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author | Liang-Chi Hsieh <viirya@appier.com> | 2015-09-21 23:46:00 -0700 |
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committer | Reynold Xin <rxin@databricks.com> | 2015-09-21 23:46:00 -0700 |
commit | 1fcefef06950e2f03477282368ca835bbf40ff24 (patch) | |
tree | 9fd7829e91af85b3f682f677721ca00b1c96eb30 /sql/core | |
parent | 781b21ba2a873ed29394c8dbc74fc700e3e0d17e (diff) | |
download | spark-1fcefef06950e2f03477282368ca835bbf40ff24.tar.gz spark-1fcefef06950e2f03477282368ca835bbf40ff24.tar.bz2 spark-1fcefef06950e2f03477282368ca835bbf40ff24.zip |
[SPARK-10446][SQL] Support to specify join type when calling join with usingColumns
JIRA: https://issues.apache.org/jira/browse/SPARK-10446
Currently the method `join(right: DataFrame, usingColumns: Seq[String])` only supports inner join. It is more convenient to have it support other join types.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes #8600 from viirya/usingcolumns_df.
Diffstat (limited to 'sql/core')
-rw-r--r-- | sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala | 22 | ||||
-rw-r--r-- | sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala | 13 |
2 files changed, 34 insertions, 1 deletions
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 8f737c2023..ba94d77b2e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -484,6 +484,26 @@ class DataFrame private[sql]( * @since 1.4.0 */ def join(right: DataFrame, usingColumns: Seq[String]): DataFrame = { + join(right, usingColumns, "inner") + } + + /** + * Equi-join with another [[DataFrame]] using the given columns. + * + * Different from other join functions, the join columns will only appear once in the output, + * i.e. similar to SQL's `JOIN USING` syntax. + * + * Note that 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. + * + * @param right Right side of the join operation. + * @param usingColumns Names of the columns to join on. This columns must exist on both sides. + * @param joinType One of: `inner`, `outer`, `left_outer`, `right_outer`, `leftsemi`. + * @group dfops + * @since 1.6.0 + */ + def join(right: DataFrame, usingColumns: Seq[String], joinType: String): DataFrame = { // Analyze the self join. The assumption is that the analyzer will disambiguate left vs right // by creating a new instance for one of the branch. val joined = sqlContext.executePlan( @@ -502,7 +522,7 @@ class DataFrame private[sql]( Join( joined.left, joined.right, - joinType = Inner, + joinType = JoinType(joinType), condition) ) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala index e2716d7841..56ad71ea4f 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala @@ -42,6 +42,19 @@ class DataFrameJoinSuite extends QueryTest with SharedSQLContext { Row(1, 2, "1", "2") :: Row(2, 3, "2", "3") :: Row(3, 4, "3", "4") :: Nil) } + test("join - join using multiple columns and specifying join type") { + val df = Seq(1, 2, 3).map(i => (i, i + 1, i.toString)).toDF("int", "int2", "str") + val df2 = Seq(1, 2, 3).map(i => (i, i + 1, (i + 1).toString)).toDF("int", "int2", "str") + + checkAnswer( + df.join(df2, Seq("int", "str"), "left"), + Row(1, 2, "1", null) :: Row(2, 3, "2", null) :: Row(3, 4, "3", null) :: Nil) + + checkAnswer( + df.join(df2, Seq("int", "str"), "right"), + Row(null, null, null, 2) :: Row(null, null, null, 3) :: Row(null, null, null, 4) :: Nil) + } + test("join - join using self join") { val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str") |