[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...

[wangyum <gi...@git.apache.org>]

Github user wangyum commented on a diff in the pull request:

    https://github.com/apache/spark/pull/22889#discussion_r230554727
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
    @@ -883,6 +883,31 @@ class Dataset[T] private[sql](
         join(right, Seq(usingColumn))
       }
     
    +  /**
    +    * Equi-join with another `DataFrame` using the given column.
    +    *
    +    * Different from other join functions, the join column will only appear once in the output,
    +    * i.e. similar to SQL's `JOIN USING` syntax.
    +    *
    +    * {{{
    +    *   // Left join of df1 and df2 using the column "user_id"
    +    *   df1.join(df2, "user_id", "left")
    +    * }}}
    +    *
    +    * @param right Right side of the join operation.
    +    * @param usingColumn Name of the column to join on. This column must exist on both sides.
    +    * @param joinType Type of join to perform. Default `inner`. Must be one of:
    +    *                 `inner`, `cross`, `outer`, `full`, `full_outer`, `left`, `left_outer`,
    +    *                 `right`, `right_outer`, `left_semi`, `left_anti`.
    +    * @note If you perform a self-join using this function without aliasing the input
    +    * `DataFrame`s, you will NOT be able to reference any columns after the join, since
    +    * there is no way to disambiguate which side of the join you would like to reference.
    +    * @group untypedrel
    +    */
    +  def join(right: Dataset[_], usingColumn: String, joinType: String): DataFrame = {
    --- End diff --
    
    What is the difference between this function and ```def join(right: Dataset[_], usingColumns: Seq[String], joinType: String)```?
    https://github.com/apache/spark/blob/v2.4.0/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala#L932-L946


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