flink 流处理_flink写入hdfs

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还是那个熟悉的例子 数字统计的例子，

数据引用环境啥的说明都在《第一个Flink程序》中，请往上个例子查看，接下来说明下这个例子是有界的数据流的单词统计。另一方面，我这边是学习的B站上的尚硅谷的教程整理的，有需求的可以去看看。

import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
/****
 * @author 
 * @date 2022/11/10
 * @desc
 */
public class BoundedStreamWordCount {
    public static void main(String[] args) throws Exception {
        // 1. 创建流式执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 2. 读取文件
        DataStreamSource<String> lineDataStreamSource = env.readTextFile("input/words.txt");
        // 3. 转换计算
        SingleOutputStreamOperator<Tuple2<String, Long>> wordAndOneTuple = lineDataStreamSource.flatMap((String line, Collector<Tuple2<String, Long>> out) -> {
            String[] words = line.split(" ");
            for (String word : words) {
                out.collect(Tuple2.of(word, 1L));
            }
        })
                .returns(Types.TUPLE( Types.STRING, Types.LONG));
        // 4. 分组操作
        KeyedStream<Tuple2<String, Long>, String> wordAndOneKeyedStream = wordAndOneTuple.keyBy(data -> data.f0);
        // 5. 求和
        SingleOutputStreamOperator<Tuple2<String, Long>> sum = wordAndOneKeyedStream.sum(1);
        // 6. 打印
        sum.print();

        // 7. 启动执行
        env.execute();
    }
}

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运行结果