Flink窗口按行为分有滑动滚动窗口，按划分标准有事件时间窗口。本节将演示各个窗口的使用。

1、 系统、软件以及前提约束

• CentOS 7 64 工作站 作者的机子ip是192.168.100.200，请读者根据自己实际情况设置
• idea 2018.1
• 在Win10中安装nc
  https://www.jianshu.com/p/4f6fb8834ad9

2、操作步骤

• 创建maven项目，加入以下依赖：

        <dependency>
            <!--spark依赖-->
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>2.2.0</version>
        </dependency>
        <!--scala依赖-->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>2.11.8</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-core</artifactId>
            <version>1.5.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.5.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.11</artifactId>
            <version>1.5.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>1.5.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka-0.10_2.11</artifactId>
            <version>1.5.0</version>
        </dependency>

• 在项目的src/main/java文件夹下创建WindowTest.java

package flink;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.SlidingEventTimeWindows;
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.assigners.TumblingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;
import org.apache.storm.trident.windowing.config.SlidingCountWindow;

public class WindowTest {
    public static void main(String[] args) throws Exception {
        // local模式
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();
        DataStream<String> text = env.socketTextStream("localhost", 9999, "\n");
        @SuppressWarnings("serial")
        DataStream<WordWithCount> windowCounts = text
                .flatMap(new FlatMapFunction<String, WordWithCount>() {
                    public void flatMap(String value, Collector<WordWithCount> out) throws Exception {
                        for (String word : value.split("\\s")) {
                            out.collect(new WordWithCount(word, 1L));
                        }
                    }
                })
                //滑动窗口按数据个数划分
//                .keyBy("word").countWindow(10,5)
                //滚动窗口按数据个数划分
//                .keyBy("word").countWindow(5)
                //滚动窗口按时间划分
//                .keyBy("word").timeWindow(Time.seconds(5))
                //滑动窗口按时间划分
                .keyBy("word").timeWindow(Time.seconds(10), Time.seconds(5))
                .reduce(new ReduceFunction<WordWithCount>() {
                    public WordWithCount reduce(WordWithCount a, WordWithCount b) throws Exception {
                        return new WordWithCount(a.word, a.count + b.count);
                    }
                });
        windowCounts.print().setParallelism(1);
        env.execute("Socket Window WordCount(zl_test)");
    }

    public static class WordWithCount {
        public String word;
        public long count;

        public WordWithCount() {
        }

        public WordWithCount(String word, long count) {
            this.word = word;
            this.count = count;
        }

        @Override
        public String toString() {
            return word + " : " + count;
        }
    }
}

• 测试
  （1）打开windows命令行，执行以下命令：

nc -l -p 9999

（2）在idea中执行WindowTest.java，注意注释部分的解释
（3）在nc窗口输入字符串，观察idea中的控制台，会有统计结果打印。
以上就是Flink窗口的测试