cloud-fan 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_r316954628
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File path: sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
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@@ -835,14 +832,12 @@ final class DataFrameWriter[T] private[sql](ds:
Dataset[T]) {
private def modeForDSV2 = mode.getOrElse(SaveMode.Append)
- private def canUseV2Source(session: SparkSession, providerClass: Class[_]):
Boolean = {
- val useV1Sources =
-
session.sessionState.conf.useV1SourceWriterList.toLowerCase(Locale.ROOT).split(",")
- val shouldUseV1Source = providerClass.newInstance() match {
- case d: DataSourceRegister if useV1Sources.contains(d.shortName()) =>
true
- case _ =>
useV1Sources.contains(providerClass.getCanonicalName.toLowerCase(Locale.ROOT))
+ private def lookupV2Provider() = {
+ DataSource.lookupDataSourceV2(source, df.sparkSession.sessionState.conf)
match {
+ // File source v2 write path is currently broken.
+ case Some(_: FileDataSourceV2) => None
Review comment:
I'm talking about `spark.read.parquet(path)`. It's nothing about the
metastore, and the behavior is well defined in Spark 2.x:
1. if no schema is specified, e.g. `spark.read.parquet(path)`, then the
schema will be inferred.
2. if schema is specified by users, e.g.
`spark.read.schema(...).parquet(path)`, then schema inference will be skipped
and Spark fails at runtime if the specified schema doesn't match the actual
file schema.
I think what you were talking about `CREATE TABLE ... USING parquet`. This
is a different case and currently file source v2 is already disabled for it.
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