Flink-06 Flink Java 3分钟上手 滚动窗口 时间驱动 Kafka TumblingWindow TimeWindowFunction TumblingProcessing 原创

代码仓库

会同步代码到 GitHub
https://github.com/turbo-duck/flink-demo

上节进度

上一节，已经实现了 Flink + Kafka + Redis 的相关配置和代码。

Kakfa Redis In Docker

KafkaProducer

StartApp

这里没有数据是正常的，因为都 写入到 Redis 了，并没有进行计算。

Redis数据

滚动窗口

Flink 中的滚动窗口（Tumbling Window）是一种常见的窗口机制，用于对数据流进行分割和处理。在滚动窗口中，时间驱动是窗口触发和关闭的关键机制。

什么是滚动窗口？

滚动窗口将数据流按照固定的时间间隔进行分割，每个时间间隔形成一个独立的窗口。滚动窗口的特点是窗口之间不重叠，每个元素只属于一个窗口。

时间驱动

核心代码

WindowedStream<Tuple2<String, Integer>, String, TimeWindow> timeWindow = keyedStream
                .window(TumblingProcessingTimeWindows.of(Time.seconds(10)));
        timeWindow.apply(new MyTimeWindowFunction()).print();

StartApp

package icu.wzk.demo06;

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.functions.KeySelector;
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.assigners.TumblingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.api.java.tuple.Tuple2;

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


/**
 * 滚动时间窗口 Tumbling Window
 * 时间对齐，窗口长度固定，没有重叠
 * @author wzk
 * @date 10:48 2024/6/22
**/
public class TumblingWindow {

    private static final Random RANDOM = new Random();

    public static void main(String[] args) throws Exception {
        //设置执行环境，类似spark中初始化sparkContext
        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.out.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 ============================
        // 每隔10s划分一个窗口
        WindowedStream<Tuple2<String, Integer>, String, TimeWindow> timeWindow = keyedStream
                .window(TumblingProcessingTimeWindows.of(Time.seconds(10)));
        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)
        );
    }
}