[大数据]Flink并行运行情况下watermark的传递机制

今天学习了watermark传递机制，弄清楚了在多并行度情况下watermark的传递机制，特此备忘
此处使用的案例是参考了尚硅谷武老师flink教程中的案例，在此表示感谢

案例源代码

package com.atguigu.apitest.window;

import com.atguigu.apitest.beans.SensorReading;
import org.apache.commons.collections.IteratorUtils;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;

import java.time.Duration;

/**
 * @author mars
 * @version 1.0
 * Create by 2022/1/8 22:57
 */

public class WindowTest3_EventTimeWindow {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

        DataStreamSource<String> dataStreamSource = env.socketTextStream("hadoop1", 9999);
        SingleOutputStreamOperator<SensorReading> map = dataStreamSource.map(new MapFunction<String, SensorReading>() {
            @Override
            public SensorReading map(String value) throws Exception {
                String[] fields = value.split(",");
                return new SensorReading(fields[0], new Long(fields[1]), new Double(fields[2]));
            }
        });

        SingleOutputStreamOperator<SensorReading> assignTimestampsAndWatermarks = map.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<SensorReading>(Time.seconds(2)) {
            @Override
            public long extractTimestamp(SensorReading element) {
                return element.getTimestamp() * 1000L;
            }
        });

        //基于事件时间的开窗聚合
        SingleOutputStreamOperator<SensorReading> cntStream = assignTimestampsAndWatermarks
                        .keyBy("id")
                        .window(TumblingEventTimeWindows.of(Time.seconds(10)))
                        .minBy("temperature");
        cntStream.print("cntStream");
        env.execute();
    }
}

pojo类

package com.atguigu.apitest.beans;

/**
 * @author mars
 * @version 1.0
 * Create by 2021/12/30 20:38
 */

/**
 * 传感器温度读数的数据类型
 */
public class SensorReading {

    private String id;
    private Long timestamp;
    private Double temperature;

    public SensorReading() {
    }

    public SensorReading(String id, Long timestamp, Double temperature) {
        this.id = id;
        this.timestamp = timestamp;
        this.temperature = temperature;
    }

    public String getId() {
        return id;
    }

    public void setId(String id) {
        this.id = id;
    }

    public Long getTimestamp() {
        return timestamp;
    }

    public void setTimestamp(Long timestamp) {
        this.timestamp = timestamp;
    }

    public Double getTemperature() {
        return temperature;
    }

    public void setTemperature(Double temperature) {
        this.temperature = temperature;
    }

    @Override
    public String toString() {
        return "SensorReading{" +
                "id='" + id + '\'' +
                ", timestamp=" + timestamp +
                ", temperature=" + temperature +
                '}';
    }
}

测试数据

sensor_1,1547718199,35.8
sensor_6,1547718201,15.4
sensor_7,1547718202,6.7
sensor_10,1547718205,38.1
sensor_1,1547718207,36.3
sensor_1,1547718209,32.8
sensor_1,1547718212,37.1

情景1：设置并行度为1时

情景2：设置并行度为4时

补充：如何知道上图中的划分的window起止点为[190,200)?

在使用事件时间语义的窗口函数中

package org.apache.flink.streaming.api.windowing.windows;

函数如下

	/**
	 * Method to get the window start for a timestamp.
	 *
	 * @param timestamp epoch millisecond to get the window start.
	 * @param offset The offset which window start would be shifted by.
	 * @param windowSize The size of the generated windows.
	 * @return window start
	 */
	public static long getWindowStartWithOffset(long timestamp, long offset, long windowSize) {
		return timestamp - (timestamp - offset + windowSize) % windowSize;
	}```
因此
1547718199 的起始点为1547718190