Github user tejasapatil commented on a diff in the pull request:
https://github.com/apache/spark/pull/17938#discussion_r115890492
--- 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
--- End diff --
I feel that examples are missing writing to partitioned + bucketed table.
eg.
```
my_dataframe.write.format("orc").partitionBy("i").bucketBy(8, "j",
"k").sortBy("j", "k").saveAsTable("my_table")
```
There could be multiple possible orderings of `partitionBy`, `bucketBy` and
`sortBy` calls. Not all of them are supported, not all of them would produce
correct outputs. I have not done any exhaustive study of the same but I think
this should be documented to guide people while using these APIs
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