基于大数据技术的开源在线教育项目 三1

kafka-topics --zookeeper cdh1.macro.com:2181,cdh2.macro.com:2181,cdh3.macro.com:2181/kafka --create --topic register_topic --partitions 10 --replication-factor 2
kafka-topics --zookeeper cdh1.macro.com:2181,cdh2.macro.com:2181,cdh3.macro.com:2181/kafka --create --topic page_topic --partitions 10 --replication-factor 2
kafka-topics --zookeeper cdh1.macro.com:2181,cdh2.macro.com:2181,cdh3.macro.com:2181/kafka --create --topic course_learn --partitions 10 --replication-factor 2
 kafka-topics --zookeeper cdh1.macro.com:2181,cdh2.macro.com:2181,cdh3.macro.com:2181/kafka --create --topic qz_log --partitions 10 --replication-factor 2

import java.util.Properties
import org.apache.kafka.clients.producer.{KafkaProducer, ProducerRecord}
import org.apache.spark.{SparkConf, SparkContext}
object CourseLearnProducer {
  def main(args: Array[String]): Unit = {
    val sparkConf = new SparkConf().setAppName("courseProducer").setMaster("local[*]")
    val ssc = new SparkContext(sparkConf)
//    System.setProperty("hadoop.home.dir", "D:\\hadoop\\hadoop-common-2.2.0-bin-master")
    val resultLog = ssc.textFile("hdfs://cdh1.macro.com:8020/user/catelf/data/course_learn.log", 10)
      .foreachPartition(partitoin => {
        val props = new Properties()
        props.put("bootstrap.servers", "cdh1.macro.com:9092,cdh2.macro.com:9092,cdh3.macro.com:9092")
        props.put("acks", "1")
        props.put("batch.size", "16384")
        props.put("linger.ms", "10")
        props.put("buffer.memory", "33554432")
        props.put("key.serializer",
          "org.apache.kafka.common.serialization.StringSerializer")
        props.put("value.serializer",
          "org.apache.kafka.common.serialization.StringSerializer")
        val producer = new KafkaProducer[String, String](props)
        partitoin.foreach(item => {
          val msg = new ProducerRecord[String, String]("course_learn", item)
          producer.send(msg)
        })
        producer.flush()
        producer.close()
      })
  }
}

import java.lang
import java.sql.ResultSet
import java.util.Random
import com.catelf.qzpoint.util.{DataSourceUtil, QueryCallback, SqlProxy}
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.kafka.common.TopicPartition
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.{Seconds, StreamingContext}
import scala.collection.mutable
object RegisterStreaming {
  private val groupid = "register_group_test"
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME", "hdfs")
    val conf = new SparkConf().setAppName(this.getClass.getSimpleName).setMaster("local[2]")
      .set("spark.streaming.kafka.maxRatePerPartition", "50")
      .set("spark.streaming.stopGracefullyOnShutdown", "true")
    val ssc = new StreamingContext(conf, Seconds(3))
    val topics = Array("register_topic")
    val kafkaMap: Map[String, Object] = Map[String, Object](
      "bootstrap.servers" -> "cdh1.macro.com:9092,cdh2.macro.com:9092,cdh3.macro.com:9092",
      "key.deserializer" -> classOf[StringDeserializer],
      "value.deserializer" -> classOf[StringDeserializer],
      "group.id" -> groupid,
      "auto.offset.reset" -> "earliest",
      "enable.auto.commit" -> (false: lang.Boolean)
    )
    ssc.checkpoint("hdfs://cdh1.macro.com:8020/user/catelf/sparkstreaming/checkpoint")
    //查询mysql中是否有偏移量
    val sqlProxy = new SqlProxy()
    val offsetMap = new mutable.HashMap[TopicPartition, Long]()
    val client = DataSourceUtil.getConnection
    try {
      sqlProxy.executeQuery(client, "select * from `offset_manager` where groupid=?", Array(groupid), new QueryCallback {
        override def process(rs: ResultSet): Unit = {
          while (rs.next()) {
            val model = new TopicPartition(rs.getString(2), rs.getInt(3))
            val offset = rs.getLong(4)
            offsetMap.put(model, offset)
          }
          rs.close() //关闭游标
        }
      })
    } catch {
      case e: Exception => e.printStackTrace()
    } finally {
      sqlProxy.shutdown(client)
    }
    //设置kafka消费数据的参数  判断本地是否有偏移量  有则根据偏移量继续消费 无则重新消费
    val stream: InputDStream[ConsumerRecord[String, String]] = if (offsetMap.isEmpty) {
      KafkaUtils.createDirectStream(
        ssc, LocationStrategies.PreferConsistent, ConsumerStrategies.Subscribe[String, String](topics, kafkaMap))
    } else {
      KafkaUtils.createDirectStream(
        ssc, LocationStrategies.PreferConsistent, ConsumerStrategies.Subscribe[String, String](topics, kafkaMap, offsetMap))
    }
    val resultDStream = stream.filter(item => item.value().split("\t").length == 3).
      mapPartitions(partitions => {
        partitions.map(item => {
          val line = item.value()
          val arr = line.split("\t")
          val app_name = arr(1) match {
            case "1" => "PC"
            case "2" => "APP"
            case _ => "Other"
          }
          (app_name, 1)
        })
      })
    resultDStream.cache()
    //    resultDStream.reduceByKeyAndWindow((x: Int, y: Int) => x + y, Seconds(60), Seconds(6)).print()
    val updateFunc = (values: Seq[Int], state: Option[Int]) => {
      val currentCount = values.sum //本批次求和
      val previousCount = state.getOrElse(0) //历史数据
      Some(currentCount + previousCount)
    }
    resultDStream.updateStateByKey(updateFunc).print()
    //处理完 业务逻辑后 手动提交offset维护到本地 mysql中
    stream.foreachRDD(rdd => {
      val sqlProxy = new SqlProxy()
      val client = DataSourceUtil.getConnection
      try {
        val offsetRanges: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
        for (or <- offsetRanges) {
          sqlProxy.executeUpdate(client, "replace into `offset_manager` (groupid,topic,`partition`,untilOffset) values(?,?,?,?)",
            Array(groupid, or.topic, or.partition.toString, or.untilOffset))
        }
      } catch {
        case e: Exception => e.printStackTrace()
      } finally {
        sqlProxy.shutdown(client)
      }
    })
    ssc.start()
    ssc.awaitTermination()
  }
}

import java.lang
import java.sql.{Connection, ResultSet}
import java.time.LocalDateTime
import java.time.format.DateTimeFormatter
import com.catelf.qzpoint.util.{DataSourceUtil, QueryCallback, SqlProxy}
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.kafka.common.TopicPartition
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.{Seconds, StreamingContext}
import scala.collection.mutable
/**
 * 知识点掌握度实时统计
 */
object QzPointStreaming {
  private val groupid = "qz_point_group"
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName(this.getClass.getSimpleName).setMaster("local[*]")
      .set("spark.streaming.kafka.maxRatePerPartition", "50")
      .set("spark.streaming.stopGracefullyOnShutdown", "true")
    val ssc = new StreamingContext(conf, Seconds(3))
    val topics = Array("qz_log")
    val kafkaMap: Map[String, Object] = Map[String, Object](
      "bootstrap.servers" -> "cdh1.macro.com:9092,cdh2.macro.com:9092,cdh3.macro.com:9092",
      "key.deserializer" -> classOf[StringDeserializer],
      "value.deserializer" -> classOf[StringDeserializer],
      "group.id" -> groupid,
      "auto.offset.reset" -> "earliest",
      "enable.auto.commit" -> (false: lang.Boolean)
    )
    //查询mysql中是否存在偏移量
    val sqlProxy = new SqlProxy()
    val offsetMap = new mutable.HashMap[TopicPartition, Long]()
    val client = DataSourceUtil.getConnection
    try {
      sqlProxy.executeQuery(client, "select * from `offset_manager` where groupid=?", Array(groupid), new QueryCallback {
        override def process(rs: ResultSet): Unit = {
          while (rs.next()) {
            val model = new TopicPartition(rs.getString(2), rs.getInt(3))
            val offset = rs.getLong(4)
            offsetMap.put(model, offset)
          }
          rs.close() //关闭游标
        }
      })
    } catch {
      case e: Exception => e.printStackTrace()
    } finally {
      sqlProxy.shutdown(client)
    }
    //设置kafka消费数据的参数  判断本地是否有偏移量  有则根据偏移量继续消费 无则重新消费
    val stream: InputDStream[ConsumerRecord[String, String]] = if (offsetMap.isEmpty) {
      KafkaUtils.createDirectStream(
        ssc, LocationStrategies.PreferConsistent, ConsumerStrategies.Subscribe[String, String](topics, kafkaMap))
    } else {
      KafkaUtils.createDirectStream(
        ssc, LocationStrategies.PreferConsistent, ConsumerStrategies.Subscribe[String, String](topics, kafkaMap, offsetMap))
    }
    //过滤不正常数据 获取数据
    val dsStream = stream.filter(item => item.value().split("\t").length == 6).
      mapPartitions(partition => partition.map(item => {
        val line = item.value()
        val arr = line.split("\t")
        val uid = arr(0) //用户id
        val courseid = arr(1) //课程id
        val pointid = arr(2) //知识点id
        val questionid = arr(3) //题目id
        val istrue = arr(4) //是否正确
        val createtime = arr(5) //创建时间
        (uid, courseid, pointid, questionid, istrue, createtime)
      }))
    dsStream.foreachRDD(rdd => {
      //获取相同用户 同一课程 同一知识点的数据
      val groupRdd = rdd.groupBy(item => item._1 + "-" + item._2 + "-" + item._3)
      groupRdd.foreachPartition(partition => {
        //在分区下获取jdbc连接
        val sqlProxy = new SqlProxy()
        val client = DataSourceUtil.getConnection
        try {
          partition.foreach { case (key, iters) =>
            qzQuestionUpdate(key, iters, sqlProxy, client) //对题库进行更新操作
          }
        } catch {
          case e: Exception => e.printStackTrace()
        }
        finally {
          sqlProxy.shutdown(client)
        }
      }
      )
    })
    //处理完 业务逻辑后 手动提交offset维护到本地 mysql中
    stream.foreachRDD(rdd => {
      val sqlProxy = new SqlProxy()
      val client = DataSourceUtil.getConnection
      try {
        val offsetRanges: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
        for (or <- offsetRanges) {
          sqlProxy.executeUpdate(client, "replace into `offset_manager` (groupid,topic,`partition`,untilOffset) values(?,?,?,?)",
            Array(groupid, or.topic, or.partition.toString, or.untilOffset))
        }
      } catch {
        case e: Exception => e.printStackTrace()
      } finally {
        sqlProxy.shutdown(client)
      }
    })
    ssc.start()
    ssc.awaitTermination()
  }
  /**
   * 对题目表进行更新操作
   *
   * @param key
   * @param iters
   * @param sqlProxy
   * @param client
   * @return
   */
  def qzQuestionUpdate(key: String, iters: Iterable[(String, String, String, String, String, String)], sqlProxy: SqlProxy, client: Connection) = {
    val keys = key.split("-")
    val userid = keys(0).toInt
    val courseid = keys(1).toInt
    val pointid = keys(2).toInt
    val array = iters.toArray
    val questionids = array.map(_._4).distinct //对当前批次的数据下questionid 去重
    //查询历史数据下的 questionid
    var questionids_history: Array[String] = Array()
    sqlProxy.executeQuery(client, "select questionids from qz_point_history where userid=? and courseid=? and pointid=?",
      Array(userid, courseid, pointid), new QueryCallback {
        override def process(rs: ResultSet): Unit = {
          while (rs.next()) {
            questionids_history = rs.getString(1).split(",")
          }
          rs.close() //关闭游标
        }
      })
    //获取到历史数据后再与当前数据进行拼接 去重
    val resultQuestionid = questionids.union(questionids_history).distinct
    val countSize = resultQuestionid.length
    val resultQuestionid_str = resultQuestionid.mkString(",")
    val qz_count = questionids.length //去重后的题个数
    var qz_sum = array.length //获取当前批次题总数
    var qz_istrue = array.filter(_._5.equals("1")).size //获取当前批次做正确的题个数
    val createtime = array.map(_._6).min //获取最早的创建时间 作为表中创建时间
    //更新qz_point_set 记录表 此表用于存当前用户做过的questionid表
    val updatetime = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss").format(LocalDateTime.now())
    sqlProxy.executeUpdate(client, "insert into qz_point_history(userid,courseid,pointid,questionids,createtime,updatetime) values(?,?,?,?,?,?) " +
      " on duplicate key update questionids=?,updatetime=?", Array(userid, courseid, pointid, resultQuestionid_str, createtime, createtime, resultQuestionid_str, updatetime))
    var qzSum_history = 0
    var istrue_history = 0
    sqlProxy.executeQuery(client, "select qz_sum,qz_istrue from qz_point_detail where userid=? and courseid=? and pointid=?",
      Array(userid, courseid, pointid), new QueryCallback {
        override def process(rs: ResultSet): Unit = {
          while (rs.next()) {
            qzSum_history += rs.getInt(1)
            istrue_history += rs.getInt(2)
          }
          rs.close()
        }
      })
    qz_sum += qzSum_history
    qz_istrue += istrue_history
    val correct_rate = qz_istrue.toDouble / qz_sum.toDouble //计算正确率
    //计算完成率
    //假设每个知识点下一共有30道题  先计算题的做题情况 再计知识点掌握度
    val qz_detail_rate = countSize.toDouble / 30 //算出做题情况乘以 正确率 得出完成率 假如30道题都做了那么正确率等于 知识点掌握度
    val mastery_rate = qz_detail_rate * correct_rate
    sqlProxy.executeUpdate(client, "insert into qz_point_detail(userid,courseid,pointid,qz_sum,qz_count,qz_istrue,correct_rate,mastery_rate,createtime,updatetime)" +
      " values(?,?,?,?,?,?,?,?,?,?) on duplicate key update qz_sum=?,qz_count=?,qz_istrue=?,correct_rate=?,mastery_rate=?,updatetime=?",
      Array(userid, courseid, pointid, qz_sum, countSize, qz_istrue, correct_rate, mastery_rate, createtime, updatetime, qz_sum, countSize, qz_istrue, correct_rate, mastery_rate, updatetime))
  }
}

import java.lang
import java.sql.{Connection, ResultSet}
import java.text.NumberFormat
import com.catelf.qzpoint.util.{DataSourceUtil, ParseJsonData, QueryCallback, SqlProxy}
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.kafka.common.TopicPartition
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkFiles}
import org.lionsoul.ip2region.{DbConfig, DbSearcher}
import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer
/**
 * 页面转换率实时统计
 */
object PageStreaming {
  private val groupid = "vip_count_groupid"
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName(this.getClass.getSimpleName).setMaster("local[*]")
      .set("spark.streaming.kafka.maxRatePerPartition", "30")
      .set("spark.streaming.stopGracefullyOnShutdown", "true")
    val ssc = new StreamingContext(conf, Seconds(3))
    val topics = Array("page_topic")
    val kafkaMap: Map[String, Object] = Map[String, Object](
      "bootstrap.servers" -> "cdh1.macro.com:9092,cdh2.macro.com:9092,cdh3.macro.com:9092",
      "key.deserializer" -> classOf[StringDeserializer],
      "value.deserializer" -> classOf[StringDeserializer],
      "group.id" -> groupid,
      "auto.offset.reset" -> "earliest",
      "enable.auto.commit" -> (false: lang.Boolean)
    )
    //查询mysql中是否存在偏移量
    val sqlProxy = new SqlProxy()
    val offsetMap = new mutable.HashMap[TopicPartition, Long]()
    val client = DataSourceUtil.getConnection
    try {
      sqlProxy.executeQuery(client, "select *from `offset_manager` where groupid=?", Array(groupid), new QueryCallback {
        override def process(rs: ResultSet): Unit = {
          while (rs.next()) {
            val model = new TopicPartition(rs.getString(2), rs.getInt(3))
            val offset = rs.getLong(4)
            offsetMap.put(model, offset)
          }
          rs.close()
        }
      })
    } catch {
      case e: Exception => e.printStackTrace()
    } finally {
      sqlProxy.shutdown(client)
    }
    //设置kafka消费数据的参数 判断本地是否有偏移量  有则根据偏移量继续消费 无则重新消费
    val stream: InputDStream[ConsumerRecord[String, String]] = if (offsetMap.isEmpty) {
      KafkaUtils.createDirectStream(
        ssc, LocationStrategies.PreferConsistent, ConsumerStrategies.Subscribe[String, String](topics, kafkaMap))
    } else {
      KafkaUtils.createDirectStream(
        ssc, LocationStrategies.PreferConsistent, ConsumerStrategies.Subscribe[String, String](topics, kafkaMap, offsetMap))
    }
    //解析json数据
    val dsStream = stream.map(item => item.value()).mapPartitions(partition => {
      partition.map(item => {
        val jsonObject = ParseJsonData.getJsonData(item)
        val uid = if (jsonObject.containsKey("uid")) jsonObject.getString("uid") else ""
        val app_id = if (jsonObject.containsKey("app_id")) jsonObject.getString("app_id") else ""
        val device_id = if (jsonObject.containsKey("device_id")) jsonObject.getString("device_id") else ""
        val ip = if (jsonObject.containsKey("ip")) jsonObject.getString("ip") else ""
        val last_page_id = if (jsonObject.containsKey("last_page_id")) jsonObject.getString("last_page_id") else ""
        val pageid = if (jsonObject.containsKey("page_id")) jsonObject.getString("page_id") else ""
        val next_page_id = if (jsonObject.containsKey("next_page_id")) jsonObject.getString("next_page_id") else ""
        (uid, app_id, device_id, ip, last_page_id, pageid, next_page_id)
      })
    }).filter(item => {
      !item._5.equals("") && !item._6.equals("") && !item._7.equals("")
    })
    dsStream.cache()
    val pageValueDStream = dsStream.map(item => (item._5 + "_" + item._6 + "_" + item._7, 1))
    val resultDStream = pageValueDStream.reduceByKey(_ + _)
    resultDStream.foreachRDD(rdd => {
      rdd.foreachPartition(partition => {
        //在分区下获取jdbc连接
        val sqlProxy = new SqlProxy()
        val client = DataSourceUtil.getConnection
        try {
          partition.foreach(item => {
            calcPageJumpCount(sqlProxy, item, client) //计算页面跳转个数
          })
        } catch {
          case e: Exception => e.printStackTrace()
        } finally {
          sqlProxy.shutdown(client)
        }
      })
    })
    ssc.sparkContext.addFile("hdfs://cdh1.macro.com:8020/user/catelf/data/ip2region.db") //广播文件
    val ipDStream = dsStream.mapPartitions(patitions => {
      val dbFile = SparkFiles.get("ip2region.db")
      val ipsearch = new DbSearcher(new DbConfig(), dbFile)
      patitions.map { item =>
        val ip = item._4
        val province = ipsearch.memorySearch(ip).getRegion().split("\\|")(2) //获取ip详情   中国|0|上海|上海市|有线通
        (province, 1l) //根据省份 统计点击个数
      }
    }).reduceByKey(_ + _)
    ipDStream.foreachRDD(rdd => {
      //查询mysql历史数据 转成rdd
      val ipSqlProxy = new SqlProxy()
      val ipClient = DataSourceUtil.getConnection
      try {
        val history_data = new ArrayBuffer[(String, Long)]()
        ipSqlProxy.executeQuery(ipClient, "select province,num from tmp_city_num_detail", null, new QueryCallback {
          override def process(rs: ResultSet): Unit = {
            while (rs.next()) {
              val tuple = (rs.getString(1), rs.getLong(2))
              history_data += tuple
            }
          }
        })
        val history_rdd = ssc.sparkContext.makeRDD(history_data)
        val resultRdd = history_rdd.fullOuterJoin(rdd).map(item => {
          val province = item._1
          val nums = item._2._1.getOrElse(0l) + item._2._2.getOrElse(0l)
          (province, nums)
        })
        resultRdd.foreachPartition(partitions => {
          val sqlProxy = new SqlProxy()
          val client = DataSourceUtil.getConnection
          try {
            partitions.foreach(item => {
              val province = item._1
              val num = item._2
              //修改mysql数据 并重组返回最新结果数据
              sqlProxy.executeUpdate(client, "insert into tmp_city_num_detail(province,num)values(?,?) on duplicate key update num=?",
                Array(province, num, num))
            })
          } catch {
            case e: Exception => e.printStackTrace()
          } finally {
              sqlProxy.shutdown(client)
          }
        })
        val top3Rdd = resultRdd.sortBy[Long](_._2, false).take(3)
        sqlProxy.executeUpdate(ipClient, "truncate table top_city_num", null)
        top3Rdd.foreach(item => {
          sqlProxy.executeUpdate(ipClient, "insert into top_city_num (province,num) values(?,?)", Array(item._1, item._2))
        })
      } catch {
        case e: Exception => e.printStackTrace()
      } finally {
        sqlProxy.shutdown(ipClient)
      }
    })
    //计算转换率
    //处理完 业务逻辑后 手动提交offset维护到本地 mysql中
    stream.foreachRDD(rdd => {
      val sqlProxy = new SqlProxy()
      val client = DataSourceUtil.getConnection
      try {
        calcJumRate(sqlProxy, client) //计算转换率
        val offsetRanges: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
        for (or <- offsetRanges) {
          sqlProxy.executeUpdate(client, "replace into `offset_manager` (groupid,topic,`partition`,untilOffset) values(?,?,?,?)",
            Array(groupid, or.topic, or.partition.toString, or.untilOffset))
        }
      } catch {
        case e: Exception => e.printStackTrace()
      } finally {
        sqlProxy.shutdown(client)
      }
    })
    ssc.start()
    ssc.awaitTermination()
  }
  /**
   * 计算页面跳转个数
   *
   * @param sqlProxy
   * @param item
   * @param client
   */
  def calcPageJumpCount(sqlProxy: SqlProxy, item: (String, Int), client: Connection): Unit = {
    val keys = item._1.split("_")
    var num: Long = item._2
    val page_id = keys(1).toInt //获取当前page_id
    val last_page_id = keys(0).toInt //获取上一page_id
    val next_page_id = keys(2).toInt //获取下页面page_id
    //查询当前page_id的历史num个数
    sqlProxy.executeQuery(client, "select num from page_jump_rate where page_id=?", Array(page_id), new QueryCallback {
      override def process(rs: ResultSet): Unit = {
        while (rs.next()) {
          num += rs.getLong(1)
        }
        rs.close()
      }
      //对num 进行修改 并且判断当前page_id是否为首页
      if (page_id == 1) {
        sqlProxy.executeUpdate(client, "insert into page_jump_rate(last_page_id,page_id,next_page_id,num,jump_rate)" +
          "values(?,?,?,?,?) on duplicate key update num=num+?", Array(last_page_id, page_id, next_page_id, num, "100%", num))
      } else {
        sqlProxy.executeUpdate(client, "insert into page_jump_rate(last_page_id,page_id,next_page_id,num)" +
          "values(?,?,?,?) on duplicate key update num=num+?", Array(last_page_id, page_id, next_page_id, num, num))
      }
    })
  }
  /**
   * 计算转换率
   */
  def calcJumRate(sqlProxy: SqlProxy, client: Connection): Unit = {
    var page1_num = 0l
    var page2_num = 0l
    var page3_num = 0l
    sqlProxy.executeQuery(client, "select num from page_jump_rate where page_id=?", Array(1), new QueryCallback {
      override def process(rs: ResultSet): Unit = {
        while (rs.next()) {
          page1_num = rs.getLong(1)
        }
      }
    })
    sqlProxy.executeQuery(client, "select num from page_jump_rate where page_id=?", Array(2), new QueryCallback {
      override def process(rs: ResultSet): Unit = {
        while (rs.next()) {
          page2_num = rs.getLong(1)
        }
      }
    })
    sqlProxy.executeQuery(client, "select num from page_jump_rate where page_id=?", Array(3), new QueryCallback {
      override def process(rs: ResultSet): Unit = {
        while (rs.next()) {
          page3_num = rs.getLong(1)
        }
      }
    })
    val nf = NumberFormat.getPercentInstance
    val page1ToPage2Rate = if (page1_num == 0) "0%" else nf.format(page2_num.toDouble / page1_num.toDouble)
    val page2ToPage3Rate = if (page2_num == 0) "0%" else nf.format(page3_num.toDouble / page2_num.toDouble)
    sqlProxy.executeUpdate(client, "update page_jump_rate set jump_rate=? where page_id=?", Array(page1ToPage2Rate, 2))
    sqlProxy.executeUpdate(client, "update page_jump_rate set jump_rate=? where page_id=?", Array(page2ToPage3Rate, 3))
  }
}