HyukjinKwon commented on a change in pull request #26907: [SPARK-30267][SQL]
Avro arrays can be of any List
URL: https://github.com/apache/spark/pull/26907#discussion_r358764878
##########
File path:
external/avro/src/test/scala/org/apache/spark/sql/avro/AvroCatalystDataConversionSuite.scala
##########
@@ -127,6 +127,25 @@ class AvroCatalystDataConversionSuite extends
SparkFunSuite
}
}
+ {
+ val seed = scala.util.Random.nextLong()
+ val rand = new scala.util.Random(seed)
+ val schema = StructType(
+ StructField("a",
+ ArrayType(
+ RandomDataGenerator.randomNestedSchema(rand, 10, testingTypes)
+ , containsNull = false)
+ , nullable = false) :: Nil
+ )
+
+ test(s"array of nested schema ${schema.catalogString} with seed $seed") {
+ val data = RandomDataGenerator.randomRow(rand, schema)
+ val converter = CatalystTypeConverters.createToCatalystConverter(schema)
+ val input = Literal.create(converter(data), schema)
+ roundTripTest(input)
+ }
+ }
Review comment:
You can do it in a bit more neater way:
```scala
test(s"array of nested schema with seed") {
val seed = scala.util.Random.nextLong()
val rand = new scala.util.Random(seed)
val schema = StructType(
StructField("a",
ArrayType(
RandomDataGenerator.randomNestedSchema(rand, 10, testingTypes),
containsNull = false),
nullable = false) :: Nil
)
withClue(s"Nested schema: $schema\nseed: $seed") {
val data = RandomDataGenerator.randomRow(rand, schema)
val converter =
CatalystTypeConverters.createToCatalystConverter(schema)
val input = Literal.create(converter(data), schema)
roundTripTest(input)
}
}
```
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