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


---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---

---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org

Reply via email to