Flink

import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

import java.util.Arrays;

public class SocketStreamWordCount {

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

    // 1. 创建流式执行环境
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

    // 1. 创建流式执行环境，本地带UI界面启动方式，需要引入指定的maven，正式环境不建议使用
    // StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(new Configuration());

    // 2. 读取文本流：hadoop102表示发送端主机名、7777表示端口号
    DataStreamSource<String> lineStream = env.socketTextStream("hadoop102", 7777);

    // 3. 转换、分组、求和，得到统计结果
    SingleOutputStreamOperator<Tuple2<String, Long>> sum = lineStream.flatMap((String line, Collector<Tuple2<String, Long>> out) -> {
        String[] words = line.split(" ");

        for (String word : words) {
            out.collect(Tuple2.of(word, 1L));
        }
    }).returns(Types.TUPLE(Types.STRING, Types.LONG))
            .keyBy(data -> data.f0)
            .sum(1);

    // 4. 打印
    sum.print();

    // 5. 执行
    env.execute();
}

}