关于java:Fllink实时计算运用九Flink-大数据实战案例二

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1. 订单领取状态跟踪统计(CEP 使用)

  • 性能

    实现对热销商品的统计,统计周期为一天,每 3 秒刷新一次数据。

  • 外围代码

    主逻辑代码实现:

      
        /**
         * 执行 Flink 工作解决
         * @throws Exception
         */
        private void executeFlinkTask() throws Exception {
    
            // 1. 创立运行环境
            StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    
            // 2. 设置 kafka 服务连贯信息
            Properties properties = new Properties();
            properties.setProperty("bootstrap.servers", "10.10.20.132:9092");
            properties.setProperty("group.id", "fink_group");
    
            // 3. 创立 Kafka 生产端
            FlinkKafkaConsumer kafkaProducer = new FlinkKafkaConsumer(
                    "orderPayment_binlog",                  // 指标 topic
                    new SimpleStringSchema(),   // 序列化 配置
                    properties);
    
            // 调试,从新从最早记录生产
            kafkaProducer.setStartFromEarliest();     // 尽可能从最早的记录开始
            env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
            env.setParallelism(1);
    
            // 4. 读取 Kafka 数据源
            DataStreamSource<String> socketStr = env.addSource(kafkaProducer);
    
            // 5. 数据过滤转换解决
            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();
                    // 过滤条件:非 DDL 操作,并且是新增的数据
                    return isDDL.equalsIgnoreCase("false") && "INSERT".equalsIgnoreCase(type);
                }
            }).flatMap(new FlatMapFunction<String, OrderPayment>() {
                @Override
                public void flatMap(String value, Collector<OrderPayment> out) throws Exception {
                    // 获取 JSON 中的 data 数据
                    JsonArray dataArray = GsonConvertUtil.getSingleton().getJsonObject(value).getAsJsonArray("data");
                    // 将 data 数据转换为 java 对象
                    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. 通过 CEP 机制,判断领取胜利的数据
            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. 定义超时数据的 TAG 标记
            OutputTag orderExpired = new OutputTag<OrderPayment>("orderExpired"){};
            DataStream<OrderPaymentResult> selectResult = patternStream.select(orderExpired,
                    new OrderExpiredMatcher(), new OrderPayedMatcher());
            selectResult.print("payed");
    
            // 8. 创立 Kafka 生产端(订单数据源)FlinkKafkaConsumer orderKafkaProducer = new FlinkKafkaConsumer(
                    "order_binlog",                  // 指标 topic
                    new SimpleStringSchema(),   // 序列化 配置
                    properties);
    
            orderKafkaProducer.setStartFromEarliest();     // 尽可能从最早的记录开始
            env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
            env.setParallelism(1);
    
            DataStreamSource<String> orderSource = env.addSource(orderKafkaProducer);
    
            // 9. 数据过滤转换解决 ( 订单数据源)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();
                    // 过滤条件:非 DDL 操作,并且是新增的数据
                    return isDDL.equalsIgnoreCase("false") && "INSERT".equalsIgnoreCase(type);
                }
            }).flatMap(new FlatMapFunction<String, Order>() {
                @Override
                public void flatMap(String value, Collector<Order> out) throws Exception {
                    // 获取 JSON 中的 data 数据
                    JsonArray dataArray = GsonConvertUtil.getSingleton().getJsonObject(value).getAsJsonArray("data");
                    // 将 data 数据转换为 java 对象
                    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. 数据源关联解决
            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. 执行工作
            env.execute("job");
        }

商品金额累加器:

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

        /**
         * 商品销售总金额累加解决
         * @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;}
    }

热销商品转换解决:

    /**
     * 热销商品,工夫窗口对象转换解决
     */
    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()));
        }
    }

热销商品的统计排行实现:

    /**
     * 热销商品的统计排行实现
     */
    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 {
            // 将数据退出到状态列表外面
            orderState.add(value);
            // 注册定时器
            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);
            }
            // 依照成交总金额,倒序排列
            orderList.sort(Comparator.comparing(HotOrder::getTotalAmount).reversed());
            orderState.clear();
            // 将数据写入至 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));

        }
    }

超时数据的匹配解决:

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("领取超时");
            return result;
        }
    }

领取胜利的匹配解决:

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("领取胜利");
            return result;
        }
    }

2. 商品 UV 统计(一般统计)

  • 性能

    统计商品在一段时间内的 UV(Unique Visitor),去重后的点击量,依据 IP 去重。

  • 外围代码

    主逻辑实现:

    public class ScreenUniqueVisitorProcessor {
    
        /**
         * 执行 flink 工作解决
         * @throws Exception
         */
        public void executeFlinkTask() throws Exception {
            // 1. 创立运行环境
            StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
            env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime);
            env.setParallelism(1);
    
            // 2. 读取 Socket 数据源
    //        DataStreamSource<String> socketStr = env.socketTextStream("localhost", 9911, "\n");
            DataStreamSource<String> socketStr = env.readTextFile("./data/goods_access.log");
    
            // 3. 数据解析转换解决
            socketStr.flatMap(new FlatMapFunction<String, GoodsAccessLog>() {
    
                @Override
                public void flatMap(String value, Collector<GoodsAccessLog> out) throws Exception {
                    // 获取 Json 中的 data 数据
                    // 依据分隔符解析数据
                    String[] arrValue = value.split("\t");
                    System.out.println("receive msg =>" + value);
                    // 将数据组装为对象
                    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. 执行工作
            env.execute("job");
        }
    
    }

热销商品的金额累加解决:

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

    /**
     * 累加统计商品销售总金额
     * @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;}
}

热销商品的数据转换解决,用于统计:

/**
 * 热销商品,在工夫窗口内,对象数据的转换解决
 */
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()));
    }
}

热销商品的统计排行解决逻辑:

/**
 * 热销商品的统计排行实现
 */
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 {
        // 将数据退出到状态列表外面
        orderState.add(value);
        // 注册定时器
        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);
        }
        // 依照成交总金额,倒序排列
        orderList.sort(Comparator.comparing(HotOrder::getTotalAmount).reversed());
        orderState.clear();
        // 将数据写入至 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 统计(布隆过滤器)

  • 性能

    性能:统计商品在一段时间内的 UV(采纳布隆过滤器),去重后的点击量,依据 IP 去重。

  • 外围代码

    主逻辑代码:

    /**
     * 执行 flink 工作解决
     * @throws Exception
     */
    public void executeFlinkTask() throws Exception {
        // 1. 创立运行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
    
        // 2. 读取 Socket 数据源
    //        DataStreamSource<String> socketStr = env.socketTextStream("localhost", 9911, "\n");
        DataStreamSource<String> socketStr = env.readTextFile("./data/goods_access.log");
        // 3. 数据解析转换解决
        socketStr.flatMap(new FlatMapFunction<String, GoodsAccessLog>() {
    
            @Override
            public void flatMap(String value, Collector<GoodsAccessLog> out) throws Exception {
                // 获取 Json 中的 data 数据
                // 依据分隔符解析数据
                String[] arrValue = value.split("\t");
                // 将数据组装为对象
                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. 执行工作
        env.execute("job");
    }

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