[SPARK-18220][SQL] read Hive orc table with varchar column should not…

… fail

## What changes were proposed in this pull request?

Spark SQL only has `StringType`, when reading hive table with varchar column, we will read that column as `StringType`. However, we still need to use varchar `ObjectInspector` to read varchar column in hive table, which means we need to know the actual column type at hive side.

In Spark 2.1, after #14363 , we parse hive type string to catalyst type, which means the actual column type at hive side is erased. Then we may use string `ObjectInspector` to read varchar column and fail.

This PR keeps the original hive column type string in the metadata of `StructField`, and use it when we convert it to a hive column.

## How was this patch tested?

newly added regression test

Author: Wenchen Fan <wenchen@databricks.com>

Closes #16060 from cloud-fan/varchar.

(cherry picked from commit 3f03c90)
Signed-off-by: Reynold Xin <rxin@databricks.com>

sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveUtils.scala

@@ -54,6 +54,14 @@ private[spark] object HiveUtils extends Logging {
   /** The version of hive used internally by Spark SQL. */
   val hiveExecutionVersion: String = "1.2.1"
 
+  /**
+   * The property key that is used to store the raw hive type string in the metadata of StructField.
+   * For example, in the case where the Hive type is varchar, the type gets mapped to a string type
+   * in Spark SQL, but we need to preserve the original type in order to invoke the correct object
+   * inspector in Hive.
+   */
+  val hiveTypeString: String = "HIVE_TYPE_STRING"
+
   val HIVE_METASTORE_VERSION = SQLConfigBuilder("spark.sql.hive.metastore.version")
     .doc("Version of the Hive metastore. Available options are " +
         s"<code>0.12.0</code> through <code>$hiveExecutionVersion</code>.")

sql/hive/src/main/scala/org/apache/spark/sql/hive/MetastoreRelation.scala

@@ -61,7 +61,12 @@ private[hive] case class MetastoreRelation(
   override protected def otherCopyArgs: Seq[AnyRef] = catalogTable :: sparkSession :: Nil
 
   private def toHiveColumn(c: StructField): FieldSchema = {
-    new FieldSchema(c.name, c.dataType.catalogString, c.getComment.orNull)
+    val typeString = if (c.metadata.contains(HiveUtils.hiveTypeString)) {
+      c.metadata.getString(HiveUtils.hiveTypeString)
+    } else {
+      c.dataType.catalogString
+    }
+    new FieldSchema(c.name, typeString, c.getComment.orNull)
   }
 
   // TODO: merge this with HiveClientImpl#toHiveTable

sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala

@@ -46,7 +46,8 @@ import org.apache.spark.sql.catalyst.catalog.CatalogTypes.TablePartitionSpec
 import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.parser.{CatalystSqlParser, ParseException}
 import org.apache.spark.sql.execution.QueryExecutionException
-import org.apache.spark.sql.types.{StructField, StructType}
+import org.apache.spark.sql.hive.HiveUtils
+import org.apache.spark.sql.types.{MetadataBuilder, StructField, StructType}
 import org.apache.spark.util.{CircularBuffer, Utils}
 
 /**
@@ -748,7 +749,12 @@ private[hive] class HiveClientImpl(
       .asInstanceOf[Class[_ <: org.apache.hadoop.hive.ql.io.HiveOutputFormat[_, _]]]
 
   private def toHiveColumn(c: StructField): FieldSchema = {
-    new FieldSchema(c.name, c.dataType.catalogString, c.getComment().orNull)
+    val typeString = if (c.metadata.contains(HiveUtils.hiveTypeString)) {
+      c.metadata.getString(HiveUtils.hiveTypeString)
+    } else {
+      c.dataType.catalogString
+    }
+    new FieldSchema(c.name, typeString, c.getComment().orNull)
   }
 
   private def fromHiveColumn(hc: FieldSchema): StructField = {
@@ -758,10 +764,13 @@ private[hive] class HiveClientImpl(
       case e: ParseException =>
         throw new SparkException("Cannot recognize hive type string: " + hc.getType, e)
     }
+
+    val metadata = new MetadataBuilder().putString(HiveUtils.hiveTypeString, hc.getType).build()
     val field = StructField(
       name = hc.getName,
       dataType = columnType,
-      nullable = true)
+      nullable = true,
+      metadata = metadata)
     Option(hc.getComment).map(field.withComment).getOrElse(field)
   }
 

sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveExternalCatalogBackwardCompatibilitySuite.scala

@@ -205,7 +205,7 @@ class HiveExternalCatalogBackwardCompatibilitySuite extends QueryTest
   test("make sure we can read table created by old version of Spark") {
     for ((tbl, expectedSchema) <- rawTablesAndExpectations) {
       val readBack = getTableMetadata(tbl.identifier.table)
-      assert(readBack.schema == expectedSchema)
+      assert(readBack.schema.sameType(expectedSchema))
 
       if (tbl.tableType == CatalogTableType.EXTERNAL) {
         // trim the URI prefix
@@ -235,7 +235,7 @@ class HiveExternalCatalogBackwardCompatibilitySuite extends QueryTest
       sql(s"ALTER TABLE ${tbl.identifier} RENAME TO $newName")
 
       val readBack = getTableMetadata(newName)
-      assert(readBack.schema == expectedSchema)
+      assert(readBack.schema.sameType(expectedSchema))
 
       // trim the URI prefix
       val actualTableLocation = new URI(readBack.storage.locationUri.get).getPath

sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala

@@ -22,6 +22,7 @@ import java.io.File
 import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.sql.{QueryTest, Row}
+import org.apache.spark.sql.hive.HiveExternalCatalog
 import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
@@ -150,6 +151,17 @@ abstract class OrcSuite extends QueryTest with TestHiveSingleton with BeforeAndA
   test("SPARK-18433: Improve DataSource option keys to be more case-insensitive") {
     assert(new OrcOptions(Map("Orc.Compress" -> "NONE")).compressionCodec == "NONE")
   }
+
+  test("SPARK-18220: read Hive orc table with varchar column") {
+    val hiveClient = spark.sharedState.externalCatalog.asInstanceOf[HiveExternalCatalog].client
+    try {
+      hiveClient.runSqlHive("CREATE TABLE orc_varchar(a VARCHAR(10)) STORED AS orc")
+      hiveClient.runSqlHive("INSERT INTO TABLE orc_varchar SELECT 'a' FROM (SELECT 1) t")
+      checkAnswer(spark.table("orc_varchar"), Row("a"))
+    } finally {
+      hiveClient.runSqlHive("DROP TABLE IF EXISTS orc_varchar")
+    }
+  }
 }
 
 class OrcSourceSuite extends OrcSuite {