[SPARK-13719][SQL] Parse JSON rows having an array type and a struct …

…type in the same fieild

## What changes were proposed in this pull request?

This apache#2400 added the support to parse JSON rows wrapped with an array. However, this throws an exception when the given data contains array data and struct data in the same field as below:

```json
{"a": {"b": 1}}
{"a": []}
```

and the schema is given as below:

```scala
val schema =
  StructType(
    StructField("a", StructType(
      StructField("b", StringType) :: Nil
    )) :: Nil)
```

- **Before**

```scala
sqlContext.read.schema(schema).json(path).show()
```

```scala
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 7 in stage 0.0 failed 4 times, most recent failure: Lost task 7.3 in stage 0.0 (TID 10, 192.168.1.170): java.lang.ClassCastException: org.apache.spark.sql.types.GenericArrayData cannot be cast to org.apache.spark.sql.catalyst.InternalRow
	at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow$class.getStruct(rows.scala:50)
	at org.apache.spark.sql.catalyst.expressions.GenericMutableRow.getStruct(rows.scala:247)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate.eval(Unknown Source)
...
```

- **After**

```scala
sqlContext.read.schema(schema).json(path).show()
```

```bash
+----+
|   a|
+----+
| [1]|
|null|
+----+
```

For other data types, in this case it converts the given values are `null` but only this case emits an exception.

This PR makes the support for wrapped rows applied only at the top level.

## How was this patch tested?

Unit tests were used and `./dev/run_tests` for code style tests.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes apache#11752 from HyukjinKwon/SPARK-3308-follow-up.

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala

@@ -20,7 +20,6 @@ package org.apache.spark.sql.execution.datasources.json
 import java.io.CharArrayWriter
 
 import com.fasterxml.jackson.core.JsonFactory
-import com.google.common.base.Objects
 import org.apache.hadoop.fs.{FileStatus, Path}
 import org.apache.hadoop.io.{LongWritable, NullWritable, Text}
 import org.apache.hadoop.mapred.{JobConf, TextInputFormat}

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala

@@ -49,8 +49,31 @@ object JacksonParser {
 
   /**
    * Parse the current token (and related children) according to a desired schema
+   * This is an wrapper for the method `convertField()` to handle a row wrapped
+   * with an array.
    */
-  def convertField(
+  def convertRootField(
+        factory: JsonFactory,
+        parser: JsonParser,
+        schema: DataType): Any = {
+    import com.fasterxml.jackson.core.JsonToken._
+    (parser.getCurrentToken, schema) match {
+      case (START_ARRAY, st: StructType) =>
+        // SPARK-3308: support reading top level JSON arrays and take every element
+        // in such an array as a row
+        convertArray(factory, parser, st)
+
+      case (START_OBJECT, ArrayType(st, _)) =>
+        // the business end of SPARK-3308:
+        // when an object is found but an array is requested just wrap it in a list
+        convertField(factory, parser, st) :: Nil
+
+      case _ =>
+        convertField(factory, parser, schema)
+    }
+  }
+
+  private def convertField(
       factory: JsonFactory,
       parser: JsonParser,
       schema: DataType): Any = {
@@ -157,19 +180,9 @@ object JacksonParser {
       case (START_OBJECT, st: StructType) =>
         convertObject(factory, parser, st)
 
-      case (START_ARRAY, st: StructType) =>
-        // SPARK-3308: support reading top level JSON arrays and take every element
-        // in such an array as a row
-        convertArray(factory, parser, st)
-
       case (START_ARRAY, ArrayType(st, _)) =>
         convertArray(factory, parser, st)
 
-      case (START_OBJECT, ArrayType(st, _)) =>
-        // the business end of SPARK-3308:
-        // when an object is found but an array is requested just wrap it in a list
-        convertField(factory, parser, st) :: Nil
-
       case (START_OBJECT, MapType(StringType, kt, _)) =>
         convertMap(factory, parser, kt)
 
@@ -264,7 +277,7 @@ object JacksonParser {
           Utils.tryWithResource(factory.createParser(record)) { parser =>
             parser.nextToken()
 
-            convertField(factory, parser, schema) match {
+            convertRootField(factory, parser, schema) match {
               case null => failedRecord(record)
               case row: InternalRow => row :: Nil
               case array: ArrayData =>

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala

@@ -65,7 +65,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
 
       Utils.tryWithResource(factory.createParser(writer.toString)) { parser =>
         parser.nextToken()
-        JacksonParser.convertField(factory, parser, dataType)
+        JacksonParser.convertRootField(factory, parser, dataType)
       }
     }
 
@@ -1426,6 +1426,23 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
     }
   }
 
+  test("Parse JSON rows having an array type and a struct type in the same field.") {
+    withTempDir { dir =>
+      val dir = Utils.createTempDir()
+      dir.delete()
+      val path = dir.getCanonicalPath
+      arrayAndStructRecords.map(record => record.replaceAll("\n", " ")).saveAsTextFile(path)
+
+      val schema =
+        StructType(
+          StructField("a", StructType(
+            StructField("b", StringType) :: Nil
+          )) :: Nil)
+      val jsonDF = sqlContext.read.schema(schema).json(path)
+      assert(jsonDF.count() == 2)
+    }
+  }
+
   test("SPARK-12872 Support to specify the option for compression codec") {
     withTempDir { dir =>
       val dir = Utils.createTempDir()

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala

@@ -209,6 +209,11 @@ private[json] trait TestJsonData {
     sqlContext.sparkContext.parallelize(
       """{"ts":1451732645}""" :: Nil)
 
+  def arrayAndStructRecords: RDD[String] =
+    sqlContext.sparkContext.parallelize(
+      """{"a": {"b": 1}}""" ::
+      """{"a": []}""" :: Nil)
+
   lazy val singleRow: RDD[String] = sqlContext.sparkContext.parallelize("""{"a":123}""" :: Nil)
 
   def empty: RDD[String] = sqlContext.sparkContext.parallelize(Seq[String]())