Flink从入门到实战四[DataStream API]-20-Sink数据输出到文件

Flink将计算结果输出到文件，代码如下：

package org.itzhimei.sink;

import org.apache.flink.api.common.functions.FlatMapFunction;
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.util.Collector;

import java.util.Arrays;

public class Sink_1_WriteAsText {

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //这里如果不设置1
        //输出到文件就会按照你本机的cpu进行并发输出
        //最终输出结果是以文件名创建的文件夹，在多个文件中并发输出结果
        env.setParallelism(1);
        DataStreamSource<String> dataStreamSource = env.fromCollection(Arrays.asList(
                "hello flink",
                "hello java",
                "hello world",
                "test",
                "source",
                "collection"));

        String output = "D:\\______flink______\\output\\writeword.txt";

        SingleOutputStreamOperator<Tuple2<String, Integer>> tuple2SingleOutputStreamOperator = dataStreamSource.flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public void flatMap(String s, Collector<Tuple2<String, Integer>> out) throws Exception {
                String[] words = s.split(" ");
                for (String word : words) {
                    out.collect(new Tuple2<>(word, 1));
                }
            }
        });
        tuple2SingleOutputStreamOperator.writeAsText(output);
        env.execute();

    }
}

/* 输出：
(hello,1)
(flink,1)
(hello,1)
(java,1)
(hello,1)
(world,1)
(test,1)
(source,1)
(collection,1)
 */

需要注意的是，输出到文件，运行环境并发度要设置为1，即env.setParallelism(1)，这里如果不设置1，输出到文件就会按照你本机的cpu进行并发输出，最终输出结果是以文件名创建的文件夹，在多个文件中并发输出结果。