<dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-scala -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_2.12</artifactId>
            <version>1.7.1</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-streaming-scala -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.12</artifactId>
            <version>1.7.1</version>
        </dependency>

package flink;

import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.tuple.Tuple2;

public class WordExample {
    public static void main(String[] args) throws Exception {

        final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        //创建构建字符串的数据集
        DataSet<String> text = env.fromElements(
                "flink test","" +
                        "I think I hear them. Stand, ho! Who‘s there?");

        //分割字符串，按照key进行分组，统计相同的key个数
        DataSet<Tuple2<String, Integer>> wordCount = text.flatMap(new LineSplitter())
                .groupBy(0).sum(1);
        
        wordCount.print();
    }
}

package flink;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;

public class LineSplitter implements FlatMapFunction<String, Tuple2<String,Integer>> {
    @Override
    public void flatMap(String o, Collector<Tuple2<String, Integer>> collector) throws Exception {
        for (String word : o.split(" ")) {
            collector.collect(new Tuple2<String, Integer>(word,1));
        }
    }
}

package flink

import org.apache.flink.api.scala._

object WordCountExample {
  def main(args: Array[String]): Unit = {
    val env = ExecutionEnvironment.getExecutionEnvironment

    val text = env.fromElements("Who‘s there?",

      "I think I hear them. Stand, ho! Who‘s there?")

    val counts = text.flatMap(_.toLowerCase().split("\\W+")filter(_.nonEmpty))
      .map((_,1)).groupBy(0).sum(1)

    counts.print()
  }
}

package flink;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;

public class WordCount {
    public static void main(String[] args) throws Exception {
        final int port;
        try {
            final ParameterTool params = ParameterTool.fromArgs(args);
            port = params.getInt("port");
        } catch (Exception e) {
            System.out.println("No port specified.Please run ‘SocketWindowWordCount--port <port>‘");
            return;
        }
        //get the execution enviroment
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        //get input data by connecting to the socket
        DataStream<String> text = env.socketTextStream("localhost", port, ‘\n‘);
        //parse the data,group it.window it,and aggregeate the counts
        DataStream<WordWithCount> windowCounts = text
                .flatMap(new FlatMapFunction<String, WordWithCount>() {
                    @Override
                    public void flatMap(String s, Collector<WordWithCount> collector) {
                        for (String word : s.split("\\s")) {
                            collector.collect(new WordWithCount(word, 1L));
                        }
                    }
                }).keyBy("word").timeWindow(Time.seconds(10), Time.seconds(5))
                .reduce(new ReduceFunction<WordWithCount>() {
                    @Override
                    public WordWithCount reduce(WordWithCount wordWithCount, WordWithCount t1) throws Exception {
                        return new WordWithCount(wordWithCount.word, wordWithCount.count + t1.count);
                    }
                });

        //print the result with a single thread,rather than in parallel
        windowCounts.print().setParallelism(1);

        env.execute("Socket Window WordCount");
    }
}

package flink;

public 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;
    }
}

package flink

import org.apache.flink.api.java.utils.ParameterTool
import org.apache.flink.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.streaming.api.windowing.time.Time

object SokcetWindowWordCount {

  case class WordWithCount(word: String, count: Long)

  def main(args: Array[String]): Unit = {
    //the port to connect to
    val port: Int = try {
      ParameterTool.fromArgs(args).getInt("port")
    } catch {
      case e: Exception => {
        System.err.println("No port specified.Please run ‘SocketWindowWordCount --port<port>‘")
        return
      }
    }
    //get the execution enviroment
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment

    //parse input data by connecting to the socket
    val text = env.socketTextStream("localhost", port, ‘\n‘)

    //parse the data.group it.window it.and aggregate the counts

    val windowCount = text
      .flatMap{w => w.split("\\s")}
      .map{w => WordWithCount(w, 1)}
      .keyBy("word")
      .timeWindow(Time.seconds(10), Time.seconds(5))

      .sum("count")

    //print the results with a single thread ,rather than in parallel
    windowCount.print().setParallelism(1)

    env.execute("Socket Window WordCount")
  }
}