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[SPARK-19543] from_json fails when the input row is empty #16881

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[SPARK-19543] from_json fails when the input row is empty #16881

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49401b1
fix
brkyvz Feb 10, 2017
6ef0f45
ready for review
brkyvz Feb 10, 2017
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2 changes: 1 addition & 1 deletion sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -496,7 +496,7 @@ case class JsonToStruct(schema: StructType, options: Map[String, String], child:
override def dataType: DataType = schema

override def nullSafeEval(json: Any): Any = {
try parser.parse(json.toString).head catch {
try parser.parse(json.toString).headOption.orNull catch {
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@HyukjinKwon HyukjinKwon Feb 10, 2017

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(Not for this PR but maybe loosely related I guess) I was thinking it is a bit odd that we support to only read the single row when it is a json array. It seems, for example,

import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = StructType(StructField("a", IntegerType) :: Nil)
Seq(("""[{"a": 1}, {"a": 2}]""")).toDF("struct").select(from_json(col("struct"), schema)).show()
+--------------------+
|jsontostruct(struct)|
+--------------------+
|                 [1]|
+--------------------+

I think maybe we should not support this in that function or it should work like a generator expression.

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@hvanhovell hvanhovell Feb 10, 2017

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@HyukjinKwon that seems fair. Feel free to work in this.

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@HyukjinKwon HyukjinKwon Feb 10, 2017

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Thank you for confirming.

case _: SparkSQLJsonProcessingException => null
}
}
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8 changes: 8 additions & 0 deletions ...alyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala
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Expand Up @@ -376,6 +376,14 @@ class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
)
}

test("SPARK-19543: from_json empty input column") {
val schema = StructType(StructField("a", IntegerType) :: Nil)
checkEvaluation(
JsonToStruct(schema, Map.empty, Literal.create(" ", StringType)),
null
)
}

test("to_json") {
val schema = StructType(StructField("a", IntegerType) :: Nil)
val struct = Literal.create(create_row(1), schema)
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