Hadoop数据类型

hadoop内置数据类型

BooleanWritable:标准布尔型数值
ByteWritable:单字节数值
DoubleWritable:双字节数值
FloatWritable:浮点数
IntWritable:整型数
LongWritable:长整型数
Text:使用UTF8格式存储的文本
NullWritable:当<key, value>中的key或value为空时使用

用户自定义数据类型的实现

1.对于只需要作为“值”出现的数据类型，实现Writable接口即可
2.对于有可能作为“键”出现的数据类型，需要实现WritableComparable接口

##实现Writable接口：

/* DataInput and DataOutput 类是java.io的类 */
public interface Writable {

     void readFields(DataInput in);

     void write(DataOutput out);

}

下面是一个小例子：

public class Point3D implement Writable {

     public float x, y, z;

     public Point3D(float fx, float fy, float fz) {

          this.x = fx;
          this.y = fy;
          this.z = fz;

     }

     public Point3D() {

          this(0.0f, 0.0f, 0.0f);

     }

     public void readFields(DataInput in) throws IOException {
          x = in.readFloat();
          y = in.readFloat();
          z = in.readFloat();
     }
     public void write(DataOutput out) throws IOException {
          out.writeFloat(x);
          out.writeFloat(y);
          out.writeFloat(z);
     }
     public String toString() {
          return Float.toString(x) + ", "
          + Float.toString(y) + ", "
          + Float.toString(z);
     }
}

2、实现WritableComparable接口

public interface WritableComparable<T> {
     public void readFields(DataInput in);
     public void write(DataOutput out);
     public int compareTo(T other);
}

先给出下面的简单例子，再做说明和扩展。

public class Point3D inplements WritableComparable {

     public float x, y, z;
      public Point3D(float fx, float fy, float fz) {

          this.x = fx;
          this.y = fy;
          this.z = fz;

     }

      public Point3D() {

          this(0.0f, 0.0f, 0.0f);

     }

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

     public String toString() {

          return Float.toString(x) + ", "
          + Float.toString(y) + ", "
          + Float.toString(z);
     }
     public float distanceFromOrigin() {
          return (float) Math.sqrt( x*x + y*y +z*z);
     }
     //影响map输出的排序，默认是升序，return值加一个负号变降序
     public int compareTo(Point3D other) {

          return Float.compareTo(distanceFromOrigin(),other.distanceFromOrigin());

     }

     public boolean equals(Object o) {

          if( !(o instanceof Point3D)) {
              return false;
          }

          Point3D other = (Point3D) o;
          return this.x == o.x && this.y == o.y && this.z == o.z;

     }

     /* 实现 hashCode() 方法很重要
     * Hadoop的Partitioners会用到这个方法，后面再说
     */
     public int hashCode() {
          return Float.floatToIntBits(x)
          ^ Float.floatToIntBits(y)
          ^ Float.floatToIntBits(z);
     }

}

自定义Hadoop数据类型后，需要明确告诉Hadoop来使用它们。这是 JobConf 所能担当的了。

void setOutputKeyClass(Class<T> theClass)
void setOutputValueClass(Class<T> theClass)

通常(默认条件下)，这个函数对Map和Reduce阶段的输出都起到作用，当然也有专门的 setMapOutputKeyClass() / setReduceOutputKeyClass() 接口。