实时数仓使用Flink识别新老客户

向右看齐 2022-11-09 14:26 216阅读 0赞

# 识别新老客户 #

1.  识别新老客户  
    本身客户端业务有新老用户的标识，但是不够准确，需要用实时计算再次确认(不涉及业务操作，只是单纯的做个状态确认)。
2.  数据拆分
3.  不同数据写入Kafka不同的Topic中

## 1. 封装kafka工具类 ##

public class MyKafkaUtil { 
    
        static Properties props = new Properties();
    
        static { 
            props.setProperty("bootstrap.servers","hadoop162:9092,hadoop163:9092");
            props.setProperty("auto.offset.reset","latest");
        }
    
    
        public  static  FlinkKafkaConsumer<String> getKafkaSource(String groupId, String topic){ 
            props.setProperty("group.id",groupId);
            return new FlinkKafkaConsumer<>(topic,new SimpleStringSchema(),props);
        }
    
        public static SinkFunction<String> getFlinkKafkaSink(String topic) { 
            //return new FlinkKafkaProducer<String>("hadoop162:9092",topic,new SimpleStringSchema());
    
            Properties props = new Properties();
            props.setProperty("bootstrap.servers","hadoop162:9092,hadoop163:9092");
            props.setProperty("transaction.timeout.ms", 1000 * 60 * 15 + "");
    
            return   new FlinkKafkaProducer<String>(topic,
                    new KafkaSerializationSchema<String>(){ 
                        @Override
                        public ProducerRecord<byte[], byte[]> serialize(String s, @Nullable Long aLong) { 
                            return new ProducerRecord<>(topic,null,s.getBytes());
                        }
                    },
                    props,
                    FlinkKafkaProducer.Semantic.EXACTLY_ONCE);
        }
    }

## 2. 封装消费Kafka数据的BaseApp类 ##

每次消费kafka数据总会有很多的模板代码, BaseApp把一些模板进行封装, 该类的子类只需要实现相应的业务逻辑即可

public abstract class BaseApp { 
        /** * 子类在此抽象方法中完成自己的业务逻辑 * * @param env 执行环境 * @param sourceStream 从Kafka直接获取得到的流 */
        protected abstract void run(StreamExecutionEnvironment env,
                                    DataStreamSource<String> sourceStream);
    
        /** * 做初始化相关工作 * * @param defaultParallelism 默认并行度 * @param groupId 消费者组 * @param topic 消费的topic */
        public void init(int defaultParallelism, String groupId, String topic) { 
            System.setProperty("HADOOP_USER_NAME", "atguigu");
            StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
            env.setParallelism(defaultParallelism);
            // 设置CK相关的参数
            // 1. 设置精准一次性保证（默认） 每5000ms开始一次checkpoint
            env.enableCheckpointing(5000, CheckpointingMode.EXACTLY_ONCE);
            // 2. Checkpoint必须在一分钟内完成，否则就会被抛弃
            env.getCheckpointConfig().setCheckpointTimeout(60000);
            // 3.开启在 job 中止后仍然保留的 externalized checkpoints
            env
                .getCheckpointConfig()
                .enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
            // 4. 设置状态后端
            env.setStateBackend(new FsStateBackend("hdfs://hadoop162:8020/gmall2021/flink/checkpoint"));
    
            DataStreamSource<String> sourceStream = env.addSource(MyKafkaUtil.getKafkaSource(groupId, topic));
    
            run(env, sourceStream);
            try { 
                env.execute();
            } catch (Exception e) { 
                e.printStackTrace();
            }
        }
    }

## 3. DWDLogApp具体实现 ##

识别新老访客

实现思路:

1.  考虑数据的乱序, 使用event-time语义
2.  按照mid分组
3.  添加5s的滚动窗口
4.  使用状态记录首次访问的时间戳
5.  如果状态为空, 则此窗口内的最小时间戳的事件为首次访问, 其他均为非首次访问
6.  如果状态不为空, 则此窗口内所有的事件均为非首次访问

public class DWDLogApp  extends BaseAPP{ 
    
        public static void main(String[] args) { 
            new DWDLogApp().init(20000,2,"DWDLogApp","DWDLogApp","ods_log");
        }
    
        @Override
        public void run(StreamExecutionEnvironment env,
                        DataStreamSource<String> sourceStream) { 
            //1.区分新老客户
            SingleOutputStreamOperator<JSONObject> validateFlatDS = distinguishNewOrOld(sourceStream);
            //validateFlatDS.print();
    
            //2.分流 启动日志在主流，其他日志在侧输出流
            Tuple3<SingleOutputStreamOperator<JSONObject>, DataStream<JSONObject>, DataStream<JSONObject>> streams = splitStram(validateFlatDS);
    
            //3.分流后的数据写入到DWD层（kafka）
            sendToKafka(streams);
    
        }

## 4. 识别新老用户 ##

//1.区分新老客户
        private SingleOutputStreamOperator<JSONObject> distinguishNewOrOld(DataStreamSource<String> sourceStream) { 
            SingleOutputStreamOperator<JSONObject> result = sourceStream
                    .map(JSON::parseObject)
                    .assignTimestampsAndWatermarks(
                            WatermarkStrategy
                                    .<JSONObject>forBoundedOutOfOrderness(Duration.ofSeconds(1))
                                    .withTimestampAssigner((obj, ts) -> obj.getLong("ts")))
                    .keyBy(line -> line.getJSONObject("common").getString("mid"))
                    .window(TumblingEventTimeWindows.of(Time.seconds(5)))
                    .process(new ProcessWindowFunction<JSONObject, JSONObject, String, TimeWindow>() { 
    
                        private ValueState<Long> firstVistedState;
    
                        @Override
                        public void open(Configuration parameters) throws Exception { 
                            firstVistedState = getRuntimeContext().getState(new ValueStateDescriptor<Long>("firstVistedState", Long.class));
                        }
    
                        @Override
                        public void process(String key,
                                            Context context,
                                            Iterable<JSONObject> iterable,
                                            Collector<JSONObject> out) throws Exception { 
                            //每条数据判断是否为新老用户
                            if (firstVistedState.value() == null) { 
                                System.out.println("这是第一次登陆");
                                //1.mid第一次登陆的窗口，按时间戳排序，把第一个登陆标记为新用户，其他都是旧用户
                                ArrayList<JSONObject> list = new ArrayList<>();
    
                                for (JSONObject object : iterable) { 
                                    list.add(object);
                                }
    
                                list.sort(Comparator.comparing(o -> o.getLong("ts")));
    
                                for (int i = 0; i < list.size(); i++) { 
                                    if (i == 0) { 
                                        list.get(i).getJSONObject("common").put("is_new", "1");
                                        firstVistedState.update(list.get(i).getLong("ts"));
                                    } else { 
                                        list.get(i).getJSONObject("common").put("is_new", "0");
                                    }
    
                                    out.collect(list.get(i));
                                }
    
                            } else { 
                                //不是这个用户的第一个窗口，所有访问都是老用户访问
                                for (JSONObject object : iterable) { 
                                    object.getJSONObject("common").put("is_new", "0");
                                    out.collect(object);
                                }
                            }
                        }
                    });
            return result;
        }
    }

## 5. 数据分流 ##

根据日志数据内容,将日志数据分为3类: 页面日志、启动日志和曝光日志。  
页面日志输出到主流,启动日志输出到启动侧输出流曝光日志输出到曝光日志侧输出流

//2.分流 启动日志在主流，其他日志在侧输出流
        private Tuple3<SingleOutputStreamOperator<JSONObject>, DataStream<JSONObject>, DataStream<JSONObject>> splitStram(SingleOutputStreamOperator<JSONObject> validateFlatDS) { 
    
            OutputTag<JSONObject> pageTag = new OutputTag<JSONObject>("page"){ };
            OutputTag<JSONObject> displayTag = new OutputTag<JSONObject>("display"){ };
    
            SingleOutputStreamOperator<JSONObject> startStream = validateFlatDS
                    .process(new ProcessFunction<JSONObject, JSONObject>() { 
                        @Override
                        public void processElement(JSONObject value,
                                                   Context context,
                                                   Collector<JSONObject> out) throws Exception { 
    
                            JSONObject start = value.getJSONObject("start");
                            if (start != null) { 
                                //这条日志是启动日志
                                out.collect(value);
                            } else { 
                                JSONObject page = value.getJSONObject("page");
                                if (page != null) { 
                                    context.output(pageTag, value);
                                }
    
                                JSONArray displays = value.getJSONArray("displays");
                                if (displays != null && displays.size() > 0) { 
                                    for (int i = 0; i < displays.size(); i++) { 
                                        JSONObject displayObject = displays.getJSONObject(i);
                                        String page_id = value.getJSONObject("page").getString("page_id");
    
                                        displayObject.put("page_id", page_id);
                                        displayObject.put("ts", value.getLong("ts"));
    
                                        context.output(displayTag, displayObject);
                                    }
                                }
                            }
                        }
                    });
         return Tuple3.of(startStream,
                    startStream.getSideOutput(pageTag),
                    startStream.getSideOutput(displayTag));
        }

## 6. 不同流写入到Kafka不同Topic ##

private void sendToKafka(Tuple3<SingleOutputStreamOperator<JSONObject>, DataStream<JSONObject>, DataStream<JSONObject>> streams) { 
            streams.f0.map(line->JSON.toJSONString(line)).addSink(MyKafkaUtil.getFlinkKafkaSink("dwd_start_log")).setParallelism(1);
            streams.f1.map(line->JSON.toJSONString(line)).addSink(MyKafkaUtil.getFlinkKafkaSink("dwd_page_log")).setParallelism(1);
            streams.f2.map(line->JSON.toJSONString(line)).addSink(MyKafkaUtil.getFlinkKafkaSink("dwd_display_log")).setParallelism(1);
        }