sparkStreaming统计各平台最近一分钟实时注册收入 时间段，平台，金额，订单数

样例数据：

__clientip=10.10.9.153&paymentstatus=0&__opip=&memberid=89385239&iamount=1&itype=16&oper_res=1&channeltype=8&__timestamp=1457252427&productid=112&selectbank=&icount=0&ordersrc=web&paymentip=61.159.104.134&orderdate=2016-03-06 16:19:55&subjecttype=zheanaiMessenger&oper_type=1&paydate=&orderamount=259.0&paymentchannel=16&oper_time=2016-03-06 16:20:27&orderid=127145727&iunit=month&bussinessid=80125727&isuse=0
__clientip=10.10.9.175&paymentstatus=0&__opip=&memberid=89378034&iamount=12&itype=17&oper_res=1&channeltype=75&__timestamp=1457252429&productid=124&selectbank=&icount=0&ordersrc=100&paymentip=59.37.137.119&orderdate=2016-03-06 16:20:29&subjecttype=zheanaiMessenger&oper_type=0&paydate=&orderamount=388.0&paymentchannel=1028&oper_time=2016-03-06 16:20:29&orderid=127145736&iunit=month&bussinessid=8012580&isuse=0
__clientip=10.10.9.153&paymentstatus=0&__opip=&memberid=75372899&iamount=12&itype=16&oper_res=1&channeltype=&__timestamp=1457252286&productid=131&selectbank=&icount=0&ordersrc=web&paymentip=113.226.244.206&orderdate=2016-03-06 16:18:06&subjecttype=zheanaiMessenger&oper_type=0&paydate=&orderamount=99.0&paymentchannel=307&oper_time=2016-03-06 16:18:06&orderid=127145700&iunit=month&bussinessid=80125477&isuse=0
__clientip=10.10.9.175&paymentstatus=0&__opip=&memberid=87634711&iamount=1&itype=16&oper_res=1&channeltype=8&__timestamp=1457252432&productid=129&selectbank=&icount=0&ordersrc=web&paymentip=114.246.35.251&orderdate=2016-03-06 16:19:05&subjecttype=zheanaiMessenger&oper_type=1&paydate=&orderamount=19.0&paymentchannel=16&oper_time=2016-03-06 16:20:32&orderid=127145713&iunit=month&bussinessid=66213022&isuse=0
__clientip=10.10.9.153&paymentstatus=0&__opip=&memberid=89172717&iamount=12&itype=17&oper_res=1&channeltype=77&__timestamp=1457252371&productid=124&selectbank=&icount=0&ordersrc=4&paymentip=111.126.43.83&orderdate=2016-03-06 16:19:31&subjecttype=zheanaiMessenger&oper_type=0&paydate=&orderamount=388.0&paymentchannel=1116&oper_time=2016-03-06 16:19:31&orderid=127145723&iunit=month&bussinessid=8012568&isuse=0

spark处理过程如下：

1.读取，ssc自带的receiver,解析(valueSplit方法 处理成kv格式)

2.过滤filterRegex，类似sql中的where条件放弃一些不需要的数据，比如只需要买单的数据而不要下单数据
3.转换,getPlatform、getFormatDate，类似case when
4.创建了一个class命名为result，重写了toString方法。该class存放从kafka中处理后的所有需要的数据字段。
5.写入MySQL，insertIntoMySQL，方法在每个partition中调用
另外代码中使用了getOrCreate以便恢复，利用了计数器简单统计了一下有效记录数

代码如下：

package com.homed.stream

/**
 * Created by hadoop on 2016/12/17.
 *
 */
import java.sql.Connection
import java.text.SimpleDateFormat
import java.util.Date

import org.apache.log4j.PropertyConfigurator
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext, Time}
import org.apache.spark.{SparkConf, SparkContext}
import org.joda.time.DateTime
import org.slf4j.LoggerFactory

import scala.collection.mutable.Map

object KafkaStreaming {

val logger = LoggerFactory.getLogger(this.getClass)
PropertyConfigurator.configure(System.getProperty("user.dir")+"\\src\\log4j.properties")

case class result(ftime:String,hour:String,orderid:Long,memberid:Long,platform:String,iamount:Double,orderamount:Double)extends Serializable{
        override def toString: String="%s\t%s\t%d\t%d\t%s\t%.2f\t%.2f".format(ftime, hour,orderid,memberid,platform,iamount,orderamount)
}

def getFormatDate(date:Date,format:SimpleDateFormat): String ={
       format.format(date)
}
def stringFormatTime(time:String,simpleformat:SimpleDateFormat): Date ={
       simpleformat.parse(time)
}

// kafka中的value解析为Map
def valueSplit(value:String): Map[String,String] ={
     val x = value.split("&")
     val valueMap:Map[String,String] = Map()
     x.foreach { kvs =>
         if (!kvs.startsWith("__")){
            val kv = kvs.split("=")
            if (kv.length==2) {
                 valueMap += (kv(0) -> kv(1))
            }
         }

}
valueMap
}

// 实现类似where的条件,tips:优先过滤条件大的减少后续操作
def filterRegex(map:Map[String,String]): Boolean ={
      //过滤操作类型，控制为支付操作
      val oper_type = map.getOrElse("oper_type","-1")
      if(!oper_type.equals("2") && !oper_type.equals("3"))
          return false
     // 过滤未支付成功记录
     if(!map.getOrElse("paymentstatus","0").equals("1"))
         return false
     // 过滤无效支付ip
     val paymentip = map.getOrElse("paymentip",null)
     if (paymentip.startsWith("10.10")||paymentip.startsWith("183.62.134")||paymentip.contains("127.0.0.1"))
         return false
    return true
}
// 实现类似 case when的方法，上报的p字段不一定为数值
def getPlatform(p:String,x:Int): String ={
      val platformname = (p,x) match{
      case (p,x) if(Array[String]("1","2","3").contains(p)) => "wap"
      case (p,x) if(Array[String]("4","8").contains(p)&& x!=18) =>"andriod"
      case (p,x) if((Array[String]("5","7","51","100").contains(p))&&(p!=18)) => "ios"
      case _ => "pc"
     }
  platformname
}
// 数据库写入
def insertIntoMySQL(con:Connection,sql:String,data:result): Unit ={
     // println(data.toString)
try {
val ps = con.prepareStatement(sql)
ps.setString(1, data.ftime)
ps.setString(2, data.hour)
ps.setLong(3,data.orderid)
ps.setLong(4, data.memberid)
ps.setString(5, data.platform)
ps.setDouble(6, data.iamount)
ps.setDouble(7, data.orderamount)
ps.executeUpdate()
ps.close()

}catch{
     case exception:Exception=>
    logger.error("Error in execution of query "+exception.getMessage+"\n-----------------------\n"+exception.printStackTrace()+"\n-----------------------------")
  }
}
def createContext(zkqurm:String,topic:scala.Predef.Map[String,Int],checkPointDir:String): StreamingContext ={

val simpleformat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
val dateFormat = new SimpleDateFormat("yyyyMMdd")
val timeFormat = new SimpleDateFormat("HH:mm")

val sql ="insert into t_ssc_toufang_result_mi(ftime,hour,orderid,memberid,platform,iamount,orderamount) values(?,?,?,?,?,?,?);"

val conf = new SparkConf()
conf.setAppName("Scala Streaming read kafka")
// VM option -Dspark.master=local
// conf.setMaster("local[4]")
val sc = new SparkContext(conf)

val totalcounts = sc.accumulator(0L,"Total count")

val ssc = new StreamingContext(sc,Seconds(60))
//ssc.checkpoint(checkPointDir)
//统计各平台最近一分钟实时注册收入 时间段，平台，金额，订单数
val lines = KafkaUtils.createStream(ssc, zkqurm, "mytopic_local",topic).map(_._2)

val filterRecord = lines.filter(x => !x.isEmpty).map(valueSplit).filter(filterRegex).map{x =>
      val orderdate = stringFormatTime(x.getOrElse("orderdate",null),simpleformat)
      val day = getFormatDate(orderdate,dateFormat)
      val hour = getFormatDate(orderdate,timeFormat)
      var orderamount = x.getOrElse("orderamount","0").toDouble
      if (x.getOrElse("oper_type",-1)==3)
          orderamount = -1*orderamount
      val res = new result(
           day
          ,hour
         ,x.getOrElse("orderid",null).toLong
         ,x.getOrElse("memberid",null).toLong
        ,getPlatform(x.getOrElse("ordersrc",null),x.getOrElse("itype",null).toInt)
         ,x.getOrElse("iamount","0").toDouble
         ,orderamount
        )
   res
}

filterRecord.foreachRDD((x: RDD[result],time: Time) =>{
     if(!x.isEmpty()) {
         // 打印一下这一批batch的处理时间段以及累计的有效记录数(不含档次)
         println("--"+new DateTime(time.milliseconds).toString("yyyy-MM-dd HH:mm:ss")+"--totalcounts:"+totalcounts.value+"-----")
         x.foreachPartition{res =>
          {
             if(!res.isEmpty){
                val connection = ConnectionPool.getConnection.getOrElse(null)
                 res.foreach {
                    r: result =>totalcounts.add(1L)
                    insertIntoMySQL(connection, sql, r)
                 }
               ConnectionPool.closeConnection(connection)
            }
         }
      }
   }
})

ssc
}
// 主函数入口=================================================================

def main(args:Array[String]): Unit ={
      val zkqurm = "10.10.10.177:2181,10.10.10.175:2181,10.10.10.179:2181"

val topic = scala.Predef.Map("t_fw_00015"->30)
      val checkPointDir ="/user/root/sparkcheck"
      val ssc = StreamingContext.getOrCreate(checkPointDir,
             () => {
                   createContext(zkqurm, topic,checkPointDir)
           })
      ssc.start()
      ssc.awaitTermination()
     }
}

连接池部分，代码如下：

package com.homed.stream

/**
* Created by hadoop on 2016/12/17.
*/

import java.sql.Connection

import com.jolbox.bonecp.{BoneCP, BoneCPConfig}
import org.slf4j.LoggerFactory

object ConnectionPool {

val logger = LoggerFactory.getLogger(this.getClass)
private val connectionPool = {
try{
Class.forName("com.mysql.jdbc.Driver")
val config = new BoneCPConfig()
config.setJdbcUrl("jdbc:mysql://localhost:3306/test")
config.setUsername("etl")
config.setPassword("xxxxx")
config.setLazyInit(true)

config.setMinConnectionsPerPartition(3)
config.setMaxConnectionsPerPartition(5)
config.setPartitionCount(5)
config.setCloseConnectionWatch(true)
config.setLogStatementsEnabled(false)

Some(new BoneCP(config))
} catch {
     case exception:Exception=>
     logger.warn("Error in creation of connection pool"+exception.printStackTrace())
     None
}
}
def getConnection:Option[Connection] ={
connectionPool match {
       case Some(connPool) => Some(connPool.getConnection)
       case None => None
}
}
def closeConnection(connection:Connection): Unit = {
    if(!connection.isClosed) {
           connection.close()

}
 }
}