[SPARK-12682][SQL] Add support for (optionally) not storing tables in hive metadata format

This PR adds a new table option (`skip_hive_metadata`) that'd allow the user to skip storing the table metadata in hive metadata format. While this could be useful in general, the specific use-case for this change is that Hive doesn't handle wide schemas well (see https://issues.apache.org/jira/browse/SPARK-12682 and https://issues.apache.org/jira/browse/SPARK-6024) which in turn prevents such tables from being queried in SparkSQL.

Author: Sameer Agarwal <[email protected]>

Closes #10826 from sameeragarwal/skip-hive-metadata.

Author: sameeragarwal

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

@@ -327,7 +327,14 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
 
     // TODO: Support persisting partitioned data source relations in Hive compatible format
     val qualifiedTableName = tableIdent.quotedString
+    val skipHiveMetadata = options.getOrElse("skipHiveMetadata", "false").toBoolean
     val (hiveCompatibleTable, logMessage) = (maybeSerDe, dataSource.relation) match {
+      case _ if skipHiveMetadata =>
+        val message =
+          s"Persisting partitioned data source relation $qualifiedTableName into " +
+            "Hive metastore in Spark SQL specific format, which is NOT compatible with Hive."
+        (None, message)
+
       case (Some(serde), relation: HadoopFsRelation)
         if relation.paths.length == 1 && relation.partitionColumns.isEmpty =>
         val hiveTable = newHiveCompatibleMetastoreTable(relation, serde)

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

@@ -900,4 +900,36 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with TestHiv
     sqlContext.sql("""use default""")
     sqlContext.sql("""drop database if exists testdb8156 CASCADE""")
   }
+
+  test("skip hive metadata on table creation") {
+    val schema = StructType((1 to 5).map(i => StructField(s"c_$i", StringType)))
+
+    catalog.createDataSourceTable(
+      tableIdent = TableIdentifier("not_skip_hive_metadata"),
+      userSpecifiedSchema = Some(schema),
+      partitionColumns = Array.empty[String],
+      bucketSpec = None,
+      provider = "parquet",
+      options = Map("path" -> "just a dummy path", "skipHiveMetadata" -> "false"),
+      isExternal = false)
+
+    // As a proxy for verifying that the table was stored in Hive compatible format, we verify that
+    // each column of the table is of native type StringType.
+    assert(catalog.client.getTable("default", "not_skip_hive_metadata").schema
+      .forall(column => HiveMetastoreTypes.toDataType(column.hiveType) == StringType))
+
+    catalog.createDataSourceTable(
+      tableIdent = TableIdentifier("skip_hive_metadata"),
+      userSpecifiedSchema = Some(schema),
+      partitionColumns = Array.empty[String],
+      bucketSpec = None,
+      provider = "parquet",
+      options = Map("path" -> "just a dummy path", "skipHiveMetadata" -> "true"),
+      isExternal = false)
+
+    // As a proxy for verifying that the table was stored in SparkSQL format, we verify that
+    // the table has a column type as array of StringType.
+    assert(catalog.client.getTable("default", "skip_hive_metadata").schema
+      .forall(column => HiveMetastoreTypes.toDataType(column.hiveType) == ArrayType(StringType)))
+  }
 }