Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/15996#discussion_r93387828
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala ---
@@ -364,48 +366,157 @@ final class DataFrameWriter[T] private[sql](ds:
Dataset[T]) {
throw new AnalysisException("Cannot create hive serde table with
saveAsTable API")
}
- val tableExists =
df.sparkSession.sessionState.catalog.tableExists(tableIdent)
-
- (tableExists, mode) match {
- case (true, SaveMode.Ignore) =>
- // Do nothing
-
- case (true, SaveMode.ErrorIfExists) =>
- throw new AnalysisException(s"Table $tableIdent already exists.")
-
- case _ =>
- val existingTable = if (tableExists) {
-
Some(df.sparkSession.sessionState.catalog.getTableMetadata(tableIdent))
- } else {
- None
- }
- val storage = if (tableExists) {
- existingTable.get.storage
- } else {
- DataSource.buildStorageFormatFromOptions(extraOptions.toMap)
- }
- val tableType = if (tableExists) {
- existingTable.get.tableType
- } else if (storage.locationUri.isDefined) {
- CatalogTableType.EXTERNAL
- } else {
- CatalogTableType.MANAGED
+ val catalog = df.sparkSession.sessionState.catalog
+ val db = tableIdent.database.getOrElse(catalog.getCurrentDatabase)
+ val tableIdentWithDB = tableIdent.copy(database = Some(db))
+ val tableName = tableIdentWithDB.unquotedString
+
+ catalog.getTableMetadataOption(tableIdentWithDB) match {
+ // If the table already exists...
+ case Some(existingTable) =>
+ mode match {
+ case SaveMode.Ignore => // Do nothing
+
+ case SaveMode.ErrorIfExists =>
+ throw new AnalysisException(s"Table $tableName already exists.
You can set SaveMode " +
+ "to SaveMode.Append to insert data into the table or set
SaveMode to " +
+ "SaveMode.Overwrite to overwrite the existing data.")
+
+ case SaveMode.Append =>
+ if (existingTable.tableType == CatalogTableType.VIEW) {
+ throw new AnalysisException("Saving data into a view is not
allowed.")
+ }
+
+ if (existingTable.provider.get == DDLUtils.HIVE_PROVIDER) {
+ throw new AnalysisException(s"Saving data in the Hive serde
table $tableName is " +
+ "not supported yet. Please use the insertInto() API as an
alternative.")
+ }
+
+ // Check if the specified data source match the data source of
the existing table.
+ val existingProvider =
DataSource.lookupDataSource(existingTable.provider.get)
+ val specifiedProvider = DataSource.lookupDataSource(source)
+ // TODO: Check that options from the resolved relation match
the relation that we are
+ // inserting into (i.e. using the same compression).
+ if (existingProvider != specifiedProvider) {
+ throw new AnalysisException(s"The format of the existing
table $tableName is " +
+ s"`${existingProvider.getSimpleName}`. It doesn't match
the specified format " +
+ s"`${specifiedProvider.getSimpleName}`.")
+ }
+
+ if (df.schema.length != existingTable.schema.length) {
+ throw new AnalysisException(
+ s"The column number of the existing table $tableName" +
+ s"(${existingTable.schema.catalogString}) doesn't match
the data schema" +
+ s"(${df.schema.catalogString})")
+ }
+
+ val resolver = df.sparkSession.sessionState.conf.resolver
+ val tableCols = existingTable.schema.map(_.name)
+
+ // As we are inserting into an existing table, we should
respect the existing schema and
+ // adjust the column order of the given dataframe according to
it, or throw exception
+ // if the column names do not match.
+ val adjustedColumns = tableCols.map { col =>
+ df.queryExecution.analyzed.resolve(Seq(col),
resolver).getOrElse {
+ val inputColumns = df.schema.map(_.name).mkString(", ")
+ throw new AnalysisException(
+ s"cannot resolve '$col' given input columns:
[$inputColumns]")
+ }
+ }
+
+ // Check if the specified partition columns match the existing
table.
+ val specifiedPartCols = CatalogUtils.normalizePartCols(
+ tableName, tableCols, partitioningColumns.getOrElse(Nil),
resolver)
+ if (specifiedPartCols != existingTable.partitionColumnNames) {
+ val existingPartCols =
existingTable.partitionColumnNames.mkString(", ")
+ throw new AnalysisException(
+ s"""
+ |Specified partitioning does not match that of the
existing table $tableName.
+ |Specified partition columns:
[${specifiedPartCols.mkString(", ")}]
+ |Existing partition columns: [$existingPartCols]
+ """.stripMargin)
+ }
+
+ // Check if the specified bucketing match the existing table.
+ val specifiedBucketSpec = getBucketSpec.map { bucketSpec =>
+ CatalogUtils.normalizeBucketSpec(tableName, tableCols,
bucketSpec, resolver)
+ }
+ if (specifiedBucketSpec != existingTable.bucketSpec) {
+ val specifiedBucketString =
+ specifiedBucketSpec.map(_.toString).getOrElse("not
bucketed")
+ val existingBucketString =
+ existingTable.bucketSpec.map(_.toString).getOrElse("not
bucketed")
+ throw new AnalysisException(
+ s"""
+ |Specified bucketing does not match that of the
existing table $tableName.
+ |Specified bucketing: $specifiedBucketString
+ |Existing bucketing: $existingBucketString
+ """.stripMargin)
+ }
+
+ if
(classOf[CreatableRelationProvider].isAssignableFrom(existingProvider)) {
+ // For data source that implementing
`CreatableRelationProvider`, we should call its
+ // `createRelation` to do the data appending.
`CreatableRelationProvider` may not
+ // provide an `InsertableRelation`, so we can't use
`DataFrameWriter.insertInto` here.
+
existingProvider.newInstance().asInstanceOf[CreatableRelationProvider].createRelation(
+ df.sparkSession.sqlContext, mode, new
CaseInsensitiveMap(extraOptions.toMap), df)
--- End diff --
In this case, does the order of columns still matter?
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