Vishal Dhavale created SPARK-36546:
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Summary: Make unionByName null-filling behavior work with array of
struct columns
Key: SPARK-36546
URL: https://issues.apache.org/jira/browse/SPARK-36546
Project: Spark
Issue Type: Improvement
Components: Spark Core
Affects Versions: 3.1.1
Reporter: Vishal Dhavale
Currently, unionByName workes with two DataFrames with slightly different
schemas. It would be good it works with an array of struct columns.
unionByName fails if we try to merge dataframe with an array of struct columns
with slightly different schema
Below is the example.
Step 1: dataframe arrayStructDf1 with columnbooksIntersted of type array of
struct
val arrayStructData = Seq(
Row("James",List(Row("Java","XX",120),Row("Scala","XA",300))),
Row("Lilly",List(Row("Java","XY",200),Row("Scala","XB",500))))
val arrayStructSchema = new StructType().add("name",StringType)
.add("booksIntersted",ArrayType(new StructType()
.add("name",StringType)
.add("author",StringType)
.add("pages",IntegerType)))
val arrayStructDf1 =
spark.createDataFrame(spark.sparkContext.parallelize(arrayStructData),arrayStructSchema)
arrayStructDf1.printSchema()
scala> arrayStructDf2.printSchema()
root
|-- name: string (nullable = true)
|-- booksIntersted: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- name: string (nullable = true)
| | |-- author: string (nullable = true)
| | |-- pages: integer (nullable = true)
Step 2: Another dataframe arrayStructDf2 with columnbooksIntersted of type
array of a struct but struct contains an extra field called "new_column"
val arrayStructData2 = Seq(
Row("James",List(Row("Java","XX",120,"new_column_data"),Row("Scala","XA",300,"new_column_data"))),
Row("Lilly",List(Row("Java","XY",200,"new_column_data"),Row("Scala","XB",500,"new_column_data"))))
val arrayStructSchemaNewClm = new StructType().add("name",StringType)
.add("booksIntersted",ArrayType(new StructType()
.add("name",StringType)
.add("author",StringType)
.add("pages",IntegerType)
.add("new_column",StringType)))
val arrayStructDf2 =
spark.createDataFrame(spark.sparkContext.parallelize(arrayStructData2),arrayStructSchemaNewClm)
arrayStructDf2.printSchema()
scala> arrayStructDf2.printSchema()
root
|-- name: string (nullable = true)
|-- booksIntersted: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- name: string (nullable = true)
| | |-- author: string (nullable = true)
| | |-- pages: integer (nullable = true)
| | |-- new_column: string (nullable = true)
Step 3: Try to merge arrayStructDf1 and arrayStructDf2 using unionByName
{code:java}
scala> arrayStructDf1.unionByName(arrayStructDf2,allowMissingColumns=true)
org.apache.spark.sql.AnalysisException: Union can only be performed on tables
with the compatible column types.
array<struct<name:string,author:string,pages:int,new_column:string>> <>
array<struct<name:string,author:string,pages:int>> at the second column of the
second table;
'Union false, false
:- LogicalRDD [name#183, booksIntersted#184], false
+- Project [name#204, booksIntersted#205]
+- LogicalRDD [name#204, booksIntersted#205], false{code}
unionByName should fill the missing data with null like it does column with
struct type
https://issues.apache.org/jira/browse/SPARK-32376
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