SparkCore 编程

import org.apache.spark.{SparkConf, SparkContext}

object wordCount {

  def main(args: Array[String]): Unit = {
    //设置本地运行  2核
    val conf = new SparkConf().setAppName("scalaWordCount").setMaster("local[2]")
    val sc = new SparkContext(conf)

    //读取文件
    //val rdd1 = sc.textFile(args(0))
    val rdd1 = sc.textFile("d:/data/word.txt");
    //切分后压平
    val rdd2 = rdd1.flatMap(x=>x.split(" "))
    //将数据都赋值 1
    val rdd3 = rdd2.map((_,1))
    //很据key  进行计算
    val rdd4 = rdd3.reduceByKey(_+_)
    //排序
    val rdd5 = rdd4.sortBy(_._2)
    //生成新文件 保存到hdfs
   // val rdd6 = rdd5.saveAsTextFile(args(1))
    println(rdd5.collect().toBuffer);

    sc.stop()
  }
}

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

object CustomSort1 {

  def main(args: Array[String]): Unit = {
    Logger.getLogger("org.apache.spark").setLevel(Level.OFF)

    val conf = new SparkConf().setAppName("IPLocation").setMaster("local[*]")
    val sc = new SparkContext(conf)
                        //id 姓名 年纪 颜值
    val users = Array("1,tom,18,100","2,lisa,19,500","3,lisi,30,50","4,zhangsan,25,100")
    val userLines = sc.makeRDD(users)
    val userRDD = userLines.map(line => {
      val fields = line.split(",")
      val num = fields(0).toInt
      val name = fields(1)
      val age = fields(2).toInt
      val rate = fields(3).toInt
      new User(num,name,age,rate)

    })

    //排序
    val sorted = userRDD.sortBy(x => x)
    println(sorted.collect().toBuffer)
  }


}

class User(val num:Int,val name:String,val age:Int,val rate:Int) extends Ordered[User] with Serializable {
  override def compare(that: User): Int = {
    //先根据颜值排序，如果颜值相同，根据年龄排序。
    if(this.rate == that.rate){
      this.age-that.age
    }else{
      that.rate-this.rate
    }
  }

  override def toString: String = {
    s"user:$num,$name,$age,$rate"
  }
}

import java.sql.{Connection, DriverManager}

import org.apache.log4j.{Level, Logger}
import org.apache.spark.rdd.JdbcRDD
import org.apache.spark.{SparkConf, SparkContext}

object JDBCDemo {
  val conn: () => Connection = () =>{
    DriverManager.getConnection("jdbc:mysql://localhost:3306/test?characterEncoding=UTF-8","root","123456")
  }

  def main(args: Array[String]): Unit = {
    Logger.getLogger("org.apache.spark").setLevel(Level.OFF)
    val conf = new SparkConf().setAppName("").setMaster("local[*]")
    val sc = new SparkContext(conf)
    val jdbcRDD = new JdbcRDD(
      sc,
      conn,
      "select * from logs where id >=? and id <=?",
      1,
      5,
      2,
      rs=>{
        val id = rs.getLong(1)
        val name = rs.getString(2)
        val age = rs.getInt(3)
        (id, name, age)
      }


    )
    val result = jdbcRDD.collect()
    println(result.toBuffer)
    sc.stop()

  }

}