keypointt commented on a change in pull request #25465: [SPARK-28747][SQL]
merge the two data source v2 fallback configs
URL: https://github.com/apache/spark/pull/25465#discussion_r315932660
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
##########
@@ -251,64 +251,62 @@ final class DataFrameWriter[T] private[sql](ds:
Dataset[T]) {
assertNotBucketed("save")
- val session = df.sparkSession
- val cls = DataSource.lookupDataSource(source, session.sessionState.conf)
- val canUseV2 = canUseV2Source(session, cls) && partitioningColumns.isEmpty
-
- // In Data Source V2 project, partitioning is still under development.
- // Here we fallback to V1 if partitioning columns are specified.
- // TODO(SPARK-26778): use V2 implementations when partitioning feature is
supported.
- if (canUseV2) {
- val provider =
cls.getConstructor().newInstance().asInstanceOf[TableProvider]
- val sessionOptions = DataSourceV2Utils.extractSessionConfigs(
- provider, session.sessionState.conf)
- val options = sessionOptions ++ extraOptions
- val dsOptions = new CaseInsensitiveStringMap(options.asJava)
-
- import
org.apache.spark.sql.execution.datasources.v2.DataSourceV2Implicits._
- provider.getTable(dsOptions) match {
- // TODO (SPARK-27815): To not break existing tests, here we treat file
source as a special
- // case, and pass the save mode to file source directly. This hack
should be removed.
- case table: FileTable =>
- val write =
table.newWriteBuilder(dsOptions).asInstanceOf[FileWriteBuilder]
- .mode(modeForDSV1) // should not change default mode for file
source.
- .withQueryId(UUID.randomUUID().toString)
- .withInputDataSchema(df.logicalPlan.schema)
- .buildForBatch()
- // The returned `Write` can be null, which indicates that we can
skip writing.
- if (write != null) {
+ lookupV2Provider() match {
+ // TODO(SPARK-26778): use V2 implementations when partition columns are
specified
+ case Some(provider) if partitioningColumns.isEmpty =>
+ saveToV2Source(provider)
+
+ case _ => saveToV1Source()
+ }
+ }
+
+ private def saveToV2Source(provider: TableProvider): Unit = {
+ val sessionOptions = DataSourceV2Utils.extractSessionConfigs(
+ provider, df.sparkSession.sessionState.conf)
+ val options = sessionOptions ++ extraOptions
+ val dsOptions = new CaseInsensitiveStringMap(options.asJava)
+
+ import
org.apache.spark.sql.execution.datasources.v2.DataSourceV2Implicits._
+ provider.getTable(dsOptions) match {
+ // TODO (SPARK-27815): To not break existing tests, here we treat file
source as a special
+ // case, and pass the save mode to file source directly. This hack
should be removed.
+ case table: FileTable =>
+ val write =
table.newWriteBuilder(dsOptions).asInstanceOf[FileWriteBuilder]
+ .mode(modeForDSV1) // should not change default mode for file source.
+ .withQueryId(UUID.randomUUID().toString)
+ .withInputDataSchema(df.logicalPlan.schema)
+ .buildForBatch()
+ // The returned `Write` can be null, which indicates that we can skip
writing.
+ if (write != null) {
+ runCommand(df.sparkSession, "save") {
+ WriteToDataSourceV2(write, df.logicalPlan)
+ }
+ }
+
+ case table: SupportsWrite if table.supports(BATCH_WRITE) =>
+ lazy val relation = DataSourceV2Relation.create(table, dsOptions)
+ modeForDSV2 match {
+ case SaveMode.Append =>
runCommand(df.sparkSession, "save") {
- WriteToDataSourceV2(write, df.logicalPlan)
+ AppendData.byName(relation, df.logicalPlan)
}
- }
- case table: SupportsWrite if table.supports(BATCH_WRITE) =>
- lazy val relation = DataSourceV2Relation.create(table, dsOptions)
- modeForDSV2 match {
- case SaveMode.Append =>
- runCommand(df.sparkSession, "save") {
- AppendData.byName(relation, df.logicalPlan)
- }
-
- case SaveMode.Overwrite if table.supportsAny(TRUNCATE,
OVERWRITE_BY_FILTER) =>
- // truncate the table
- runCommand(df.sparkSession, "save") {
- OverwriteByExpression.byName(relation, df.logicalPlan,
Literal(true))
- }
-
- case other =>
- throw new AnalysisException(s"TableProvider implementation
$source cannot be " +
- s"written with $other mode, please use Append or Overwrite " +
- "modes instead.")
- }
+ case SaveMode.Overwrite if table.supportsAny(TRUNCATE,
OVERWRITE_BY_FILTER) =>
+ // truncate the table
+ runCommand(df.sparkSession, "save") {
+ OverwriteByExpression.byName(relation, df.logicalPlan,
Literal(true))
+ }
- // Streaming also uses the data source V2 API. So it may be that the
data source implements
- // v2, but has no v2 implementation for batch writes. In that case, we
fall back to saving
- // as though it's a V1 source.
- case _ => saveToV1Source()
- }
- } else {
- saveToV1Source()
+ case other =>
+ throw new AnalysisException(s"TableProvider implementation $source
cannot be " +
+ s"written with $other mode, please use Append or Overwrite " +
Review comment:
```use `Append` or `Overwrite` save mode instead``` ?
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