Github user cloud-fan commented on a diff in the pull request:

    https://github.com/apache/spark/pull/14155#discussion_r75451232
  
    --- Diff: 
sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala ---
    @@ -144,16 +161,147 @@ private[spark] class HiveExternalCatalog(client: 
HiveClient, hadoopConf: Configu
         assert(tableDefinition.identifier.database.isDefined)
         val db = tableDefinition.identifier.database.get
         requireDbExists(db)
    +    verifyTableProperties(tableDefinition)
    +
    +    if (tableDefinition.provider == Some("hive") || 
tableDefinition.tableType == VIEW) {
    +      client.createTable(tableDefinition, ignoreIfExists)
    +    } else {
    +      val tableProperties = tableMetadataToProperties(tableDefinition)
    +
    +      def newSparkSQLSpecificMetastoreTable(): CatalogTable = {
    +        tableDefinition.copy(
    +          schema = new StructType,
    +          partitionColumnNames = Nil,
    +          bucketSpec = None,
    +          properties = tableDefinition.properties ++ tableProperties)
    +      }
    +
    +      def newHiveCompatibleMetastoreTable(serde: HiveSerDe, path: String): 
CatalogTable = {
    +        tableDefinition.copy(
    +          storage = tableDefinition.storage.copy(
    +            locationUri = Some(new Path(path).toUri.toString),
    +            inputFormat = serde.inputFormat,
    +            outputFormat = serde.outputFormat,
    +            serde = serde.serde
    +          ),
    +          properties = tableDefinition.properties ++ tableProperties)
    +      }
    +
    +      val qualifiedTableName = tableDefinition.identifier.quotedString
    +      val maybeSerde = 
HiveSerDe.sourceToSerDe(tableDefinition.provider.get)
    +      val maybePath = new 
CaseInsensitiveMap(tableDefinition.storage.properties).get("path")
    +      val skipHiveMetadata = tableDefinition.storage.properties
    +        .getOrElse("skipHiveMetadata", "false").toBoolean
    +
    +      val (hiveCompatibleTable, logMessage) = (maybeSerde, maybePath) 
match {
    +        case _ if skipHiveMetadata =>
    +          val message =
    +            s"Persisting data source table $qualifiedTableName into Hive 
metastore in" +
    +              "Spark SQL specific format, which is NOT compatible with 
Hive."
    +          (None, message)
    +
    +        // our bucketing is un-compatible with hive(different hash 
function)
    +        case _ if tableDefinition.bucketSpec.nonEmpty =>
    +          val message =
    +            s"Persisting bucketed data source table $qualifiedTableName 
into " +
    +              "Hive metastore in Spark SQL specific format, which is NOT 
compatible with Hive. "
    +          (None, message)
    +
    +        case (Some(serde), Some(path)) =>
    +          val message =
    +            s"Persisting data source table $qualifiedTableName with a 
single input path " +
    +              s"into Hive metastore in Hive compatible format."
    +          (Some(newHiveCompatibleMetastoreTable(serde, path)), message)
    +
    +        case (Some(_), None) =>
    +          val message =
    +            s"Data source table $qualifiedTableName is not file based. 
Persisting it into " +
    +              s"Hive metastore in Spark SQL specific format, which is NOT 
compatible with Hive."
    +          (None, message)
    +
    +        case _ =>
    +          val provider = tableDefinition.provider.get
    +          val message =
    +            s"Couldn't find corresponding Hive SerDe for data source 
provider $provider. " +
    +              s"Persisting data source table $qualifiedTableName into Hive 
metastore in " +
    +              s"Spark SQL specific format, which is NOT compatible with 
Hive."
    +          (None, message)
    +      }
    +
    +      (hiveCompatibleTable, logMessage) match {
    +        case (Some(table), message) =>
    +          // We first try to save the metadata of the table in a Hive 
compatible way.
    +          // If Hive throws an error, we fall back to save its metadata in 
the Spark SQL
    +          // specific way.
    +          try {
    +            logInfo(message)
    +            saveTableIntoHive(table, ignoreIfExists)
    +          } catch {
    +            case NonFatal(e) =>
    +              val warningMessage =
    +                s"Could not persist 
${tableDefinition.identifier.quotedString} in a Hive " +
    +                  "compatible way. Persisting it into Hive metastore in 
Spark SQL specific format."
    +              logWarning(warningMessage, e)
    +              saveTableIntoHive(newSparkSQLSpecificMetastoreTable(), 
ignoreIfExists)
    +          }
    +
    +        case (None, message) =>
    +          logWarning(message)
    +          saveTableIntoHive(newSparkSQLSpecificMetastoreTable(), 
ignoreIfExists)
    +      }
    +    }
    +  }
    +
    +  private def tableMetadataToProperties(table: CatalogTable): Map[String, 
String] = {
    +    val properties = new scala.collection.mutable.HashMap[String, String]
    +    properties.put(DATASOURCE_PROVIDER, table.provider.get)
    +
    +    // Serialized JSON schema string may be too long to be stored into a 
single metastore table
    +    // property. In this case, we split the JSON string and store each 
part as a separate table
    +    // property.
    +    val threshold = 4000
    +    val schemaJsonString = table.schema.json
    +    // Split the JSON string.
    +    val parts = schemaJsonString.grouped(threshold).toSeq
    +    properties.put(DATASOURCE_SCHEMA_NUMPARTS, parts.size.toString)
    +    parts.zipWithIndex.foreach { case (part, index) =>
    +      properties.put(s"$DATASOURCE_SCHEMA_PART_PREFIX$index", part)
    +    }
    +
    +    if (table.partitionColumnNames.nonEmpty) {
    +      properties.put(DATASOURCE_SCHEMA_NUMPARTCOLS, 
table.partitionColumnNames.length.toString)
    +      table.partitionColumnNames.zipWithIndex.foreach { case (partCol, 
index) =>
    +        properties.put(s"$DATASOURCE_SCHEMA_PARTCOL_PREFIX$index", partCol)
    +      }
    +    }
    +
    +    if (table.bucketSpec.isDefined) {
    +      val BucketSpec(numBuckets, bucketColumnNames, sortColumnNames) = 
table.bucketSpec.get
    +
    +      properties.put(DATASOURCE_SCHEMA_NUMBUCKETS, numBuckets.toString)
    +      properties.put(DATASOURCE_SCHEMA_NUMBUCKETCOLS, 
bucketColumnNames.length.toString)
    +      bucketColumnNames.zipWithIndex.foreach { case (bucketCol, index) =>
    +        properties.put(s"$DATASOURCE_SCHEMA_BUCKETCOL_PREFIX$index", 
bucketCol)
    +      }
    +
    +      if (sortColumnNames.nonEmpty) {
    +        properties.put(DATASOURCE_SCHEMA_NUMSORTCOLS, 
sortColumnNames.length.toString)
    +        sortColumnNames.zipWithIndex.foreach { case (sortCol, index) =>
    +          properties.put(s"$DATASOURCE_SCHEMA_SORTCOL_PREFIX$index", 
sortCol)
    +        }
    +      }
    +    }
    +
    +    properties.toMap
    +  }
     
    -    if (
    +  private def saveTableIntoHive(tableDefinition: CatalogTable, 
ignoreIfExists: Boolean): Unit = {
         // If this is an external data source table...
    -      tableDefinition.properties.contains("spark.sql.sources.provider") &&
    -        tableDefinition.tableType == CatalogTableType.EXTERNAL &&
    -        // ... that is not persisted as Hive compatible format (external 
tables in Hive compatible
    -        // format always set `locationUri` to the actual data location and 
should NOT be hacked as
    -        // following.)
    -        tableDefinition.storage.locationUri.isEmpty
    -    ) {
    +    if (tableDefinition.tableType == EXTERNAL &&
    +      // ... that is not persisted as Hive compatible format (external 
tables in Hive compatible
    +      // format always set `locationUri` to the actual data location and 
should NOT be hacked as
    +      // following.)
    +      tableDefinition.storage.locationUri.isEmpty) {
    --- End diff --
    
    I added a check at the beginning to make sure we are saving a data source 
table.


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