Flink读取网络数据

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-streaming-java_2.11</artifactId>
    <version>1.5.6</version>
</dependency>

package com.jd.xq;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
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;
/**
 * @author duanxiaoqiu
 * @Date 2019-08-16 15:45
 **/
public class StartJob {
    public static void main(String[] args) throws Exception {
        // 创建 execution environment
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 通过连接 socket 获取输入数据，这里连接到本地9000端口，如果9000端口已被占用，请换一个端口
        DataStream<String> text = env.socketTextStream("localhost", 9000, "\n");
        // 解析数据，按 word 分组，开窗，聚合
        DataStream<Tuple2<String, Integer>> windowCounts = text
                .flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {
                    @Override
                    public void flatMap(String value, Collector<Tuple2<String, Integer>> out) {
                        for (String word : value.split("\\s")) {
                            out.collect(Tuple2.of(word, 1));
                        }
                    }
                })
                .keyBy(0)
                .timeWindow(Time.seconds(5))
                .sum(1);
        // 将结果打印到控制台，注意这里使用的是单线程打印，而非多线程
        windowCounts.print().setParallelism(1);
        env.execute("Socket Window WordCount");
    }
}