[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
Github user arman1371 closed the pull request at: https://github.com/apache/spark/pull/22889 --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/22889#discussion_r230614726
--- 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 --
Normally, we do not add such an API.
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[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
Github user arman1371 commented on a diff in the pull request:
https://github.com/apache/spark/pull/22889#discussion_r230575855
--- 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 --
Yes, for scala api it's just 5 characters but in java api it's very hard to
change `String` to `Seq[String]`
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[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
Github user dongjoon-hyun commented on a diff in the pull request: https://github.com/apache/spark/pull/22889#discussion_r230571447 --- 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. --- End diff -- Please fix the indentation here. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/22889#discussion_r230571437
--- 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 --
@arman1371 . We understand that this PR is trying to add a syntactic sugar.
But you need only 5 characters, `Seq(` and `)`, to use the existing general
API. Personally, I agree with @wangyum . I prefer not to add this.
Historically,
1. Spark 1.4 adds `Seq[String]` version was added later to support PySpark
(SPARK-7990)
2. Spark 1.6 adds `join` type to `Seq[String]` version (SPARK-10446)
It's a long time ago. Given that, I guess Apache Spark community
intentionally didn't add the `String` version for this in order to keep
`Dataset` simple in terms of the number of APIs. Anyway, since you need an
answer, let's ask the general opinion again to make a decision.
Hi, @rxin, @cloud-fan , @gatorsmile . Did we explicitly decide not to add
this API ? It seems that @arman1371 wants to add this for feature parity with
PySpark at Spark 3.0.0.
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[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
Github user wangyum commented on a diff in the pull request:
https://github.com/apache/spark/pull/22889#discussion_r230570164
--- 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 --
cc @dongjoon-hyun
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[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
Github user arman1371 commented on a diff in the pull request:
https://github.com/apache/spark/pull/22889#discussion_r230560687
--- 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 --
no, it's a simpler implementation. as i said we have both of `def
join(right: Dataset[_], usingColumn: String)` and `def join(right: Dataset[_],
usingColumns: Seq[String])`. based on your opinion the first function should be
removed.
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[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
Github user wangyum commented on a diff in the pull request:
https://github.com/apache/spark/pull/22889#discussion_r230559647
--- 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 --
Cloud we close this?
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[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
Github user arman1371 commented on a diff in the pull request:
https://github.com/apache/spark/pull/22889#discussion_r230559472
--- 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 --
The answer of both questions are yes.
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[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
Github user wangyum commented on a diff in the pull request:
https://github.com/apache/spark/pull/22889#discussion_r230557937
--- 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 --
@arman1371 What do you think? ```def join(right: Dataset[_], usingColumn:
String, joinType: String)``` only support one column. right?
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[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
Github user wangyum commented on a diff in the pull request:
https://github.com/apache/spark/pull/22889#discussion_r230555316
--- 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 --
So in your case. Could you replace ```df1.join(df2, "user_id", "left")```
with ```df1.join(df2, Seq("user_id"), "left")```?
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[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
Github user arman1371 commented on a diff in the pull request:
https://github.com/apache/spark/pull/22889#discussion_r230554936
--- 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 --
It uses String parameter for usingColumns instead of Seq[String].
It is like `def join(right: Dataset[_], usingColumn: String)` and `def
join(right: Dataset[_], usingColumns: Seq[String])` that was implemented before.
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[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
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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[GitHub] spark pull request #22889: [SPARK-25882][SQL] Added a function to join two d...
GitHub user arman1371 opened a pull request: https://github.com/apache/spark/pull/22889 [SPARK-25882][SQL] Added a function to join two datasets using one column with join type parameter ## What changes were proposed in this pull request? Added a function to join two datasets using one column with join type parameter You can merge this pull request into a Git repository by running: $ git pull https://github.com/arman1371/spark master Alternatively you can review and apply these changes as the patch at: https://github.com/apache/spark/pull/22889.patch To close this pull request, make a commit to your master/trunk branch with (at least) the following in the commit message: This closes #22889 commit 16d07c6da03706b206f0d24317e2ff8b1e489cf3 Author: Arman Date: 2018-10-30T08:46:11Z Added function to join two datasets using one column and get the join type as a parameter --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
