大数据之spark_spark的案例分析

计算订单分类成交金额

需求
在给定的订单数据，根据订单的分类ID进行聚合，然后按照订单分类名称，统计出某一天商品各个分类的成交金额

数据样例

{"cid": 1, "money": 600.0, "longitude":116.397128,"latitude":39.916527,"oid":"o123", }
"oid":"o112", "cid": 3, "money": 200.0, "longitude":118.396128,"latitude":35.916527}
{"oid":"o124", "cid": 2, "money": 200.0, "longitude":117.397128,"latitude":38.916527}
{"oid":"o125", "cid": 3, "money": 100.0, "longitude":118.397128,"latitude":35.916527}
{"oid":"o127", "cid": 1, "money": 100.0, "longitude":116.395128,"latitude":39.916527}
{"oid":"o128", "cid": 2, "money": 200.0, "longitude":117.396128,"latitude":38.916527}
{"oid":"o129", "cid": 3, "money": 300.0, "longitude":115.398128,"latitude":35.916527}
{"oid":"o130", "cid": 2, "money": 100.0, "longitude":116.397128,"latitude":39.916527}
{"oid":"o131", "cid": 1, "money": 100.0, "longitude":117.394128,"latitude":38.916527}
{"oid":"o132", "cid": 3, "money": 200.0, "longitude":118.396128,"latitude":35.916527}

字段说明
oid：订单id，String类型
cid: 商品分类id，Int类型
money: 订单金额，Double类型
longitude: 经度，Double类型
latitude: 纬度，Double类型

分类信息
1，家具
2，手机
3，服装

计算结果保存MySQL中

导入Maven依赖

<dependency>
    <groupId>com.alibabagroupId>
    <artifactId>fastjsonartifactId>
    <version>1.2.57version>
dependency>

<dependency>
    <groupId>mysqlgroupId>
    <artifactId>mysql-connector-javaartifactId>
    <version>5.1.47version>
dependency>

代码实现

//case class 会自动生成get和set方法还会自动实现序列化接口
//如果是普通的class类,要手动实现序列化接口Serializable并书写get,set方法,或在定义属性时添加注解@BeanProperty自动生成get,set方法
case class  CaseOrderBean(var oid:String, cid:String,var money:Double,var longitude:String,var latitude:String)

import java.sql.{Connection, Date, DriverManager, PreparedStatement, SQLException}
import com.alibaba.fastjson.{JSON, JSONException}
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

object OrderCount {

  def main(args: Array[String]): Unit = {
    val isLocal = args(0).toBoolean
    //创建SparkConf，然后创建SparkContext
    val conf = new SparkConf().setAppName(this.getClass.getSimpleName)
    if (isLocal) {
      conf.setMaster("local[*]")
    }
    val sc = new SparkContext(conf)
    //创建RDD
    val lines: RDD[String] = sc.textFile(args(1))

    //解析JSON
    val beanRDD: RDD[CaseOrderBean] = lines.map(line => {
      var bean: CaseOrderBean = null
      try {
        bean = JSON.parseObject(line, classOf[CaseOrderBean])
      } catch {
        case e: JSONException => {
          //单独处理有问题的数据，可以记录日志后存储起来
        }
      }
      bean
    })
    //过滤有问题的数据,因为遇到有问题的数据时,CaseOrderBean返回的null,所以需要筛选出不为null的数据
    val filtered: RDD[CaseOrderBean] = beanRDD.filter(_ != null)
    //将数据转成元组，分组聚合
    val cidAndMoney = filtered.map(bean => {
      val cid = bean.cid
      val money = bean.money
      (cid, money)
    })
    //分组聚合的RDD
    val reduced: RDD[(Int, Double)] = cidAndMoney.reduceByKey(_ + _)
    //在创建一个RDD
    val categoryLines: RDD[String] = sc.textFile(args(2))
    //分类RDD
    val cidAndCName: RDD[(Int, String)] = categoryLines.map(line => {
      val fields = line.split(",")
      val cid = fields(0).toInt
      val cname = fields(1)
      (cid, cname)
    })
    val joined: RDD[(Int, (Double, String))] = reduced.join(cidAndCName)
    //将join后的数据在进行处理
    val result: RDD[(String, Double)] = joined.map(t => (t._2._2, t._2._1))
    //正确的姿势
    //将数据写入到MySQL
    result.foreachPartition(it: Iterator[(String, Double)]) => {
    //创建MySQL连接
    var conn: Connection = null
    var statement: PreparedStatement = null
    try {
      conn = DriverManager.getConnection(
        "jdbc:mysql://localhost:3306/bigdata?characterEncoding=UTF-8", 
        "root", 
        "123456")
      statement = conn.prepareStatement("INSERT INTO t_result values (null, ?, ?, ?)")
      //将迭代器中的数据写入到MySQL
      it.foreach(t => {
        statement.setString(1, t._1)
        statement.setDouble(2, t._2)
        statement.setDate(3, new Date(System.currentTimeMillis()))
        //执行executeUpdate,单条写入
        //statement.executeUpdate()
        //批量写入
        statement.addBatch()
      })
      statement.executeBatch()
      ()
    } catch {
      case e: SQLException => {
        //单独处理有问题的数据
      }
    } finally {
      //释放MySQL的资源
      if (statement != null) {
        statement.close()
      }
      if (conn != null) {
        conn.close()
      }
    }
	sc.stop()
  }
}