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From gatorsmile <>
Subject [GitHub] spark pull request #17938: [SPARK-20694][DOCS][SQL] Document DataFrameWriter...
Date Sun, 14 May 2017 03:36:34 GMT
Github user gatorsmile commented on a diff in the pull request:
    --- Diff: docs/ ---
    @@ -581,6 +581,113 @@ Starting from Spark 2.1, persistent datasource tables have per-partition
     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 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 data-lang="java"  markdown="1">
    +{% include_example write_sorting_and_bucketing java/org/apache/spark/examples/sql/
    +<div data-lang="python"  markdown="1">
    +{% include_example write_sorting_and_bucketing python/sql/ %}
    +<div data-lang="sql"  markdown="1">
    +{% highlight sql %}
    +CREATE TABLE users_bucketed_by_name(
    +  name STRING,
    +  favorite_color STRING,
    +  favorite_NUMBERS array<integer>
    +) USING parquet 
    --- End diff --
    Could you please use the same table names `people_bucketed` with the same column names
in the example? Thanks!

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