阿里云中Flink提交作业流程

访问阿里云首页面：https://www.aliyun.com/

选择"按量付费"

通过选择区域，看哪个区域有虚拟交换机。

查看创建的工作空间，当工作空间状态为运行中时，点击控制台。

开通完成后，会有一个控制台：

可以选择SQL开发或者作业运维运行自己的Flink程序。

java 复制代码

package com.bigdata.day06;

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.MapFunction;
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.streaming.api.functions.source.SourceFunction;

import java.util.Random;

/**
 * @基本功能:
 * @program:FlinkDemo
 * @author: 闫哥
 * @create:2023-11-24 14:34:50
 **/
public class _03TestAliyun {

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

        //1. env-准备环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);

        //2. source-加载数据
        DataStreamSource<String> dataStreamSource = env.addSource(new SourceFunction<String>() {
            @Override
            public void run(SourceContext<String> ctx) throws Exception {

                String[] words = {"hello", "world", "bigdata"};
                Random random = new Random();
                while (true) {
                    String word = words[random.nextInt(words.length)];
                    int num = random.nextInt(10);
                    ctx.collect(word + "," + num);
                    Thread.sleep(500);
                }
            }

            @Override
            public void cancel() {

            }
        });

        //3. transformation-数据处理转换
        //2. source-加载数据
        SingleOutputStreamOperator<Tuple2<String, Integer>> mapStream = dataStreamSource.map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String s) throws Exception {
                String[] arr = s.split(",");

                return Tuple2.of(arr[0], Integer.valueOf(arr[1]));
            }
        });
        //3. transformation-数据处理转换
        SingleOutputStreamOperator<Tuple2<String, Integer>> result = mapStream.keyBy(0).sum(1);


        result.print();
        //4. sink-数据输出


        //5. execute-执行
        env.execute();
    }
}