Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/10498#discussion_r48820679
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala ---
@@ -189,13 +220,43 @@ final class DataFrameWriter private[sql](df:
DataFrame) {
ifNotExists = false)).toRdd
}
- private def normalizedParCols: Option[Seq[String]] =
partitioningColumns.map { parCols =>
- parCols.map { col =>
- df.logicalPlan.output
- .map(_.name)
- .find(df.sqlContext.analyzer.resolver(_, col))
- .getOrElse(throw new AnalysisException(s"Partition column $col not
found in existing " +
- s"columns (${df.logicalPlan.output.map(_.name).mkString(",
")})"))
+ private def normalizedParCols: Option[Seq[String]] =
partitioningColumns.map { cols =>
+ cols.map(normalize(_, "Partition"))
+ }
+
+ private def normalizedBucketCols: Option[Seq[String]] =
bucketingColumns.map { cols =>
+ cols.map(normalize(_, "Bucketing"))
+ }
+
+ private def normalizedSortCols: Option[Seq[String]] = sortingColumns.map
{ cols =>
+ cols.map(normalize(_, "Sorting"))
+ }
+
+ private def getBucketSpec: Option[BucketSpec] = {
+ if (sortingColumns.isDefined) {
+ require(numBuckets.isDefined, "sortBy must be used together with
bucketBy")
+ }
+
+ for {
+ n <- numBuckets
+ cols <- normalizedBucketCols
+ } yield {
+ require(n > 0, "Bucket number must be greater than 0.")
+ BucketSpec(n, cols, normalizedSortCols)
+ }
+ }
+
+ private def normalize(columnName: String, columnType: String): String = {
+ val validColumnNames = df.logicalPlan.output.map(_.name)
+ validColumnNames.find(df.sqlContext.analyzer.resolver(_, columnName))
+ .getOrElse(throw new AnalysisException(s"$columnType column
$columnName not found in " +
+ s"existing columns (${validColumnNames.mkString(", ")})"))
+ }
+
+ private def assertNotBucketed(): Unit = {
+ if (numBuckets.isDefined || sortingColumns.isDefined) {
--- End diff --
If users just wanna sort the data, they can call `DataFrame.sort` before
write. In this context, the `sortingColumns` is part of the bucketing
information and should be used together with `bucketingColumns`.
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