---
layout: global
title: Spark Streaming Custom Receivers
---

Spark Streaming can receive streaming data from any arbitrary data source beyond
the one's for which it has in-built support (that is, beyond Flume, Kafka, Kinesis, files, sockets, etc.).
This requires the developer to implement a *receiver* that is customized for receiving data from
the concerned data source. This guide walks through the process of implementing a custom receiver
and using it in a Spark Streaming application.

### Implementing a Custom Receiver

This starts with implementing a [Receiver](api/scala/index.html#org.apache.spark.streaming.receiver.Receiver).
A custom receiver must extend this abstract class by implementing two methods
- `onStart()`: Things to do to start receiving data.
- `onStop()`: Things to do to stop receiving data.

Note that `onStart()` and `onStop()` must not block indefinitely. Typically, onStart() would start the threads
that responsible for receiving the data and `onStop()` would ensure that the receiving by those threads
are stopped. The receiving threads can also use `isStopped()`, a `Receiver` method, to check whether they
should stop receiving data.

Once the data is received, that data can be stored inside Spark
by calling `store(data)`, which is a method provided by the
[Receiver](api/scala/index.html#org.apache.spark.streaming.receiver.Receiver) class.
There are number of flavours of `store()` which allow you store the received data
record-at-a-time or as whole collection of objects / serialized bytes.

Any exception in the receiving threads should be caught and handled properly to avoid silent
failures of the receiver. `restart(<exception>)` will restart the receiver by
asynchronously calling `onStop()` and then calling `onStart()` after a delay.
`stop(<exception>)` will call `onStop()` and terminate the receiver. Also, `reportError(<error>)`
reports a error message to the driver (visible in the logs and UI) without stopping / restarting
the receiver.

The following is a custom receiver that receives a stream of text over a socket. It treats
'\n' delimited lines in the text stream as records and stores them with Spark. If the receiving thread
has any error connecting or receiving, the receiver is restarted to make another attempt to connect.

<div class="codetabs">
<div data-lang="scala"  markdown="1" >

{% highlight scala %}

class CustomReceiver(host: String, port: Int)
  extends Receiver[String](StorageLevel.MEMORY_AND_DISK_2) with Logging {

  def onStart() {
    // Start the thread that receives data over a connection
    new Thread("Socket Receiver") {
      override def run() { receive() }
    }.start()
  }

  def onStop() {
   // There is nothing much to do as the thread calling receive()
   // is designed to stop by itself isStopped() returns false
  }

  /** Create a socket connection and receive data until receiver is stopped */
  private def receive() {
    var socket: Socket = null
    var userInput: String = null
    try {
     // Connect to host:port
     socket = new Socket(host, port)

     // Until stopped or connection broken continue reading
     val reader = new BufferedReader(new InputStreamReader(socket.getInputStream(), "UTF-8"))
     userInput = reader.readLine()
     while(!isStopped && userInput != null) {
       store(userInput)
       userInput = reader.readLine()
     }
     reader.close()
     socket.close()

     // Restart in an attempt to connect again when server is active again
     restart("Trying to connect again")
    } catch {
     case e: java.net.ConnectException =>
       // restart if could not connect to server
       restart("Error connecting to " + host + ":" + port, e)
     case t: Throwable =>
       // restart if there is any other error
       restart("Error receiving data", t)
    }
  }
}

{% endhighlight %}

</div>
<div data-lang="java" markdown="1">

{% highlight java %}

public class JavaCustomReceiver extends Receiver<String> {

  String host = null;
  int port = -1;

  public JavaCustomReceiver(String host_ , int port_) {
    super(StorageLevel.MEMORY_AND_DISK_2());
    host = host_;
    port = port_;
  }

  public void onStart() {
    // Start the thread that receives data over a connection
    new Thread()  {
      @Override public void run() {
        receive();
      }
    }.start();
  }

  public void onStop() {
    // There is nothing much to do as the thread calling receive()
    // is designed to stop by itself isStopped() returns false
  }

  /** Create a socket connection and receive data until receiver is stopped */
  private void receive() {
    Socket socket = null;
    String userInput = null;

    try {
      // connect to the server
      socket = new Socket(host, port);

      BufferedReader reader = new BufferedReader(new InputStreamReader(socket.getInputStream()));

      // Until stopped or connection broken continue reading
      while (!isStopped() && (userInput = reader.readLine()) != null) {
        System.out.println("Received data '" + userInput + "'");
        store(userInput);
      }
      reader.close();
      socket.close();

      // Restart in an attempt to connect again when server is active again
      restart("Trying to connect again");
    } catch(ConnectException ce) {
      // restart if could not connect to server
      restart("Could not connect", ce);
    } catch(Throwable t) {
      // restart if there is any other error
      restart("Error receiving data", t);
    }
  }
}

{% endhighlight %}

</div>
</div>


### Using the custom receiver in a Spark Streaming application

The custom receiver can be used in a Spark Streaming application by using
`streamingContext.receiverStream(<instance of custom receiver>)`. This will create
input DStream using data received by the instance of custom receiver, as shown below

<div class="codetabs">
<div data-lang="scala"  markdown="1" >

{% highlight scala %}
// Assuming ssc is the StreamingContext
val customReceiverStream = ssc.receiverStream(new CustomReceiver(host, port))
val words = lines.flatMap(_.split(" "))
...
{% endhighlight %}

The full source code is in the example [CustomReceiver.scala](https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala).

</div>
<div data-lang="java" markdown="1">

{% highlight java %}
// Assuming ssc is the JavaStreamingContext
JavaDStream<String> customReceiverStream = ssc.receiverStream(new JavaCustomReceiver(host, port));
JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() { ... });
...
{% endhighlight %}

The full source code is in the example [JavaCustomReceiver.java](https://github.com/apache/spark/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java).

</div>
</div>



### Implementing and Using a Custom Actor-based Receiver

Custom [Akka Actors](http://doc.akka.io/docs/akka/2.2.4/scala/actors.html) can also be used to
receive data. The [`ActorHelper`](api/scala/index.html#org.apache.spark.streaming.receiver.ActorHelper)
trait can be applied on any Akka actor, which allows received data to be stored in Spark using
 `store(...)` methods. The supervisor strategy of this actor can be configured to handle failures, etc.

{% highlight scala %}
class CustomActor extends Actor with ActorHelper {
  def receive = {
   case data: String => store(data)
  }
}
{% endhighlight %}

And a new input stream can be created with this custom actor as

{% highlight scala %}
// Assuming ssc is the StreamingContext
val lines = ssc.actorStream[String](Props(new CustomActor()), "CustomReceiver")
{% endhighlight %}

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.