diff --git a/docs/configuration.md b/docs/configuration.md
index cee59cf2aa05f489906aa850f7558c5399a51a24..1e95b862441f5d62a0a9950cd8ba00486564a92a 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1564,8 +1564,8 @@ spark.sql("SET -v").show(n=200, truncate=False)
 <div data-lang="r"  markdown="1">
 
 {% highlight r %}
-# sqlContext is an existing sqlContext.
-properties <- sql(sqlContext, "SET -v")
+sparkR.session()
+properties <- sql("SET -v")
 showDF(properties, numRows = 200, truncate = FALSE)
 {% endhighlight %}
 
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index db06a65b994be131534c17b0e2f29e75ecdae6cf..2ee3b80185c2ffd8a8e38dc5c21cd5a483b58478 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -1534,7 +1534,7 @@ See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/ma
 ***
 
 ## DataFrame and SQL Operations
-You can easily use [DataFrames and SQL](sql-programming-guide.html) operations on streaming data. You have to create a SQLContext using the SparkContext that the StreamingContext is using. Furthermore this has to done such that it can be restarted on driver failures. This is done by creating a lazily instantiated singleton instance of SQLContext. This is shown in the following example. It modifies the earlier [word count example](#a-quick-example) to generate word counts using DataFrames and SQL. Each RDD is converted to a DataFrame, registered as a temporary table and then queried using SQL.
+You can easily use [DataFrames and SQL](sql-programming-guide.html) operations on streaming data. You have to create a SparkSession using the SparkContext that the StreamingContext is using. Furthermore this has to done such that it can be restarted on driver failures. This is done by creating a lazily instantiated singleton instance of SparkSession. This is shown in the following example. It modifies the earlier [word count example](#a-quick-example) to generate word counts using DataFrames and SQL. Each RDD is converted to a DataFrame, registered as a temporary table and then queried using SQL.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
@@ -1546,9 +1546,9 @@ val words: DStream[String] = ...
 
 words.foreachRDD { rdd =>
 
-  // Get the singleton instance of SQLContext
-  val sqlContext = SQLContext.getOrCreate(rdd.sparkContext)
-  import sqlContext.implicits._
+  // Get the singleton instance of SparkSession
+  val spark = SparkSession.builder.config(rdd.sparkContext.getConf).getOrCreate()
+  import spark.implicits._
 
   // Convert RDD[String] to DataFrame
   val wordsDataFrame = rdd.toDF("word")
@@ -1558,7 +1558,7 @@ words.foreachRDD { rdd =>
 
   // Do word count on DataFrame using SQL and print it
   val wordCountsDataFrame = 
-    sqlContext.sql("select word, count(*) as total from words group by word")
+    spark.sql("select word, count(*) as total from words group by word")
   wordCountsDataFrame.show()
 }
 
@@ -1593,8 +1593,8 @@ words.foreachRDD(
     @Override
     public Void call(JavaRDD<String> rdd, Time time) {
 
-      // Get the singleton instance of SQLContext
-      SQLContext sqlContext = SQLContext.getOrCreate(rdd.context());
+      // Get the singleton instance of SparkSession
+      SparkSession spark = SparkSession.builder().config(rdd.sparkContext().getConf()).getOrCreate();
 
       // Convert RDD[String] to RDD[case class] to DataFrame
       JavaRDD<JavaRow> rowRDD = rdd.map(new Function<String, JavaRow>() {
@@ -1604,14 +1604,14 @@ words.foreachRDD(
           return record;
         }
       });
-      DataFrame wordsDataFrame = sqlContext.createDataFrame(rowRDD, JavaRow.class);
+      DataFrame wordsDataFrame = spark.createDataFrame(rowRDD, JavaRow.class);
 
       // Creates a temporary view using the DataFrame
       wordsDataFrame.createOrReplaceTempView("words");
 
       // Do word count on table using SQL and print it
       DataFrame wordCountsDataFrame =
-          sqlContext.sql("select word, count(*) as total from words group by word");
+          spark.sql("select word, count(*) as total from words group by word");
       wordCountsDataFrame.show();
       return null;
     }
@@ -1624,11 +1624,14 @@ See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/ma
 <div data-lang="python" markdown="1">
 {% highlight python %}
 
-# Lazily instantiated global instance of SQLContext
-def getSqlContextInstance(sparkContext):
-    if ('sqlContextSingletonInstance' not in globals()):
-        globals()['sqlContextSingletonInstance'] = SQLContext(sparkContext)
-    return globals()['sqlContextSingletonInstance']
+# Lazily instantiated global instance of SparkSession
+def getSparkSessionInstance(sparkConf):
+    if ('sparkSessionSingletonInstance' not in globals()):
+        globals()['sparkSessionSingletonInstance'] = SparkSession\
+            .builder\
+            .config(conf=sparkConf)\
+            .getOrCreate()
+    return globals()['sparkSessionSingletonInstance']
 
 ...
 
@@ -1639,18 +1642,18 @@ words = ... # DStream of strings
 def process(time, rdd):
     print("========= %s =========" % str(time))
     try:
-        # Get the singleton instance of SQLContext
-        sqlContext = getSqlContextInstance(rdd.context)
+        # Get the singleton instance of SparkSession
+        spark = getSparkSessionInstance(rdd.context.getConf())
 
         # Convert RDD[String] to RDD[Row] to DataFrame
         rowRdd = rdd.map(lambda w: Row(word=w))
-        wordsDataFrame = sqlContext.createDataFrame(rowRdd)
+        wordsDataFrame = spark.createDataFrame(rowRdd)
 
         # Creates a temporary view using the DataFrame
         wordsDataFrame.createOrReplaceTempView("words")
 
         # Do word count on table using SQL and print it
-        wordCountsDataFrame = sqlContext.sql("select word, count(*) as total from words group by word")
+        wordCountsDataFrame = spark.sql("select word, count(*) as total from words group by word")
         wordCountsDataFrame.show()
     except:
         pass