spark git commit: [SPARK-15863][SQL][DOC][SPARKR] sql programming guide updates to include sparkSession in R

2016-06-20 Thread lian 
Repository: spark
Updated Branches:
  refs/heads/branch-2.0 4fc4eb943 -> dbf7f48b6


[SPARK-15863][SQL][DOC][SPARKR] sql programming guide updates to include 
sparkSession in R

## What changes were proposed in this pull request?

Update doc as per discussion in PR #13592

## How was this patch tested?

manual

shivaram liancheng

Author: Felix Cheung 

Closes #13799 from felixcheung/rsqlprogrammingguide.

(cherry picked from commit 58f6e27dd70f476f99ac8204e6b405bced4d6de1)
Signed-off-by: Cheng Lian 


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/dbf7f48b
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/dbf7f48b
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/dbf7f48b

Branch: refs/heads/branch-2.0
Commit: dbf7f48b6e73f3500b0abe9055ac204a3f756418
Parents: 4fc4eb9
Author: Felix Cheung 
Authored: Tue Jun 21 13:56:37 2016 +0800
Committer: Cheng Lian 
Committed: Tue Jun 21 13:57:03 2016 +0800

--
 docs/sparkr.md|  2 +-
 docs/sql-programming-guide.md | 34 --
 2 files changed, 17 insertions(+), 19 deletions(-)
--


http://git-wip-us.apache.org/repos/asf/spark/blob/dbf7f48b/docs/sparkr.md
--
diff --git a/docs/sparkr.md b/docs/sparkr.md
index 023bbcd..f018901 100644
--- a/docs/sparkr.md
+++ b/docs/sparkr.md
@@ -152,7 +152,7 @@ write.df(people, path="people.parquet", source="parquet", 
mode="overwrite")
 
 ### From Hive tables
 
-You can also create SparkDataFrames from Hive tables. To do this we will need 
to create a SparkSession with Hive support which can access tables in the Hive 
MetaStore. Note that Spark should have been built with [Hive 
support](building-spark.html#building-with-hive-and-jdbc-support) and more 
details can be found in the [SQL programming 
guide](sql-programming-guide.html#starting-point-sqlcontext). In SparkR, by 
default it will attempt to create a SparkSession with Hive support enabled 
(`enableHiveSupport = TRUE`).
+You can also create SparkDataFrames from Hive tables. To do this we will need 
to create a SparkSession with Hive support which can access tables in the Hive 
MetaStore. Note that Spark should have been built with [Hive 
support](building-spark.html#building-with-hive-and-jdbc-support) and more 
details can be found in the [SQL programming 
guide](sql-programming-guide.html#starting-point-sparksession). In SparkR, by 
default it will attempt to create a SparkSession with Hive support enabled 
(`enableHiveSupport = TRUE`).
 
 
 {% highlight r %}

http://git-wip-us.apache.org/repos/asf/spark/blob/dbf7f48b/docs/sql-programming-guide.md
--
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index d93f30b..4206f73 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -107,19 +107,17 @@ spark = SparkSession.build \
 
 
 
-Unlike Scala, Java, and Python API, we haven't finished migrating `SQLContext` 
to `SparkSession` for SparkR yet, so
-the entry point into all relational functionality in SparkR is still the
-`SQLContext` class in Spark 2.0. To create a basic `SQLContext`, all you need 
is a `SparkContext`.
+The entry point into all functionality in Spark is the 
[`SparkSession`](api/R/sparkR.session.html) class. To initialize a basic 
`SparkSession`, just call `sparkR.session()`:
 
 {% highlight r %}
-spark <- sparkRSQL.init(sc)
+sparkR.session()
 {% endhighlight %}
 
-Note that when invoked for the first time, `sparkRSQL.init()` initializes a 
global `SQLContext` singleton instance, and always returns a reference to this 
instance for successive invocations. In this way, users only need to initialize 
the `SQLContext` once, then SparkR functions like `read.df` will be able to 
access this global instance implicitly, and users don't need to pass the 
`SQLContext` instance around.
+Note that when invoked for the first time, `sparkR.session()` initializes a 
global `SparkSession` singleton instance, and always returns a reference to 
this instance for successive invocations. In this way, users only need to 
initialize the `SparkSession` once, then SparkR functions like `read.df` will 
be able to access this global instance implicitly, and users don't need to pass 
the `SparkSession` instance around.
 
 
 
-`SparkSession` (or `SQLContext` for SparkR) in Spark 2.0 provides builtin 
support for Hive features including the ability to
+`SparkSession` in Spark 2.0 provides builtin support for Hive features 
including the ability to
 write queries using HiveQL, access to Hive UDFs, and the ability to read data 
from Hive tables.
 To use

spark git commit: [SPARK-15863][SQL][DOC][SPARKR] sql programming guide updates to include sparkSession in R

2016-06-20 Thread lian 
Repository: spark
Updated Branches:
  refs/heads/master 07367533d -> 58f6e27dd


[SPARK-15863][SQL][DOC][SPARKR] sql programming guide updates to include 
sparkSession in R

## What changes were proposed in this pull request?

Update doc as per discussion in PR #13592

## How was this patch tested?

manual

shivaram liancheng

Author: Felix Cheung 

Closes #13799 from felixcheung/rsqlprogrammingguide.


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/58f6e27d
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/58f6e27d
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/58f6e27d

Branch: refs/heads/master
Commit: 58f6e27dd70f476f99ac8204e6b405bced4d6de1
Parents: 0736753
Author: Felix Cheung 
Authored: Tue Jun 21 13:56:37 2016 +0800
Committer: Cheng Lian 
Committed: Tue Jun 21 13:56:37 2016 +0800

--
 docs/sparkr.md|  2 +-
 docs/sql-programming-guide.md | 34 --
 2 files changed, 17 insertions(+), 19 deletions(-)
--


http://git-wip-us.apache.org/repos/asf/spark/blob/58f6e27d/docs/sparkr.md
--
diff --git a/docs/sparkr.md b/docs/sparkr.md
index 023bbcd..f018901 100644
--- a/docs/sparkr.md
+++ b/docs/sparkr.md
@@ -152,7 +152,7 @@ write.df(people, path="people.parquet", source="parquet", 
mode="overwrite")
 
 ### From Hive tables
 
-You can also create SparkDataFrames from Hive tables. To do this we will need 
to create a SparkSession with Hive support which can access tables in the Hive 
MetaStore. Note that Spark should have been built with [Hive 
support](building-spark.html#building-with-hive-and-jdbc-support) and more 
details can be found in the [SQL programming 
guide](sql-programming-guide.html#starting-point-sqlcontext). In SparkR, by 
default it will attempt to create a SparkSession with Hive support enabled 
(`enableHiveSupport = TRUE`).
+You can also create SparkDataFrames from Hive tables. To do this we will need 
to create a SparkSession with Hive support which can access tables in the Hive 
MetaStore. Note that Spark should have been built with [Hive 
support](building-spark.html#building-with-hive-and-jdbc-support) and more 
details can be found in the [SQL programming 
guide](sql-programming-guide.html#starting-point-sparksession). In SparkR, by 
default it will attempt to create a SparkSession with Hive support enabled 
(`enableHiveSupport = TRUE`).
 
 
 {% highlight r %}

http://git-wip-us.apache.org/repos/asf/spark/blob/58f6e27d/docs/sql-programming-guide.md
--
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index d93f30b..4206f73 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -107,19 +107,17 @@ spark = SparkSession.build \
 
 
 
-Unlike Scala, Java, and Python API, we haven't finished migrating `SQLContext` 
to `SparkSession` for SparkR yet, so
-the entry point into all relational functionality in SparkR is still the
-`SQLContext` class in Spark 2.0. To create a basic `SQLContext`, all you need 
is a `SparkContext`.
+The entry point into all functionality in Spark is the 
[`SparkSession`](api/R/sparkR.session.html) class. To initialize a basic 
`SparkSession`, just call `sparkR.session()`:
 
 {% highlight r %}
-spark <- sparkRSQL.init(sc)
+sparkR.session()
 {% endhighlight %}
 
-Note that when invoked for the first time, `sparkRSQL.init()` initializes a 
global `SQLContext` singleton instance, and always returns a reference to this 
instance for successive invocations. In this way, users only need to initialize 
the `SQLContext` once, then SparkR functions like `read.df` will be able to 
access this global instance implicitly, and users don't need to pass the 
`SQLContext` instance around.
+Note that when invoked for the first time, `sparkR.session()` initializes a 
global `SparkSession` singleton instance, and always returns a reference to 
this instance for successive invocations. In this way, users only need to 
initialize the `SparkSession` once, then SparkR functions like `read.df` will 
be able to access this global instance implicitly, and users don't need to pass 
the `SparkSession` instance around.
 
 
 
-`SparkSession` (or `SQLContext` for SparkR) in Spark 2.0 provides builtin 
support for Hive features including the ability to
+`SparkSession` in Spark 2.0 provides builtin support for Hive features 
including the ability to
 write queries using HiveQL, access to Hive UDFs, and the ability to read data 
from Hive tables.
 To use these features, you do not need to have an existing Hive setup.
 
@@ -175,7 +173,7 @@ df.show()
 
 
 
-With a `SQLContext`,