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• 带状态的算子：UpdateStateByKey
• 实战：计算到目前为止累积出现的单词个数写入到MySQL中
• 基于window的统计
• 实战：黑名单过滤
• 实战：Spark Streaming整合Spark SQL实战

updateStateByKey算子

需求：统计到目前为止累计出现单词的个数（需要保持以前的状态）

import org.apache.spark.SparkConfimport org.apache.spark.streaming.{
   Seconds, StreamingContext}/**  * 使用Spark Streaming完成有状态统计  */object StatefulWordCount {
     def main(args: Array[String]): Unit = {
       val sparkConf = new SparkConf().setAppName("StatefulWordCount").setMaster("local[2]")    val ssc = new StreamingContext(sparkConf, Seconds(5))    // 如果使用了stateful的算子，必须要设置checkpoint    // 在生产环境中，建议大家把checkpoint设置到HDFS的某个文件夹中    ssc.checkpoint(".")    val lines = ssc.socketTextStream("192.168.0.230", 6789)    val result = lines.flatMap(_.split(" ")).map((_,1))    val state = result.updateStateByKey[Int](updateFunction _)    state.print()    ssc.start()    ssc.awaitTermination()  }  /**    * 把当前的数据去更新已有的或者是老的数据    * @param currentValues  当前的    * @param preValues  老的    * @return    */  def updateFunction(currentValues: Seq[Int], preValues: Option[Int]): Option[Int] = {
       val current = currentValues.sum    val pre = preValues.getOrElse(0)    Some(current + pre)  }}

计算到目前为止累计出现的单词个数写入到MySQL

• 使用Spark Streaming进行词频统计
• Spark Streaming统计结果写入到MySQL中去

import java.sql.DriverManagerimport org.apache.spark.SparkConfimport org.apache.spark.streaming.{
   Seconds, StreamingContext}/**  * 使用Spark Streaming完成词频统计，并将结果写入到MySQL数据库中  */object ForeachRDDApp {
     def main(args: Array[String]): Unit = {
       val sparkConf = new SparkConf().setAppName("ForeachRDDApp").setMaster("local[2]")    val ssc = new StreamingContext(sparkConf, Seconds(5))    val lines = ssc.socketTextStream("192.168.0.230", 6789)    val result = lines.flatMap(_.split(" ")).map((_, 1)).reduceByKey(_ + _)    //result.print()  //此处仅仅是将统计结果输出到控制台    //TODO... 将结果写入到MySQL    //    result.foreachRDD(rdd =>{
       //      val connection = createConnection()  // executed at the driver    //      rdd.foreach {
    record =>    //        val sql = "insert into wordcount(word, wordcount) values('"+record._1 + "'," + record._2 +")"    //        connection.createStatement().execute(sql)    //      }    //    })    result.print()    result.foreachRDD(rdd => {
         rdd.foreachPartition(partitionOfRecords => {
             val connection = createConnection()          partitionOfRecords.foreach(record => {
               val sql = "insert into wordcount(word, wordcount) values('" + record._1 + "'," + record._2 + ")"            connection.createStatement().execute(sql)          })          connection.close()      })    })    ssc.start()    ssc.awaitTermination()  }  /**    * 获取MySQL的连接    */  def createConnection() = {
       Class.forName("com.mysql.jdbc.Driver")    DriverManager.getConnection("jdbc:mysql://192.168.0.230:3306/imooc_spark", "root", "root")  }}

window：定时的进行一个时间段的处理

window length：窗口的长度

sliding interval：窗口的间隔

这两个参数和我们batch size有关系的：倍数

每隔多久计算某个范围内的数据：每隔10秒计算前10分钟wc

==>每隔sliding interval统计前window length的值

val windowedWordCounts = pairs.reduceByKeyAndWindow((a:Int,b:Int) => (a + b), Seconds(30), Seconds(10))

黑名单过滤

• transform 算子的使用
• Spark Streaming整合RDD进行操作

需求：

访问日志 ==> DStream

201808085,ml 201892505,ml 201581512,zg

==> （ml:201808085,ml）

(ml,201892505,ml) (zg,201581512,zg)

黑名单列表 ==>RDD

ml zg

==>(ml:true)

==>201581512,zg

leftjoin

(ml:<201808085,ml>,< true>) (ml:<201892505,ml>,< true>) (zg:<201581512,zg>,< false>)

import org.apache.spark.SparkConfimport org.apache.spark.streaming.{
   Seconds, StreamingContext}/**  * 黑名单过滤  */object TransformApp {
     def main(args: Array[String]): Unit = {
       val sparkConf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount")    /**      * 创建StreamingContext需要两个参数：SparkConf和batch interval      */    val ssc = new StreamingContext(sparkConf, Seconds(5))    /**      * 构建黑名单      */    val blacks = List("zs", "ls")    val blacksRDD = ssc.sparkContext.parallelize(blacks).map(x => (x, true))    val lines = ssc.socketTextStream("192.168.0.230", 6789)    val clicklog = lines.map(x => (x.split(",")(1), x)).transform(rdd => {
         rdd.leftOuterJoin(blacksRDD)        .filter(x=> x._2._2.getOrElse(false) != true)        .map(x=>x._2._1)    })    clicklog.print()    ssc.start()    ssc.awaitTermination()  }}

Spark Streaming整合Spark SQL完成词频统计

import org.apache.spark.SparkConfimport org.apache.spark.rdd.RDDimport org.apache.spark.sql.SparkSessionimport org.apache.spark.streaming.{
   Seconds, StreamingContext, Time}/**  * Spark Streaming整合Spark SQL完成词频统计操作  */object SqlNetworkWordCount {
     def main(args: Array[String]): Unit = {
       val sparkConf = new SparkConf().setAppName("ForeachRDDApp").setMaster("local[2]")    val ssc = new StreamingContext(sparkConf, Seconds(5))    val lines = ssc.socketTextStream("localhost", 6789)    val words = lines.flatMap(_.split(" "))    // Convert RDDs of the words DStream to DataFrame and run SQL query    words.foreachRDD {
    (rdd: RDD[String], time: Time) =>      val spark = SparkSessionSingleton.getInstance(rdd.sparkContext.getConf)      import spark.implicits._      // Convert RDD[String] to RDD[case class] to DataFrame      val wordsDataFrame = rdd.map(w => Record(w)).toDF()      // Creates a temporary view using the DataFrame      wordsDataFrame.createOrReplaceTempView("words")      // Do word count on table using SQL and print it      val wordCountsDataFrame =        spark.sql("select word, count(*) as total from words group by word")      println(s"========= $time =========")      wordCountsDataFrame.show()    }    ssc.start()    ssc.awaitTermination()  }  /** Case class for converting RDD to DataFrame */  case class Record(word: String)  /** Lazily instantiated singleton instance of SparkSession */  object SparkSessionSingleton {
       @transient  private var instance: SparkSession = _    def getInstance(sparkConf: SparkConf): SparkSession = {
         if (instance == null) {
           instance = SparkSession          .builder          .config(sparkConf)          .getOrCreate()      }      instance    }  }}

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