Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/10604#discussion_r49810775
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala ---
@@ -664,6 +669,32 @@ abstract class HadoopFsRelation private[sql](
})
}
+ /**
+ * Groups the input files by bucket id, if bucketing is enabled and this
data source is bucketed.
+ * Returns None if there exists any malformed bucket files.
+ */
+ private def groupBucketFiles(
+ files: Array[FileStatus]): Option[scala.collection.Map[Int,
Array[FileStatus]]] = {
+ malformedBucketFile = false
+ if (getBucketSpec.isDefined) {
+ val groupedBucketFiles = mutable.HashMap.empty[Int,
mutable.ArrayBuffer[FileStatus]]
+ for (file <- files) {
+ val bucketId = BucketingUtils.getBucketId(file.getPath.getName)
+ if (bucketId.isEmpty) {
+ logError(s"File ${file.getPath} is supposed to be a bucket file,
but there is no " +
+ "bucket id information in file name, fallback to non-bucketing
mode.")
+ malformedBucketFile = true
+ return None
--- End diff --
you can consider just walking through the data in two passes. the first
pass to find the error, and the second pass to do the grouping.
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