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[Doc] bucketing is applicable to all file-based data sources #16349

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[Doc] bucketing is applicable to all file-based data sources #16349

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6 changes: 3 additions & 3 deletions sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -150,7 +150,7 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) {
* predicates on the partitioned columns. In order for partitioning to work well, the number
* of distinct values in each column should typically be less than tens of thousands.
*
* This was initially applicable for Parquet but in 1.5+ covers JSON, text, ORC and avro as well.
* This is applicable for all file-based data sources (e.g. Parquet, JSON) staring Spark 2.1.0.
*
* @since 1.4.0
*/
Expand All @@ -164,7 +164,7 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) {
* Buckets the output by the given columns. If specified, the output is laid out on the file
* system similar to Hive's bucketing scheme.
*
* This is applicable for Parquet, JSON and ORC.
* This is applicable for all file-based data sources (e.g. Parquet, JSON) staring Spark 2.1.0.
*
* @since 2.0
*/
Expand All @@ -178,7 +178,7 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) {
/**
* Sorts the output in each bucket by the given columns.
*
* This is applicable for Parquet, JSON and ORC.
* This is applicable for all file-based data sources (e.g. Parquet, JSON) staring Spark 2.1.0.
*
* @since 2.0
*/
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