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

docs/streaming-programming-guide.md

@@ -66,7 +66,7 @@ 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.
@@ -98,7 +98,7 @@ 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(_ + _)

examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala

@@ -22,7 +22,6 @@ import java.util.Properties
 import kafka.producer._
 
 import org.apache.spark.streaming._
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.kafka._
 import org.apache.spark.SparkConf
 

examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala

@@ -23,7 +23,6 @@ import java.net.Socket
 import org.apache.spark.{SparkConf, Logging}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Seconds, StreamingContext}
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.receiver.Receiver
 
 /**

examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala

@@ -19,7 +19,6 @@ package org.apache.spark.examples.streaming
 
 import org.apache.spark.SparkConf
 import org.apache.spark.streaming.{Seconds, StreamingContext}
-import org.apache.spark.streaming.StreamingContext._
 
 /**
  * Counts words in new text files created in the given directory

examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala

@@ -22,7 +22,6 @@ import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence
 
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Seconds, StreamingContext}
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.mqtt._
 import org.apache.spark.SparkConf
 

examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala

@@ -19,7 +19,6 @@ package org.apache.spark.examples.streaming
 
 import org.apache.spark.SparkConf
 import org.apache.spark.streaming.{Seconds, StreamingContext}
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.storage.StorageLevel
 
 /**

examples/src/main/scala/org/apache/spark/examples/streaming/QueueStream.scala

@@ -22,7 +22,6 @@ import scala.collection.mutable.SynchronizedQueue
 import org.apache.spark.SparkConf
 import org.apache.spark.rdd.RDD
 import org.apache.spark.streaming.{Seconds, StreamingContext}
-import org.apache.spark.streaming.StreamingContext._
 
 object QueueStream {
 

examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala

@@ -25,7 +25,6 @@ import com.google.common.io.Files
 import org.apache.spark.SparkConf
 import org.apache.spark.rdd.RDD
 import org.apache.spark.streaming.{Time, Seconds, StreamingContext}
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.util.IntParam
 
 /**

examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala

@@ -20,7 +20,6 @@ package org.apache.spark.examples.streaming
 import org.apache.spark.SparkConf
 import org.apache.spark.HashPartitioner
 import org.apache.spark.streaming._
-import org.apache.spark.streaming.StreamingContext._
 
 /**
  * Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every

examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala

@@ -23,7 +23,6 @@ import org.apache.spark.SparkConf
 import org.apache.spark.SparkContext._
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Seconds, StreamingContext}
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.twitter._
 
 // scalastyle:off

examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala

@@ -18,7 +18,6 @@
 package org.apache.spark.examples.streaming
 
 import org.apache.spark.streaming.{Seconds, StreamingContext}
-import StreamingContext._
 import org.apache.spark.SparkContext._
 import org.apache.spark.streaming.twitter._
 import org.apache.spark.SparkConf

examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala

@@ -24,7 +24,6 @@ import akka.zeromq.Subscribe
 import akka.util.ByteString
 
 import org.apache.spark.streaming.{Seconds, StreamingContext}
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.zeromq._
 
 import scala.language.implicitConversions

examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala

@@ -19,7 +19,6 @@ package org.apache.spark.examples.streaming.clickstream
 
 import org.apache.spark.SparkContext._
 import org.apache.spark.streaming.{Seconds, StreamingContext}
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.examples.streaming.StreamingExamples
 // scalastyle:off
 /** Analyses a streaming dataset of web page views. This class demonstrates several types of

mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala

@@ -24,7 +24,6 @@ import org.apache.spark.SparkContext._
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.dstream.DStream
 import org.apache.spark.util.Utils
 import org.apache.spark.util.random.XORShiftRandom

mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala

@@ -22,7 +22,6 @@ import scala.reflect.ClassTag
 import org.apache.spark.Logging
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mllib.linalg.Vector
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.dstream.DStream
 
 /**

streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala

@@ -22,7 +22,6 @@ import java.util.concurrent.atomic.AtomicInteger
 
 import scala.collection.Map
 import scala.collection.mutable.Queue
-import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
 import akka.actor.{Props, SupervisorStrategy}
@@ -523,9 +522,11 @@ object StreamingContext extends Logging {
 
   private[streaming] val DEFAULT_CLEANER_TTL = 3600
 
-  implicit def toPairDStreamFunctions[K, V](stream: DStream[(K, V)])
+  @deprecated("Replaced by implicit functions in the DStream companion object. This is " +
+    "kept here only for backward compatibility.", "1.3.0")
+  def toPairDStreamFunctions[K, V](stream: DStream[(K, V)])
       (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null) = {
-    new PairDStreamFunctions[K, V](stream)
+    DStream.toPairDStreamFunctions(stream)(kt, vt, ord)
   }
 
   /**

streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala

@@ -36,7 +36,6 @@ import org.apache.spark.api.java.function.{Function => JFunction, Function2 => J
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming._
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.dstream.DStream
 
 /**
@@ -815,6 +814,6 @@ object JavaPairDStream {
 
   def scalaToJavaLong[K: ClassTag](dstream: JavaPairDStream[K, Long])
   : JavaPairDStream[K, JLong] = {
-    StreamingContext.toPairDStreamFunctions(dstream.dstream).mapValues(new JLong(_))
+    DStream.toPairDStreamFunctions(dstream.dstream).mapValues(new JLong(_))
   }
 }

streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala

@@ -20,16 +20,16 @@ package org.apache.spark.streaming.dstream
 
 import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
 
-import scala.deprecated
 import scala.collection.mutable.HashMap
+import scala.language.implicitConversions
 import scala.reflect.ClassTag
 import scala.util.matching.Regex
 
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.rdd.{BlockRDD, RDD}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming._
-import org.apache.spark.streaming.StreamingContext._
+import org.apache.spark.streaming.StreamingContext.rddToFileName
 import org.apache.spark.streaming.scheduler.Job
 import org.apache.spark.util.{CallSite, MetadataCleaner, Utils}
 
@@ -48,8 +48,7 @@ import org.apache.spark.util.{CallSite, MetadataCleaner, Utils}
  * `window`. In addition, [[org.apache.spark.streaming.dstream.PairDStreamFunctions]] contains
  * operations available only on DStreams of key-value pairs, such as `groupByKeyAndWindow` and
  * `join`. These operations are automatically available on any DStream of pairs
- * (e.g., DStream[(Int, Int)] through implicit conversions when
- * `org.apache.spark.streaming.StreamingContext._` is imported.
+ * (e.g., DStream[(Int, Int)] through implicit conversions.
  *
  * DStreams internally is characterized by a few basic properties:
  *  - A list of other DStreams that the DStream depends on
@@ -802,10 +801,21 @@ abstract class DStream[T: ClassTag] (
   }
 }
 
-private[streaming] object DStream {
+object DStream {
+
+  // `toPairDStreamFunctions` was in SparkContext before 1.3 and users had to
+  // `import StreamingContext._` to enable it. Now we move it here to make the compiler find
+  // it automatically. However, we still keep the old function in StreamingContext for backward
+  // compatibility and forward to the following function directly.
+
+  implicit def toPairDStreamFunctions[K, V](stream: DStream[(K, V)])
+      (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null):
+    PairDStreamFunctions[K, V] = {
+    new PairDStreamFunctions[K, V](stream)
+  }
 
   /** Get the creation site of a DStream from the stack trace of when the DStream is created. */
-  def getCreationSite(): CallSite = {
+  private[streaming] def getCreationSite(): CallSite = {
     val SPARK_CLASS_REGEX = """^org\.apache\.spark""".r
     val SPARK_STREAMING_TESTCLASS_REGEX = """^org\.apache\.spark\.streaming\.test""".r
     val SPARK_EXAMPLES_CLASS_REGEX = """^org\.apache\.spark\.examples""".r

streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala

@@ -27,12 +27,10 @@ import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat}
 import org.apache.spark.{HashPartitioner, Partitioner, SerializableWritable}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.streaming.{Duration, Time}
-import org.apache.spark.streaming.StreamingContext._
+import org.apache.spark.streaming.StreamingContext.rddToFileName
 
 /**
  * Extra functions available on DStream of (key, value) pairs through an implicit conversion.
- * Import `org.apache.spark.streaming.StreamingContext._` at the top of your program to use
- * these functions.
  */
 class PairDStreamFunctions[K, V](self: DStream[(K,V)])
     (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K])

streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala

@@ -17,8 +17,6 @@
 
 package org.apache.spark.streaming.dstream
 
-import org.apache.spark.streaming.StreamingContext._
-
 import org.apache.spark.rdd.RDD
 import org.apache.spark.rdd.{CoGroupedRDD, MapPartitionsRDD}
 import org.apache.spark.Partitioner

streaming/src/main/scala/org/apache/spark/streaming/package.scala

@@ -26,7 +26,7 @@ package org.apache.spark
  * available only on DStreams
  * of key-value pairs, such as `groupByKey` and `reduceByKey`. These operations are automatically
  * available on any DStream of the right type (e.g. DStream[(Int, Int)] through implicit
- * conversions when you `import org.apache.spark.streaming.StreamingContext._`.
+ * conversions.
  *
  * For the Java API of Spark Streaming, take a look at the
  * [[org.apache.spark.streaming.api.java.JavaStreamingContext]] which serves as the entry point, and

streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala

@@ -28,7 +28,6 @@ import org.apache.spark.{SparkConf, SparkException}
 import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.{BlockRDD, RDD}
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.dstream.{DStream, WindowedDStream}
 import org.apache.spark.HashPartitioner
 

streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala

@@ -30,7 +30,6 @@ import org.apache.hadoop.io.{IntWritable, Text}
 import org.apache.hadoop.mapred.TextOutputFormat
 import org.apache.hadoop.mapreduce.lib.output.{TextOutputFormat => NewTextOutputFormat}
 
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.dstream.{DStream, FileInputDStream}
 import org.apache.spark.streaming.util.ManualClock
 import org.apache.spark.util.Utils

streaming/src/test/scala/org/apache/spark/streaming/MasterFailureTest.scala

@@ -20,7 +20,6 @@ package org.apache.spark.streaming
 import org.apache.spark.Logging
 import org.apache.spark.streaming.dstream.DStream
 import org.apache.spark.util.Utils
-import org.apache.spark.streaming.StreamingContext._
 
 import scala.util.Random
 import scala.collection.mutable.ArrayBuffer

streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala

@@ -17,7 +17,6 @@
 
 package org.apache.spark.streaming
 
-import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.dstream.DStream
 import org.apache.spark.storage.StorageLevel
 

streaming/src/test/scala/org/apache/spark/streamingtest/ImplicitSuite.scala

@@ -0,0 +1,35 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streamingtest
+
+/**
+ * A test suite to make sure all `implicit` functions work correctly.
+ *
+ * As `implicit` is a compiler feature, we don't need to run this class.
+ * What we need to do is making the compiler happy.
+ */
+class ImplicitSuite {
+
+  // We only want to test if `implict` works well with the compiler, so we don't need a real DStream.
+  def mockDStream[T]: org.apache.spark.streaming.dstream.DStream[T] = null
+
+  def testToPairDStreamFunctions(): Unit = {
+    val rdd: org.apache.spark.streaming.dstream.DStream[(Int, Int)] = mockDStream
+    rdd.groupByKey()
+  }
+}