【Flink、java】

依赖 

<dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-streaming-java_2.11</artifactId>
      <version>1.14.6</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-clients</artifactId>
      <version>${flink.version}</version>
    </dependency>

 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<String> lineDS = env.readTextFile("input/word.txt");
        // TODO: 2023/9/26 切分转换
        FlatMapOperator<String, Tuple2<String, Integer>> wordAndOne = lineDS.flatMap(new FlatMapFunction>() {
            @Override
            public void flatMap(String s, Collector<Tuple2<String, Integer>> 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<String, Integer> 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<Tuple2<String, Integer>> wordAndOneGroupBY = wordAndOne.groupBy(0);
        // TODO: 2023/9/26 各分组内聚合
        AggregateOperator<Tuple2<String, Integer>> sum = wordAndOneGroupBY.sum(1);//1是位置，表示第二个元素
        // TODO: 2023/9/26 输出
        sum.print();
    }
}