SparkStreaming与SparkSQL集成分析数据并将结果存入MySQL

一、前提说明

---

1. 安装了Flume
2. 本案例实现流程图：
   
3. 本案例实现的功能是：实现wordcount功能，并将每次的分析结果保存到数据库中

二、实现步骤

---

• 在MySQL创建top表，就只有两个字段：key和value
• 在pom.xml中确保已经添加了MySQL数据库的驱动
• 编写如下代码

  import java.net.InetAddress
  import java.sql.DriverManager
  
  import org.apache.spark.SparkConf
  import org.apache.spark.sql.SparkSession
  import org.apache.spark.storage.StorageLevel
  import org.apache.spark.streaming.dstream.{
         DStream, ReceiverInputDStream}
  import org.apache.spark.streaming.flume.{
         FlumeUtils, SparkFlumeEvent}
  import org.apache.spark.streaming.{
         Seconds, StreamingContext}
  
  case class Count(key:String,value:Int)
  
  object FlumeDemoMySQL {
         
    def main(args: Array[String]): Unit = {
         
      //创建一个Context对象: StreamingContext (SparkContext, SQLContext)
      val conf = new SparkConf().setAppName("MyNetworkWordCount").setMaster("local[2]")
      val ssc = new StreamingContext(conf,Seconds(5))
  
      // 创建FlumeStream， 特别注意，hostname是windows上的虚拟网卡net8的ip
      val flumeDStream: ReceiverInputDStream[SparkFlumeEvent] = FlumeUtils.createPollingStream(ssc, "niit01", 8888)
      // 读取数据进行处理，即进行transform变换
      val flatMapDStream: DStream[String] = flumeDStream.flatMap(e => {
         
        val body = e.event.getBody
        val str = new String(body.array())
        val strings = str.split(" ")
        strings
      })
  
      val mapDStream = flatMapDStream.map((_, 1))
  
      val spark = SparkSession.builder().getOrCreate()
      // 引入spark隐式转换函数
      import spark.implicits._
  
      val transformDStream: DStream[Count] = mapDStream.transform(t => {
         
        t.map(x => Count(x._1, x._2))
      })
  
      // 打印结果
      //transformDStream.print()
  
      transformDStream.foreachRDD(c => {
         
        val dataFrame = c.toDF()
        dataFrame.createOrReplaceTempView("top")
        spark.sql("select key,sum(value) as total from top group by key order by total desc")
          .foreachPartition(x=>{
         
            //x.foreach(println)
            val connection = DriverManager.getConnection("jdbc:mysql://127.0.0.1:3306/niit?characterEncoding=UTF-8", "root", "123456")
            val preparedStatement = connection.prepareStatement("insert into realtime values(?,?)")
            x.foreach(r => {
         
              val key = r.get(0).toString
              val value = r.get(1).toString.toInt
              println(r.toString())
              println("key: " + key + ", value: " + value)
              val address = InetAddress.getLocalHost.getHostAddress
              val hostName = InetAddress.getLocalHost.getHostName
              val threadId = Thread.currentThread().getId
              val threadName = Thread.currentThread().getName
              println("HostAddress: " + address + ", HostName: " + hostName + ",threadId: " + threadId + ",threadName: " + threadName)
              preparedStatement.setString(1,key.toString)
              preparedStatement.setInt(2,value.toString.toInt)
              preparedStatement.executeUpdate()
            })
            if (preparedStatement != null) preparedStatement.close()
            if (connection != null) connection.close()
          })
      })
  
      // 开启
      ssc.start()
      ssc.awaitTermination()
  
      // 关闭资源
      spark.stop()
    }
  }
  
  

• 启动程序
• 配置Flume，配置文件内容可参考：Flume之Pull模式
• 编写测试数据到某个文件，保存文件后，将该文件复制到Flume配置文件中所指定的路径
• 查看IDEA程序控制台的结果