Github user marmbrus commented on a diff in the pull request: https://github.com/apache/spark/pull/11509#discussion_r55081339 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala --- @@ -464,215 +378,140 @@ abstract class OutputWriter { } } -/** - * ::Experimental:: - * A [[BaseRelation]] that provides much of the common code required for relations that store their - * data to an HDFS compatible filesystem. - * - * For the read path, similar to [[PrunedFilteredScan]], it can eliminate unneeded columns and - * filter using selected predicates before producing an RDD containing all matching tuples as - * [[Row]] objects. In addition, when reading from Hive style partitioned tables stored in file - * systems, it's able to discover partitioning information from the paths of input directories, and - * perform partition pruning before start reading the data. Subclasses of [[HadoopFsRelation()]] - * must override one of the four `buildScan` methods to implement the read path. - * - * For the write path, it provides the ability to write to both non-partitioned and partitioned - * tables. Directory layout of the partitioned tables is compatible with Hive. - * - * @constructor This constructor is for internal uses only. The [[PartitionSpec]] argument is for - * implementing metastore table conversion. - * - * @param maybePartitionSpec An [[HadoopFsRelation]] can be created with an optional - * [[PartitionSpec]], so that partition discovery can be skipped. - * - * @since 1.4.0 - */ -@Experimental -abstract class HadoopFsRelation private[sql]( - maybePartitionSpec: Option[PartitionSpec], - parameters: Map[String, String]) - extends BaseRelation with FileRelation with Logging { +case class HadoopFsRelation( + sqlContext: SQLContext, + location: FileCatalog, + partitionSchema: StructType, --- End diff -- The `partitionSchema` is immutable and only tells you the names / types of the partitioning columns. The `partitionSpec` actually enumerates all the possible values along with the files for each set of values. This can change overtime, even for a single instance of a DataFrame. We should think about reducing the redundancy here if possible though as today the spec also includes another copy of the schema
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