Fllink實時計算運用（九）Flink 大數據實戰案例二

1. 訂單支付狀態跟蹤統計（CEP運用）

• 功能

  實現對熱銷商品的統計， 統計週期爲一天， 每3秒刷新一次數據。

• 核心代碼

  主邏輯代碼實現：

  /**
       * 執行Flink任務處理
       * @throws Exception
       */
      private void executeFlinkTask() throws Exception {
  
          // 1. 建立運行環境
          StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
  
          // 2. 設置kafka服務鏈接信息
          Properties properties = new Properties();
          properties.setProperty("bootstrap.servers", "10.10.20.132:9092");
          properties.setProperty("group.id", "fink_group");
  
          // 3. 建立Kafka消費端
          FlinkKafkaConsumer kafkaProducer = new FlinkKafkaConsumer(
                  "orderPayment_binlog",                  // 目標 topic
                  new SimpleStringSchema(),   // 序列化 配置
                  properties);
  
          // 調試，從新從最先記錄消費
          kafkaProducer.setStartFromEarliest();     // 儘量從最先的記錄開始
          env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
          env.setParallelism(1);
  
          // 4. 讀取Kafka數據源
          DataStreamSource<String> socketStr = env.addSource(kafkaProducer);
  
          // 5. 數據過濾轉換處理
          DataStream<OrderPayment> orderPaymentDataStream = socketStr.filter(new FilterFunction<String>() {
              @Override
              public boolean filter(String value) throws Exception {
                  JsonObject jsonObject = GsonConvertUtil.getSingleton().getJsonObject(value);
                  String isDDL = jsonObject.get("isDdl").getAsString();
                  String type = jsonObject.get("type").getAsString();
                  // 過濾條件： 非DDL操做， 而且是新增的數據
                  return isDDL.equalsIgnoreCase("false") && "INSERT".equalsIgnoreCase(type);
              }
          }).flatMap(new FlatMapFunction<String, OrderPayment>() {
              @Override
              public void flatMap(String value, Collector<OrderPayment> out) throws Exception {
                  // 獲取JSON中的data數據
                  JsonArray dataArray = GsonConvertUtil.getSingleton().getJsonObject(value).getAsJsonArray("data");
                  // 將data數據轉換爲java對象
                  for(int i =0; i< dataArray.size(); i++) {
                      JsonObject jsonObject = dataArray.get(i).getAsJsonObject();
                      OrderPayment orderPayment = GsonConvertUtil.getSingleton().cvtJson2Obj(jsonObject, OrderPayment.class);
                      System.out.println("orderPayment => " + orderPayment);
                      out.collect(orderPayment);
                  }
              }
          })
          .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<OrderPayment>(Time.seconds(0)) {
              @Override
              public long extractTimestamp(OrderPayment element) {
                  return element.getUpdateTime();
              }
          })
          .keyBy(OrderPayment::getOrderId);
    
          // 6.經過CEP機制， 判斷支付成功的數據
          Pattern<OrderPayment, ?> pattern = Pattern.<OrderPayment>begin("begin")
                  .where(new SimpleCondition<OrderPayment>() {
                      @Override
                      public boolean filter(OrderPayment value) throws Exception {
                          return value.getStatus() == 0;
                      }
                  }).next("follow").where(new SimpleCondition<OrderPayment>() {
                      @Override
                      public boolean filter(OrderPayment value) throws Exception {
                          return value.getStatus() == 1;
                      }
                  }).within(Time.seconds(15)).times(1);
  
          PatternStream<OrderPayment> patternStream = CEP.pattern(orderPaymentDataStream, pattern);
          // 7.定義超時數據的TAG標記
          OutputTag orderExpired = new OutputTag<OrderPayment>("orderExpired"){};
          DataStream<OrderPaymentResult> selectResult = patternStream.select(orderExpired,
                  new OrderExpiredMatcher(), new OrderPayedMatcher());
          selectResult.print("payed");
  
          // 8. 建立Kafka消費端（訂單數據源）
          FlinkKafkaConsumer orderKafkaProducer = new FlinkKafkaConsumer(
                  "order_binlog",                  // 目標 topic
                  new SimpleStringSchema(),   // 序列化 配置
                  properties);
  
          orderKafkaProducer.setStartFromEarliest();     // 儘量從最先的記錄開始
          env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
          env.setParallelism(1);
  
          DataStreamSource<String> orderSource = env.addSource(orderKafkaProducer);
  
          // 9. 數據過濾轉換處理(訂單數據源）
          DataStream<Order> orderDataStream = orderSource.filter(new FilterFunction<String>() {
              @Override
              public boolean filter(String value) throws Exception {
                  JsonObject jsonObject = GsonConvertUtil.getSingleton().getJsonObject(value);
                  String isDDL = jsonObject.get("isDdl").getAsString();
                  String type = jsonObject.get("type").getAsString();
                  // 過濾條件： 非DDL操做， 而且是新增的數據
                  return isDDL.equalsIgnoreCase("false") && "INSERT".equalsIgnoreCase(type);
              }
          }).flatMap(new FlatMapFunction<String, Order>() {
              @Override
              public void flatMap(String value, Collector<Order> out) throws Exception {
                  // 獲取JSON中的data數據
                  JsonArray dataArray = GsonConvertUtil.getSingleton().getJsonObject(value).getAsJsonArray("data");
                  // 將data數據轉換爲java對象
                  for(int i =0; i< dataArray.size(); i++) {
                      JsonObject jsonObject = dataArray.get(i).getAsJsonObject();
                      Order order = GsonConvertUtil.getSingleton().cvtJson2Obj(jsonObject, Order.class);
                      System.out.println("order => " + order);
                      out.collect(order);
                  }
              }
          })
          .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<Order>(Time.seconds(0)) {
              @Override
              public long extractTimestamp(Order element) {
                  return element.getExecTime();
              }
          });
  
          // 10. 數據源關聯處理
          orderDataStream.keyBy(Order::getId).intervalJoin(selectResult.keyBy(OrderPaymentResult::getOrderId))
                  .between(Time.seconds(0), Time.seconds(15))
                  .process(new ProcessJoinFunction<Order, OrderPaymentResult, JoinOrderPayment>() {
                      @Override
                      public void processElement(Order left, OrderPaymentResult right, Context ctx, Collector<JoinOrderPayment> out) throws Exception {
                          JoinOrderPayment joinResult = JoinOrderPayment.build(left, right);
                          out.collect(joinResult);
                      }
                  })
                  .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<JoinOrderPayment>(Time.seconds(0)) {
                      @Override
                      public long extractTimestamp(JoinOrderPayment element) {
                          return element.getUpdateTime();
                      }
                  })
                  .keyBy(JoinOrderPayment::getGoodsId)
                  .timeWindow(Time.hours(24), Time.seconds(3))
                  .aggregate(new TotalAmount(), new AmountWindow())
                  .keyBy(HotOrder::getTimeWindow)
                  .process(new TopNHotOrder());
  
          // 11. 執行任務
          env.execute("job");
      }

商品金額累加器：

/**
     * 商品金額累加器
     */
    private static class TotalAmount implements AggregateFunction<JoinOrderPayment, JoinOrderPayment, JoinOrderPayment> {
        @Override
        public JoinOrderPayment createAccumulator() {
            JoinOrderPayment order = new JoinOrderPayment();
            order.setTotalAmount(0l);
            return order;
        }

        /**
         * 商品銷售總金額累加處理
         * @param value
         * @param accumulator
         * @return
         */
        @Override
        public JoinOrderPayment add(JoinOrderPayment value, JoinOrderPayment accumulator) {
            accumulator.setGoodsId(value.getGoodsId());
            accumulator.setGoodsName((value.getGoodsName()));
            accumulator.setStatus(value.getStatus());
            accumulator.setUpdateTime(value.getUpdateTime());
            accumulator.setTotalAmount(accumulator.getTotalAmount() + (value.getExecPrice() * value.getExecVolume()));
            return accumulator;
        }

        @Override
        public JoinOrderPayment getResult(JoinOrderPayment accumulator) {
            return accumulator;
        }

        @Override
        public JoinOrderPayment merge(JoinOrderPayment a, JoinOrderPayment b) {
            return null;
        }
    }

熱銷商品轉換處理：

/**
     * 熱銷商品， 時間窗口對象轉換處理
     */
    private static class AmountWindow implements WindowFunction<JoinOrderPayment, HotOrder, Long, TimeWindow> {

        @Override
        public void apply(Long goodsId, TimeWindow window, Iterable<JoinOrderPayment> input, Collector<HotOrder> out) throws Exception {
            JoinOrderPayment order = input.iterator().next();
            out.collect(new HotOrder(order.getGoodsId(), order.getGoodsName(), order.getTotalAmount(), window.getEnd()));
        }
    }

熱銷商品的統計排行實現：

/**
     * 熱銷商品的統計排行實現
     */
    private class TopNHotOrder extends KeyedProcessFunction<Long, HotOrder, String> {

        private ListState<HotOrder> orderState;

        @Override
        public void processElement(HotOrder value, Context ctx, Collector<String> out) throws Exception {
            // 將數據加入到狀態列表裏面
            orderState.add(value);
            // 註冊定時器
            ctx.timerService().registerEventTimeTimer(value.getTimeWindow());
        }

        @Override
        public void onTimer(long timestamp, OnTimerContext ctx, Collector<String> out) throws Exception {
            List<HotOrder> orderList = new ArrayList<>();
            for(HotOrder order : orderState.get()){
                orderList.add(order);
            }
            // 按照成交總金額， 倒序排列
            orderList.sort(Comparator.comparing(HotOrder::getTotalAmount).reversed());
            orderState.clear();
            // 將數據寫入至ES
            HotOrderRepository hotOrderRepository = (HotOrderRepository) ApplicationContextUtil.getBean("hotOrderRepository");
            StringBuffer strBuf = new StringBuffer();
            for(HotOrder order: orderList) {
                order.setId(order.getGoodsId());
                order.setCreateDate(new Date(order.getTimeWindow()));
                hotOrderRepository.save(order);
                strBuf.append(order).append("\n");
                System.out.println("result => " + order);
            }
            out.collect(strBuf.toString());
        }

        @Override
        public void open(Configuration parameters) throws Exception {
            super.open(parameters);
            orderState = getRuntimeContext().getListState(new ListStateDescriptor<HotOrder>("hot-order", HotOrder.class));

        }
    }

超時數據的匹配處理:

private class OrderExpiredMatcher implements PatternTimeoutFunction<OrderPayment, OrderPaymentResult> {
        @Override
        public OrderPaymentResult timeout(Map<String, List<OrderPayment>> map, long l) throws Exception {
            OrderPaymentResult result = new OrderPaymentResult();
            OrderPayment payment = map.get("begin").iterator().next();
            result.setOrderId(payment.getOrderId());
            result.setStatus(payment.getStatus());
            result.setUpdateTime(payment.getUpdateTime());
            result.setMessage("支付超時");
            return result;
        }
    }

支付成功的匹配處理:

private class OrderPayedMatcher implements PatternSelectFunction<OrderPayment, OrderPaymentResult> {

        @Override
        public OrderPaymentResult select(Map<String, List<OrderPayment>> map) throws Exception {
            OrderPaymentResult result = new OrderPaymentResult();
            OrderPayment payment = map.get("follow").iterator().next();
            result.setOrderId(payment.getOrderId());
            result.setStatus(payment.getStatus());
            result.setUpdateTime(payment.getUpdateTime());
            result.setMessage("支付成功");
            return result;
        }
    }

2. 商品UV統計（普通統計）

• 功能

  統計商品在一段時間內的UV（Unique Visitor），去重後的點擊量， 根據IP去重。

• 核心代碼

  主邏輯實現：

  public class ScreenUniqueVisitorProcessor {
  
      /**
       * 執行flink任務處理
       * @throws Exception
       */
      public void executeFlinkTask() throws Exception {
          // 1. 建立運行環境
          StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
          env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime);
          env.setParallelism(1);
  
          // 2. 讀取Socket數據源
  //        DataStreamSource<String> socketStr = env.socketTextStream("localhost", 9911, "\n");
          DataStreamSource<String> socketStr = env.readTextFile("./data/goods_access.log");
  
          // 3. 數據解析轉換處理
          socketStr.flatMap(new FlatMapFunction<String, GoodsAccessLog>() {
  
              @Override
              public void flatMap(String value, Collector<GoodsAccessLog> out) throws Exception {
                  // 獲取Json中的data數據
                  // 根據分隔符解析數據
                  String[] arrValue = value.split("\t");
                  System.out.println("receive msg => " + value);
                  // 將數據組裝爲對象
                  GoodsAccessLog log = new GoodsAccessLog();
                  for(int i=0; i<arrValue.length; i++) {
                      if(i == 0) {
                          log.setIp(arrValue[i]);
                      }else if( i== 1) {
                          log.setAccessTime(Long.valueOf(arrValue[i]));
                      }else if( i== 2) {
                          log.setEventType(arrValue[i]);
                      }else if( i== 3) {
                          log.setGoodsId(arrValue[i]);
                      }
                  }
                  out.collect(log);
              }
          })
          .filter(new FilterFunction<GoodsAccessLog>() {
              @Override
              public boolean filter(GoodsAccessLog value) throws Exception {
                  return value.getEventType().equals("view");
              }
          })
          .keyBy(GoodsAccessLog::getGoodsId)
          .timeWindow(Time.seconds(10))
          .process( new ProcessWindowFunction<GoodsAccessLog, Map<String, String> , String, TimeWindow>(){
              @Override
              public void process(String key, Context context, Iterable<GoodsAccessLog> elements, Collector<Map<String, String>> out) throws Exception {
                  Set<String> ipSet = new HashSet<>();
                  Map<String, String> goodsUV = new LinkedHashMap<>();
                  elements.forEach( log -> {
                      ipSet.add(log.getIp());
                  });
                  goodsUV.put(key , context.window().getEnd() + ":" + ipSet.size());
                  out.collect(goodsUV);
              }
          })
          .print("uv result").setParallelism(1);
  
          // 5. 執行任務
          env.execute("job");
      }
  
  }

熱銷商品的金額累加處理：

/**
 * 商品金額累加器
 */
private static class TotalAmount implements AggregateFunction<Order, Order, Order> {
    @Override
    public Order createAccumulator() {
        Order order = new Order();
        order.setTotalAmount(0l);
        return order;
    }

    /**
     * 累加統計商品銷售總金額
     * @param value
     * @param accumulator
     * @return
     */
    @Override
    public Order add(Order value, Order accumulator) {
        accumulator.setGoodsId(value.getGoodsId());
        accumulator.setGoodsName((value.getGoodsName()));
        accumulator.setTotalAmount(accumulator.getTotalAmount() + (value.getExecPrice() * value.getExecVolume()));
        return accumulator;
    }

    @Override
    public Order getResult(Order accumulator) {
        return accumulator;
    }

    @Override
    public Order merge(Order a, Order b) {
        return null;
    }
}

熱銷商品的數據轉換處理， 用於統計：

/**
 * 熱銷商品， 在時間窗口內， 對象數據的轉換處理
 */
private static class AmountWindow implements WindowFunction<Order, HotOrder, Long, TimeWindow> {

    @Override
    public void apply(Long goodsId, TimeWindow window, Iterable<Order> input, Collector<HotOrder> out) throws Exception {
        Order order = input.iterator().next();
        out.collect(new HotOrder(goodsId, order.getGoodsName(), order.getTotalAmount(), window.getEnd()));
    }
}

熱銷商品的統計排行處理邏輯：

/**
 * 熱銷商品的統計排行實現
 */
private class TopNHotOrder extends KeyedProcessFunction<Long, HotOrder, String> {

    private ListState<HotOrder> orderState;

    @Override
    public void processElement(HotOrder value, Context ctx, Collector<String> out) throws Exception {
        // 將數據加入到狀態列表裏面
        orderState.add(value);
        // 註冊定時器
        ctx.timerService().registerEventTimeTimer(value.getTimeWindow());
    }

    @Override
    public void onTimer(long timestamp, OnTimerContext ctx, Collector<String> out) throws Exception {
        List<HotOrder> orderList = new ArrayList<>();
        for(HotOrder order : orderState.get()){
            orderList.add(order);
        }
        // 按照成交總金額， 倒序排列
        orderList.sort(Comparator.comparing(HotOrder::getTotalAmount).reversed());
        orderState.clear();
        // 將數據寫入至ES
        HotOrderRepository hotOrderRepository = (HotOrderRepository) ApplicationContextUtil.getBean("hotOrderRepository");
        StringBuffer strBuf = new StringBuffer();
        for(HotOrder order: orderList) {
            order.setId(order.getGoodsId());
            order.setCreateDate(new Date(order.getTimeWindow()));
            hotOrderRepository.save(order);
            strBuf.append(order).append("\n");
            System.out.println("result => " + order);
        }
        out.collect(strBuf.toString());
    }

    @Override
    public void open(Configuration parameters) throws Exception {
        super.open(parameters);
        orderState = getRuntimeContext().getListState(new ListStateDescriptor<HotOrder>("hot-order", HotOrder.class));

    }
}

3. 商品UV統計（布隆過濾器）

• 功能

  功能： 統計商品在一段時間內的UV（採用布隆過濾器），去重後的點擊量， 根據IP去重。

• 核心代碼

  主邏輯代碼：

  /**
   * 執行flink任務處理
   * @throws Exception
   */
  public void executeFlinkTask() throws Exception {
      // 1. 建立運行環境
      StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
      env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
  
      // 2. 讀取Socket數據源
  //        DataStreamSource<String> socketStr = env.socketTextStream("localhost", 9911, "\n");
      DataStreamSource<String> socketStr = env.readTextFile("./data/goods_access.log");
      // 3. 數據解析轉換處理
      socketStr.flatMap(new FlatMapFunction<String, GoodsAccessLog>() {
  
          @Override
          public void flatMap(String value, Collector<GoodsAccessLog> out) throws Exception {
              // 獲取Json中的data數據
              // 根據分隔符解析數據
              String[] arrValue = value.split("\t");
              // 將數據組裝爲對象
              GoodsAccessLog log = new GoodsAccessLog();
              for(int i=0; i<arrValue.length; i++) {
                  if(i == 0) {
                      log.setIp(arrValue[i]);
                  }else if( i== 1) {
                      log.setAccessTime(Long.valueOf(arrValue[i]));
                  }else if( i== 2) {
                      log.setEventType(arrValue[i]);
                  }else if( i== 3) {
                      log.setGoodsId(arrValue[i]);
                  }
              }
              out.collect(log);
          }
      })
      .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<GoodsAccessLog>(Time.seconds(0)) {
          @Override
          public long extractTimestamp(GoodsAccessLog element) {
              return element.getAccessTime();
          }
      })
      .filter(new FilterFunction<GoodsAccessLog>() {
          @Override
          public boolean filter(GoodsAccessLog value) throws Exception {
              return value.getEventType().equals("view");
          }
      })
      .keyBy(GoodsAccessLog::getGoodsId)
      .timeWindow(Time.minutes(30))
      .trigger(new CustomWindowTrigger())
      .process(new CustomUVBloom())
      .keyBy(0)
      .timeWindow(Time.seconds(3))
      .max(1)
      .print("uv result => ").setParallelism(1);
      // 5. 執行任務
      env.execute("job");
  }

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