Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/14155#discussion_r74684229
--- Diff:
sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala ---
@@ -144,16 +161,141 @@ 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 provider = tableDefinition.provider.get
+ val partitionColumns = tableDefinition.partitionColumnNames
+ val bucketSpec = tableDefinition.bucketSpec
+
+ val tableProperties = new scala.collection.mutable.HashMap[String,
String]
+ tableProperties.put(DATASOURCE_PROVIDER, provider)
+
+ // 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 = tableDefinition.schema.json
+ // Split the JSON string.
+ val parts = schemaJsonString.grouped(threshold).toSeq
+ tableProperties.put(DATASOURCE_SCHEMA_NUMPARTS, parts.size.toString)
+ parts.zipWithIndex.foreach { case (part, index) =>
+ tableProperties.put(s"$DATASOURCE_SCHEMA_PART_PREFIX$index", part)
+ }
+
+ if (partitionColumns.nonEmpty) {
+ tableProperties.put(DATASOURCE_SCHEMA_NUMPARTCOLS,
partitionColumns.length.toString)
+ partitionColumns.zipWithIndex.foreach { case (partCol, index) =>
+ tableProperties.put(s"$DATASOURCE_SCHEMA_PARTCOL_PREFIX$index",
partCol)
+ }
+ }
+
+ if (bucketSpec.isDefined) {
+ val BucketSpec(numBuckets, bucketColumnNames, sortColumnNames) =
bucketSpec.get
+
+ tableProperties.put(DATASOURCE_SCHEMA_NUMBUCKETS,
numBuckets.toString)
+ tableProperties.put(DATASOURCE_SCHEMA_NUMBUCKETCOLS,
bucketColumnNames.length.toString)
+ bucketColumnNames.zipWithIndex.foreach { case (bucketCol, index) =>
+
tableProperties.put(s"$DATASOURCE_SCHEMA_BUCKETCOL_PREFIX$index", bucketCol)
+ }
+
+ if (sortColumnNames.nonEmpty) {
+ tableProperties.put(DATASOURCE_SCHEMA_NUMSORTCOLS,
sortColumnNames.length.toString)
+ sortColumnNames.zipWithIndex.foreach { case (sortCol, index) =>
+
tableProperties.put(s"$DATASOURCE_SCHEMA_SORTCOL_PREFIX$index", sortCol)
+ }
+ }
+ }
+
+ def newSparkSQLSpecificMetastoreTable(): CatalogTable = {
+ CatalogTable(
+ identifier = tableDefinition.identifier,
+ tableType = tableDefinition.tableType,
+ storage = tableDefinition.storage,
+ schema = new StructType(),
+ properties = tableDefinition.properties ++ tableProperties.toMap)
+ }
+
+ def newHiveCompatibleMetastoreTable(serde: HiveSerDe, path: String):
CatalogTable = {
+ tableDefinition.copy(properties =
tableProperties.toMap).withNewStorage(
+ locationUri = Some(new Path(path).toUri.toString),
+ inputFormat = serde.inputFormat,
+ outputFormat = serde.outputFormat,
+ serde = serde.serde)
+ }
+
+ val qualifiedTableName = tableDefinition.identifier.quotedString
+ val maybePath = new
CaseInsensitiveMap(tableDefinition.storage.properties).get("path")
--- End diff --
Previous code create a `DataSource` and resolve it, just to get the paths
for this data source. We can get the path from data source options directly.
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