fqaiser94 commented on a change in pull request #27066:
URL: https://github.com/apache/spark/pull/27066#discussion_r448418602
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
##########
@@ -923,4 +923,501 @@ class ColumnExpressionSuite extends QueryTest with
SharedSparkSession {
val inSet = InSet(Literal("a"), Set("a", "b").map(UTF8String.fromString))
assert(inSet.sql === "('a' IN ('a', 'b'))")
}
+
+ def checkAnswerAndSchema(
+ df: => DataFrame,
+ expectedAnswer: Seq[Row],
+ expectedSchema: StructType): Unit = {
+
+ checkAnswer(df, expectedAnswer)
+ assert(df.schema == expectedSchema)
+ }
+
+ private lazy val structType = StructType(Seq(
+ StructField("a", IntegerType, nullable = false),
+ StructField("b", IntegerType, nullable = true),
+ StructField("c", IntegerType, nullable = false)))
+
+ private lazy val structLevel1: DataFrame = spark.createDataFrame(
+ sparkContext.parallelize(Row(Row(1, null, 3)) :: Nil),
+ StructType(Seq(StructField("a", structType, nullable = false))))
+
+ private lazy val nullStructLevel1: DataFrame = spark.createDataFrame(
+ sparkContext.parallelize(Row(null) :: Nil),
+ StructType(Seq(StructField("a", structType, nullable = true))))
+
+ private lazy val structLevel2: DataFrame = spark.createDataFrame(
+ sparkContext.parallelize(Row(Row(Row(1, null, 3))) :: Nil),
+ StructType(Seq(
+ StructField("a", StructType(Seq(
+ StructField("a", structType, nullable = false))),
+ nullable = false))))
+
+ private lazy val nullStructLevel2: DataFrame = spark.createDataFrame(
+ sparkContext.parallelize(Row(Row(null)) :: Nil),
+ StructType(Seq(
+ StructField("a", StructType(Seq(
+ StructField("a", structType, nullable = true))),
+ nullable = false))))
+
+ private lazy val structLevel3: DataFrame = spark.createDataFrame(
+ sparkContext.parallelize(Row(Row(Row(Row(1, null, 3)))) :: Nil),
+ StructType(Seq(
+ StructField("a", StructType(Seq(
+ StructField("a", StructType(Seq(
+ StructField("a", structType, nullable = false))),
+ nullable = false))),
+ nullable = false))))
+
+ test("withField should throw an exception if called on a non-StructType
column") {
+ intercept[AnalysisException] {
+ testData.withColumn("key", $"key".withField("a", lit(2)))
+ }.getMessage should include("struct argument should be struct type, got:
int")
+ }
+
+ test("withField should throw an exception if either fieldName or col
argument are null") {
+ an[java.lang.NullPointerException] should be thrownBy {
+ structLevel1.withColumn("a", $"a".withField(null, lit(2)))
+ }
+
+ an[java.lang.NullPointerException] should be thrownBy {
+ structLevel1.withColumn("a", $"a".withField("b", null))
+ }
+
+ an[java.lang.NullPointerException] should be thrownBy {
+ structLevel1.withColumn("a", $"a".withField(null, null))
+ }
+ }
+
+ test("withField should throw an exception if any intermediate structs don't
exist") {
+ intercept[AnalysisException] {
+ structLevel2.withColumn("a", 'a.withField("x.b", lit(2)))
+ }.getMessage should include("No such struct field x in a")
+
+ intercept[AnalysisException] {
+ structLevel3.withColumn("a", 'a.withField("a.x.b", lit(2)))
+ }.getMessage should include("No such struct field x in a")
+ }
+
+ test("withField should throw an exception if any intermediate field is not a
struct") {
+ intercept[AnalysisException] {
+ structLevel1.withColumn("a", 'a.withField("b.a", lit(2)))
+ }.getMessage should include("struct argument should be struct type, got:
int")
+
+ intercept[AnalysisException] {
+ val structLevel2: DataFrame = spark.createDataFrame(
+ sparkContext.parallelize(Row(Row(Row(1, null, 3), 4)) :: Nil),
+ StructType(Seq(
+ StructField("a", StructType(Seq(
+ StructField("a", structType, nullable = false),
+ StructField("a", IntegerType, nullable = false))),
+ nullable = false))))
+
+ structLevel2.withColumn("a", 'a.withField("a.b", lit(2)))
+ }.getMessage should include("Ambiguous reference to fields")
+ }
+
+ test("withField should add field with no name") {
Review comment:
I don't have a use case to support this behaviour.
The main reason I decided to allow this behaviour is to keep in line with
the behaviour of other similar functions.
For example, `Dataset.withColumn` allows you to create columns with empty
string as a name.
```
scala> Seq(1).toDF("col1").withColumn("", lit(2)).printSchema
root
|-- col1: integer (nullable = false)
|-- : integer (nullable = false)
```
Similarly, `org.apache.spark.sql.functions.struct` allows you to create a
struct with fields which have empty string as a name.
```
scala> Seq(1).toDF("col1").withColumn("col2", struct(lit(1).as("a"),
lit(2).as(""))).printSchema
root
|-- col1: integer (nullable = false)
|-- col2: struct (nullable = false)
| |-- a: integer (nullable = false)
| |-- : integer (nullable = false)
```
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