利用HDFS、RabbitMQ、MongoDB实现统计

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业务分析

IPTV行业桌面数据分析，分析用户行为数据。每天数据量可达3000万。统计用户访问量（PV）、UV（独立用户）、VV（视频播放次数）、
DAU（日活）、WAU（周活）、MAU（月活）、月开机率、点击次数、排行榜数据等等。

架构设计

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• client上报数据存入缓存中
• 定时将缓存的字符流刷新到文件，并将文件上传到hdfs
• 通过mq 客户端发送至服务端
• mq服务端监听到hdfs进行处理（ 将字节数组反序列化为实体Bean）
• 将其实体Bean写入mongo数据库
• 利用mongoDB聚合函数aggregate()查询（分库分表）

环境部署

HADOOP环境搭建：[Hadoop环境搭建地址]
RabbitMQ环境搭建：[RabbitMQ环境搭建地址]

部分代码

客户端收集数据

@RequestMapping(value = "/singleData/{policyId}")
 public ResponseData postSingleData(@PathVariable("policyId") String policyId, HttpServletRequest resuest) {
     ResponseData response = getResponseData();
     try {
         ServletInputStream inputStream = resuest.getInputStream();

         StringBuffer sb = new StringBuffer();
         BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(inputStream, "utf-8"));
         String data = null;
         while ((data = bufferedReader.readLine()) != null) {
             sb.append(data);
             sb.append("\n");
         }
         if (sb.length() <= 0) {
             throw new Exception("请提供提交数据");
         }
         DataStr s = new DataStr(policyId, sb);
         service.receiveNewData(s);
     } catch (Exception e) {
         response.setCode(0);
         response.setMsg(e.getMessage());
     }

     return response;
 }

定时将缓存的字符流刷新到文件

private void flushData() throws Exception {
      Queue dataCache = DataPond.getDataCache();
      DataStr dataIte = null;
      Integer size = dataCache.size();
      logger.info("There are [" + size + "] of datas in queue");
      while (size > 0 && (dataIte = dataCache.poll()) != null) {
          String policyId = dataIte.getPolicyId();
          Map fileCache = DataPond.getFileCache();
          FileStruct fileStruct = fileCache.get(policyId);
          if (fileStruct == null) {
              fileStruct = new FileStruct(policyId);
              fileCache.put(policyId, fileStruct);
          }
          fileStruct.write(dataIte.getData().toString());
          fileStruct.write("\n");
          size--;
      }
  }

文件上传到hdfs,并通过mq发送

private void uploadFilesAndSendToMQ() throws Exception {
       /* 遍历文件 */
       Map fileCache = DataPond.getFileCache();

       Set keySet = fileCache.keySet();
       for (String key : keySet) {
           FileStruct fs = fileCache.get(key);
           /* 标记是否可以被flush，并上传hdfs */
           Boolean shallBeFlush = false;
           if (fs.getFielSize() >= SystemChangeableConstant.MAX_FILESIZE) {
               shallBeFlush = true;
           }
           if (System.currentTimeMillis() - fs.getLastUpdateTime() >= SystemChangeableConstant.MAX_FILE_NOACTION_TIMESPAN) {
               shallBeFlush = true;
           }
           if (shallBeFlush) {
               if (!hdfsUtil.isOpened()) {
                   //TODO 临时获取hadoop环境变量
                   System.setProperty("hadoop.home.dir", "D:\\hadoop-2.6.5");
                   hdfsUtil.open();
               }
               logger.info("File of policy [" + key + "] is full and will send out!");
               fs.flush();
               try {
                   transferFileToDfs(fs);
                   logger.info("File of policy [" + key + "] send to hdfs success");
               } catch (Exception e) {
                   logger.error("File of policy [" + key + "] send to hdfs fail as " + e.getMessage());
               }
               try {
                   sendToMq(fs);
                   logger.info("File of policy [" + key + "] send to MQ success");
               } catch (Exception e) {
                   logger.error("File of policy [" + key + "] send to MQ fail as " + e.getMessage());
               }
               fileCache.remove(key);
               fs.destroy();
           }
       }
       hdfsUtil.close();
   }

mq服务端监听到hdfs进行处理

/* 提取command放入线程对象 */
  Consumer consumer = new DefaultConsumer(channel) {
      @Override
      public void handleDelivery(String consumerTag, Envelope envelope, AMQP.BasicProperties properties, byte[] body) throws IOException {
          logger.info("Router [" + SystemConstant.NEW_FILE_QUEUE + "] received new file");
          FileStruct fileStruct = (FileStruct) SerializingUtil.deserialize(body);
          factory.createNewTask(fileStruct);
      }
  };
  channel.basicConsume(SystemConstant.NEW_FILE_QUEUE, true, consumer);

数据入mongo

    for (HashMap aMap : parsedDatas) {
       // 预置的默认时间列
       aMap.put(DataTable.DEFAUL_TTIMECOLUMN, now);

       dataTable.genId(aMap);
       String tableName = dataTable.getSplitTableName(aMap);
       Document doc = new Document(aMap);

       if (dataTable.hasIdentify()) {
           mongoConnector.insertSingle(tableName, doc);
       } else {
           List list = tableToDatasMap.get(tableName);
           if (list == null) {
               list = new ArrayList(parsedDatas.size() / 2);
               tableToDatasMap.put(tableName, list);
           }
           list.add(doc);
       }
   }
   for (String key : tableToDatasMap.keySet()) {
       mongoConnector.insert(key, tableToDatasMap.get(key));
   }

聚合分析

public List> getExhibitionCount(Date start , Date end ) throws ParseException {
     List> map = new ArrayList>();
      //获取区间日期天数
     int days = differentDaysByMillisecond(start, end);
     for (int i = 0 ;i < days + 1 ; i++){
         Aggregation aggregation = null;
         aggregation = Aggregation.newAggregation(
                 Aggregation.group("templateCode","columnId","positionId").sum("duration").as("duration"),
                 Aggregation.project("templateCode","columnId","positionId","duration")
         );
         String tableName = this.getTableName(start);
         AggregationResults aggregate = mongoTemplate.aggregate(aggregation, tableName, HashMap.class);
         //key 日期  value 当天数据
         List mappedResults = aggregate.getMappedResults();
         System.out.print("处理后数据：" + mappedResults);
         for (Map adCountDtoMap:mappedResults) {
             String json = JsonUtils.obj2Str(adCountDtoMap);
             AdCountDto adCountDto = JsonUtils.str2Obj(json, AdCountDto.class);
             adCountDto.setDate(dateFormatCheck.format(new Date()));
             createIndex(COL_NAME);
             mongoTemplate.insert(adCountDto,COL_NAME);
         }
         start = dataPlus(start ,1);
     }
     return map;
 }