[DOC] bucketing is applicable to all file-based data sources

## What changes were proposed in this pull request?
Starting Spark 2.1.0, bucketing feature is available for all file-based data sources. This patch fixes some function docs that haven't yet been updated to reflect that.

## How was this patch tested?
N/A

Author: Reynold Xin <rxin@databricks.com>

Closes #16349 from rxin/ds-doc.

sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala

@@ -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
    */
@@ -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
    */
@@ -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
    */