Spark机器学习

1 在线学习

模型随着接收的新消息，不断更新自己；而不是像离线训练一次次重新训练。

2 Spark Streaming

• 离散化流(DStream)

• 输入源：Akka actors、消息队列、Flume、Kafka、……
  

  http://spark.apache.org/docs/latest/streaming-programming-guide.html

• 类群(lineage)：应用到RDD上的转换算子和执行算子的集合

3 MLib+Streaming应用

3.0 build.sbt

依赖Spark MLlib和Spark Streaming

name := "scala-spark-streaming-app"

version := "1.0"

scalaVersion := "2.11.7"

libraryDependencies += "org.apache.spark" %% "spark-mllib" % "1.5.1"

libraryDependencies += "org.apache.spark" %% "spark-streaming" % "1.5.1"

使用国内镜像仓库

~/.sbt/repositories

[repositories]
local
osc: http://maven.oschina.net/content/groups/public/
typesafe: http://repo.typesafe.com/typesafe/ivy-releases/, [organization]/[module]/(scala_[scalaVersion]/)(sbt_[sbtVersion]/)[revision]/[type]s/[artifact](-[classifier]).[ext], bootOnly
sonatype-oss-releases
maven-central
sonatype-oss-snapshots

3.1 生产消息

object StreamingProducer {

  def main(args: Array[String]) {

    val random = new Random()

    // Maximum number of events per second
    val MaxEvents = 6

    // Read the list of possible names
    val namesResource = this.getClass.getResourceAsStream("/names.csv")
    val names = scala.io.Source.fromInputStream(namesResource)
      .getLines()
      .toList
      .head
      .split(",")
      .toSeq

    // Generate a sequence of possible products
    val products = Seq(
      "iPhone Cover" -> 9.99,
      "Headphones" -> 5.49,
      "Samsung Galaxy Cover" -> 8.95,
      "iPad Cover" -> 7.49
    )

    /** Generate a number of random product events */
    def generateProductEvents(n: Int) = {
      (1 to n).map { i =>
        val (product, price) = products(random.nextInt(products.size))
        val user = random.shuffle(names).head
        (user, product, price)
      }
    }

    // create a network producer
    val listener = new ServerSocket(9999)
    println("Listening on port: 9999")

    while (true) {
      val socket = listener.accept()
      new Thread() {
        override def run = {
          println("Got client connected from: " + socket.getInetAddress)
          val out = new PrintWriter(socket.getOutputStream(), true)

          while (true) {
            Thread.sleep(1000)
            val num = random.nextInt(MaxEvents)
            val productEvents = generateProductEvents(num)
            productEvents.foreach{ event =>
              out.write(event.productIterator.mkString(","))
              out.write("\n")
            }
            out.flush()
            println(s"Created $num events...")
          }
          socket.close()
        }
      }.start()
    }
  }
}

sbt run

Multiple main classes detected, select one to run:

 [1] MonitoringStreamingModel
 [2] SimpleStreamingApp
 [3] SimpleStreamingModel
 [4] StreamingAnalyticsApp
 [5] StreamingModelProducer
 [6] StreamingProducer
 [7] StreamingStateApp

Enter number: 6

3.2 打印消息

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Spark机器学习· 实时机器学习