Flink-08 Flink Java 3分钟上手滑动窗口 SlidingWindow 时间驱动事件驱动 TimeWindow CountWindow GlobalWindow 原创

代码仓库

会同步代码到 GitHub
https://github.com/turbo-duck/flink-demo
在这里插入图片描述

上节进度

上节修改了数据源从 Socket 到 kafka。此外，完成了 滚动窗口-事件驱动。

核心代码

每三个数据(key相同数据)触发一次事件

WindowedStream<Tuple2<String, Integer>, String, GlobalWindow> countWindow = keyedStream.countWindow(3);
        countWindow.apply(new MyCountWindowFunction()).print();

运行效果如下图
请添加图片描述

滑动窗口

请添加图片描述

什么是滑动窗口

Flink 的滑动窗口（Sliding Window）是一种在流处理应用中使用的窗口类型，用于对连续流数据进行分割和处理。
滑动窗口相对于滚动窗口（Tumbling Window）来说更灵活，因为它允许窗口在时间上重叠，从而可以更加精细地分析流数据。

滑动窗口按照固定的时间间隔（滑动步长）在数据流上滑动，并生成多个窗口。
这些窗口可以重叠，因此每条数据可能会被分配到多个窗口中。每个窗口都会独立地进行计算和聚合操作。

|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|
  0    5    10   15   20   25   30   35   40   45   50   55   60  (时间轴)

窗口 1：从 0 分钟到 10 分钟
窗口 2：从 5 分钟到 15 分钟
窗口 3：从 10 分钟到 20 分钟
窗口 4：从 15 分钟到 25 分钟
窗口 5：从 20 分钟到 30 分钟
...

时间驱动

StartApp

package icu.wzk.demo07;

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.windows.GlobalWindow;

import java.text.SimpleDateFormat;
import java.util.Random;

/**
 * 滑动窗口 SlidingWindow
 * 窗口长度固定 可以有重叠
 * 基于时间驱动、基于事件驱动
 * @author wzk
 * @date 10:51 2024/6/22
**/
public class SlidingWindow {

    private static final Random RANDOM = new Random();

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        DataStreamSource<String> dataStreamSource = env.socketTextStream("0.0.0.0", 9999);
        SingleOutputStreamOperator<Tuple2<String, Integer>> mapStream = dataStreamSource.map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String value) throws Exception {
                SimpleDateFormat format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss.SSS");
                long timeMillis = System.currentTimeMillis();
                int random = RANDOM.nextInt(10);
                System.err.println("value : " + value + " random : " + random + " timestamp : " + timeMillis + "|" + format.format(timeMillis));
                return new Tuple2<>(value, random);
            }
        });
        KeyedStream<Tuple2<String, Integer>, String> keyedStream = mapStream
                .keyBy(new KeySelector<Tuple2<String, Integer>, String>() {
                    @Override
                    public String getKey(Tuple2<String, Integer> value) throws Exception {
                        return value.f0;
                    }
                });

        // ==================== 时间驱动 ============================
        // 基于时间驱动，每隔5s计算一下最近10s的数据
        WindowedStream<Tuple2<String, Integer>, Tuple, TimeWindow> timeWindow = keyedStream.timeWindow(Time.seconds(10), Time.seconds(5));
        timeWindow.sum(1).print();
        timeWindow.apply(new MyTimeWindowFunction()).print();
        env.execute();
    }

}

MyTimeWindowFunction

package icu.wzk.demo06;

import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;

import java.text.SimpleDateFormat;


/**
 * 基于时间驱动 TimeWindow
 * @author wzk
 * @date 10:26 2024/6/22
**/
public class MyTimeWindowFunction implements WindowFunction<Tuple2<String,Integer>, String, String, TimeWindow> {

    @Override
    public void apply(String s, TimeWindow window, Iterable<Tuple2<String, Integer>> input, Collector<String> out) throws Exception {
        SimpleDateFormat format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss.SSS");

        int sum = 0;

        for(Tuple2<String,Integer> tuple2 : input){
            sum +=tuple2.f1;
        }

        long start = window.getStart();
        long end = window.getEnd();

        out.collect("key:" + s + " value: " + sum + "| window_start :"
                + format.format(start) + "  window_end :" + format.format(end)
        );
    }
}

事件驱动

StartApp

package icu.wzk.demo07;

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.windows.GlobalWindow;

import java.text.SimpleDateFormat;
import java.util.Random;

/**
 * 滑动窗口 SlidingWindow
 * 窗口长度固定 可以有重叠
 * 基于时间驱动、基于事件驱动
 * @author wzk
 * @date 10:51 2024/6/22
**/
public class SlidingWindow {

    private static final Random RANDOM = new Random();

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        DataStreamSource<String> dataStreamSource = env.socketTextStream("0.0.0.0", 9999);
        SingleOutputStreamOperator<Tuple2<String, Integer>> mapStream = dataStreamSource.map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String value) throws Exception {
                SimpleDateFormat format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss.SSS");
                long timeMillis = System.currentTimeMillis();
                int random = RANDOM.nextInt(10);
                System.err.println("value : " + value + " random : " + random + " timestamp : " + timeMillis + "|" + format.format(timeMillis));
                return new Tuple2<>(value, random);
            }
        });
        KeyedStream<Tuple2<String, Integer>, String> keyedStream = mapStream
                .keyBy(new KeySelector<Tuple2<String, Integer>, String>() {
                    @Override
                    public String getKey(Tuple2<String, Integer> value) throws Exception {
                        return value.f0;
                    }
                });
        // =================== 事件驱动 =============================
        //基于事件驱动，每隔2个事件，触发一次计算，本次窗口的大小为3，代表窗口里的每种事件最多为3个
        WindowedStream<Tuple2<String, Integer>, String, GlobalWindow> countWindow = keyedStream
                .countWindow(3, 2);
        countWindow.sum(1).print();
        countWindow.apply(new MyCountWindowFunction()).print();
        env.execute();
    }
}

MyCountWindowFunction

package icu.wzk.demo06;


import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.windowing.windows.GlobalWindow;
import org.apache.flink.util.Collector;

import java.text.SimpleDateFormat;


/**
 * 基于事件驱动 GlobalWindow
 * @author wzk
 * @date 10:27 2024/6/22
**/
public class MyCountWindowFunction implements WindowFunction<Tuple2<String, Integer>, String, String, GlobalWindow> {

    @Override
    public void apply(String s, GlobalWindow window, Iterable<Tuple2<String, Integer>> input, Collector<String> out) throws Exception {
        SimpleDateFormat format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss.SSS");
        int sum = 0;
        for (Tuple2<String, Integer> tuple2 : input){
            sum += tuple2.f1;
        }
        // 无用的时间戳，默认值为： Long.MAX_VALUE,因为基于事件计数的情况下，不关心时间。
        long maxTimestamp = window.maxTimestamp();
        out.collect("key:" + s + " value: " + sum + "| maxTimeStamp :"
                + maxTimestamp + "," + format.format(maxTimestamp)
        );
    }
}