flink(入门)——wordcount批处理

复习一下，写一个简单的flink批处理小程序：

1. 创建maven项目，引入依赖（注意引入 flink-clients_2.12，flink1.11后 flink-java 移除了这个依赖，需要手动添加，否则会报错 No ExecutorFactory found to execute the application）

       <dependencies>
           <dependency>
               <groupId>org.apache.flink</groupId>
               <artifactId>flink-java</artifactId>
               <version>1.12.2</version>
           </dependency>
           <dependency>
               <groupId>org.apache.flink</groupId>
               <artifactId>flink-streaming-java_2.12</artifactId>
               <version>1.12.2</version>
           </dependency>
           <dependency>
               <groupId>org.apache.flink</groupId>
               <artifactId>flink-clients_2.12</artifactId>
               <version>1.12.2</version>
           </dependency>
       </dependencies>

2. 创建WordCount类
1. 创建执行环境
2. 从文件中读取数据（有界流 也就是批数据）
3. 对数据进行处理、输出

   package com.jy.bjz.wc;
   
   import org.apache.flink.api.common.functions.FlatMapFunction;
   import org.apache.flink.api.java.DataSet;
   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.tuple.Tuple2;
   import org.apache.flink.util.Collector;
   
   /**
    * TODO
    *
    * @author baojiazhong
    * @since 2021/9/8 13:51
    */
   // 批处理
   public class WordCount {
       public static void main(String[] args) throws Exception {
           // 创建执行环境
           ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
           // 从文件读取数据
           String inputPath = "C:\\Users\\15868\\Desktop\\TL\\flink\\learn\\src\\main\\resources\\hello.txt";
           DataSet<String> inputData = env.readTextFile(inputPath);
           // 自定义实现flatmap方法
           DataSet<Tuple2<String, Integer>> resultDataSet = inputData.flatMap(new myFlatMapper())
                   .groupBy(0)
                   .sum(1);
           // 打印输出
           resultDataSet.print();
   
       }
   
       // 自定义 实现flatmapper方法
       public static class myFlatMapper implements FlatMapFunction<String, Tuple2<String, Integer>> {
   
           public void flatMap(String s, Collector<Tuple2<String, Integer>> collector) throws Exception {
               String[] words = s.split(" ");
               for (String word : words) {
                   collector.collect(new Tuple2<String, Integer>(word, 1));
               }
           }
       }
   
   
   }

3. 总结
   1. flink中较多方法都是比较喜欢自定义去实现接口
   2. 使用的tuple代表参数个数，例如 tuple2 就有两个参数，tuple25 就有25个参数