flink-watermark

一.背景

     当我们统计用户点击的时候，有时候会因为各种情况数据延迟，我们需要一个允许最大的延迟范围进行统计。这里的延迟统计分为两种：

       模拟初始数据：早上10:00 11.10 用户点击了一次，但是延迟到10:00 11.15 才发送过来，允许最大延迟5秒， 5秒窗口统计。我们希望还是能统计到

       

二.基本代码

@Data
public class UserTimeInfo implements Serializable {

    private String userId;
    /** 实际时间-偏移量 偏移后的时间*/
    private Timestamp pTime;
    public UserTimeInfo() {
    }
    public UserTimeInfo(String userId, Timestamp pTime) {
        this.userId = userId;
        this.pTime = pTime;
    }
}

 

public class UserTimeSource implements SourceFunction {


    /**
     * 为了id 统计方便，我们只留一个id
     */
    static String[] userIds = {"id->"};
    Random random = new Random();
    /**
     * 模拟发送20次
     */
    int times = 20;

    @Override
    public void run(SourceContext sc) throws Exception {
        while (true) {
            TimeUnit.SECONDS.sleep(1);
            int m = (int) (System.currentTimeMillis() % userIds.length);
            // 随机延迟几秒
            int defTime = random.nextInt(5);
            // 发送时间
            DateTime dateTime = new DateTime();
            // 计算延迟后的时间，并且打印时间
            DateTime dateTimePrint = dateTime.plusSeconds(-defTime);
            System.out.println("实际时间:" + print(dateTime) + ",延迟:" + defTime + ":-->" + print(dateTimePrint));
            // 发送延迟时间
            dateTime = dateTime.plusSeconds(-defTime);
            sc.collect(new UserTimeInfo(userIds[m], new Timestamp(dateTime.getMillis())));
            // 只持续固定时间方便观察
            if (--times == 0) {
                break;
            }
        }
    }

    @Override
    public void cancel() {
        System.out.println("cancel to do ...");
    }

    private static String print(DateTime dateTime) {
        return dateTime.toString("yyyy-MM-dd hh:mm:ss");
    }
}

 

三.定义我们的两种watermark

    a. 基于系统时间 

   

/**
 * 这里逻辑，模拟按系统时间进行统计
 * 所有数据和系统自身时间有关
 */
public class UserTimeWaterMarkBySystem implements AssignerWithPeriodicWatermarks {
    /**
     * 默认允许 5秒延迟
     */
    long maxDelayTime = 5000;
    /**
     * 该时间由于基于系统时间来做，
     * 如果10:00 11:10 秒用户点击的数据，然后延迟，实际收到的时间是10.00 11:15  
     * a.根据系统时间 想减，小于5秒就会统计到
     * b.注意，如果程序挂了，12点重启消费这个数据，就统计不到了
     * @return
     */
    @Nullable
    @Override
    public Watermark getCurrentWatermark() {
        return new Watermark(System.currentTimeMillis() - maxDelayTime);
    }
    @Override
    public long extractTimestamp(UserTimeInfo element, long previousElementTimestamp) {
        long timestamp = element.getPTime().getTime();
        return timestamp;
    }
}

 

  b.根据数据自生时间进行做延迟判断

   

public class UserTimeWaterMarkByRowTime implements AssignerWithPeriodicWatermarks {
    /**
     * 默认允许 5秒延迟
     */
    long maxDelayTime = 5000;

    /**
     * 该时间由于基于数据时间来做，
     * 如果10:00 11:10 秒用户点击的数据，然后延迟，实际收到的时间是10.00 11:15
     * a.根据系统时间 想减，小于5秒就会统计到
     * b.只要消息 时间延迟小于5 就能被统计。 
     * 这种对点击事件来说，更符合要求
     * @return
     */
    private long currentMaxTimestamp;
    
    @Nullable
    @Override
    public Watermark getCurrentWatermark() {
        return new Watermark(currentMaxTimestamp - maxDelayTime);
    }
    @Override
    public long extractTimestamp(UserTimeInfo element, long previousElementTimestamp) {
        long timestamp = element.getPTime().getTime();
        currentMaxTimestamp = Math.max(timestamp, currentMaxTimestamp);
        return timestamp;
    }
}

 

四.source 类，和以前一样

  

public class UserTimeWaterMarkApp {
    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        DataStream userInfoDataStream = env.addSource(new UserTimeSource());
        //  UserTimeWaterMarkByRowTime 这个时间可以替换
        DataStream timedData = userInfoDataStream.assignTimestampsAndWatermarks(new UserTimeWaterMarkByRowTime());
        StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);
        tableEnv.registerDataStream("test", timedData, "userId,pTime.rowtime");
        Table result = tableEnv.sqlQuery("SELECT userId,TUMBLE_END(pTime, INTERVAL '5' SECOND) as pTime,count(1) as cnt FROM  test" +
                " GROUP BY TUMBLE(pTime, INTERVAL '5' SECOND),userId ");
        // deal with (Tuple2 value) -> out.collect(row)
        SingleOutputStreamOperator allClick = tableEnv.toRetractStream(result, Row.class)
                .flatMap((Tuple2 value, Collector out) -> {
                    out.collect(value.f1);
                }).returns(Row.class);
        // add sink or print
        allClick.print();
        env.execute("test");
    }

}

 

 

public class UserTimeWaterMarkApp {
    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        DataStream userInfoDataStream = env.addSource(new UserTimeSource());

        DataStream timedData = userInfoDataStream.assignTimestampsAndWatermarks(new UserTimeWaterMarkByRowTime());
        StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);
        tableEnv.registerDataStream("test", timedData, "userId,pTime.rowtime");
        Table result = tableEnv.sqlQuery("SELECT userId,TUMBLE_END(pTime, INTERVAL '5' SECOND) as pTime,count(1) as cnt FROM  test" +
                " GROUP BY TUMBLE(pTime, INTERVAL '5' SECOND),userId ");
        // deal with (Tuple2 value) -> out.collect(row)
        SingleOutputStreamOperator allClick = tableEnv.toRetractStream(result, Row.class)
                .flatMap((Tuple2 value, Collector out) -> {
                    out.collect(value.f1);
                }).returns(Row.class);
        // add sink or print
        allClick.print();
        env.execute("test");
    }

 

小结：

   1.这个是基于flink 1.7 跑的

   2.代码比较简单，也好理解，有问题直接私信我 

 

 

 

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