2 files changed, 21 insertions, 3 deletions
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index 4c1341ed5d..2be22761e8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -32,7 +32,7 @@ import org.apache.spark.sql.execution.command.DDLUtils
 import org.apache.spark.sql.execution.datasources.DataSource
 import org.apache.spark.sql.execution.datasources.jdbc._
 import org.apache.spark.sql.execution.datasources.json.JsonInferSchema
-import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.types.{StringType, StructType}
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -365,6 +365,15 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
         createParser)
     }
 
+    // Check a field requirement for corrupt records here to throw an exception in a driver side
+    schema.getFieldIndex(parsedOptions.columnNameOfCorruptRecord).foreach { corruptFieldIndex =>
+      val f = schema(corruptFieldIndex)
+      if (f.dataType != StringType || !f.nullable) {
+        throw new AnalysisException(
+          "The field for corrupt records must be string type and nullable")
+      }
+    }
+
     val parsed = jsonDataset.rdd.mapPartitions { iter =>
       val parser = new JacksonParser(schema, parsedOptions)
       iter.flatMap(parser.parse(_, createParser, UTF8String.fromString))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonFileFormat.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonFileFormat.scala
index 2cbf4ea7be..902fee5a7e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonFileFormat.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonFileFormat.scala
@@ -22,13 +22,13 @@ import org.apache.hadoop.fs.{FileStatus, Path}
 import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext}
 
 import org.apache.spark.internal.Logging
-import org.apache.spark.sql.{Row, SparkSession}
+import org.apache.spark.sql.{AnalysisException, SparkSession}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.json.{JacksonGenerator, JacksonParser, JSONOptions}
 import org.apache.spark.sql.catalyst.util.CompressionCodecs
 import org.apache.spark.sql.execution.datasources._
 import org.apache.spark.sql.sources._
-import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.types.{StringType, StructType}
 import org.apache.spark.util.SerializableConfiguration
 
 class JsonFileFormat extends TextBasedFileFormat with DataSourceRegister {
@@ -102,6 +102,15 @@ class JsonFileFormat extends TextBasedFileFormat with DataSourceRegister {
       sparkSession.sessionState.conf.sessionLocalTimeZone,
       sparkSession.sessionState.conf.columnNameOfCorruptRecord)
 
+    // Check a field requirement for corrupt records here to throw an exception in a driver side
+    dataSchema.getFieldIndex(parsedOptions.columnNameOfCorruptRecord).foreach { corruptFieldIndex =>
+      val f = dataSchema(corruptFieldIndex)
+      if (f.dataType != StringType || !f.nullable) {
+        throw new AnalysisException(
+          "The field for corrupt records must be string type and nullable")
+      }
+    }
+
     (file: PartitionedFile) => {
       val parser = new JacksonParser(requiredSchema, parsedOptions)
       JsonDataSource(parsedOptions).readFile(