我是map-reduce的新手。我想知道在hadoop中实现自定义数据类型时,readfields和write方法的用途是什么?例如,

     public class Point3D implements Writable {
     public float x;
     public float y;
     public float z;

  public Point3D(float x, float y, float z) {
    this.x = x;
    this.y = y;
    this.z = z;
  }

  public Point3D() {
    this(0.0f, 0.0f, 0.0f);
  }

  public void write(DataOutput out) throws IOException {
    out.writeFloat(x);
    out.writeFloat(y);
    out.writeFloat(z);
  }

  public void readFields(DataInput in) throws IOException {
    x = in.readFloat();
    y = in.readFloat();
    z = in.readFloat();
  }

  public String toString() {
    return Float.toString(x) + ", "
    + Float.toString(y) + ", "
    + Float.toString(z);
  }
  public void set(float x, float y, float z)
 {
 this.x=x;
 this.y=y;
 this.z=z;
 }
}

在上面的示例中,自定义记录读取器使用set方法设置x,y和z的值。因此,我们最终在映射器中获得了这些值。但是对可写的readfealds和write()方法有什么需求呢?
请救命

最佳答案

readFileds()和write()方法用于读取和写入序列化数据以在网络上传输。

以下问题解释了对可写文件的需求。

What is the reason for having Writable wrapper classes in Hadoop MapReduce for Java types?

关于java - 在hadoop.io API中可写类中使用readFields(),我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/24361094/

10-11 21:35