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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2021/01/09 12:07:16 UTC
[GitHub] [spark] maropu commented on a change in pull request #31103: [SPARK-34002][SQL] Fix the usage of encoder in ScalaUDF
maropu commented on a change in pull request #31103:
URL: https://github.com/apache/spark/pull/31103#discussion_r554406326
##########
File path: sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala
##########
@@ -1982,8 +1982,34 @@ class DatasetSuite extends QueryTest
assert(timezone == "Asia/Shanghai")
}
}
+
+ test("SPARK-34002: Fix broken Option input/output in UDF") {
+ def f1(bar: Bar): Option[Bar] = {
+ None
+ }
+
+ def f2(bar: Option[Bar]): Option[Bar] = {
+ bar
+ }
+
+ val udf1: UserDefinedFunction = udf(f1 _).withName("f1")
+ val udf2: UserDefinedFunction = udf(f2 _).withName("f2")
Review comment:
nit: `val udf1 = udf(f1 _).withName("f1")`
##########
File path: sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala
##########
@@ -1982,8 +1982,34 @@ class DatasetSuite extends QueryTest
assert(timezone == "Asia/Shanghai")
}
}
+
+ test("SPARK-34002: Fix broken Option input/output in UDF") {
+ def f1(bar: Bar): Option[Bar] = {
+ None
+ }
+
+ def f2(bar: Option[Bar]): Option[Bar] = {
+ bar
+ }
+
+ val udf1: UserDefinedFunction = udf(f1 _).withName("f1")
+ val udf2: UserDefinedFunction = udf(f2 _).withName("f2")
+
+ val df = (1 to 2).map(i => Tuple1(Bar(1))).toDF("c0")
+ val newDf = df
+ .withColumn("c1", udf1(col("c0")))
+ .withColumn("c2", udf2(col("c1")))
+ val schema = newDf.schema
+ assert(schema == StructType(
Review comment:
`assert(newDf.schema == StructType(`
##########
File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/encoders/ExpressionEncoder.scala
##########
@@ -304,8 +304,13 @@ case class ExpressionEncoder[T](
StructField(s.name, s.dataType, s.nullable)
})
+ /**
+ * This is used for `ScalaUDF` (see `UDFRegistration`). As the serialization in `ScalaUDF` is for
+ * individual column, not the whole row, we just take the data type of vanilla object serializer,
+ * not `serializer` which is transformed somehow.
+ */
def dataTypeAndNullable: Schema = {
- val dataType = if (isSerializedAsStruct) schema else schema.head.dataType
+ val dataType = objSerializer.dataType
Schema(dataType, objSerializer.nullable)
Review comment:
Btw, we don't need to move this thin helper func into the `UDFRegistration` side? It seems this func exists now for the specific use for udf-related code.
##########
File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/encoders/ExpressionEncoder.scala
##########
@@ -304,8 +304,13 @@ case class ExpressionEncoder[T](
StructField(s.name, s.dataType, s.nullable)
})
+ /**
+ * This is used for `ScalaUDF` (see `UDFRegistration`). As the serialization in `ScalaUDF` is for
+ * individual column, not the whole row, we just take the data type of vanilla object serializer,
+ * not `serializer` which is transformed somehow.
+ */
def dataTypeAndNullable: Schema = {
- val dataType = if (isSerializedAsStruct) schema else schema.head.dataType
+ val dataType = objSerializer.dataType
Schema(dataType, objSerializer.nullable)
Review comment:
nit: Schema(objSerializer.dataType, objSerializer.nullable)
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