nirav patel created SPARK-28630:
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Summary: Union fails when column order is different
Key: SPARK-28630
URL: https://issues.apache.org/jira/browse/SPARK-28630
Project: Spark
Issue Type: Improvement
Components: Spark Core
Affects Versions: 2.2.3
Reporter: nirav patel
I am trying to union 2 df which has same number of columns and types but in
different order. It fails.
val df1 = sparkSession.sparkContext.parallelize(List(
(1, 5, true),
(2, 3, false),
(4, 4, true)
)).toDF("id","age", "vaccinated")
val df3 = sparkSession.sparkContext.parallelize(List(
(1, true, 6),
(2, false, 3),
(3, false, 2)
)).toDF("id","vaccinated", "age")
df1.union(df3)
Actual output:
org.apache.spark.sql.AnalysisException: Union can only be performed on tables
with the same number of columns
Expected output:
It should read schema (column names and/Or types ). I can see sometime you want
to ignore column name and just want to do merge based on types. So May be
introduce an option whether to use names followed by type or just type while
doing merge.
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