Spark Streaming实时数据分析

 

[kfk@bigdata-pro01 softwares]$ sudo rpm -ivh nc-1.84-22.el6.x86_64.rpm 
Preparing...                ########################################### [100%]
   1:nc                     ########################################### [100%]
[kfk@bigdata-pro01 softwares]$ 

 

重新启用一个远程连接窗口

bin/run-example streaming.NetworkWordCount localhost 9999 

 回到这边输入一些信息

 看到这边就有数据接收到了

我们退出，换个方式启动

我们在这边再输入一些数据

这边处理得非常快

 

 因为打印的日志信息太多了，我修改一下配置文件(3个节点都修改吧，保守一点了)

我们在来跑一下

 再回到这边我们敲几个字母进去

 

 把同样的单词多次输入我们看看是什么结果

可以看到他会统计

 

 

 

 

 

我们启动一下spark-shell,发现报错了

 

 是因为我们前面配置了hive到spark sql 里面，我们先配回来（3个节点都修改）

 

再启动一下

我们输入代码

scala> import org.apache.spark.streaming._
import org.apache.spark.streaming._

scala> import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.StreamingContext._

scala> val ssc = new StreamingContext(sc, Seconds(5))
ssc: org.apache.spark.streaming.StreamingContext = org.apache.spark.streaming.StreamingContext@431f8830

scala> val lines = ssc.socketTextStream("localhost", 9999)
lines: org.apache.spark.streaming.dstream.ReceiverInputDStream[String] = org.apache.spark.streaming.dstream.SocketInputDStream@23f27434

scala> val words = lines.flatMap(_.split(" "))
words: org.apache.spark.streaming.dstream.DStream[String] = org.apache.spark.streaming.dstream.FlatMappedDStream@2c3df478

scala> val pairs = words.map(word => (word, 1))
pairs: org.apache.spark.streaming.dstream.DStream[(String, Int)] = org.apache.spark.streaming.dstream.MappedDStream@6d3dc0c5

scala> val wordCounts = pairs.reduceByKey(_ + _)
wordCounts: org.apache.spark.streaming.dstream.DStream[(String, Int)] = org.apache.spark.streaming.dstream.ShuffledDStream@8fa4647

scala> wordCounts.print()

scala> 

最后启动一下服务发现报错了

是因为没有启动nc

 现在把他启动

 我们敲进去一些数据

 

 退出再启动一次

 再次把代码放进来

 我们在nc那边输入数据

 回到这边看看结果

打开我们的idea

package com.spark.test

import org.apache.spark.sql.SparkSession
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.{SparkConf, SparkContext}
object Test {

  def main(args: Array[String]): Unit = {
   val spark= SparkSession
       .builder
         .master("local[2]")
         .appName("HdfsTest")
           .getOrCreate()

    val ssc = new  StreamingContext(spark.sparkContext, Seconds(5));
    val lines = ssc.socketTextStream("localhost", 9999)
    val words = lines.flatMap(_.split(" "))
  }
}

 

 

package com.spark.test

import org.apache.spark.sql.SparkSession
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.{Seconds, StreamingContext}

object TestStreaming {
  def main(args: Array[String]): Unit = {
    val spark= SparkSession.builder.master("local[2]")
      .appName("streaming").getOrCreate()

    val sc=spark.sparkContext;
    val ssc = new StreamingContext(sc, Seconds(5))
    val lines = ssc.socketTextStream("bigdata-pro01.kfk.com", 9999)
    //flatMap运算
    val words = lines.flatMap(_.split(" ")).map(words=>(words,1)).reduceByKey(_+_)
    words.print()
    //map reduce 计算
   // val wordCounts = words.map(x =>(x, 1)).reduceByKey(_ + _)
   // wordCounts.print()
    ssc.start()
    ssc.awaitTermination()

  }
}

 

 

 这个过程呢要这样操作，先把程序运行，再启动nc,再到nc界面输入单词

 

package com.spark.test

import java.sql.DriverManager

import org.apache.spark.sql.SparkSession
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.{Seconds, StreamingContext}

object TestStreaming {
  def main(args: Array[String]): Unit = {
    val spark= SparkSession.builder.master("local[2]")
      .appName("streaming").getOrCreate()

    val sc=spark.sparkContext;
    val ssc = new StreamingContext(sc, Seconds(5))
    val lines = ssc.socketTextStream("bigdata-pro01.kfk.com", 9999)
    //flatMap运算
    val words = lines.flatMap(_.split(" ")).map(words=>(words,1)).reduceByKey(_+_)

    words.foreachRDD(rdd=>rdd.foreachPartition(line=>{
        Class.forName("com.mysql.jdbc.Driver")
      val conn= DriverManager.
        getConnection("jdbc:mysql://bigdata-pro01.kfk.com:3306/test","root","root")
      try {
        for(row <-line ) {
          val sql = "insert into webCount(titleName,count)values('" +row._1+ "',"+row._2+")"
          conn.prepareStatement(sql).executeUpdate()
        }
      }finally {
       conn.close()
      }

    }))


    words.print()
    //map reduce 计算
   // val wordCounts = words.map(x =>(x, 1)).reduceByKey(_ + _)
   // wordCounts.print()
    ssc.start()
    ssc.awaitTermination()

  }
}

 我们把代码拷进来

 

import java.sql.DriverManager
import org.apache.spark.sql.SparkSession
import org.apache.spark.streaming.{Seconds, StreamingContext}

 val sc=spark.sparkContext;
    val ssc = new StreamingContext(sc, Seconds(5))
    val lines = ssc.socketTextStream("bigdata-pro01.kfk.com", 9999)
    val words = lines.flatMap(_.split(" ")).map(words=>(words,1)).reduceByKey(_+_)
    words.foreachRDD(rdd=>rdd.foreachPartition(line=>{
        Class.forName("com.mysql.jdbc.Driver")
      val conn= DriverManager.
        getConnection("jdbc:mysql://bigdata-pro01.kfk.com:3306/test","root","root")
      try {
        for(row <-line ) {
          val sql = "insert into webCount(titleName,count)values('" +row._1+ "',"+row._2+")"
          conn.prepareStatement(sql).executeUpdate()
        }
      }finally {
       conn.close()
      }
    }))
    ssc.start()
    ssc.awaitTermination()

 我们输入数据

 

我们通过mysql查看一下表里面的数据