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About Nicolas A Perez

Nicolas A Perez
Nicolas is a software engineer at IPC, an independent SUBWAY® franchisee-owned and operated purchasing cooperative, where I work on their Big Data Platform. Very interested in Apache Spark, Hadoop, distributed systems, algorithms, and functional programming, especially in the Scala programming language.

How to Integrate Custom Data Sources Into Apache Spark

Streaming data is a hot topic these days, and Apache Spark is an excellent framework for streaming. In this blog post, I’ll show you how to integrate custom data sources into Spark.

Spark Streaming gives us the ability to stream from a variety of sources while using the same concise API for accessing data streams, performing SQL queries, or creating machine learning algorithms. These abilities make Spark a preferable framework for streaming (or any type of workflow) applications, since we can use all aspects of the framework.

The challenge is figuring out how to integrate custom data sources into Spark so we can leverage its power without needing to change to more standard sources. It might seem logical to change, but in some cases it is just not possible or convenient to do so.

Streaming Custom Receivers

Spark offers different extension points, as we could see when we extended the Data Source API here in order to integrate our custom data store into Spark SQL.

In this example, we are going to do the same, but we are also going to extend the streaming API so we can stream from anywhere.

In order to implement our custom receiver, we need to extend the Receiver[A] class. Note that it has type annotation, so we can enforce type safety on our DStream from the streaming client side point of view.

We are going to use this custom receiver to stream orders that one of our applications send over a socket.

The structure of the data traveling through the network looks like this:

1 5
1 1 2
2 1 1
2 1 1
4 1 1
2 2
1 2 2

We first receive the order ID and the total amount of the order, and then we receive the line items of the order. The first value is the item ID, the second is the order ID, (which matches the order ID value) and then the cost of the item. In this example, we have two orders. The first one has four items and the second one has only one item.

The idea is to hide all of this from our Spark application, so what it receives on the DStream is a complete order defined on a stream as follows:

val orderStream: DStream[Order] = .....
val orderStream: DStream[Order] = .....

At the same time, we are also using the receiver to stream our custom streaming source. Even though it sends the data over a socket, it will be quite complicated to use the standard socket stream from Spark, since we will not be able to control how the data is coming in and we will have the problem of conforming orders on the application itself. This could be very complicated, since once we are in the app space we are running in parallel, and it is hard to sync all of this incoming data. However, in the receiver space it is easy to create orders from the raw input text.

Let’s take a look at what our initial implementation looks like.

case class Order(id: Int, total: Int, items: List[Item] = null)
case class Item(id: Int, cost: Int)

class OrderReceiver(host: String, port: Int) extends Receiver[Order](StorageLevel.MEMORY_ONLY)  {

  override def onStart(): Unit = {

    println("starting...")

    val thread = new Thread("Receiver") {
      override def run() {receive() }
    }

    thread.start()
  }

  override def onStop(): Unit = stop("I am done")

  def receive() = ....
}
case class Order(id: Int, total: Int, items: List[Item] = null)
case class Item(id: Int, cost: Int)

class OrderReceiver(host: String, port: Int) extends Receiver[Order](StorageLevel.MEMORY_ONLY)  {

  override def onStart(): Unit = {

    println("starting...")

    val thread = new Thread("Receiver") {
      override def run() {receive() }
    }

    thread.start()
  }

  override def onStop(): Unit = stop("I am done")

  def receive() = ....
}

Our OrderReceiver extends Receiver[Order] which allows us to store an Order (type annotated) inside Spark. We also need to implement the onStart() and onStop() methods. Note that onStart() creates a thread so it is non-blocking, which is very important for proper behavior.

Now, let’s take a look at the receive method, where the magic really happens.

def receive() = {
    val socket = new Socket(host, port)
    var currentOrder: Order = null
    var currentItems: List[Item] = null

    val reader = new BufferedReader(new InputStreamReader (socket.getInputStream(), "UTF-8"))

    while (!isStopped()) {
      var userInput = reader.readLine()

      if (userInput == null) stop("Stream has ended")
      else {
        val parts = userInput.split(" ")

        if (parts.length == 2) {
          if (currentOrder != null) {
            store(Order(currentOrder.id, currentOrder.total, currentItems))
          }

          currentOrder = Order(parts(0).toInt, parts(1).toInt)
          currentItems = List[Item]()
        }
        else {
          currentItems = Item(parts(0).toInt, parts(1).toInt) :: currentItems
        }
      }
    }
  }
def receive() = {
    val socket = new Socket(host, port)
    var currentOrder: Order = null
    var currentItems: List[Item] = null

    val reader = new BufferedReader(new InputStreamReader (socket.getInputStream(), "UTF-8"))

    while (!isStopped()) {
      var userInput = reader.readLine()

      if (userInput == null) stop("Stream has ended")
      else {
        val parts = userInput.split(" ")

        if (parts.length == 2) {
          if (currentOrder != null) {
            store(Order(currentOrder.id, currentOrder.total, currentItems))
          }

          currentOrder = Order(parts(0).toInt, parts(1).toInt)
          currentItems = List[Item]()
        }
        else {
          currentItems = Item(parts(0).toInt, parts(1).toInt) :: currentItems
        }
      }
    }
  }

Here, we create a socket and point it to our source and then we just simply start reading from it until a stop command has been dispatched, or our socket has no more data on it. Note that we are reading the same structure we have defined previously (how our data is being sent). Once we have completely read an Order, we call store(…) so it gets saved into Spark.

There is nothing left to do here but to use our receiver in our application, which look like this:

val config = new SparkConf().setAppName("streaming")
val sc = new SparkContext(config)
val ssc = new StreamingContext(sc, Seconds(5))
 
val stream: DStream[Order] = ssc.receiverStream(new OrderReceiver(port))
val config = new SparkConf().setAppName("streaming")
val sc = new SparkContext(config)
val ssc = new StreamingContext(sc, Seconds(5))
 
val stream: DStream[Order] = ssc.receiverStream(new OrderReceiver(port))

Note how we have created the stream using our custom OrderReceiver (the val stream has been annotated only for clarity but it is not required). From now on, we use the stream (DString[Order]) as any other stream we have used in any other application.

stream.foreachRDD { rdd =>
      rdd.foreach(order => {
            println(order.id))              
            order.items.foreach(println)
      }
    }
stream.foreachRDD { rdd =>
      rdd.foreach(order => {
            println(order.id))              
            order.items.foreach(println)
      }
    }

Summary

Spark Streaming comes in very handy when processing sources that generate endless data. You can use the same API that you use for Spark SQL and other components in the system, but it is also flexible enough to be extended to meet your particular needs.

Reference: How to Integrate Custom Data Sources Into Apache Spark from our JCG partner Nicolas A Perez at the Mapr blog.
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