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("
")
}
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
object SimpleStreamingApp {
def main(args: Array[String]) {
val ssc = new StreamingContext("local[2]", "First Streaming App", Seconds(10))
val stream = ssc.socketTextStream("localhost", 9999)
// here we simply print out the first few elements of each batch
stream.print()
ssc.start()
ssc.awaitTermination()
}
}
sbt run
Enter number: 2
3.3 流式分析
object StreamingAnalyticsApp {
def main(args: Array[String]) {
val ssc = new StreamingContext("local[2]", "First Streaming App", Seconds(10))
val stream = ssc.socketTextStream("localhost", 9999)
// create stream of events from raw text elements
val events = stream.map { record =>
val event = record.split(",")
(event(0), event(1), event(2))
}
/*
We compute and print out stats for each batch.
Since each batch is an RDD, we call forEeachRDD on the DStream, and apply the usual RDD functions
we used in Chapter 1.
*/
events.foreachRDD { (rdd, time) =>
val numPurchases = rdd.count()
val uniqueUsers = rdd.map { case (user, _, _) => user }.distinct().count()
val totalRevenue = rdd.map { case (_, _, price) => price.toDouble }.sum()
val productsByPopularity = rdd
.map { case (user, product, price) => (product, 1) }
.reduceByKey(_ + _)
.collect()
.sortBy(-_._2)
val mostPopular = productsByPopularity(0)
val formatter = new SimpleDateFormat
val dateStr = formatter.format(new Date(time.milliseconds))
println(s"== Batch start time: $dateStr ==")
println("Total purchases: " + numPurchases)
println("Unique users: " + uniqueUsers)
println("Total revenue: " + totalRevenue)
println("Most popular product: %s with %d purchases".format(mostPopular._1, mostPopular._2))
}
// start the context
ssc.start()
ssc.awaitTermination()
}
}
sbt run
Enter number: 4
3.4 有状态的流计算
object StreamingStateApp {
import org.apache.spark.streaming.StreamingContext._
def updateState(prices: Seq[(String, Double)], currentTotal: Option[(Int, Double)]) = {
val currentRevenue = prices.map(_._2).sum
val currentNumberPurchases = prices.size
val state = currentTotal.getOrElse((0, 0.0))
Some((currentNumberPurchases + state._1, currentRevenue + state._2))
}
def main(args: Array[String]) {
val ssc = new StreamingContext("local[2]", "First Streaming App", Seconds(10))
// for stateful operations, we need to set a checkpoint location
ssc.checkpoint("/tmp/sparkstreaming/")
val stream = ssc.socketTextStream("localhost", 9999)
// create stream of events from raw text elements
val events = stream.map { record =>
val event = record.split(",")
(event(0), event(1), event(2).toDouble)
}
val users = events.map { case (user, product, price) => (user, (product, price)) }
val revenuePerUser = users.updateStateByKey(updateState)
revenuePerUser.print()
// start the context
ssc.start()
ssc.awaitTermination()
}
}
sbt run
Enter number: 7
4 线性流回归
线性回归StreamingLinearRegressionWithSGD
- trainOn
- predictOn
4.1 流数据生成器
object StreamingModelProducer {
import breeze.linalg._
def main(args: Array[String]) {
// Maximum number of events per second
val MaxEvents = 100
val NumFeatures = 100
val random = new Random()
/** Function to generate a normally distributed dense vector */
def generateRandomArray(n: Int) = Array.tabulate(n)(_ => random.nextGaussian())
// Generate a fixed random model weight vector
val w = new DenseVector(generateRandomArray(NumFeatures))
val intercept = random.nextGaussian() * 10
/** Generate a number of random product events */
def generateNoisyData(n: Int) = {
(1 to n).map { i =>
val x = new DenseVector(generateRandomArray(NumFeatures))
val y: Double = w.dot(x)
val noisy = y + intercept //+ 0.1 * random.nextGaussian()
(noisy, x)
}
}
// 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 data = generateNoisyData(num)
data.foreach { case (y, x) =>
val xStr = x.data.mkString(",")
val eventStr = s"$y $xStr"
out.write(eventStr)
out.write("
")
}
out.flush()
println(s"Created $num events...")
}
socket.close()
}
}.start()
}
}
}
sbt run
Enter number: 5
4.2 流回归模型
object SimpleStreamingModel {
def main(args: Array[String]) {
val ssc = new StreamingContext("local[2]", "First Streaming App", Seconds(10))
val stream = ssc.socketTextStream("localhost", 9999)
val NumFeatures = 100
val zeroVector = DenseVector.zeros[Double](NumFeatures)
val model = new StreamingLinearRegressionWithSGD()
.setInitialWeights(Vectors.dense(zeroVector.data))
.setNumIterations(1)
.setStepSize(0.01)
// create a stream of labeled points
val labeledStream: DStream[LabeledPoint] = stream.map { event =>
val split = event.split(" ")
val y = split(0).toDouble
val features: Array[Double] = split(1).split(",").map(_.toDouble)
LabeledPoint(label = y, features = Vectors.dense(features))
}
// train and test model on the stream, and print predictions for illustrative purposes
model.trainOn(labeledStream)
//model.predictOn(labeledStream).print()
ssc.start()
ssc.awaitTermination()
}
}
sbt run
Enter number: 5
5 流K-均值
- K-均值聚类：StreamingKMeans
6 评估
object MonitoringStreamingModel {
def main(args: Array[String]) {
val ssc = new StreamingContext("local[2]", "First Streaming App", Seconds(10))
val stream = ssc.socketTextStream("localhost", 9999)
val NumFeatures = 100
val zeroVector = DenseVector.zeros[Double](NumFeatures)
val model1 = new StreamingLinearRegressionWithSGD()
.setInitialWeights(Vectors.dense(zeroVector.data))
.setNumIterations(1)
.setStepSize(0.01)
val model2 = new StreamingLinearRegressionWithSGD()
.setInitialWeights(Vectors.dense(zeroVector.data))
.setNumIterations(1)
.setStepSize(1.0)
// create a stream of labeled points
val labeledStream = stream.map { event =>
val split = event.split(" ")
val y = split(0).toDouble
val features = split(1).split(",").map(_.toDouble)
LabeledPoint(label = y, features = Vectors.dense(features))
}
// train both models on the same stream
model1.trainOn(labeledStream)
model2.trainOn(labeledStream)
// use transform to create a stream with model error rates
val predsAndTrue = labeledStream.transform { rdd =>
val latest1 = model1.latestModel()
val latest2 = model2.latestModel()
rdd.map { point =>
val pred1 = latest1.predict(point.features)
val pred2 = latest2.predict(point.features)
(pred1 - point.label, pred2 - point.label)
}
}
// print out the MSE and RMSE metrics for each model per batch
predsAndTrue.foreachRDD { (rdd, time) =>
val mse1 = rdd.map { case (err1, err2) => err1 * err1 }.mean()
val rmse1 = math.sqrt(mse1)
val mse2 = rdd.map { case (err1, err2) => err2 * err2 }.mean()
val rmse2 = math.sqrt(mse2)
println(
s"""
|-------------------------------------------
|Time: $time
|-------------------------------------------
""".stripMargin)
println(s"MSE current batch: Model 1: $mse1; Model 2: $mse2")
println(s"RMSE current batch: Model 1: $rmse1; Model 2: $rmse2")
println("...
")
}
ssc.start()
ssc.awaitTermination()
}
}
sbt run
Enter number: 1