使用 mapWithState计算单词个数

package allinone

import org.apache.spark.streaming._
import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.SparkSession

/**
  * Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every
  * second starting with initial value of word count.
  * Usage: StatefulNetworkWordCount <hostname> <port>
  *   <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive
  *   data.
  *
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
  *    `spark-submit.sh allinone.StatefulNetworkWordCount localhost 9999`
  */

object StatefulNetworkWordCount {
  def main(args: Array[String]) {
    if (args.length < 2) {
      System.err.println("Usage: StatefulNetworkWordCount <hostname> <port>")
      System.exit(1)
    }

    // 本地模式运行,便于测试
    Logger.getLogger("org").setLevel(Level.WARN)
    val spark = SparkSession.builder()
      .appName(this.getClass.getName)
      .master("local[2]")
      .getOrCreate()

    spark.sparkContext.setLogLevel("WARN")

    // Create the context with a 1 second batch size
    val ssc = new StreamingContext(spark.sparkContext, Seconds(10))
    ssc.checkpoint("/tmp/word-count-map-with-state")

    // Initial state RDD for mapWithState operation
    val initialRDD = ssc.sparkContext.parallelize(List(("hello", 1), ("world", 1)))

    // Create a ReceiverInputDStream on target ip:port and count the
    // words in input stream of \n delimited test (eg. generated by 'nc')
    val lines = ssc.socketTextStream(args(0), args(1).toInt)
    val words = lines.flatMap(_.split(" "))
    val wordDstream = words.map(x => (x, 1))

    // Update the cumulative count using mapWithState
    // This will give a DStream made of state (which is the cumulative count of the words)
    // 定义一个接收三个参数的匿名函数
    val mappingFunc = (word: String, one: Option[Int], state: State[Int]) => {
      val sum = one.getOrElse(0) + state.getOption.getOrElse(0)  // 当前值加上上一个批次的该状态的值
      val output = (word, sum) // 输出单词和该单词出现的次数
      state.update(sum) // 更新当前的状态
      output // 该匿名函数的返回值为输出结果
    }

    // 使用 mapWithState 更新状态
    val stateDstream = wordDstream.mapWithState(StateSpec.function(mappingFunc).initialState(initialRDD))
    stateDstream.print()
    ssc.start()
    ssc.awaitTermination()
  }
}