Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/9712#discussion_r44860429 --- 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 -- I have a question here. According to the Javadoc of `Row`, a user should use and only use `Row` for `StructType` field. It's ok to support `Product` too, but do we have a reason for this? Is it needed for the UDT stuff? Sorry I'm not familiar with UDT handling, it will be good if you can explain it in detail, thanks!
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