Simplify code.

Author: yhuai

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

@@ -69,22 +69,16 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
         val table = synchronized {
           client.getTable(in.database, in.name)
         }
-        val schemaString = Option(table.getProperty("spark.sql.sources.schema"))
-          .orElse {
-            // If spark.sql.sources.schema is not defined, we either splitted the schema to multiple
-            // parts or the schema was not defined. To determine if the schema was defined,
-            // we check spark.sql.sources.schema.numOfParts.
-            Option(table.getProperty("spark.sql.sources.schema.numOfParts")) match {
-              case Some(numOfParts) =>
-                val parts = (0 until numOfParts.toInt).map { index =>
-                  Option(table.getProperty(s"spark.sql.sources.schema.part.${index}"))
-                    .getOrElse("Could not read schema from the metastore because it is corrupted.")
-                }
-                // Stick all parts back to a single schema string in the JSON representation.
-                Some(parts.mkString)
-              case None => None // The schema was not defined.
+        val schemaString = Option(table.getProperty("spark.sql.sources.schema.numOfParts")) match {
+          case Some(numOfParts) =>
+            val parts = (0 until numOfParts.toInt).map { index =>
+              Option(table.getProperty(s"spark.sql.sources.schema.part.${index}"))
+                .getOrElse("Could not read schema from the metastore because it is corrupted.")
             }
-          }
+            // Stick all parts back to a single schema string in the JSON representation.
+            Some(parts.mkString)
+          case None => None // The schema was not defined.
+        }
 
         val userSpecifiedSchema =
           schemaString.flatMap(s => Some(DataType.fromJson(s).asInstanceOf[StructType]))
@@ -133,23 +127,12 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
     if (userSpecifiedSchema.isDefined) {
       val threshold = hive.conf.schemaStringLengthThreshold
       val schemaJsonString = userSpecifiedSchema.get.json
-      // Check if the size of the JSON string of the schema exceeds the threshold.
-      if (schemaJsonString.size > threshold) {
-        // Need to split the string.
-        val parts = schemaJsonString.grouped(threshold).toSeq
-        // First, record the total number of parts we have.
-        tbl.setProperty("spark.sql.sources.schema.numOfParts", parts.size.toString)
-        // Second, write every part to table property.
-        parts.zipWithIndex.foreach {
-          case (part, index) =>
-            tbl.setProperty(s"spark.sql.sources.schema.part.${index}", part)
-        }
-      } else {
-        // The length is less than the threshold, just put it in the table property.
-        tbl.setProperty("spark.sql.sources.schema.numOfParts", "1")
-        // We use spark.sql.sources.schema instead of using spark.sql.sources.schema.part.0
-        // because users may have already created data source tables in metastore.
-        tbl.setProperty("spark.sql.sources.schema", schemaJsonString)
+      // Split the JSON string.
+      val parts = schemaJsonString.grouped(threshold).toSeq
+      tbl.setProperty("spark.sql.sources.schema.numOfParts", parts.size.toString)
+      parts.zipWithIndex.foreach {
+        case (part, index) =>
+          tbl.setProperty(s"spark.sql.sources.schema.part.${index}", part)
       }
     }
     options.foreach { case (key, value) => tbl.setSerdeParam(key, value) }