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Empty arrays returns the expected datatype #274

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Aug 29, 2022
Merged

Empty arrays returns the expected datatype #274

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12 changes: 9 additions & 3 deletions core/src/main/scala/doric/syntax/ArrayColumns.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -2,10 +2,11 @@ package doric
package syntax

import scala.language.higherKinds
import scala.reflect.ClassTag

import cats.data.Kleisli
import cats.implicits._
import doric.types.CollectionType
import doric.types.{CollectionType, LiteralSparkType, SparkType}

import org.apache.spark.sql.{Column, Dataset, functions => f}
import org.apache.spark.sql.catalyst.expressions._
Expand Down Expand Up @@ -58,8 +59,13 @@ private[syntax] trait ArrayColumns {
* @see org.apache.spark.sql.functions.array
* @todo scaladoc link (issue #135)
*/
def array[T](cols: DoricColumn[T]*): ArrayColumn[T] =
cols.toList.traverse(_.elem).map(f.array(_: _*)).toDC
def array[T: SparkType: ClassTag](
cols: DoricColumn[T]*
)(implicit lt: LiteralSparkType[Array[T]]): ArrayColumn[T] =
if (cols.nonEmpty)
cols.toList.traverse(_.elem).map(f.array(_: _*)).toDC
else
lit(Array.empty[T])

/**
* Creates a new list column. The input columns must all have the same data type.
Expand Down
15 changes: 15 additions & 0 deletions core/src/test/scala/doric/syntax/ArrayColumnsSpec.scala
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Expand Up @@ -2,6 +2,7 @@ package doric
package syntax

import doric.sem.{ChildColumnNotFound, ColumnTypeError, DoricMultiError, SparkErrorWrapper}
import doric.types.SparkType

import org.apache.spark.sql.{Row, functions => f}
import org.apache.spark.sql.types.{IntegerType, LongType, StringType}
Expand Down Expand Up @@ -300,6 +301,20 @@ class ArrayColumnsSpec extends DoricTestElements {
List(Some(Array("a", "b")))
)
}

it("should work be of the expected type when is empty") {
val df = spark
.range(5)
.select(
array[Long]().as("l"),
array[String]().as("s"),
array[(String, String)]().as("r")
)

df("l").expr.dataType === SparkType[Array[Long]].dataType
df("s").expr.dataType === SparkType[Array[String]].dataType
df("r").expr.dataType === SparkType[Array[(String, String)]].dataType
}
}

describe("list doric function") {
Expand Down