Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/17938#discussion_r115923548
--- Diff: docs/sql-programming-guide.md ---
@@ -581,6 +581,46 @@ Starting from Spark 2.1, persistent datasource tables
have per-partition metadat
Note that partition information is not gathered by default when creating
external datasource tables (those with a `path` option). To sync the partition
information in the metastore, you can invoke `MSCK REPAIR TABLE`.
+### Bucketing, Sorting and Partitioning
+
+For file-based data source it is also possible to bucket and and sort or
partition the output.
+Bucketing and sorting is applicable only to persistent tables:
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+{% include_example write_sorting_and_bucketing
scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% include_example write_sorting_and_bucketing
java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
+</div>
+
+<div data-lang="python" markdown="1">
+{% include_example write_sorting_and_bucketing python/sql/datasource.py %}
+</div>
+
+</div>
+
+while partitioning can be used with both `save` and `saveAsTable`:
--- End diff --
like @tejasapatil suggested, we should give one more example about
partitioned and bucketed table, so that users know they can use bucketing and
partitioning at the same time
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