataset.as` before joining them, and specify the column using qualified
name, e.g. `df.as("a").join(df.as("b"), $"a.id" > $"b.id")`. You can also set
spark.sql.analyzer.failAmbiguousSelfJoin to false to disable this check.;
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