Spark Streaming 实现网站热词排序

桃扇骨 2022-10-05 01:52 168阅读 0赞

### 网站热词排序 ###

*   *  一、创建数据库来保存数据
     *  二、创建HotWordBySort.scala，实现功能
     *  三、在master节点 nc -lk 9999

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[1、MySQL安装教程][1_MySQL]

[2、Spark Streaming 实现网站热词排序][2_Spark Streaming]

[3、Spark Streaming 实时计算框架][3_Spark Streaming]

[4、Spark Streaming 整合 Kafka，实现交流][4_Spark Streaming _ Kafka]

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网站热词排序能够分析出用户对网站内容的喜好程度，以此来增加用户感兴趣的内容，从而提升用户访问网站的流量。利用SparkStreaming计数就可以编程实现热词排序的需求，并将排名前三的热词信息输出到MySQL数据表保存。

## 一、创建数据库来保存数据 ##

mysql -uroot -pabc123456
    show databases;
    create database hotword;
    use hotword;
    create table searchKeyWord(insert_time data,keyword varchar(30),search_count integer);

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc3NTI1NQ_size_16_color_FFFFFF_t_70]

## 二、创建HotWordBySort.scala，实现功能 ##

import java.sql.{
        DriverManager, Statement}
    import org.apache.spark.{
        SparkConf, SparkContext}
    import org.apache.spark.streaming.{
        Seconds, StreamingContext}
    import org.apache.spark.streaming.dstream.{
        DStream, ReceiverInputDStream}
    
    object HotWordBySort {
        
      def main(args: Array[String]): Unit = {
        
        // 1.创建SparkConf对象
        val sparkConf: SparkConf = new SparkConf()
          .setAppName("HotWordBySort").setMaster("local[2]")
        // 2.创建SparkContext对象
        val sc: SparkContext = new SparkContext(sparkConf)
        // 3.设置日志级别
        sc.setLogLevel("WARN")
        // 4.创建StreamingContext，需要两个参数，分别为SparkContext和批处理时间间隔
        val ssc: StreamingContext = new StreamingContext(sc, Seconds(5))
        // 5.连接socket服务，需要socket服务地址、端口号及存储级别(默认的)
        val dstream: ReceiverInputDStream[String] = ssc
          .socketTextStream("192.168.142.128", 9999)
      
        // 6.通过逗号分隔第一个字段和第二个字段
        val itemPairs: DStream[(String, Int)] = dstream.map(line => (line
          .split(",")(0), 1))
        // 7.调用reduceByKeyAndWindow操作，需要三个参数
        val itemCount: DStream[(String, Int)] = itemPairs.reduceByKeyAndWindow((v1: Int, v2: Int) => 
          v1 + v2, Seconds(60), Seconds(10)) // 计算10s内出现的单词次数；
        // 8.Dstream没有sortByKey操作，所以排序用transform实现，false降序，take(3)取前3
        val hotWord = itemCount.transform(itemRDD => {
        
          val top3: Array[(String, Int)] = itemRDD.map(pair => (pair._2, pair._1))
            .sortByKey(false).map(pair => (pair._2, pair._1)).take(3)
          //9.将本地的集合（排名前三热词组成的集合）转成RDD
          ssc.sparkContext.makeRDD(top3)
        })
        
        // 10. 调用foreachRDD操作，将输出的数据保存到mysql数据库的表中
        hotWord.foreachRDD(rdd => {
        
          val url = "jdbc:mysql://192.168.142.128:3306/hotword"
          val user = "root"
          val password = "abc123456"
          Class.forName("com.mysql.jdbc.Driver")
          val conn1 = DriverManager.getConnection(url, user, password)
          conn1.prepareStatement("delete from searchKeyWord where 1=1")
            .executeUpdate()
          conn1.close()
          rdd.foreachPartition(partitionOfRecords => {
        
            val url = "jdbc:mysql://192.168.142.128:3306/hotword"
            val user = "root"
            val password = "abc123456"
            Class.forName("com.mysql.jdbc.Driver")
            val conn2 = DriverManager.getConnection(url, user, password)
            conn2.setAutoCommit(false)
            val stat: Statement = conn2.createStatement()
            partitionOfRecords.foreach(record => {
        
              stat.addBatch("insert into searchKeyWord(insert_time, keyword, search_count) values (now(), ' "+record._1+"','"+record._2+"') ")
            })
            stat.executeBatch()
            conn2.commit()
            conn2.close()
          })
        })
        ssc.start()
        ssc.awaitTermination()
        ssc.stop()
      }
    }

执行程序。

## 三、在master节点 nc -lk 9999 ##

输入命令：nc -lk 9999 (master)
    
    输入数据：
    hadoop,111        
    spark,222      
    hadoop,222
    hadoop,222
    hive,222
    hive,333

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc3NTI1NQ_size_16_color_FFFFFF_t_70 1]  
登陆MySQL查看数据表，发现前三位热词在表中了。

[1_MySQL]: https://blog.csdn.net/weixin_44775255/article/details/115662187
[2_Spark Streaming]: https://blog.csdn.net/weixin_44775255/article/details/117601297
[3_Spark Streaming]: https://blog.csdn.net/weixin_44775255/article/details/116784428
[4_Spark Streaming _ Kafka]: https://blog.csdn.net/weixin_44775255/article/details/117602040
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