06、Flink实战：DataStream之DataSourceAPI(五）RichParallelSourceFunction自定义多并行DataSource-程序员快看-教程，程序员编程资料站

自定义多并行DataSource必须继承 RichParallelSourceFunction 类，并重写run()和cancel()方法。

import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;
import java.io.RandomAccessFile;

// 这里的泛型Tuple2是该source输出的数据类型
public class MyParallelFileSource extends RichParallelSourceFunction<Tuple2<String,String>>{
    private String path = "C:\\Users\\Dell\\Desktop\\test";

    private Boolean flag = true;
    public MyParallelFileSource() {
    }

    public MyParallelFileSource(String path) {
        this.path = path;
    }

    /**
     * run()方法，用于一直运行产生数据
     * @param ctx
     * @throws Exception
     */
    @Override
    public void run(SourceContext<Tuple2<String, String>> ctx) throws Exception {
        //获取当前 subTask 的 index 值
        int subtaskIndex = getRuntimeContext().getIndexOfThisSubtask();
        //定义用于读取的文件路径
        RandomAccessFile randomAccessFile = new RandomAccessFile(path+"/"+subtaskIndex+".txt", "r");
        //多并行线程不安全问题。需要加锁。
        final Object checkpointLock = ctx.getCheckpointLock();//最好用final修饰
        while (flag) {   //无限循环，用于一直读取数据
            String line = randomAccessFile.readLine();
            if (line != null) {
                line = new String(line.getBytes("ISO-8859-1"), "UTF-8");

                synchronized (checkpointLock){
                    //将数据发送出去
                    ctx.collect(Tuple2.of(subtaskIndex+"",line));
                }
            }else{
                Thread.sleep(1000);
            }
        }
    }

    /**
     * cancel() 方法，用于关闭Source
     */
    @Override
    public void cancel() {
        flag = false;
    }
}

import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

public class ReadTxtContentJob {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStreamSource<Tuple2<String, String>> streamSource = env.addSource(new MyParallelFileSource());

        streamSource.print();

        env.execute("ReadTxtContentJob");
    }
}

输出结果如下：