[
https://issues.apache.org/jira/browse/SPARK-36546?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Vishal Dhavale updated SPARK-36546:
-----------------------------------
Description:
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
{code:java}
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)
{code}
Step 2: Another dataframe arrayStructDf2 with column booksIntersted of type
array of a struct but struct contains an extra field called "new_column"
{code:java}
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){code}
Step3: Merge arrayStructDf1 and arrayStructDf2 using unionByName
We see the error org.apache.spark.sql.AnalysisException: Union can only be
performed on tables with the compatible column types.
{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
was:
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
{code:java}
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)
| | |-- new_column: string (nullable = true)
{code}
Step 2: Another dataframe arrayStructDf2 with column booksIntersted of type
array of a struct but struct contains an extra field called "new_column"
{code:java}
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(){code}
We see the below error when we try to use unionByName arrayStructDf1 and
arrayStructDf2
{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
> 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
> Priority: Major
>
> 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
> {code:java}
> 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)
> {code}
>
> Step 2: Another dataframe arrayStructDf2 with column booksIntersted of type
> array of a struct but struct contains an extra field called "new_column"
> {code:java}
> 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){code}
>
> Step3: Merge arrayStructDf1 and arrayStructDf2 using unionByName
> We see the error org.apache.spark.sql.AnalysisException: Union can only be
> performed on tables with the compatible column types.
> {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
>
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