第一章.项目需求三:购物券风险预警

1.需求分析

一.简介

实时预警,是一种经常出现在实时计算中的业务类型,根据日志数据中系统报错异常,或者用户行为异常的检测,产生对应预警日志,预警日志通过图形化界面的展示,可以提醒监控方,需要及时核查问题,并采取应对措施

二.需求说明

需求:同一设备,五分钟内使用2个及以上不同账号登录且都增加了收货地址,达到以上要求则产生一条预警日志,并且同一设备,每分钟只记录一次预警

三.数据处理流程图

2.编辑子模块(gmall-realtime)

一.pom.xml

<dependency>
      <groupId>org.elasticsearch</groupId>
      <artifactId>elasticsearch-hadoop</artifactId>
      <version>6.6.0</version>
 </dependency>
 <dependency>
     <groupId>com.alibaba</groupId>
      <artifactId>druid</artifactId>
     <version>1.0.5</version>
 </dependency>

二.样例类

package com.atguigu.realtime.beans
import scala.collection.mutable
import scala.collection.mutable.ListBuffer
/**
 * Created by Smexy on 2021/9/30
 */
// 对应 actions[{action},... ]
case class Action(action_id:String,
                  item:String,
                  item_type:String,
                  ts:Long
                 )
// 对应common部分
case class CommonInfo(
                       ar:String,
                       ba:String,
                       ch:String,
                       is_new:Int,
                       md:String,
                       mid:String,
                       os:String,
                       uid:Long,
                       vc:String
                     )
// 对应Kafka中的一条数据
case class ActionsLog(
                       actions: List[Action],
                       ts:Long,
                       common:CommonInfo
                     )
//预警日志
case class AlertInfo(id:String,
                          // 这个设备过去5分组，登录过的用户
                           uids:mutable.Set[String],
                          // 这个设备过去5分组，登录过的用户 如果收藏了商品，请将收藏商品的id记录下来
                           itemIds:mutable.Set[String],
                          //  这个设备过去5分组，登录过的用户所产生的所有行为
                           events:ListBuffer[String],
                          // 预警日志的ts
                           ts:Long)

三.ES相关(创建index)

PUT _template/gmall_coupon_alert_template
{
  "index_patterns": ["gmall_coupon_alert*"],
  "settings": {
    "number_of_shards": 3
  },
  "aliases" : { 
    "{index}-query": {},
    "gmall_coupon_alert-query":{}
  },
   "mappings": {
     "_doc":{  
       "properties":{
         "mid":{
           "type":"keyword"
         },
         "uids":{
           "type":"keyword"
         },
         "itemIds":{
           "type":"keyword"
         },
         "events":{
           "type":"keyword"
         },
         "ts":{
           "type":"date"
         } 
       }
     }
   }
}

四.Alert实现

package com.atguigu.realtime.app
import com.alibaba.fastjson.{JSON, JSONObject}
import com.atguigu.constants.MyConstants
import com.atguigu.realtime.beans.{Action, ActionsLog, AlertInfo, CommonInfo}
import com.atguigu.realtime.utils.MyKafkaUtil
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka010.{CanCommitOffsets, HasOffsetRanges, OffsetRange}
import org.apache.spark.streaming.{Minutes, Seconds, StreamingContext}
import java.time.{Instant, LocalDate, LocalDateTime, ZoneId}
import java.time.format.DateTimeFormatter
import scala.collection.mutable
import scala.util.control.Breaks
/**
 *  at least once + ES(幂等输出,偏移量维护在kafka)
 */
object AlertApp extends BaseApp {
  override var appName: String = "AlertApp"
  override var batchDuration: Int = 10
  val groupId = "gmallrealtime"
  def main(args: Array[String]): Unit = {
    //将ES的配置写入conf
    val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName(appName)
    conf.set("es.index.auto.create", "true")
    conf.set("es.nodes", "hadoop102")
    conf.set("es.port", "9200")
    ssc = new StreamingContext(conf,Seconds(batchDuration))
    runApp{
      //1.获取初始DS
      val ds = MyKafkaUtil.getKafkaStream(Array(MyConstants.ACTIONS_LOG), ssc, groupId)
      var offsetRanges:Array[OffsetRange] = null
      //2.获取偏移量信息
      val ds1 = ds.transform(rdd => {
        offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
        //3.封装样例类
        rdd.map(record => {
          //获取actions
          val jSONObject:JSONObject = JSON.parseObject(record.value())
          /**
           * 导入静态方法,封装了scala集合和java集合的转换方法
           * scala集合转java集合 scala集合.asJava
           * java集合转scala集合 java集合.asScala
           */
          import collection.JavaConverters._
          //将util.list转为scala的list
          val actions: List[Action] = JSON.parseArray(jSONObject.getString("actions"), classOf[Action]).asScala.toList
          //获取common
          val common = JSON.parseObject(jSONObject.getString("common"), classOf[CommonInfo])
          ActionsLog(actions, jSONObject.getLong("ts"), common)
        })
      })
      //4.开窗5分钟,按照(mid,uid)分组
      val ds2 = ds1.window(Minutes(5))
        .map(log => {
          ((log.common.mid, log.common.uid), log)
        })
        .groupByKey()
      //5.根据需求,计算每个用户是否进行收货地址修改
      val ds3 = ds2.map {
        case ((mid, uid), actionLogs) => {
          //假设不需要预警
          var ifNeedAlert: Boolean = false
          //判断是否有需要预警的嫌疑
          Breaks.breakable {
            actionLogs.foreach(actionLogs => {
              actionLogs.actions.foreach(action => {
                if (action.action_id == "trade_add_address") {
                  ifNeedAlert = true
                  //不需要往下遍历,跳出循环
                  Breaks.break
                }
              })
            })
          }
          //如果需要预警,返回这个人此期间产生的Logs
          if(ifNeedAlert){
            (mid,actionLogs)
          }else{
            (null,null)
          }
        }
      }
      //6.过滤(null,null)
      val ds4 = ds3.filter(_._1 != null)
      //7.继续将同一个设备,有嫌疑的用户及日志,分到同一组
      val ds5 = ds4.groupByKey()
      //8.过去5分钟,修改收货地址用户超过2个及以上的设备才需要预警
      val ds6 = ds5.filter(_._2.size >= 2)
      //9.压平
      val ds7 = ds6.mapValues(_.flatten)
      //10.生成预警信息
      val ds8 = ds7.map {
        case (mid, actionsLogs) => {
          var uids = mutable.Set[String]()
          var itemIds = mutable.Set[String]()
          var events = mutable.ListBuffer[String]()
          actionsLogs.foreach(actionsLog => {
            uids.add(actionsLog.common.uid.toString)
            actionsLog.actions.foreach(action => {
              if (action.action_id == "favor_add") {
                itemIds.add(action.item)
              }
              events.append(action.action_id)
            })
          })
          // id = mid_yyyy-MM-dd HH:mm
          val formatter1: DateTimeFormatter = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm")
          val ts: Long = System.currentTimeMillis()
          val time: LocalDateTime = LocalDateTime.ofInstant(Instant.ofEpochMilli(ts), ZoneId.of("Asia/Shanghai"))
          var id = mid + "_" + time.format(formatter1)
          AlertInfo(id, uids, itemIds, events, ts)
        }
      }
      ds8.cache()
      ds8.print(100)
      //为了使用ESRdd的静态方法
      import org.elasticsearch.spark._
      //11.写到ES
      ds8.foreachRDD(rdd=>{
        rdd.saveToEs("/gmall_coupon_alert"+ LocalDate.now()+"/_doc" ,Map("es.mapping.id" -> "id") )
        //提交偏移量
        ds.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
      })
    }
  }
}

五.启动APP

ES客户端查看

3.Kibana发布可视化界面

一.创建索引样式

二.做树状图

三.做饼图

四.看板展示

第二章.项目需求四:灵活分析

1.需求分析

一.灵活查询的场景

数仓中存储了大量的明细数据,但是Hadoop存储的数仓计算必须经过MR,所以即时交互性非常糟糕,为了方便数据分析人员查看信息,数据平台需要提供一个能够根据文字及选项等条件,进行灵活分析判断的数据功能

二.需求详细

输入参数

返回结果

三.数据处理流程图

2.编辑子模块(gmall-canalclient)

编写canal客户端采集明细数据到kafka

package com.atguigu.client;
import com.alibaba.fastjson.JSONObject;
import com.alibaba.otter.canal.client.CanalConnector;
import com.alibaba.otter.canal.client.CanalConnectors;
import com.alibaba.otter.canal.protocol.CanalEntry;
import com.alibaba.otter.canal.protocol.Message;
import com.atguigu.constants.MyConstants;
import com.google.protobuf.ByteString;
import com.google.protobuf.InvalidProtocolBufferException;
import java.net.InetSocketAddress;
import java.util.List;
//canalclient连接服务端
public class CanalClient2 {
    public static void main(String[] args) throws InterruptedException, InvalidProtocolBufferException {
        //1.创建一个客户端对象
        CanalConnector connector = CanalConnectors.newSingleConnector(new InetSocketAddress("hadoop103", 11111), "example", null, null);
        //2.连接
        connector.connect();
        //3.发请求
        connector.subscribe("sparkproduct.*");
        //4.拉数据
        while(true){
            Message message = connector.get(100);
            //判断是否拉取到了数据
            if(message.getId() == -1){
                System.out.println("歇会再去");
                Thread.sleep(5000);
                continue;
            }
            //解析数据(message->拉取一次,多条SQL,造成的写操作变化)
            //List<CanalEntry.Entry> entrys:->保存的就是多条SQL引起的变化
            List<CanalEntry.Entry> entries = message.getEntries();
            for (CanalEntry.Entry entry : entries) {
                //Entry:一个SQL引起的变化
                //只要insert语句
                if(entry.getEntryType().equals(CanalEntry.EntryType.ROWDATA)){
                    //ByteStrinng storeValue_存的是一条SQL引起的数据变化,,不能直接使用,必须反序列化
                    String tableName = entry.getHeader().getTableName();
                    ByteString storeValue = entry.getStoreValue();
                    parseDate(tableName,storeValue);
                }
            }
        }
        //
    }
    private static void parseDate(String tableName,ByteString storeValue) throws InvalidProtocolBufferException {
        //反序列化
        CanalEntry.RowChange rowChange = CanalEntry.RowChange.parseFrom(storeValue);
        CanalEntry.EventType eventType = rowChange.getEventType();
        //只要insert
       if (tableName.equals("order_info") && eventType.equals(CanalEntry.EventType.INSERT) ){
           sendDataToKafka(rowChange,MyConstants.GMALL_ORDER_INFO);
       }else if(tableName.equals("order_detail") && eventType.equals(CanalEntry.EventType.INSERT)){
           sendDataToKafka(rowChange,MyConstants.GMALL_ORDER_DETAIL);
       }else if(tableName.equals("user_info") && (eventType.equals(CanalEntry.EventType.INSERT) || eventType.equals(CanalEntry.EventType.UPDATE))){
           sendDataToKafka(rowChange,MyConstants.GMALL_USER_INFO);
       }
    }
    public static void sendDataToKafka(CanalEntry.RowChange rowChange,String topicName){
        List<CanalEntry.RowData> rowDatasList = rowChange.getRowDatasList();
        //将每一行数据的所有列封装为json字符串
        for (CanalEntry.RowData rowData : rowDatasList) {
            JSONObject jsonObject = new JSONObject();
            //获取insert后的列的信息
            List<CanalEntry.Column> afterColumnsList = rowData.getAfterColumnsList();
            for (CanalEntry.Column column : afterColumnsList) {
                jsonObject.put(column.getName(),column.getValue());
            }
            System.out.println(jsonObject.toJSONString());
            //生产到kafka
            MyProducer.sendData(topicName,jsonObject.toJSONString());
        }
    }
}

3.编辑子模块(gmall-realtime)

一.数据处理流程

二.样例类

package com.atguigu.realtime.beans
case class OrderDetail(id:String,
                       order_id: String,
                       sku_name: String,
                       sku_id: String,
                       order_price: String,
                       img_url: String,
                       sku_num: String)
case class UserInfo(id:String,
                    login_name:String,
                    user_level:String,
                    birthday:String,
                    gender:String)
import java.text.SimpleDateFormat
import java.util
case class SaleDetail(
                       var order_detail_id:String =null,
                       var order_id: String=null,
                       var order_status:String=null,
                       var create_time:String=null,
                       var user_id: String=null,
                       var sku_id: String=null,
                       var user_gender: String=null,
                       var user_age: Int=0,
                       var user_level: String=null,
                       var sku_price: Double=0D,
                       var sku_name: String=null,
                       var dt:String=null) {
  def this(orderInfo:OrderInfo,orderDetail: OrderDetail) {
    this
    mergeOrderInfo(orderInfo)
    mergeOrderDetail(orderDetail)
  }
  def mergeOrderInfo(orderInfo:OrderInfo): Unit ={
    if(orderInfo!=null){
      this.order_id=orderInfo.id
      this.order_status=orderInfo.order_status
      this.create_time=orderInfo.create_time
      this.dt=orderInfo.create_date
      this.user_id=orderInfo.user_id
    }
  }
  def mergeOrderDetail(orderDetail: OrderDetail): Unit ={
    if(orderDetail!=null){
      this.order_detail_id=orderDetail.id
      this.sku_id=orderDetail.sku_id
      this.sku_name=orderDetail.sku_name
      this.sku_price=orderDetail.order_price.toDouble
    }
  }
  def mergeUserInfo(userInfo: UserInfo): Unit ={
    if(userInfo!=null){
      this.user_id=userInfo.id
      val formattor = new SimpleDateFormat("yyyy-MM-dd")
      val date: util.Date = formattor.parse(userInfo.birthday)
      val curTs: Long = System.currentTimeMillis()
      val  betweenMs= curTs-date.getTime
      val age=betweenMs/1000L/60L/60L/24L/365L
      this.user_age=age.toInt
      this.user_gender=userInfo.gender
      this.user_level=userInfo.user_level
    }
  }
}

三.采集user_Info进入缓存(redis)

• 使用order_Info.userid和userinfo.id
• 当前这笔订单的userid是当前新注册的用户,还是之前注册的用户?都有可能!需要join user_info的全量用户
• 全量的user_info存储在哪里
• mysql中user_info性能不太好
• 将mysql中的全量user_info存入到redis中
• 目的:在redis中有全量的用户信息
• 写一个程序,批量将mysql的user_info表的数据读入,写出到redis!
• 如果可以获取全量用户的binlog日志,也可以使用canal,读取全量的binlog日志,将数据生产到kafka,再写入到redis(采用)

package com.atguigu.realtime.app
import com.alibaba.fastjson.JSON
import com.atguigu.constants.MyConstants
import com.atguigu.realtime.utils.{MyKafkaUtil, RedisUtil}
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka010.{CanCommitOffsets, HasOffsetRanges}
import org.apache.spark.streaming.{Seconds, StreamingContext}
object UserApp extends BaseApp {
  override var appName: String = "UserApp"
  override var batchDuration: Int = 10
  val groupId = "gmallrealtime"
  def main(args: Array[String]): Unit = {
    ssc = new StreamingContext(new SparkConf().setMaster("local[*]").setAppName(appName),Seconds(batchDuration))
    runApp{
      //获取初始DS
      val ds = MyKafkaUtil.getKafkaStream(Array(MyConstants.GMALL_USER_INFO), ssc, groupId)
      ds.foreachRDD(rdd=>{
        if(!rdd.isEmpty()){
          //获取偏移量
          val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
          rdd.foreachPartition(partition => {
            val jedis = RedisUtil.getJedisClient()
            partition.foreach(record => {
              val jSONObject = JSON.parseObject(record.value())
              jedis.set("userInfo:"+jSONObject.getString("id"),record.value())
            })
            jedis.close()
          })
          ds.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
        }
      })
    }
  }
}

四.ES索引建立

PUT  _template/gmall2021_sale_detail_template
{
   "index_patterns": ["gmall2021_sale_detail*"],                  
    "settings": {                                               
      "number_of_shards": 3
    },
    "aliases" : { 
      "{index}-query": {},
      "gmall2021_sale_detail-query":{}
    },
    "mappings" : {
      "_doc" : {
        "properties" : {
          "order_detail_id" : {
            "type" :   "keyword"
          },
          "order_id" : {
            "type" : "keyword" 
          },
          "create_time" : {
            "type" : "date" ,
            "format" : "yyyy-MM-dd HH:mm:ss"
          },
          "dt" : {
            "type" : "date"
          },
          "order_status" : {
                "type" : "keyword" 
          },
          "sku_id" : {
                "type" : "keyword"
          },
          "sku_name" : {
            "type" : "text",
            "analyzer": "ik_max_word"
          },
          "sku_price" : {
            "type" : "float"
          },
          "user_age" : {
            "type" : "long"
          },
          "user_gender" : {
            "type" : "keyword" 
          },
          "user_id" : {
            "type" : "keyword" 
          },
          "user_level" : {
            "type" : "keyword",
            "index" : false 
          }
        }
      }
    }
  }

五.双流join

package com.atguigu.realtime.app
import com.alibaba.fastjson.JSON
import com.atguigu.constants.MyConstants
import com.atguigu.realtime.beans.{OrderDetail, OrderInfo, SaleDetail, UserInfo}
import com.atguigu.realtime.utils.{MyKafkaUtil, RedisUtil}
import com.google.gson.Gson
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.kafka010.{CanCommitOffsets, HasOffsetRanges, OffsetRange}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import java.time.{LocalDate, LocalDateTime}
import java.time.format.DateTimeFormatter
import scala.collection.mutable
/**
 * SparkStreaming中双流join的注意事项
 *  1.两个流必须从一个StreamingContext中获取
 *  2.只有DStream[K-V],才能调用join算子
 *  3.join的原理是根据key进行join
 *  4.在join时,只有要当join的数据,位于同一批次,被处理时才能join上
 *  5.如果由于网络延迟,导致两个流要join的数据,位于不同的批次被消费,此时无法join
 */
object SaleDetailApp extends BaseApp {
  override var appName: String = "SaleDetailApp"
  override var batchDuration: Int = 10
  val groupId = "gmallrealtime"
  def main(args: Array[String]): Unit = {
    //将es的配置写入sparkconf
    val conf: SparkConf = new SparkConf().setMaster("local[4]").setAppName(appName)
    conf.set("es.index.auto.create", "true")
    conf.set("es.nodes", "hadoop102")
    conf.set("es.port", "9200")
    ssc = new StreamingContext(conf, Seconds(batchDuration))
    runApp{
      //获取初始DS
      val ds1 = MyKafkaUtil.getKafkaStream(Array(MyConstants.GMALL_ORDER_DETAIL), ssc, groupId)
      val ds2 = MyKafkaUtil.getKafkaStream(Array(MyConstants.GMALL_ORDER_INFO), ssc, groupId)
      //获取当前批次的偏移量,将初始DS,转换为DS[K-V]
      var orderInfoOffsetRanges:Array[OffsetRange] = null
      var orderDetailOffsetRanges:Array[OffsetRange] = null
      val ds3 = ds1.transform(rdd => {
        orderDetailOffsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
        rdd.map(record => {
          val orderDetail = JSON.parseObject(record.value(), classOf[OrderDetail])
          (orderDetail.order_id, orderDetail)
        })
      })
      val ds4 = ds2.transform(rdd => {
        orderInfoOffsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
        parseRecordToOrderInfo(rdd)
      })
      val ds5 = ds4.fullOuterJoin(ds3)
      //使用缓存处理join后的数据,同一批次能join的就join,不能join的就写入缓存,等待后续批次
      //缓存使用的是redis,存在读写redis,以分区为单位获取连接
      val ds6 = ds5.mapPartitions(partition => {
        val jedis = RedisUtil.getJedisClient()
        val gson = new Gson()
        //准备一个集合,存放可以上的明细数据
        val results = mutable.ListBuffer[SaleDetail]()
        partition.foreach {
          case (orderId, (orderInfoOption, orderDetailOption)) => {
            if (orderInfoOption != None) {
              val oi = orderInfoOption.get
              if (orderDetailOption != None) {
                val od = orderDetailOption.get
                //关联当前批次可以join上的数据
                val saleDetail = new SaleDetail(oi, od)
                results.append(saleDetail)
              }
              //写入redis
              jedis.setex("orderInfo:" + orderId, 5 * 2 * 60, gson.toJson(oi))
              //从Redis中找,有没有之前批次早到的orderDetail,有就关联
              val set = jedis.smembers("orderDetail:" + orderId)
              //如果可以不存在,不会报空指针异常,此时set是一个[],并不是null
              set.forEach(orderDetailStr => {
                val orderDetail = JSON.parseObject(orderDetailStr, classOf[OrderDetail])
                val saleDetail = new SaleDetail(oi, orderDetail)
                results.append(saleDetail)
              })
            } else {
              val od = orderDetailOption.get
              //到缓存中找有没有早到的orderInfo,有就关联
              val str = jedis.get("orderInfo:" + orderId)
              if (str != null) {
                val info = JSON.parseObject(str, classOf[OrderInfo])
                val saleDetail = new SaleDetail(info, od)
                results.append(saleDetail)
              } else {
                //到缓存中没有找到早到的orderInfo,说明orderDetail早到了,写入缓存
                jedis.sadd("orderDetail:" + orderId, gson.toJson(od))
                //设置过期时间
                jedis.expire("orderDetail:" + orderId, 5 * 2 * 60)
              }
            }
          }
        }
        jedis.close()
        results.iterator
      })
      //ds6是已经将orderInfo和orderDetail join后的DS,需要从redis中根据ds6的userId获取用户的其他信息
      val ds7 = ds6.mapPartitions(partition => {
        val jedis = RedisUtil.getJedisClient()
        val it = partition.map(saleDetail => {
          val userInfoStr = jedis.get("userInfo:" + saleDetail.user_id)
          val userInfo = JSON.parseObject(userInfoStr, classOf[UserInfo])
          saleDetail.mergeUserInfo(userInfo)
          saleDetail
        })
        jedis.close()
        it
      })
      ds7.cache()
      println("即将写入:" + ds7.count().print())
      ds7.print(1000)
      import org.elasticsearch.spark._
      //写入ES
      ds7.foreachRDD(rdd => {
        // resource:  目的地    cfg: 配置
        rdd.saveToEs("/gmall2021_sale_detail"+ LocalDate.now()+"/_doc" ,Map("es.mapping.id" -> "order_detail_id") )
        // 提交偏移量
        ds1.asInstanceOf[CanCommitOffsets].commitAsync(orderDetailOffsetRanges)
        ds2.asInstanceOf[CanCommitOffsets].commitAsync(orderInfoOffsetRanges)
      })
    }
  }
  def parseRecordToOrderInfo(rdd: RDD[ConsumerRecord[String, String]]):RDD[(String,OrderInfo)] = {
    rdd.map(record=>{
      val orderInfo = JSON.parseObject(record.value(), classOf[OrderInfo])
      //"create_time": "2021-09-28 21:12:40",
      val formatter1 = DateTimeFormatter.ofPattern("yyyy-MM-dd")
      val formatter2 = DateTimeFormatter.ofPattern("HH")
      val formatter3 = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss")
      // 先将 "2021-09-28 21:12:40" 转换为  LocalDateTime对象
      val localDateTime = LocalDateTime.parse(orderInfo.create_time, formatter3)
      orderInfo.create_date=localDateTime.format(formatter1)
      orderInfo.create_hour=localDateTime.format(formatter2)
      (orderInfo.id,orderInfo)
    })
  }
}

4.编辑子模块(gmall-publisher)

输入路径及参数

http://localhost:8070/sale_detail?date=2021-08-21&startpage=1&size=5&keyword=小米手机

一.pom.xml

<!--- ES依赖包-->
<dependency>
    <groupId>io.searchbox</groupId>
    <artifactId>jest</artifactId>
    <version>5.3.3</version>
</dependency>

<dependency>
    <groupId>net.java.dev.jna</groupId>
    <artifactId>jna</artifactId>
    <version>4.5.2</version>
</dependency>

<dependency>
    <groupId>org.codehaus.janino</groupId>
    <artifactId>commons-compiler</artifactId>
    <version>2.7.8</version>
</dependency>

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-data-elasticsearch</artifactId>
</dependency>

二.配置 application.properties

#es
spring.elasticsearch.jest.uris=http://hadoop102:9200

三.业务代码实现

1. pojo层

option.java(一个统计选项)

package com.example.gmallpublisher.pojo;

import lombok.AllArgsConstructor;
import lombok.Data;

@Data
@AllArgsConstructor
public class Option {
    String name;
    Double value;
}

SaleDetail.java(销售统计详情)

package com.example.gmallpublisher.pojo;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;

/**
 * Created by Smexy on 2021/10/8
 */
@Data
@NoArgsConstructor
@AllArgsConstructor
public class SaleDetail {

    private String order_detail_id;
    private String order_id;
    private String order_status;
    private String create_time;
    private String user_id;
    private String sku_id;
    private String user_gender;
    private Integer user_age;
    private String user_level;
    private Double sku_price;
    private String sku_name;
    private String dt;

    private String  es_metadata_id;
}

Stat.java(一组统计选项)

import lombok.AllArgsConstructor;
import lombok.Data;
import java.util.List;
@Data
@AllArgsConstructor
public class Stat {
    String title;
    List<Option> options;
}

1. dao层

单独创建dao层的原因

• 为了和Mybatis的mapper进行区分
• 因为在主程序上标注了@MapperScan,如果将ESDao也放入mapper包下,此时spring也会为ESDao提供代理对象,在创建对象时会报错,在创建对象时,会根据ESDao中的方法名,去匹配SQL,并没有SQL,报错

ESDao.java

package com.example.gmallpublisher.dao;

import com.alibaba.fastjson.JSONObject;

import java.io.IOException;

/**
 * http://localhost:8070/sale_detail?date=2021-08-21&startpage=1&size=5&keyword=小米手机
 */

public interface ESDao {
    JSONObject getData(String data,Integer startpage,Integer size,String keyword) throws IOException;
}

ESDaoImpl.java

package com.example.gmallpublisher.dao;

import com.alibaba.fastjson.JSONObject;
import com.example.gmallpublisher.pojo.Option;
import com.example.gmallpublisher.pojo.SaleDetail;
import com.example.gmallpublisher.pojo.Stat;
import com.ibm.icu.text.DecimalFormat;
import io.searchbox.client.JestClient;
import io.searchbox.core.Search;
import io.searchbox.core.SearchResult;
import io.searchbox.core.search.aggregation.MetricAggregation;
import io.searchbox.core.search.aggregation.TermsAggregation;
import org.elasticsearch.index.query.MatchQueryBuilder;
import org.elasticsearch.search.aggregations.AggregationBuilders;
import org.elasticsearch.search.aggregations.bucket.terms.TermsBuilder;
import org.elasticsearch.search.builder.SearchSourceBuilder;
import org.springframework.beans.factory.annotation.Autowired;



import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

/**
 * http://localhost:8070/sale_detail?date=2021-08-21&startpage=1&size=5&keyword=小米手机
 * date:查询date这一天的购物明细
 *     作用,生成要查询的indexName:gmall2021_sale_detail2021-10-09
 *  size:要查询多少条数据
 *  startpage:查询哪一页的数据
 *  keyword:检索的关键字
 *
 */
@Repository
public class ESDaoImpl implements ESDao{
    @Autowired
    private JestClient jestClient;
    public SearchResult getResult(String date,Integer startpage,Integer size,String keyword) throws IOException {
        int from =(startpage - 1) * size;
        String indexName = "gmall2021_sale_detail" + date;
        //构造match{}
        MatchQueryBuilder matchQueryBuilder = new MatchQueryBuilder("sku_name", keyword);
        //构造aggs:{}
        TermsBuilder termsBuilder1 = AggregationBuilders.terms("gender_count").field("user_gender").size(2);
        TermsBuilder termsBuilder2 = AggregationBuilders.terms("age_count").field("user_age").size(150);
        //构造外层{}
        String queryString = new SearchSourceBuilder().query(matchQueryBuilder).aggregation(termsBuilder1).aggregation(termsBuilder2)
                .from(from).size(size).toString();
        Search search = new Search.Builder(queryString).addIndex(indexName).addType("_doc").build();
        SearchResult searchResult = jestClient.execute(search);
        return searchResult;

    }
    //detail:[{SaleDetail},{}]
    public ArrayList<SaleDetail> getDetail(SearchResult result){
        ArrayList<SaleDetail> results = new ArrayList<>();
        List<SearchResult.Hit<SaleDetail, Void>> hits = result.getHits(SaleDetail.class);
        for (SearchResult.Hit<SaleDetail, Void> hit : hits) {
            SaleDetail source = hit.source;
            source.setEs_metadata_id(hit.id);
            results.add(source);
        }
        return results;

    }
    public Stat getAgeStat(SearchResult result){
        MetricAggregation aggregations = result.getAggregations();
        TermsAggregation age_count = aggregations.getTermsAggregation("age_count");
        List<TermsAggregation.Entry> buckets = age_count.getBuckets();
        int ageFrom20To30 = 0;
        int agelt20 = 0;
        int agegt30 = 0;
        for (TermsAggregation.Entry bucket : buckets) {
            if (Integer.parseInt(bucket.getKey())<20){
                agelt20 += bucket.getCount();
            }else if(Integer.parseInt(bucket.getKey()) >=30){
                agegt30 += bucket.getCount();
            }else{
                ageFrom20To30 += bucket.getCount();
            }
        }
        double sumCount = ageFrom20To30 + agelt20 + agegt30;
        DecimalFormat decimalFormat = new DecimalFormat("###.##");
        ArrayList<Option> options = new ArrayList<Option>();
        options.add(new Option("20岁到30岁",Double.parseDouble(decimalFormat.format(ageFrom20To30 / sumCount * 100))));
        options.add(new Option("30岁及30岁以上",Double.parseDouble(decimalFormat.format(agegt30 / sumCount * 100))));
        options.add(new Option("20岁以下",Double.parseDouble(decimalFormat.format(agelt20 / sumCount * 100))));

        return new Stat("用户年龄占比",options);

    }
    public Stat getGenderStat(SearchResult result){

        MetricAggregation aggregations = result.getAggregations();

        TermsAggregation age_count = aggregations.getTermsAggregation("gender_count");

        List<TermsAggregation.Entry> buckets = age_count.getBuckets();

        int maleCount = 0;
        int femaleCount = 0;


        for (TermsAggregation.Entry bucket : buckets) {

            if (bucket.getKey().equals("M")){
                maleCount += bucket.getCount();
            }else{
                femaleCount+= bucket.getCount();
            }

        }

        double sumCount = maleCount + femaleCount ;

        DecimalFormat decimalFormat = new DecimalFormat("###.##");

        ArrayList<Option> options = new ArrayList<>();

        options.add(new Option("男",Double.parseDouble(decimalFormat.format(maleCount / sumCount * 100))));
        options.add(new Option("女",Double.parseDouble(decimalFormat.format(femaleCount / sumCount * 100))));


        return new Stat("用户性别占比",options);

    }




    @Override
    public JSONObject getData(String date, Integer startpage, Integer size, String keyword) throws IOException {
        SearchResult searchResult = getResult(date, startpage, size, keyword);

        JSONObject jsonObject = new JSONObject();

        ArrayList<Stat> stats = new ArrayList<>();

        stats.add(getAgeStat(searchResult));
        stats.add(getGenderStat(searchResult));

        jsonObject.put("total",searchResult.getTotal());
        jsonObject.put("stats",stats);
        jsonObject.put("detail",getDetail(searchResult));


        return jsonObject;
    }
}

1. service层

PublisherService.java

package com.example.gmallpublisher.service;

import com.alibaba.fastjson.JSONObject;
import com.example.gmallpublisher.pojo.DAUPerHour;
import com.example.gmallpublisher.pojo.GMVPerHour;

import java.io.IOException;
import java.util.List;

public interface PublisherService {
    //查询单日日活
    Integer getDAUByDate(String date);
    //查询当日新增的设备数
    Integer getNewMidCountByDate(String date);
    //查询当日分时的设备数
    List<DAUPerHour> getDAUPerHourData(String date);
    //查询当天累计的GMV
    Double getGMVByDate(String date);
    //查询当天分时GMV
    List<GMVPerHour> getGMVPerHourDate(String date);
    //灵活分析查询
    JSONObject getData(String date, Integer startpage, Integer size, String keyword) throws IOException;

}

PublisherServiceImpl.java

package com.example.gmallpublisher.service;

import com.alibaba.fastjson.JSONObject;
import com.example.gmallpublisher.dao.ESDao;
import com.example.gmallpublisher.mapper.DAUMapper;
import com.example.gmallpublisher.mapper.GMVMapper;
import com.example.gmallpublisher.pojo.DAUPerHour;


import com.example.gmallpublisher.pojo.GMVPerHour;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;


import java.io.IOException;
import java.util.List;

@Service
public class PublisherServiceImpl implements PublisherService {


    @Autowired
    private DAUMapper dauMapper;
    @Autowired
    private GMVMapper gmvMapper;
    @Autowired
    private ESDao esDao;
    @Override
    public Integer getDAUByDate(String date) {
        System.out.println("遵循的业务步骤");
        return dauMapper.getDAUByDate(date);
    }

    @Override
    public Integer getNewMidCountByDate(String date) {
        System.out.println("遵循的业务步骤");
        return dauMapper.getNewMidCountByDate(date);
    }

    @Override
    public List<DAUPerHour> getDAUPerHourData(String date) {
        System.out.println("遵循业务步骤");
        return dauMapper.getDAUPerHourData(date);
    }

    @Override
    public Double getGMVByDate(String date) {
        System.out.println("遵循业务步骤");
        return gmvMapper.getGMVByDate(date);
    }

    @Override
    public List<GMVPerHour> getGMVPerHourDate(String date) {
        System.out.println("遵循业务步骤");
        return gmvMapper.getGMVPerHourDate(date);
    }
    @Override
    public JSONObject getData(String date, Integer startpage, Integer size, String keyword) throws IOException {
        return esDao.getData(date,startpage,size,keyword);
    }
}

1. controller层

    @Autowired
    private PublisherService publisherService;
    @RequestMapping(value="/sale_detail")
    public Object handle3(String date,Integer startpage,Integer size,String keyword) throws IOException {
        return publisherService.getData(date,startpage,size,keyword);
    }

1. index.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>首页</title>
</head>
<body>
<a href=" http://localhost:8070/realtime-total?date=2021-10-07">请求今天DAU和GMV</a> <br>
<a href=" http://localhost:8070/realtime-hours?id=dau&date=2021-10-07">请求今天和昨天分时DAU</a>
<br/>
<a href=" http://localhost:8070/realtime-hours?id=order_amount&date=2021-10-07">请求今天和昨天分时GMV</a>
<a href=" http://localhost:8070/sale_detail?date=2021-10-09&startpage=2&size=10&keyword=小米手机">请求10-09日购物明细</a>

</body>
</html>

四.运行程序