Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/9712#discussion_r44872999
--- Diff:
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/encoders/RowEncoderSuite.scala
---
@@ -68,7 +117,36 @@ class RowEncoderSuite extends SparkFunSuite {
.add("structOfArray", new StructType().add("array", arrayOfString))
.add("structOfMap", new StructType().add("map", mapOfString))
.add("structOfArrayAndMap",
- new StructType().add("array", arrayOfString).add("map",
mapOfString)))
+ new StructType().add("array", arrayOfString).add("map",
mapOfString))
+ .add("structOfUDT", structOfUDT))
+
+ test(s"encode/decode: arrayOfUDT") {
+ val schema = new StructType()
+ .add("arrayOfUDT", arrayOfUDT)
+
+ val encoder = RowEncoder(schema)
+
+ val input: Row = Row(Seq(new ExamplePoint(0.1, 0.2), new
ExamplePoint(0.3, 0.4)))
+ val row = encoder.toRow(input)
+ val convertedBack = encoder.fromRow(row)
+ assert(input.getSeq[ExamplePoint](0) ==
convertedBack.getSeq[ExamplePoint](0))
+ }
+
+ test(s"encode/decode: Product") {
+ val schema = new StructType()
+ .add("structAsProduct",
+ new StructType()
+ .add("int", IntegerType)
+ .add("string", StringType)
+ .add("double", DoubleType))
+
+ val encoder = RowEncoder(schema)
+
+ val input: Row = Row((100, "test", 0.123))
--- End diff --
If we have an input parameter mapping to a `StructType` field in an
`InternalRow`, we will use `Row` as its input type. E.g.,
`sqlContext.udf.register("udfFunc", (ns: Row) => { (ns.getInt(0),
ns.getString(1)) })`. But we can't use `Row` as output type for an UDF. Because
we can still get the input schema of `ScalaUDF`'s children expressions later if
we can't infer input types correctly by using `schemaFor`. However, the output
types of the UDF can be only inferred by `schemaFor`.
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