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WriteSchemaConverter#serializeColumn should not upcast #48

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WriteSchemaConverter#serializeColumn should not upcast #48

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4 changes: 3 additions & 1 deletion data-source/src/main/scala/tech/ytsaurus/spyt/serializers/YtLogicalType.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -75,7 +75,9 @@ sealed abstract class AtomicYtLogicalType(name: String,
sealed trait CompositeYtLogicalType extends YtLogicalType {
override def columnValueType: ColumnValueType = ColumnValueType.ANY

override def getName(isColumnType: Boolean): String = ColumnValueType.ANY.getName
override def getName(isColumnType: Boolean): String = throw new IllegalStateException(
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I'd prefer to preserve backward compatibility and introduce a config option to change this behaviour

"unable to serialize a complex type: use spark.yt.write.typeV3.enabled"
)
}

sealed abstract class CompositeYtLogicalTypeAlias(name: String,
Expand Down
64 changes: 33 additions & 31 deletions data-source/src/test/scala/tech/ytsaurus/spyt/format/types/ComplexTypeTest.scala
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
package tech.ytsaurus.spyt.format.types

import com.google.common.base.Throwables
import org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkException
import org.apache.spark.sql.types._
Expand Down Expand Up @@ -469,30 +470,28 @@ class ComplexTypeTest extends FlatSpec with Matchers with LocalSpark with TmpDir
res.select($"value.*").as[TestSmall].collect() should contain theSameElementsAs Seq(testRowSmall)
}

it should "write dataset with complex types" in {
it should "not write dataset with complex types" in {
import spark.implicits._

Seq(
(Seq(1, 2, 3), A(1, Some("a")), Map("1" -> 0.1)),
(Seq(4, 5, 6), A(2, None), Map("2" -> 0.3))
)
.toDF("a", "b", "c").coalesce(1)
.write.mode(SaveMode.Overwrite)
.yt(tmpPath)

val res = spark.read
.schemaHint(
"a" -> ArrayType(LongType),
"b" -> StructType(Seq(StructField("field1", LongType), StructField("field2", StringType))),
"c" -> MapType(StringType, DoubleType)
try {
Seq(
(Seq(1, 2, 3), A(1, Some("a")), Map("1" -> 0.1)),
(Seq(4, 5, 6), A(2, None), Map("2" -> 0.3))
)
.yt(tmpPath)

res.columns should contain theSameElementsAs Seq("a", "b", "c")
res.select("a", "b", "c").collect() should contain theSameElementsAs Seq(
Row(Seq(1, 2, 3), Row(1, "a"), Map("1" -> 0.1)),
Row(Seq(4, 5, 6), Row(2, null), Map("2" -> 0.3))
)
.toDF("a", "b", "c").coalesce(1)
.write.mode(SaveMode.Overwrite)
.yt(tmpPath)
fail("was supposed to fail")
} catch {
case t: Throwable =>
val cause = Throwables.getRootCause(t)
if (
!cause.isInstanceOf[IllegalStateException]
|| cause.asInstanceOf[IllegalStateException].getMessage != "unable to serialize a complex type: use spark.yt.write.typeV3.enabled"
) {
throw t
}
}
}

it should "sort map data while writing" in {
Expand Down Expand Up @@ -528,16 +527,19 @@ class ComplexTypeTest extends FlatSpec with Matchers with LocalSpark with TmpDir
)
val df = data.toDF("map1", "map2")

df.write.yt(tmpPath)

val res = spark.read
.yt(tmpPath)
.select('map1.cast(BinaryType), 'map2.cast(BinaryType))
.as[(Option[Array[Byte]], Option[Array[Byte]])]
.collect()
.map{case (x,y) => x.map(_.toList) -> y.map(_.toList)}

res should contain theSameElementsAs binaryData
try {
df.write.yt(tmpPath)
fail("was supposed to fail")
} catch {
case t: Throwable =>
val cause = Throwables.getRootCause(t)
if (
!cause.isInstanceOf[IllegalStateException]
|| cause.asInstanceOf[IllegalStateException].getMessage != "unable to serialize a complex type: use spark.yt.write.typeV3.enabled"
) {
throw t
}
}
}
}

Expand Down
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