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https://issues.apache.org/jira/browse/SPARK-21677?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16121658#comment-16121658
]
Hyukjin Kwon commented on SPARK-21677:
--------------------------------------
[~cjm], I was thinking like
{code}
spark.sql("""SELECT json_tuple('{"a":1, "b":2}', cast(NULL AS STRING), 'b',
cast(NULL AS STRING), 'a');""").show()
+----+---+----+---+
| c0| c1| c2| c3|
+----+---+----+---+
|null| 2|null| 1|
+----+---+----+---+
{code}
I think this could be at least consistent with Hive's implementation:
{code}
hive> SELECT json_tuple('{"a":1, "b":2}', cast(NULL AS STRING), 'b', cast(NULL
AS STRING), 'a');
...
NULL 2 NULL 1
{code}
> json_tuple throws NullPointException when column is null as string type.
> ------------------------------------------------------------------------
>
> Key: SPARK-21677
> URL: https://issues.apache.org/jira/browse/SPARK-21677
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Hyukjin Kwon
> Priority: Minor
> Labels: Starter
>
> I was testing {{json_tuple}} before using this to extract values from JSONs
> in my testing cluster but I found it could actually throw
> {{NullPointException}} as below sometimes:
> {code}
> scala> Seq(("""{"Hyukjin": 224, "John":
> 1225}""")).toDS.selectExpr("json_tuple(value, trim(' Hyukjin '))").show()
> +---+
> | c0|
> +---+
> |224|
> +---+
> scala> Seq(("""{"Hyukjin": 224, "John":
> 1225}""")).toDS.selectExpr("json_tuple(value, trim(' Jackson '))").show()
> +----+
> | c0|
> +----+
> |null|
> +----+
> scala> Seq(("""{"Hyukjin": 224, "John":
> 1225}""")).toDS.selectExpr("json_tuple(value, trim(null))").show()
> ...
> java.lang.NullPointerException
> at
> org.apache.spark.sql.catalyst.expressions.JsonTuple$$anonfun$foldableFieldNames$1.apply(jsonExpressions.scala:367)
> at
> org.apache.spark.sql.catalyst.expressions.JsonTuple$$anonfun$foldableFieldNames$1.apply(jsonExpressions.scala:366)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> at
> org.apache.spark.sql.catalyst.expressions.JsonTuple.foldableFieldNames$lzycompute(jsonExpressions.scala:366)
> at
> org.apache.spark.sql.catalyst.expressions.JsonTuple.foldableFieldNames(jsonExpressions.scala:365)
> at
> org.apache.spark.sql.catalyst.expressions.JsonTuple.constantFields$lzycompute(jsonExpressions.scala:373)
> at
> org.apache.spark.sql.catalyst.expressions.JsonTuple.constantFields(jsonExpressions.scala:373)
> at
> org.apache.spark.sql.catalyst.expressions.JsonTuple.org$apache$spark$sql$catalyst$expressions$JsonTuple$$parseRow(jsonExpressions.scala:417)
> at
> org.apache.spark.sql.catalyst.expressions.JsonTuple$$anonfun$eval$4.apply(jsonExpressions.scala:401)
> at
> org.apache.spark.sql.catalyst.expressions.JsonTuple$$anonfun$eval$4.apply(jsonExpressions.scala:400)
> at org.apache.spark.util.Utils$.tryWithResource(Utils.scala:2559)
> at
> org.apache.spark.sql.catalyst.expressions.JsonTuple.eval(jsonExpressions.scala:400)
> {code}
> It sounds we should show explicit error messages or return {{NULL}}.
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