Flink1.18环境代码编写

package com.xiaohu.env;

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.configuration.RestOptions;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.LocalStreamEnvironment;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

/*
    DataStreamApi
 */
public class EnvDemo {
    public static void main(String[] args) throws Exception {
        //创建执行环境
//        StreamExecutionEnvironment
//                .createLocalEnvironment() //创建本地环境
//                .createRemoteEnvironment("master",8081,"/xx/xx")  //创建远程环境
//                .getExecutionEnvironment() //使用默认配置获取环境，底层会进行区分远程或者本地

        //创建flink配置文件对象
        Configuration conf = new Configuration();
        conf.set(RestOptions.BIND_PORT,"8082"); //修改ui界面的端口号，默认是8081
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(conf);

        //设置流处理环境还是批处理环境 DataSet API已经过时了，现在都是一套代码，进行设置
//        env.setRuntimeMode(RuntimeExecutionMode.BATCH); //批处理
//        env.setRuntimeMode(RuntimeExecutionMode.STREAMING); //流处理，默认就是流处理
        //一般情况下，不会在代码中指定，不够灵活，一般都是在提交的时候，使用命令进行指定 flink run  -Dexecution.runtime-mode=BATCH【STREAMING】 ...

        DataStreamSource<String> socketDS = env.socketTextStream("master", 7777);

        socketDS.flatMap(new FlatMapFunction<String, Tuple2<String,Long>>() {
            @Override
            public void flatMap(String s, Collector<Tuple2<String, Long>> collector) throws Exception {
                String[] words = s.split(" ");
                for (String word : words) {
                    Tuple2<String, Long> tuple2 = Tuple2.of(word, 1L);
                    collector.collect(tuple2);
                }
            }
        }).keyBy(new KeySelector<Tuple2<String, Long>, String>() {
            @Override
            public String getKey(Tuple2<String, Long> stringLongTuple2) throws Exception {
                return stringLongTuple2.f0;
            }
        }).sum(1).print();


        //一个execute或executeAsync方法触发一个Job作业
        //flink是事件驱动执行，是延迟执行或者懒执行
        env.execute("DataStreamApi测试无界流读取socket数据");
        //可以提交多次
        //env..execute() 但是这种，按照代码顺序执行，等前面的job执行完才可以，会进行阻塞
        //新版本有个env.executeAsync() 异步执行，就不会发生阻塞了，都用这个提交，上面的也用这个方法提交，用的比较少
        //有几个executeAsync()，就会有几个job,对应jobmanager中就会有几个jobmaster



    }
}