Flink滑动窗口（Sliding）中window和windowAll的区别

滑动窗口的使用，主要是计算，在reduce之前添加滑动窗口，设置好间隔和所统计的时间，然后再进行reduce计算数据即可。

窗口设置好时间间隔，和处理时间窗口的时间，比如将滑动窗口的时间间隔都设置为5s,处理时间为15s,意思是每隔五秒，就处理15s秒的数据

滑动窗口（window）

比如打了3s的输入，到了第五秒的时候，滑动window开始处理15秒的数据，数据就像滑动一样，用一个线段展示。

代码展示：

import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.SlidingProcessingTimeWindows;
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;

public class Demo4Window {
    public static void main(String[] args) throws Exception {
        //1、创建环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        //2、读取数据
        DataStream<String> linesDS = env.socketTextStream("master", 8888);

        //使用lambda表达式处理数据
        DataStream<String> wordsDS = linesDS
                .flatMap((line, out) -> {
                    for (String word : line.split(",")) {
                        out.collect(word);
                    }
                }, Types.STRING);

        DataStream<Tuple2<String, Integer>> kvDS = wordsDS
                .map(word -> Tuple2.of(word, 1))
                //指定返回类型
                .returns(Types.TUPLE(Types.STRING, Types.INT));

        KeyedStream<Tuple2<String, Integer>, String> keyByDS = kvDS.keyBy(kv -> kv.f0);

        /*
         * SlidingProcessingTimeWindows:滑动的处理时间窗口
         */
        WindowedStream<Tuple2<String, Integer>, String, TimeWindow> windowDS = keyByDS
                //每隔5秒计算最近15秒的数据
                .window(SlidingProcessingTimeWindows.of(Time.seconds(15), Time.seconds(5)));


        //kv1代表之前的结果（状态），kv2代码最新一条数据
        //reduce：有状态计算
        DataStream<Tuple2<String, Integer>> countDS = windowDS
                .reduce((kv1, kv2) -> Tuple2.of(kv1.f0, kv1.f1 + kv2.f1));

        countDS.print();

        //execute方法会触发任务执行（任务调度）
        env.execute("lambda");
    }
}

滑动窗口（windowAll） 

将同一个窗口的数据放在一起计算，将之前计算的结果与最新统计的结果相加

 代码展示：

import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.AllWindowedStream;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.SlidingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;

public class Demo4WindowAll {
    public static void main(String[] args) throws Exception {
        //1、创建环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        //2、读取数据
        DataStream<String> linesDS = env.socketTextStream("master", 8888);

        //使用lambda表达式处理数据
        DataStream<String> wordsDS = linesDS
                .flatMap((line, out) -> {
                    for (String word : line.split(",")) {
                        out.collect(word);
                    }
                }, Types.STRING);

        DataStream<Tuple2<String, Integer>> kvDS = wordsDS
                .map(word -> Tuple2.of(word, 1))
                //指定返回类型
                .returns(Types.TUPLE(Types.STRING, Types.INT));

        /*
         * SlidingProcessingTimeWindows:滑动的处理时间窗口
         */
        AllWindowedStream<Tuple2<String, Integer>, TimeWindow> windowAllDS = kvDS
                //每隔5秒计算最近15秒的数据
                //windowAll:将同一个窗口的数据发一起进行计算
                .windowAll(SlidingProcessingTimeWindows.of(Time.seconds(15), Time.seconds(5)));

        //kv1代表之前的结果（状态），kv2代码最新一条数据
        //reduce：有状态计算
        DataStream<Tuple2<String, Integer>> countDS = windowAllDS
                .reduce((kv1, kv2) -> Tuple2.of(kv1.f0, kv1.f1 + kv2.f1));

        countDS.print();

        //execute方法会触发任务执行（任务调度）
        env.execute("lambda");
    }
}