【Flink、java】

依赖 

      org.apache.flink
      flink-streaming-java_2.11
      1.14.6
    
    
      org.apache.flink
      flink-clients
      ${flink.version}
    

 快速上手

1.增添依赖

2.在根目录，添加input文件

 DataSet API实现wordcount（已经不能用了）

package org.example;
/*
 * @Auther:huangzhiyang
 * @Date:2023/9/26
 * @Description:wc
 */

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.AggregateOperator;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.operators.FlatMapOperator;
import org.apache.flink.api.java.operators.UnsortedGrouping;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;

public class wordCountBatchDemo {
    public static void main(String[] args) throws Exception {
        // TODO: 2023/9/26 创建执行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        // TODO: 2023/9/26 读取数据
        DataSource lineDS = env.readTextFile("input/word.txt");
        // TODO: 2023/9/26 切分转换
        FlatMapOperator> wordAndOne = lineDS.flatMap(new FlatMapFunction>() {
            @Override
            public void flatMap(String s, Collector> collector) throws Exception {
                // TODO: 2023/9/26 按照空格切分单词
                String[] words = s.split(" ");
                // TODO: 2023/9/26 将单词转为tuple2
                for (String word : words) {
                    Tuple2 tuple2 = Tuple2.of(word, 1);
                    // TODO: 2023/9/26 使用collector向下游发送数据
                    collector.collect(tuple2);
                }
            }
        });
        // TODO: 2023/9/26 按照word分组
        UnsortedGrouping> wordAndOneGroupBY = wordAndOne.groupBy(0);
        // TODO: 2023/9/26 各分组内聚合
        AggregateOperator> sum = wordAndOneGroupBY.sum(1);//1是位置，表示第二个元素
        // TODO: 2023/9/26 输出
        sum.print();
    }
}