[SPARK-13888][DOC] Remove Akka Receiver doc and refer to the DStream Akka project

## What changes were proposed in this pull request?

I have copied the docs of Streaming Akka to https://github.com/spark-packages/dstream-akka/blob/master/README.md

So we can remove them from Spark now.

## How was this patch tested?

Only document changes.

(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #11711 from zsxwing/remove-akka-doc.

docs/streaming-custom-receivers.md

@@ -256,64 +256,3 @@ The following table summarizes the characteristics of both types of receivers
   <td></td>
 </tr>
 </table>
-
-## Implementing and Using a Custom Actor-based Receiver
-
-Custom [Akka Actors](http://doc.akka.io/docs/akka/2.3.11/scala/actors.html) can also be used to
-receive data. Here are the instructions.
-
-1. **Linking:** You need to add the following dependency to your SBT or Maven project (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information).
-
-		groupId = org.apache.spark
-		artifactId = spark-streaming-akka_{{site.SCALA_BINARY_VERSION}}
-		version = {{site.SPARK_VERSION_SHORT}}
-
-2. **Programming:**
-
-	<div class="codetabs">
-	<div data-lang="scala"  markdown="1" >
-
-	You need to extend [`ActorReceiver`](api/scala/index.html#org.apache.spark.streaming.akka.ActorReceiver)
-	so as to store received data into Spark using `store(...)` methods. The supervisor strategy of
-	this actor can be configured to handle failures, etc.
-
-		class CustomActor extends ActorReceiver {
-		  def receive = {
-		    case data: String => store(data)
-		  }
-		}
-
-		// A new input stream can be created with this custom actor as
-		val ssc: StreamingContext = ...
-		val lines = AkkaUtils.createStream[String](ssc, Props[CustomActor](), "CustomReceiver")
-
-	See [ActorWordCount.scala](https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala) for an end-to-end example.
-	</div>
-	<div data-lang="java" markdown="1">
-
-	You need to extend [`JavaActorReceiver`](api/scala/index.html#org.apache.spark.streaming.akka.JavaActorReceiver)
-	so as to store received data into Spark using `store(...)` methods. The supervisor strategy of
-	this actor can be configured to handle failures, etc.
-
-		class CustomActor extends JavaActorReceiver {
-		  @Override
-		  public void onReceive(Object msg) throws Exception {
-		    store((String) msg);
-		  }
-		}
-
-		// A new input stream can be created with this custom actor as
-		JavaStreamingContext jssc = ...;
-		JavaDStream<String> lines = AkkaUtils.<String>createStream(jssc, Props.create(CustomActor.class), "CustomReceiver");
-
-	See [JavaActorWordCount.scala](https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/JavaActorWordCount.scala) for an end-to-end example.
-	</div>
-	</div>
-
-3. **Deploying:** As with any Spark applications, `spark-submit` is used to launch your application.
-You need to package `spark-streaming-akka_{{site.SCALA_BINARY_VERSION}}` and its dependencies into
-the application JAR. Make sure `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}`
-are marked as `provided` dependencies as those are already present in a Spark installation. Then
-use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide).
-
-<span class="badge" style="background-color: grey">Python API</span> Since actors are available only in the Java and Scala libraries, AkkaUtils is not available in the Python API.

docs/streaming-programming-guide.md

@@ -594,7 +594,7 @@ data from a source and stores it in Spark's memory for processing.
 Spark Streaming provides two categories of built-in streaming sources.
 
 - *Basic sources*: Sources directly available in the StreamingContext API.
-  Examples: file systems, socket connections, and Akka actors.
+  Examples: file systems, and socket connections.
 - *Advanced sources*: Sources like Kafka, Flume, Kinesis, Twitter, etc. are available through
   extra utility classes. These require linking against extra dependencies as discussed in the
   [linking](#linking) section.
@@ -631,7 +631,7 @@ as well as to run the receiver(s).
 We have already taken a look at the `ssc.socketTextStream(...)` in the [quick example](#a-quick-example)
 which creates a DStream from text
 data received over a TCP socket connection. Besides sockets, the StreamingContext API provides
-methods for creating DStreams from files and Akka actors as input sources.
+methods for creating DStreams from files as input sources.
 
 - **File Streams:** For reading data from files on any file system compatible with the HDFS API (that is, HDFS, S3, NFS, etc.), a DStream can be created as:
 
@@ -658,17 +658,12 @@ methods for creating DStreams from files and Akka actors as input sources.
 
 	<span class="badge" style="background-color: grey">Python API</span> `fileStream` is not available in the Python API, only	`textFileStream` is	available.
 
-- **Streams based on Custom Actors:** DStreams can be created with data streams received through Akka
-  actors by using `AkkaUtils.createStream(ssc, actorProps, actor-name)`. See the [Custom Receiver
-  Guide](streaming-custom-receivers.html) for more details.
-
-  <span class="badge" style="background-color: grey">Python API</span> Since actors are available only in the Java and Scala
-  libraries, `AkkaUtils.createStream` is not available in the Python API.
+- **Streams based on Custom Receivers:** DStreams can be created with data streams received through custom receivers. See the [Custom Receiver
+  Guide](streaming-custom-receivers.html) and [DStream Akka](https://github.com/spark-packages/dstream-akka) for more details.
 
 - **Queue of RDDs as a Stream:** For testing a Spark Streaming application with test data, one can also create a DStream based on a queue of RDDs, using `streamingContext.queueStream(queueOfRDDs)`. Each RDD pushed into the queue will be treated as a batch of data in the DStream, and processed like a stream.
 
-For more details on streams from sockets, files, and actors,
-see the API documentations of the relevant functions in
+For more details on streams from sockets and files, see the API documentations of the relevant functions in
 [StreamingContext](api/scala/index.html#org.apache.spark.streaming.StreamingContext) for
 Scala, [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html)
 for Java, and [StreamingContext](api/python/pyspark.streaming.html#pyspark.streaming.StreamingContext) for Python.
@@ -2439,13 +2434,8 @@ that can be called to store the data in Spark. So, to migrate your custom networ
 BlockGenerator object (does not exist any more in Spark 1.0 anyway), and use `store(...)` methods on
 received data.
 
-**Actor-based Receivers**: Data could have been received using any Akka Actors by extending the actor class with
-`org.apache.spark.streaming.receivers.Receiver` trait. This has been renamed to
-[`org.apache.spark.streaming.receiver.ActorHelper`](api/scala/index.html#org.apache.spark.streaming.receiver.ActorHelper)
-and the `pushBlock(...)` methods to store received data has been renamed to `store(...)`. Other helper classes in
-the `org.apache.spark.streaming.receivers` package were also moved
-to [`org.apache.spark.streaming.receiver`](api/scala/index.html#org.apache.spark.streaming.receiver.package)
-package and renamed for better clarity.
+**Actor-based Receivers**: The Actor-based Receiver APIs have been moved to [DStream Akka](https://github.com/spark-packages/dstream-akka).
+Please refer to the project for more details.
 
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