[SPARK-4608][Streaming] Reorganize StreamingContext implicit to improve API convenience

There is only one implicit function `toPairDStreamFunctions` in `StreamingContext`. This PR did similar reorganization like [SPARK-4397](https://issues.apache.org/jira/browse/SPARK-4397).

Compiled the following codes with Spark Streaming 1.1.0 and ran it with this PR. Everything is fine.
```Scala
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._

object StreamingApp {

  def main(args: Array[String]) {
    val conf = new SparkConf().setMaster("local[2]").setAppName("FileWordCount")
    val ssc = new StreamingContext(conf, Seconds(10))
    val lines = ssc.textFileStream("/some/path")
    val words = lines.flatMap(_.split(" "))
    val pairs = words.map(word => (word, 1))
    val wordCounts = pairs.reduceByKey(_ + _)
    wordCounts.print()

    ssc.start()
    ssc.awaitTermination()
  }
}
```

Author: zsxwing <zsxwing@gmail.com>

Closes #3464 from zsxwing/SPARK-4608 and squashes the following commits:

aa6d44a [zsxwing] Fix a copy-paste error
f74c190 [zsxwing] Merge branch 'master' into SPARK-4608
e6f9cc9 [zsxwing] Update the docs
27833bb [zsxwing] Remove `import StreamingContext._`
c15162c [zsxwing] Reorganize StreamingContext implicit to improve API convenience

1 files changed, 2 insertions, 2 deletions

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 1ac5b9e863..01450efe35 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -75,7 +75,7 @@ main entry point for all streaming functionality. We create a local StreamingCon
 {% highlight scala %}
 import org.apache.spark._
 import org.apache.spark.streaming._
-import org.apache.spark.streaming.StreamingContext._
+import org.apache.spark.streaming.StreamingContext._ // not necessary in Spark 1.3+
 
 // Create a local StreamingContext with two working thread and batch interval of 1 second.
 // The master requires 2 cores to prevent from a starvation scenario.
@@ -107,7 +107,7 @@ each line will be split into multiple words and the stream of words is represent
 `words` DStream.  Next, we want to count these words.
 
 {% highlight scala %}
-import org.apache.spark.streaming.StreamingContext._
+import org.apache.spark.streaming.StreamingContext._ // not necessary in Spark 1.3+
 // Count each word in each batch
 val pairs = words.map(word => (word, 1))
 val wordCounts = pairs.reduceByKey(_ + _)