Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/14155#discussion_r75063920
--- 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
--- End diff --
Let's mention the conf at here.
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