[SPARK-15315][SQL] Adding error check to the CSV datasource writer for...

[SPARK-15315][SQL] Adding error check to  the CSV datasource writer for unsupported complex data types.

## What changes were proposed in this pull request?

Adds error handling to the CSV writer  for unsupported complex data types.  Currently garbage gets written to the output csv files if the data frame schema has complex data types.

## How was this patch tested?

Added new unit test case.

Author: sureshthalamati <suresh.thalamati@gmail.com>

Closes #13105 from sureshthalamati/csv_complex_types_SPARK-15315.

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/DefaultSource.scala

@@ -32,7 +32,7 @@ import org.apache.spark.sql.catalyst.expressions.JoinedRow
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
 import org.apache.spark.sql.execution.datasources._
 import org.apache.spark.sql.sources._
-import org.apache.spark.sql.types.{StringType, StructField, StructType}
+import org.apache.spark.sql.types._
 import org.apache.spark.util.SerializableConfiguration
 
 /**
@@ -86,6 +86,7 @@ class DefaultSource extends FileFormat with DataSourceRegister {
       job: Job,
       options: Map[String, String],
       dataSchema: StructType): OutputWriterFactory = {
+    verifySchema(dataSchema)
     val conf = job.getConfiguration
     val csvOptions = new CSVOptions(options)
     csvOptions.compressionCodec.foreach { codec =>
@@ -172,4 +173,15 @@ class DefaultSource extends FileFormat with DataSourceRegister {
         .mapPartitions(_.map(pair => new String(pair._2.getBytes, 0, pair._2.getLength, charset)))
     }
   }
+
+  private def verifySchema(schema: StructType): Unit = {
+    schema.foreach { field =>
+      field.dataType match {
+        case _: ArrayType | _: MapType | _: StructType =>
+          throw new UnsupportedOperationException(
+            s"CSV data source does not support ${field.dataType.simpleString} data type.")
+        case _ =>
+      }
+    }
+  }
 }

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala

@@ -31,6 +31,8 @@ import org.apache.spark.sql.test.{SharedSQLContext, SQLTestUtils}
 import org.apache.spark.sql.types._
 
 class CSVSuite extends QueryTest with SharedSQLContext with SQLTestUtils {
+  import testImplicits._
+
   private val carsFile = "cars.csv"
   private val carsMalformedFile = "cars-malformed.csv"
   private val carsFile8859 = "cars_iso-8859-1.csv"
@@ -582,4 +584,24 @@ class CSVSuite extends QueryTest with SharedSQLContext with SQLTestUtils {
 
     assert(numbers.count() == 8)
   }
+
+  test("error handling for unsupported data types.") {
+    withTempDir { dir =>
+      val csvDir = new File(dir, "csv").getCanonicalPath
+      var msg = intercept[UnsupportedOperationException] {
+        Seq((1, "Tesla")).toDF("a", "b").selectExpr("struct(a, b)").write.csv(csvDir)
+      }.getMessage
+      assert(msg.contains("CSV data source does not support struct<a:int,b:string> data type"))
+
+      msg = intercept[UnsupportedOperationException] {
+        Seq((1, Map("Tesla" -> 3))).toDF("id", "cars").write.csv(csvDir)
+      }.getMessage
+      assert(msg.contains("CSV data source does not support map<string,int> data type"))
+
+      msg = intercept[UnsupportedOperationException] {
+        Seq((1, Array("Tesla", "Chevy", "Ford"))).toDF("id", "brands").write.csv(csvDir)
+      }.getMessage
+      assert(msg.contains("CSV data source does not support array<string> data type"))
+    }
+  }
 }